[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the amount of cash provided by operating activities?

Dialogue history:
[Turn 1] what was the amount of cash provided by operating activities?

Pre-table text:
liquidity and capital resources we maintained a strong financial position throughout fiscal year 2019 .
as of 30 september 2019 , our consolidated balance sheet included cash and cash items of $ 2248.7 .
we continue to have consistent access to commercial paper markets , and cash flows from operating and financing activities are expected to meet liquidity needs for the foreseeable future .
as of 30 september 2019 , we had $ 971.5 of foreign cash and cash items compared to a total amount of cash and cash items of $ 2248.7 .
as a result of the tax act , we do not expect that a significant portion of our foreign subsidiaries' and affiliates' earnings will be subject to u.s .
income tax upon subsequent repatriation to the united states .
the repatriation of these earnings may be subject to foreign withholding and other taxes depending on the country in which the subsidiaries and affiliates reside .
however , because we have significant current investment plans outside the u.s. , it is our intent to permanently reinvest the majority of our foreign cash and cash items that would be subject to additional taxes outside the u.s .
refer to note 23 , income taxes , for additional information .
the table below summarizes our cash flows from operating activities , investing activities , and financing activities from continuing operations as reflected on the consolidated statements of cash flows: .

Table:
cash provided by ( used for )	2019	2018
operating activities	$ 2969.9	$ 2547.2
investing activities	-2113.4 ( 2113.4 )	-1641.6 ( 1641.6 )
financing activities	-1370.5 ( 1370.5 )	-1359.8 ( 1359.8 )

Post-table text:
operating activities for the fiscal year ended 30 september 2019 , cash provided by operating activities was $ 2969.9 .
income from continuing operations of $ 1760.0 was adjusted for items including depreciation and amortization , deferred income taxes , impacts from the tax act , a charge for the facility closure of one of our customers , undistributed earnings of unconsolidated affiliates , gain on sale of assets and investments , share-based compensation , noncurrent capital lease receivables , and certain other adjustments .
the caption "gain on sale of assets and investments" includes a gain of $ 14.1 recognized on the disposition of our interest in high-tech gases ( beijing ) co. , ltd. , a previously held equity investment in our industrial gases 2013 asia segment .
refer to note 7 , acquisitions , to the consolidated financial statements for additional information .
the working capital accounts were a use of cash of $ 25.3 , primarily driven by $ 69.0 from trade receivables and $ 41.8 from payables and accrued liabilities , partially offset by $ 79.8 from other receivables .
the use of cash within "payables and accrued liabilities" was primarily driven by a $ 48.9 decrease in accrued utilities and a $ 30.3 decrease in accrued interest , partially offset by a $ 51.6 increase in customer advances primarily related to sale of equipment activity .
the decrease in accrued utilities was primarily driven by a contract modification to a tolling arrangement in india and lower utility costs in the industrial gases 2013 americas segment .
the source of cash from other receivables of $ 79.8 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures and the collection of value added taxes .
for the fiscal year ended 30 september 2018 , cash provided by operating activities was $ 2547.2 , including income from continuing operations of $ 1455.6 .
other adjustments of $ 131.6 include a $ 54.9 net impact from the remeasurement of intercompany transactions .
the related hedging instruments that eliminate the earnings impact are included as a working capital adjustment in other receivables or payables and accrued liabilities .
in addition , other adjustments were impacted by cash received from the early termination of a cross currency swap of $ 54.4 , as well as the excess of pension expense over pension contributions of $ 23.5 .
the working capital accounts were a use of cash of $ 265.4 , primarily driven by payables and accrued liabilities , inventories , and trade receivables , partially offset by other receivables .
the use of cash in payables and accrued liabilities of $ 277.7 includes a decrease in customer advances of $ 145.7 primarily related to sale of equipment activity and $ 67.1 for maturities of forward exchange contracts that hedged foreign currency exposures .
the use of cash in inventories primarily resulted from the purchase of helium molecules .
in addition , inventories reflect the noncash impact of our change in accounting for u.s .
inventories from lifo to fifo .
the source of cash from other receivables of $ 128.3 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was cash spent from investing activities?

Dialogue history:
[Turn 1] what was the amount of cash provided by operating activities?
[Turn 2] what was cash spent from investing activities?

Pre-table text:
liquidity and capital resources we maintained a strong financial position throughout fiscal year 2019 .
as of 30 september 2019 , our consolidated balance sheet included cash and cash items of $ 2248.7 .
we continue to have consistent access to commercial paper markets , and cash flows from operating and financing activities are expected to meet liquidity needs for the foreseeable future .
as of 30 september 2019 , we had $ 971.5 of foreign cash and cash items compared to a total amount of cash and cash items of $ 2248.7 .
as a result of the tax act , we do not expect that a significant portion of our foreign subsidiaries' and affiliates' earnings will be subject to u.s .
income tax upon subsequent repatriation to the united states .
the repatriation of these earnings may be subject to foreign withholding and other taxes depending on the country in which the subsidiaries and affiliates reside .
however , because we have significant current investment plans outside the u.s. , it is our intent to permanently reinvest the majority of our foreign cash and cash items that would be subject to additional taxes outside the u.s .
refer to note 23 , income taxes , for additional information .
the table below summarizes our cash flows from operating activities , investing activities , and financing activities from continuing operations as reflected on the consolidated statements of cash flows: .

Table:
cash provided by ( used for )	2019	2018
operating activities	$ 2969.9	$ 2547.2
investing activities	-2113.4 ( 2113.4 )	-1641.6 ( 1641.6 )
financing activities	-1370.5 ( 1370.5 )	-1359.8 ( 1359.8 )

Post-table text:
operating activities for the fiscal year ended 30 september 2019 , cash provided by operating activities was $ 2969.9 .
income from continuing operations of $ 1760.0 was adjusted for items including depreciation and amortization , deferred income taxes , impacts from the tax act , a charge for the facility closure of one of our customers , undistributed earnings of unconsolidated affiliates , gain on sale of assets and investments , share-based compensation , noncurrent capital lease receivables , and certain other adjustments .
the caption "gain on sale of assets and investments" includes a gain of $ 14.1 recognized on the disposition of our interest in high-tech gases ( beijing ) co. , ltd. , a previously held equity investment in our industrial gases 2013 asia segment .
refer to note 7 , acquisitions , to the consolidated financial statements for additional information .
the working capital accounts were a use of cash of $ 25.3 , primarily driven by $ 69.0 from trade receivables and $ 41.8 from payables and accrued liabilities , partially offset by $ 79.8 from other receivables .
the use of cash within "payables and accrued liabilities" was primarily driven by a $ 48.9 decrease in accrued utilities and a $ 30.3 decrease in accrued interest , partially offset by a $ 51.6 increase in customer advances primarily related to sale of equipment activity .
the decrease in accrued utilities was primarily driven by a contract modification to a tolling arrangement in india and lower utility costs in the industrial gases 2013 americas segment .
the source of cash from other receivables of $ 79.8 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures and the collection of value added taxes .
for the fiscal year ended 30 september 2018 , cash provided by operating activities was $ 2547.2 , including income from continuing operations of $ 1455.6 .
other adjustments of $ 131.6 include a $ 54.9 net impact from the remeasurement of intercompany transactions .
the related hedging instruments that eliminate the earnings impact are included as a working capital adjustment in other receivables or payables and accrued liabilities .
in addition , other adjustments were impacted by cash received from the early termination of a cross currency swap of $ 54.4 , as well as the excess of pension expense over pension contributions of $ 23.5 .
the working capital accounts were a use of cash of $ 265.4 , primarily driven by payables and accrued liabilities , inventories , and trade receivables , partially offset by other receivables .
the use of cash in payables and accrued liabilities of $ 277.7 includes a decrease in customer advances of $ 145.7 primarily related to sale of equipment activity and $ 67.1 for maturities of forward exchange contracts that hedged foreign currency exposures .
the use of cash in inventories primarily resulted from the purchase of helium molecules .
in addition , inventories reflect the noncash impact of our change in accounting for u.s .
inventories from lifo to fifo .
the source of cash from other receivables of $ 128.3 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what is cash provided from operating activities less cash spent from investing activities?

Dialogue history:
[Turn 1] what was the amount of cash provided by operating activities?
[Turn 2] what was cash spent from investing activities?
[Turn 3] what is cash provided from operating activities less cash spent from investing activities?

Pre-table text:
liquidity and capital resources we maintained a strong financial position throughout fiscal year 2019 .
as of 30 september 2019 , our consolidated balance sheet included cash and cash items of $ 2248.7 .
we continue to have consistent access to commercial paper markets , and cash flows from operating and financing activities are expected to meet liquidity needs for the foreseeable future .
as of 30 september 2019 , we had $ 971.5 of foreign cash and cash items compared to a total amount of cash and cash items of $ 2248.7 .
as a result of the tax act , we do not expect that a significant portion of our foreign subsidiaries' and affiliates' earnings will be subject to u.s .
income tax upon subsequent repatriation to the united states .
the repatriation of these earnings may be subject to foreign withholding and other taxes depending on the country in which the subsidiaries and affiliates reside .
however , because we have significant current investment plans outside the u.s. , it is our intent to permanently reinvest the majority of our foreign cash and cash items that would be subject to additional taxes outside the u.s .
refer to note 23 , income taxes , for additional information .
the table below summarizes our cash flows from operating activities , investing activities , and financing activities from continuing operations as reflected on the consolidated statements of cash flows: .

Table:
cash provided by ( used for )	2019	2018
operating activities	$ 2969.9	$ 2547.2
investing activities	-2113.4 ( 2113.4 )	-1641.6 ( 1641.6 )
financing activities	-1370.5 ( 1370.5 )	-1359.8 ( 1359.8 )

Post-table text:
operating activities for the fiscal year ended 30 september 2019 , cash provided by operating activities was $ 2969.9 .
income from continuing operations of $ 1760.0 was adjusted for items including depreciation and amortization , deferred income taxes , impacts from the tax act , a charge for the facility closure of one of our customers , undistributed earnings of unconsolidated affiliates , gain on sale of assets and investments , share-based compensation , noncurrent capital lease receivables , and certain other adjustments .
the caption "gain on sale of assets and investments" includes a gain of $ 14.1 recognized on the disposition of our interest in high-tech gases ( beijing ) co. , ltd. , a previously held equity investment in our industrial gases 2013 asia segment .
refer to note 7 , acquisitions , to the consolidated financial statements for additional information .
the working capital accounts were a use of cash of $ 25.3 , primarily driven by $ 69.0 from trade receivables and $ 41.8 from payables and accrued liabilities , partially offset by $ 79.8 from other receivables .
the use of cash within "payables and accrued liabilities" was primarily driven by a $ 48.9 decrease in accrued utilities and a $ 30.3 decrease in accrued interest , partially offset by a $ 51.6 increase in customer advances primarily related to sale of equipment activity .
the decrease in accrued utilities was primarily driven by a contract modification to a tolling arrangement in india and lower utility costs in the industrial gases 2013 americas segment .
the source of cash from other receivables of $ 79.8 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures and the collection of value added taxes .
for the fiscal year ended 30 september 2018 , cash provided by operating activities was $ 2547.2 , including income from continuing operations of $ 1455.6 .
other adjustments of $ 131.6 include a $ 54.9 net impact from the remeasurement of intercompany transactions .
the related hedging instruments that eliminate the earnings impact are included as a working capital adjustment in other receivables or payables and accrued liabilities .
in addition , other adjustments were impacted by cash received from the early termination of a cross currency swap of $ 54.4 , as well as the excess of pension expense over pension contributions of $ 23.5 .
the working capital accounts were a use of cash of $ 265.4 , primarily driven by payables and accrued liabilities , inventories , and trade receivables , partially offset by other receivables .
the use of cash in payables and accrued liabilities of $ 277.7 includes a decrease in customer advances of $ 145.7 primarily related to sale of equipment activity and $ 67.1 for maturities of forward exchange contracts that hedged foreign currency exposures .
the use of cash in inventories primarily resulted from the purchase of helium molecules .
in addition , inventories reflect the noncash impact of our change in accounting for u.s .
inventories from lifo to fifo .
the source of cash from other receivables of $ 128.3 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what is that less cash spent in financing activities?

Dialogue history:
[Turn 1] what was the amount of cash provided by operating activities?
[Turn 2] what was cash spent from investing activities?
[Turn 3] what is cash provided from operating activities less cash spent from investing activities?
[Turn 4] what is that less cash spent in financing activities?

Pre-table text:
liquidity and capital resources we maintained a strong financial position throughout fiscal year 2019 .
as of 30 september 2019 , our consolidated balance sheet included cash and cash items of $ 2248.7 .
we continue to have consistent access to commercial paper markets , and cash flows from operating and financing activities are expected to meet liquidity needs for the foreseeable future .
as of 30 september 2019 , we had $ 971.5 of foreign cash and cash items compared to a total amount of cash and cash items of $ 2248.7 .
as a result of the tax act , we do not expect that a significant portion of our foreign subsidiaries' and affiliates' earnings will be subject to u.s .
income tax upon subsequent repatriation to the united states .
the repatriation of these earnings may be subject to foreign withholding and other taxes depending on the country in which the subsidiaries and affiliates reside .
however , because we have significant current investment plans outside the u.s. , it is our intent to permanently reinvest the majority of our foreign cash and cash items that would be subject to additional taxes outside the u.s .
refer to note 23 , income taxes , for additional information .
the table below summarizes our cash flows from operating activities , investing activities , and financing activities from continuing operations as reflected on the consolidated statements of cash flows: .

Table:
cash provided by ( used for )	2019	2018
operating activities	$ 2969.9	$ 2547.2
investing activities	-2113.4 ( 2113.4 )	-1641.6 ( 1641.6 )
financing activities	-1370.5 ( 1370.5 )	-1359.8 ( 1359.8 )

Post-table text:
operating activities for the fiscal year ended 30 september 2019 , cash provided by operating activities was $ 2969.9 .
income from continuing operations of $ 1760.0 was adjusted for items including depreciation and amortization , deferred income taxes , impacts from the tax act , a charge for the facility closure of one of our customers , undistributed earnings of unconsolidated affiliates , gain on sale of assets and investments , share-based compensation , noncurrent capital lease receivables , and certain other adjustments .
the caption "gain on sale of assets and investments" includes a gain of $ 14.1 recognized on the disposition of our interest in high-tech gases ( beijing ) co. , ltd. , a previously held equity investment in our industrial gases 2013 asia segment .
refer to note 7 , acquisitions , to the consolidated financial statements for additional information .
the working capital accounts were a use of cash of $ 25.3 , primarily driven by $ 69.0 from trade receivables and $ 41.8 from payables and accrued liabilities , partially offset by $ 79.8 from other receivables .
the use of cash within "payables and accrued liabilities" was primarily driven by a $ 48.9 decrease in accrued utilities and a $ 30.3 decrease in accrued interest , partially offset by a $ 51.6 increase in customer advances primarily related to sale of equipment activity .
the decrease in accrued utilities was primarily driven by a contract modification to a tolling arrangement in india and lower utility costs in the industrial gases 2013 americas segment .
the source of cash from other receivables of $ 79.8 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures and the collection of value added taxes .
for the fiscal year ended 30 september 2018 , cash provided by operating activities was $ 2547.2 , including income from continuing operations of $ 1455.6 .
other adjustments of $ 131.6 include a $ 54.9 net impact from the remeasurement of intercompany transactions .
the related hedging instruments that eliminate the earnings impact are included as a working capital adjustment in other receivables or payables and accrued liabilities .
in addition , other adjustments were impacted by cash received from the early termination of a cross currency swap of $ 54.4 , as well as the excess of pension expense over pension contributions of $ 23.5 .
the working capital accounts were a use of cash of $ 265.4 , primarily driven by payables and accrued liabilities , inventories , and trade receivables , partially offset by other receivables .
the use of cash in payables and accrued liabilities of $ 277.7 includes a decrease in customer advances of $ 145.7 primarily related to sale of equipment activity and $ 67.1 for maturities of forward exchange contracts that hedged foreign currency exposures .
the use of cash in inventories primarily resulted from the purchase of helium molecules .
in addition , inventories reflect the noncash impact of our change in accounting for u.s .
inventories from lifo to fifo .
the source of cash from other receivables of $ 128.3 was primarily due to the maturities of forward exchange contracts that hedged foreign currency exposures. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the percentage of total segment operations that was made of investment banking in 2005?

Dialogue history:
[Turn 1] what was the percentage of total segment operations that was made of investment banking in 2005?

Pre-table text:
segment results 2013 operating basis ( a ) ( b ) ( table continued from previous page ) year ended december 31 , operating earnings return on common equity 2013 goodwill ( c ) .

Table:
year ended december 31 , ( in millions except ratios )	year ended december 31 , 2005	year ended december 31 , 2004	year ended december 31 , change	2005	2004
investment bank	$ 3658	$ 2948	24% ( 24 % )	18% ( 18 % )	17% ( 17 % )
retail financial services	3427	2199	56	26	24
card services	1907	1274	50	16	17
commercial banking	1007	608	66	30	29
treasury & securities services	1037	440	136	55	17
asset & wealth management	1216	681	79	51	17
corporate	-1731 ( 1731 )	61	nm	nm	nm
total	$ 10521	$ 8211	28% ( 28 % )	17% ( 17 % )	16% ( 16 % )

Post-table text:
jpmorgan chase & co .
/ 2005 annual report 35 and are retained in corporate .
these retained expenses include parent company costs that would not be incurred if the segments were stand-alone businesses ; adjustments to align certain corporate staff , technology and operations allocations with market prices ; and other one-time items not aligned with the business segments .
during 2005 , the firm refined cost allocation methodologies related to certain corporate functions , technology and operations expenses in order to improve transparency , consistency and accountability with regard to costs allocated across business segments .
prior periods have not been revised to reflect these new cost allocation methodologies .
capital allocation each business segment is allocated capital by taking into consideration stand- alone peer comparisons , economic risk measures and regulatory capital requirements .
the amount of capital assigned to each business is referred to as equity .
at the time of the merger , goodwill , as well as the associated capital , was allocated solely to corporate .
effective january 2006 , the firm expects to refine its methodology for allocating capital to the business segments to include any goodwill associated with line of business-directed acquisitions since the merger .
u.s .
gaap requires the allocation of goodwill to the business segments for impairment testing ( see critical accounting estimates used by the firm and note 15 on pages 81 2013 83 and 114 2013116 , respectively , of this annual report ) .
see the capital management section on page 56 of this annual report for a discussion of the equity framework .
credit reimbursement tss reimburses the ib for credit portfolio exposures the ib manages on behalf of clients the segments share .
at the time of the merger , the reimbursement methodology was revised to be based upon pre-tax earnings , net of the cost of capital related to those exposures .
prior to the merger , the credit reimbursement was based upon pre-tax earnings , plus the allocated capital associated with the shared clients .
tax-equivalent adjustments segment and firm results reflect revenues on a tax-equivalent basis for segment reporting purposes .
refer to explanation and reconciliation of the firm 2019s non-gaap financial measures on page 31 of this annual report for additional details .
description of business segment reporting methodology results of the business segments are intended to reflect each segment as if it were essentially a stand-alone business .
the management reporting process that derives these results allocates income and expense using market-based methodologies .
effective with the merger on july 1 , 2004 , several of the allocation methodologies were revised , as noted below .
as prior periods have not been revised to reflect these new methodologies , they are not comparable to the presentation of periods beginning with the third quarter of 2004 .
further , the firm continues to assess the assumptions , methodologies and reporting reclassifications used for segment reporting , and further refinements may be implemented in future periods .
revenue sharing when business segments join efforts to sell products and services to the firm 2019s clients , the participating business segments agree to share revenues from those transactions .
these revenue-sharing agreements were revised on the merger date to provide consistency across the lines of business .
funds transfer pricing funds transfer pricing ( 201cftp 201d ) is used to allocate interest income and expense to each business and transfer the primary interest rate risk exposures to corporate .
the allocation process is unique to each business and considers the interest rate risk , liquidity risk and regulatory requirements of its stand- alone peers .
business segments may retain certain interest rate exposures , subject to management approval , that would be expected in the normal operation of a similar peer business .
in the third quarter of 2004 , ftp was revised to conform the policies of the combined firms .
expense allocation where business segments use services provided by support units within the firm , the costs of those support units are allocated to the business segments .
those expenses are allocated based upon their actual cost , or the lower of actual cost or market cost , as well as upon usage of the services provided .
effective with the third quarter of 2004 , the cost allocation methodologies of the heritage firms were aligned to provide consistency across the business segments .
in addition , expenses related to certain corporate functions , technology and operations ceased to be allocated to the business segments .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what would 2005 operating income be without the commercial banking segment?

Dialogue history:
[Turn 1] what was the percentage of total segment operations that was made of investment banking in 2005?
[Turn 2] what would 2005 operating income be without the commercial banking segment?

Pre-table text:
segment results 2013 operating basis ( a ) ( b ) ( table continued from previous page ) year ended december 31 , operating earnings return on common equity 2013 goodwill ( c ) .

Table:
year ended december 31 , ( in millions except ratios )	year ended december 31 , 2005	year ended december 31 , 2004	year ended december 31 , change	2005	2004
investment bank	$ 3658	$ 2948	24% ( 24 % )	18% ( 18 % )	17% ( 17 % )
retail financial services	3427	2199	56	26	24
card services	1907	1274	50	16	17
commercial banking	1007	608	66	30	29
treasury & securities services	1037	440	136	55	17
asset & wealth management	1216	681	79	51	17
corporate	-1731 ( 1731 )	61	nm	nm	nm
total	$ 10521	$ 8211	28% ( 28 % )	17% ( 17 % )	16% ( 16 % )

Post-table text:
jpmorgan chase & co .
/ 2005 annual report 35 and are retained in corporate .
these retained expenses include parent company costs that would not be incurred if the segments were stand-alone businesses ; adjustments to align certain corporate staff , technology and operations allocations with market prices ; and other one-time items not aligned with the business segments .
during 2005 , the firm refined cost allocation methodologies related to certain corporate functions , technology and operations expenses in order to improve transparency , consistency and accountability with regard to costs allocated across business segments .
prior periods have not been revised to reflect these new cost allocation methodologies .
capital allocation each business segment is allocated capital by taking into consideration stand- alone peer comparisons , economic risk measures and regulatory capital requirements .
the amount of capital assigned to each business is referred to as equity .
at the time of the merger , goodwill , as well as the associated capital , was allocated solely to corporate .
effective january 2006 , the firm expects to refine its methodology for allocating capital to the business segments to include any goodwill associated with line of business-directed acquisitions since the merger .
u.s .
gaap requires the allocation of goodwill to the business segments for impairment testing ( see critical accounting estimates used by the firm and note 15 on pages 81 2013 83 and 114 2013116 , respectively , of this annual report ) .
see the capital management section on page 56 of this annual report for a discussion of the equity framework .
credit reimbursement tss reimburses the ib for credit portfolio exposures the ib manages on behalf of clients the segments share .
at the time of the merger , the reimbursement methodology was revised to be based upon pre-tax earnings , net of the cost of capital related to those exposures .
prior to the merger , the credit reimbursement was based upon pre-tax earnings , plus the allocated capital associated with the shared clients .
tax-equivalent adjustments segment and firm results reflect revenues on a tax-equivalent basis for segment reporting purposes .
refer to explanation and reconciliation of the firm 2019s non-gaap financial measures on page 31 of this annual report for additional details .
description of business segment reporting methodology results of the business segments are intended to reflect each segment as if it were essentially a stand-alone business .
the management reporting process that derives these results allocates income and expense using market-based methodologies .
effective with the merger on july 1 , 2004 , several of the allocation methodologies were revised , as noted below .
as prior periods have not been revised to reflect these new methodologies , they are not comparable to the presentation of periods beginning with the third quarter of 2004 .
further , the firm continues to assess the assumptions , methodologies and reporting reclassifications used for segment reporting , and further refinements may be implemented in future periods .
revenue sharing when business segments join efforts to sell products and services to the firm 2019s clients , the participating business segments agree to share revenues from those transactions .
these revenue-sharing agreements were revised on the merger date to provide consistency across the lines of business .
funds transfer pricing funds transfer pricing ( 201cftp 201d ) is used to allocate interest income and expense to each business and transfer the primary interest rate risk exposures to corporate .
the allocation process is unique to each business and considers the interest rate risk , liquidity risk and regulatory requirements of its stand- alone peers .
business segments may retain certain interest rate exposures , subject to management approval , that would be expected in the normal operation of a similar peer business .
in the third quarter of 2004 , ftp was revised to conform the policies of the combined firms .
expense allocation where business segments use services provided by support units within the firm , the costs of those support units are allocated to the business segments .
those expenses are allocated based upon their actual cost , or the lower of actual cost or market cost , as well as upon usage of the services provided .
effective with the third quarter of 2004 , the cost allocation methodologies of the heritage firms were aligned to provide consistency across the business segments .
in addition , expenses related to certain corporate functions , technology and operations ceased to be allocated to the business segments .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the value of credit net in 2017?

Dialogue history:
[Turn 1] what was the value of credit net in 2017?

Pre-table text:
the goldman sachs group , inc .
and subsidiaries notes to consolidated financial statements in the tables above : 2030 the gross fair values exclude the effects of both counterparty netting and collateral netting , and therefore are not representative of the firm 2019s exposure .
2030 counterparty netting is reflected in each level to the extent that receivable and payable balances are netted within the same level and is included in counterparty netting in levels .
where the counterparty netting is across levels , the netting is included in cross-level counterparty netting .
2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
significant unobservable inputs the table below presents the amount of level 3 assets ( liabilities ) , and ranges , averages and medians of significant unobservable inputs used to value the firm 2019s level 3 derivatives .
level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december $ in millions 2017 2016 .

Table:
$ in millions	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2017	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2016
interest rates net	$ -410 ( 410 )	$ -381 ( 381 )
correlation	( 10 ) % (  % ) to 95% ( 95 % ) ( 71%/79% ( 71%/79 % ) )	( 10 ) % (  % ) to 86% ( 86 % ) ( 56%/60% ( 56%/60 % ) )
volatility ( bps )	31 to 150 ( 84/78 )	31 to 151 ( 84/57 )
credit net	$ 1505	$ 2504
correlation	28% ( 28 % ) to 84% ( 84 % ) ( 61%/60% ( 61%/60 % ) )	35% ( 35 % ) to 91% ( 91 % ) ( 65%/68% ( 65%/68 % ) )
credit spreads ( bps )	1 to 633 ( 69/42 )	1 to 993 ( 122/73 )
upfront credit points	0 to 97 ( 42/38 )	0 to 100 ( 43/35 )
recovery rates	22% ( 22 % ) to 73% ( 73 % ) ( 68%/73% ( 68%/73 % ) )	1% ( 1 % ) to 97% ( 97 % ) ( 58%/70% ( 58%/70 % ) )
currencies net	$ -181 ( 181 )	$ 3
correlation	49% ( 49 % ) to 72% ( 72 % ) ( 61%/62% ( 61%/62 % ) )	25% ( 25 % ) to 70% ( 70 % ) ( 50%/55% ( 50%/55 % ) )
commodities net	$ 47	$ 73
volatility	9% ( 9 % ) to 79% ( 79 % ) ( 24%/24% ( 24%/24 % ) )	13% ( 13 % ) to 68% ( 68 % ) ( 33%/33% ( 33%/33 % ) )
natural gas spread	$ ( 2.38 ) to $ 3.34 ( $ ( 0.22 ) /$ ( 0.12 ) )	$ ( 1.81 ) to $ 4.33 ( $ ( 0.14 ) /$ ( 0.05 ) )
oil spread	$ ( 2.86 ) to $ 23.61 ( $ 6.47/$ 2.35 )	$ ( 19.72 ) to $ 64.92 ( $ 25.30/$ 16.43 )
equities net	$ -1249 ( 1249 )	$ -3416 ( 3416 )
correlation	( 36 ) % (  % ) to 94% ( 94 % ) ( 50%/52% ( 50%/52 % ) )	( 39 ) % (  % ) to 88% ( 88 % ) ( 41%/41% ( 41%/41 % ) )
volatility	4% ( 4 % ) to 72% ( 72 % ) ( 24%/22% ( 24%/22 % ) )	5% ( 5 % ) to 72% ( 72 % ) ( 24%/23% ( 24%/23 % ) )

Post-table text:
in the table above : 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
2030 ranges represent the significant unobservable inputs that were used in the valuation of each type of derivative .
2030 averages represent the arithmetic average of the inputs and are not weighted by the relative fair value or notional of the respective financial instruments .
an average greater than the median indicates that the majority of inputs are below the average .
for example , the difference between the average and the median for credit spreads and oil spread inputs indicates that the majority of the inputs fall in the lower end of the range .
2030 the ranges , averages and medians of these inputs are not representative of the appropriate inputs to use when calculating the fair value of any one derivative .
for example , the highest correlation for interest rate derivatives is appropriate for valuing a specific interest rate derivative but may not be appropriate for valuing any other interest rate derivative .
accordingly , the ranges of inputs do not represent uncertainty in , or possible ranges of , fair value measurements of the firm 2019s level 3 derivatives .
2030 interest rates , currencies and equities derivatives are valued using option pricing models , credit derivatives are valued using option pricing , correlation and discounted cash flow models , and commodities derivatives are valued using option pricing and discounted cash flow models .
2030 the fair value of any one instrument may be determined using multiple valuation techniques .
for example , option pricing models and discounted cash flows models are typically used together to determine fair value .
therefore , the level 3 balance encompasses both of these techniques .
2030 correlation within currencies and equities includes cross- product type correlation .
2030 natural gas spread represents the spread per million british thermal units of natural gas .
2030 oil spread represents the spread per barrel of oil and refined products .
range of significant unobservable inputs the following is information about the ranges of significant unobservable inputs used to value the firm 2019s level 3 derivative instruments : 2030 correlation .
ranges for correlation cover a variety of underliers both within one product type ( e.g. , equity index and equity single stock names ) and across product types ( e.g. , correlation of an interest rate and a currency ) , as well as across regions .
generally , cross-product type correlation inputs are used to value more complex instruments and are lower than correlation inputs on assets within the same derivative product type .
2030 volatility .
ranges for volatility cover numerous underliers across a variety of markets , maturities and strike prices .
for example , volatility of equity indices is generally lower than volatility of single stocks .
2030 credit spreads , upfront credit points and recovery rates .
the ranges for credit spreads , upfront credit points and recovery rates cover a variety of underliers ( index and single names ) , regions , sectors , maturities and credit qualities ( high-yield and investment-grade ) .
the broad range of this population gives rise to the width of the ranges of significant unobservable inputs .
130 goldman sachs 2017 form 10-k .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was it in 2016?

Dialogue history:
[Turn 1] what was the value of credit net in 2017?
[Turn 2] what was it in 2016?

Pre-table text:
the goldman sachs group , inc .
and subsidiaries notes to consolidated financial statements in the tables above : 2030 the gross fair values exclude the effects of both counterparty netting and collateral netting , and therefore are not representative of the firm 2019s exposure .
2030 counterparty netting is reflected in each level to the extent that receivable and payable balances are netted within the same level and is included in counterparty netting in levels .
where the counterparty netting is across levels , the netting is included in cross-level counterparty netting .
2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
significant unobservable inputs the table below presents the amount of level 3 assets ( liabilities ) , and ranges , averages and medians of significant unobservable inputs used to value the firm 2019s level 3 derivatives .
level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december $ in millions 2017 2016 .

Table:
$ in millions	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2017	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2016
interest rates net	$ -410 ( 410 )	$ -381 ( 381 )
correlation	( 10 ) % (  % ) to 95% ( 95 % ) ( 71%/79% ( 71%/79 % ) )	( 10 ) % (  % ) to 86% ( 86 % ) ( 56%/60% ( 56%/60 % ) )
volatility ( bps )	31 to 150 ( 84/78 )	31 to 151 ( 84/57 )
credit net	$ 1505	$ 2504
correlation	28% ( 28 % ) to 84% ( 84 % ) ( 61%/60% ( 61%/60 % ) )	35% ( 35 % ) to 91% ( 91 % ) ( 65%/68% ( 65%/68 % ) )
credit spreads ( bps )	1 to 633 ( 69/42 )	1 to 993 ( 122/73 )
upfront credit points	0 to 97 ( 42/38 )	0 to 100 ( 43/35 )
recovery rates	22% ( 22 % ) to 73% ( 73 % ) ( 68%/73% ( 68%/73 % ) )	1% ( 1 % ) to 97% ( 97 % ) ( 58%/70% ( 58%/70 % ) )
currencies net	$ -181 ( 181 )	$ 3
correlation	49% ( 49 % ) to 72% ( 72 % ) ( 61%/62% ( 61%/62 % ) )	25% ( 25 % ) to 70% ( 70 % ) ( 50%/55% ( 50%/55 % ) )
commodities net	$ 47	$ 73
volatility	9% ( 9 % ) to 79% ( 79 % ) ( 24%/24% ( 24%/24 % ) )	13% ( 13 % ) to 68% ( 68 % ) ( 33%/33% ( 33%/33 % ) )
natural gas spread	$ ( 2.38 ) to $ 3.34 ( $ ( 0.22 ) /$ ( 0.12 ) )	$ ( 1.81 ) to $ 4.33 ( $ ( 0.14 ) /$ ( 0.05 ) )
oil spread	$ ( 2.86 ) to $ 23.61 ( $ 6.47/$ 2.35 )	$ ( 19.72 ) to $ 64.92 ( $ 25.30/$ 16.43 )
equities net	$ -1249 ( 1249 )	$ -3416 ( 3416 )
correlation	( 36 ) % (  % ) to 94% ( 94 % ) ( 50%/52% ( 50%/52 % ) )	( 39 ) % (  % ) to 88% ( 88 % ) ( 41%/41% ( 41%/41 % ) )
volatility	4% ( 4 % ) to 72% ( 72 % ) ( 24%/22% ( 24%/22 % ) )	5% ( 5 % ) to 72% ( 72 % ) ( 24%/23% ( 24%/23 % ) )

Post-table text:
in the table above : 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
2030 ranges represent the significant unobservable inputs that were used in the valuation of each type of derivative .
2030 averages represent the arithmetic average of the inputs and are not weighted by the relative fair value or notional of the respective financial instruments .
an average greater than the median indicates that the majority of inputs are below the average .
for example , the difference between the average and the median for credit spreads and oil spread inputs indicates that the majority of the inputs fall in the lower end of the range .
2030 the ranges , averages and medians of these inputs are not representative of the appropriate inputs to use when calculating the fair value of any one derivative .
for example , the highest correlation for interest rate derivatives is appropriate for valuing a specific interest rate derivative but may not be appropriate for valuing any other interest rate derivative .
accordingly , the ranges of inputs do not represent uncertainty in , or possible ranges of , fair value measurements of the firm 2019s level 3 derivatives .
2030 interest rates , currencies and equities derivatives are valued using option pricing models , credit derivatives are valued using option pricing , correlation and discounted cash flow models , and commodities derivatives are valued using option pricing and discounted cash flow models .
2030 the fair value of any one instrument may be determined using multiple valuation techniques .
for example , option pricing models and discounted cash flows models are typically used together to determine fair value .
therefore , the level 3 balance encompasses both of these techniques .
2030 correlation within currencies and equities includes cross- product type correlation .
2030 natural gas spread represents the spread per million british thermal units of natural gas .
2030 oil spread represents the spread per barrel of oil and refined products .
range of significant unobservable inputs the following is information about the ranges of significant unobservable inputs used to value the firm 2019s level 3 derivative instruments : 2030 correlation .
ranges for correlation cover a variety of underliers both within one product type ( e.g. , equity index and equity single stock names ) and across product types ( e.g. , correlation of an interest rate and a currency ) , as well as across regions .
generally , cross-product type correlation inputs are used to value more complex instruments and are lower than correlation inputs on assets within the same derivative product type .
2030 volatility .
ranges for volatility cover numerous underliers across a variety of markets , maturities and strike prices .
for example , volatility of equity indices is generally lower than volatility of single stocks .
2030 credit spreads , upfront credit points and recovery rates .
the ranges for credit spreads , upfront credit points and recovery rates cover a variety of underliers ( index and single names ) , regions , sectors , maturities and credit qualities ( high-yield and investment-grade ) .
the broad range of this population gives rise to the width of the ranges of significant unobservable inputs .
130 goldman sachs 2017 form 10-k .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what is the net change?

Dialogue history:
[Turn 1] what was the value of credit net in 2017?
[Turn 2] what was it in 2016?
[Turn 3] what is the net change?

Pre-table text:
the goldman sachs group , inc .
and subsidiaries notes to consolidated financial statements in the tables above : 2030 the gross fair values exclude the effects of both counterparty netting and collateral netting , and therefore are not representative of the firm 2019s exposure .
2030 counterparty netting is reflected in each level to the extent that receivable and payable balances are netted within the same level and is included in counterparty netting in levels .
where the counterparty netting is across levels , the netting is included in cross-level counterparty netting .
2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
significant unobservable inputs the table below presents the amount of level 3 assets ( liabilities ) , and ranges , averages and medians of significant unobservable inputs used to value the firm 2019s level 3 derivatives .
level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december $ in millions 2017 2016 .

Table:
$ in millions	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2017	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2016
interest rates net	$ -410 ( 410 )	$ -381 ( 381 )
correlation	( 10 ) % (  % ) to 95% ( 95 % ) ( 71%/79% ( 71%/79 % ) )	( 10 ) % (  % ) to 86% ( 86 % ) ( 56%/60% ( 56%/60 % ) )
volatility ( bps )	31 to 150 ( 84/78 )	31 to 151 ( 84/57 )
credit net	$ 1505	$ 2504
correlation	28% ( 28 % ) to 84% ( 84 % ) ( 61%/60% ( 61%/60 % ) )	35% ( 35 % ) to 91% ( 91 % ) ( 65%/68% ( 65%/68 % ) )
credit spreads ( bps )	1 to 633 ( 69/42 )	1 to 993 ( 122/73 )
upfront credit points	0 to 97 ( 42/38 )	0 to 100 ( 43/35 )
recovery rates	22% ( 22 % ) to 73% ( 73 % ) ( 68%/73% ( 68%/73 % ) )	1% ( 1 % ) to 97% ( 97 % ) ( 58%/70% ( 58%/70 % ) )
currencies net	$ -181 ( 181 )	$ 3
correlation	49% ( 49 % ) to 72% ( 72 % ) ( 61%/62% ( 61%/62 % ) )	25% ( 25 % ) to 70% ( 70 % ) ( 50%/55% ( 50%/55 % ) )
commodities net	$ 47	$ 73
volatility	9% ( 9 % ) to 79% ( 79 % ) ( 24%/24% ( 24%/24 % ) )	13% ( 13 % ) to 68% ( 68 % ) ( 33%/33% ( 33%/33 % ) )
natural gas spread	$ ( 2.38 ) to $ 3.34 ( $ ( 0.22 ) /$ ( 0.12 ) )	$ ( 1.81 ) to $ 4.33 ( $ ( 0.14 ) /$ ( 0.05 ) )
oil spread	$ ( 2.86 ) to $ 23.61 ( $ 6.47/$ 2.35 )	$ ( 19.72 ) to $ 64.92 ( $ 25.30/$ 16.43 )
equities net	$ -1249 ( 1249 )	$ -3416 ( 3416 )
correlation	( 36 ) % (  % ) to 94% ( 94 % ) ( 50%/52% ( 50%/52 % ) )	( 39 ) % (  % ) to 88% ( 88 % ) ( 41%/41% ( 41%/41 % ) )
volatility	4% ( 4 % ) to 72% ( 72 % ) ( 24%/22% ( 24%/22 % ) )	5% ( 5 % ) to 72% ( 72 % ) ( 24%/23% ( 24%/23 % ) )

Post-table text:
in the table above : 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
2030 ranges represent the significant unobservable inputs that were used in the valuation of each type of derivative .
2030 averages represent the arithmetic average of the inputs and are not weighted by the relative fair value or notional of the respective financial instruments .
an average greater than the median indicates that the majority of inputs are below the average .
for example , the difference between the average and the median for credit spreads and oil spread inputs indicates that the majority of the inputs fall in the lower end of the range .
2030 the ranges , averages and medians of these inputs are not representative of the appropriate inputs to use when calculating the fair value of any one derivative .
for example , the highest correlation for interest rate derivatives is appropriate for valuing a specific interest rate derivative but may not be appropriate for valuing any other interest rate derivative .
accordingly , the ranges of inputs do not represent uncertainty in , or possible ranges of , fair value measurements of the firm 2019s level 3 derivatives .
2030 interest rates , currencies and equities derivatives are valued using option pricing models , credit derivatives are valued using option pricing , correlation and discounted cash flow models , and commodities derivatives are valued using option pricing and discounted cash flow models .
2030 the fair value of any one instrument may be determined using multiple valuation techniques .
for example , option pricing models and discounted cash flows models are typically used together to determine fair value .
therefore , the level 3 balance encompasses both of these techniques .
2030 correlation within currencies and equities includes cross- product type correlation .
2030 natural gas spread represents the spread per million british thermal units of natural gas .
2030 oil spread represents the spread per barrel of oil and refined products .
range of significant unobservable inputs the following is information about the ranges of significant unobservable inputs used to value the firm 2019s level 3 derivative instruments : 2030 correlation .
ranges for correlation cover a variety of underliers both within one product type ( e.g. , equity index and equity single stock names ) and across product types ( e.g. , correlation of an interest rate and a currency ) , as well as across regions .
generally , cross-product type correlation inputs are used to value more complex instruments and are lower than correlation inputs on assets within the same derivative product type .
2030 volatility .
ranges for volatility cover numerous underliers across a variety of markets , maturities and strike prices .
for example , volatility of equity indices is generally lower than volatility of single stocks .
2030 credit spreads , upfront credit points and recovery rates .
the ranges for credit spreads , upfront credit points and recovery rates cover a variety of underliers ( index and single names ) , regions , sectors , maturities and credit qualities ( high-yield and investment-grade ) .
the broad range of this population gives rise to the width of the ranges of significant unobservable inputs .
130 goldman sachs 2017 form 10-k .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the 2016 value?

Dialogue history:
[Turn 1] what was the value of credit net in 2017?
[Turn 2] what was it in 2016?
[Turn 3] what is the net change?
[Turn 4] what was the 2016 value?

Pre-table text:
the goldman sachs group , inc .
and subsidiaries notes to consolidated financial statements in the tables above : 2030 the gross fair values exclude the effects of both counterparty netting and collateral netting , and therefore are not representative of the firm 2019s exposure .
2030 counterparty netting is reflected in each level to the extent that receivable and payable balances are netted within the same level and is included in counterparty netting in levels .
where the counterparty netting is across levels , the netting is included in cross-level counterparty netting .
2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
significant unobservable inputs the table below presents the amount of level 3 assets ( liabilities ) , and ranges , averages and medians of significant unobservable inputs used to value the firm 2019s level 3 derivatives .
level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december $ in millions 2017 2016 .

Table:
$ in millions	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2017	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2016
interest rates net	$ -410 ( 410 )	$ -381 ( 381 )
correlation	( 10 ) % (  % ) to 95% ( 95 % ) ( 71%/79% ( 71%/79 % ) )	( 10 ) % (  % ) to 86% ( 86 % ) ( 56%/60% ( 56%/60 % ) )
volatility ( bps )	31 to 150 ( 84/78 )	31 to 151 ( 84/57 )
credit net	$ 1505	$ 2504
correlation	28% ( 28 % ) to 84% ( 84 % ) ( 61%/60% ( 61%/60 % ) )	35% ( 35 % ) to 91% ( 91 % ) ( 65%/68% ( 65%/68 % ) )
credit spreads ( bps )	1 to 633 ( 69/42 )	1 to 993 ( 122/73 )
upfront credit points	0 to 97 ( 42/38 )	0 to 100 ( 43/35 )
recovery rates	22% ( 22 % ) to 73% ( 73 % ) ( 68%/73% ( 68%/73 % ) )	1% ( 1 % ) to 97% ( 97 % ) ( 58%/70% ( 58%/70 % ) )
currencies net	$ -181 ( 181 )	$ 3
correlation	49% ( 49 % ) to 72% ( 72 % ) ( 61%/62% ( 61%/62 % ) )	25% ( 25 % ) to 70% ( 70 % ) ( 50%/55% ( 50%/55 % ) )
commodities net	$ 47	$ 73
volatility	9% ( 9 % ) to 79% ( 79 % ) ( 24%/24% ( 24%/24 % ) )	13% ( 13 % ) to 68% ( 68 % ) ( 33%/33% ( 33%/33 % ) )
natural gas spread	$ ( 2.38 ) to $ 3.34 ( $ ( 0.22 ) /$ ( 0.12 ) )	$ ( 1.81 ) to $ 4.33 ( $ ( 0.14 ) /$ ( 0.05 ) )
oil spread	$ ( 2.86 ) to $ 23.61 ( $ 6.47/$ 2.35 )	$ ( 19.72 ) to $ 64.92 ( $ 25.30/$ 16.43 )
equities net	$ -1249 ( 1249 )	$ -3416 ( 3416 )
correlation	( 36 ) % (  % ) to 94% ( 94 % ) ( 50%/52% ( 50%/52 % ) )	( 39 ) % (  % ) to 88% ( 88 % ) ( 41%/41% ( 41%/41 % ) )
volatility	4% ( 4 % ) to 72% ( 72 % ) ( 24%/22% ( 24%/22 % ) )	5% ( 5 % ) to 72% ( 72 % ) ( 24%/23% ( 24%/23 % ) )

Post-table text:
in the table above : 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
2030 ranges represent the significant unobservable inputs that were used in the valuation of each type of derivative .
2030 averages represent the arithmetic average of the inputs and are not weighted by the relative fair value or notional of the respective financial instruments .
an average greater than the median indicates that the majority of inputs are below the average .
for example , the difference between the average and the median for credit spreads and oil spread inputs indicates that the majority of the inputs fall in the lower end of the range .
2030 the ranges , averages and medians of these inputs are not representative of the appropriate inputs to use when calculating the fair value of any one derivative .
for example , the highest correlation for interest rate derivatives is appropriate for valuing a specific interest rate derivative but may not be appropriate for valuing any other interest rate derivative .
accordingly , the ranges of inputs do not represent uncertainty in , or possible ranges of , fair value measurements of the firm 2019s level 3 derivatives .
2030 interest rates , currencies and equities derivatives are valued using option pricing models , credit derivatives are valued using option pricing , correlation and discounted cash flow models , and commodities derivatives are valued using option pricing and discounted cash flow models .
2030 the fair value of any one instrument may be determined using multiple valuation techniques .
for example , option pricing models and discounted cash flows models are typically used together to determine fair value .
therefore , the level 3 balance encompasses both of these techniques .
2030 correlation within currencies and equities includes cross- product type correlation .
2030 natural gas spread represents the spread per million british thermal units of natural gas .
2030 oil spread represents the spread per barrel of oil and refined products .
range of significant unobservable inputs the following is information about the ranges of significant unobservable inputs used to value the firm 2019s level 3 derivative instruments : 2030 correlation .
ranges for correlation cover a variety of underliers both within one product type ( e.g. , equity index and equity single stock names ) and across product types ( e.g. , correlation of an interest rate and a currency ) , as well as across regions .
generally , cross-product type correlation inputs are used to value more complex instruments and are lower than correlation inputs on assets within the same derivative product type .
2030 volatility .
ranges for volatility cover numerous underliers across a variety of markets , maturities and strike prices .
for example , volatility of equity indices is generally lower than volatility of single stocks .
2030 credit spreads , upfront credit points and recovery rates .
the ranges for credit spreads , upfront credit points and recovery rates cover a variety of underliers ( index and single names ) , regions , sectors , maturities and credit qualities ( high-yield and investment-grade ) .
the broad range of this population gives rise to the width of the ranges of significant unobservable inputs .
130 goldman sachs 2017 form 10-k .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the change over the 2016 value?

Dialogue history:
[Turn 1] what was the value of credit net in 2017?
[Turn 2] what was it in 2016?
[Turn 3] what is the net change?
[Turn 4] what was the 2016 value?
[Turn 5] what was the change over the 2016 value?

Pre-table text:
the goldman sachs group , inc .
and subsidiaries notes to consolidated financial statements in the tables above : 2030 the gross fair values exclude the effects of both counterparty netting and collateral netting , and therefore are not representative of the firm 2019s exposure .
2030 counterparty netting is reflected in each level to the extent that receivable and payable balances are netted within the same level and is included in counterparty netting in levels .
where the counterparty netting is across levels , the netting is included in cross-level counterparty netting .
2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
significant unobservable inputs the table below presents the amount of level 3 assets ( liabilities ) , and ranges , averages and medians of significant unobservable inputs used to value the firm 2019s level 3 derivatives .
level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december $ in millions 2017 2016 .

Table:
$ in millions	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2017	level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2016
interest rates net	$ -410 ( 410 )	$ -381 ( 381 )
correlation	( 10 ) % (  % ) to 95% ( 95 % ) ( 71%/79% ( 71%/79 % ) )	( 10 ) % (  % ) to 86% ( 86 % ) ( 56%/60% ( 56%/60 % ) )
volatility ( bps )	31 to 150 ( 84/78 )	31 to 151 ( 84/57 )
credit net	$ 1505	$ 2504
correlation	28% ( 28 % ) to 84% ( 84 % ) ( 61%/60% ( 61%/60 % ) )	35% ( 35 % ) to 91% ( 91 % ) ( 65%/68% ( 65%/68 % ) )
credit spreads ( bps )	1 to 633 ( 69/42 )	1 to 993 ( 122/73 )
upfront credit points	0 to 97 ( 42/38 )	0 to 100 ( 43/35 )
recovery rates	22% ( 22 % ) to 73% ( 73 % ) ( 68%/73% ( 68%/73 % ) )	1% ( 1 % ) to 97% ( 97 % ) ( 58%/70% ( 58%/70 % ) )
currencies net	$ -181 ( 181 )	$ 3
correlation	49% ( 49 % ) to 72% ( 72 % ) ( 61%/62% ( 61%/62 % ) )	25% ( 25 % ) to 70% ( 70 % ) ( 50%/55% ( 50%/55 % ) )
commodities net	$ 47	$ 73
volatility	9% ( 9 % ) to 79% ( 79 % ) ( 24%/24% ( 24%/24 % ) )	13% ( 13 % ) to 68% ( 68 % ) ( 33%/33% ( 33%/33 % ) )
natural gas spread	$ ( 2.38 ) to $ 3.34 ( $ ( 0.22 ) /$ ( 0.12 ) )	$ ( 1.81 ) to $ 4.33 ( $ ( 0.14 ) /$ ( 0.05 ) )
oil spread	$ ( 2.86 ) to $ 23.61 ( $ 6.47/$ 2.35 )	$ ( 19.72 ) to $ 64.92 ( $ 25.30/$ 16.43 )
equities net	$ -1249 ( 1249 )	$ -3416 ( 3416 )
correlation	( 36 ) % (  % ) to 94% ( 94 % ) ( 50%/52% ( 50%/52 % ) )	( 39 ) % (  % ) to 88% ( 88 % ) ( 41%/41% ( 41%/41 % ) )
volatility	4% ( 4 % ) to 72% ( 72 % ) ( 24%/22% ( 24%/22 % ) )	5% ( 5 % ) to 72% ( 72 % ) ( 24%/23% ( 24%/23 % ) )

Post-table text:
in the table above : 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts .
2030 ranges represent the significant unobservable inputs that were used in the valuation of each type of derivative .
2030 averages represent the arithmetic average of the inputs and are not weighted by the relative fair value or notional of the respective financial instruments .
an average greater than the median indicates that the majority of inputs are below the average .
for example , the difference between the average and the median for credit spreads and oil spread inputs indicates that the majority of the inputs fall in the lower end of the range .
2030 the ranges , averages and medians of these inputs are not representative of the appropriate inputs to use when calculating the fair value of any one derivative .
for example , the highest correlation for interest rate derivatives is appropriate for valuing a specific interest rate derivative but may not be appropriate for valuing any other interest rate derivative .
accordingly , the ranges of inputs do not represent uncertainty in , or possible ranges of , fair value measurements of the firm 2019s level 3 derivatives .
2030 interest rates , currencies and equities derivatives are valued using option pricing models , credit derivatives are valued using option pricing , correlation and discounted cash flow models , and commodities derivatives are valued using option pricing and discounted cash flow models .
2030 the fair value of any one instrument may be determined using multiple valuation techniques .
for example , option pricing models and discounted cash flows models are typically used together to determine fair value .
therefore , the level 3 balance encompasses both of these techniques .
2030 correlation within currencies and equities includes cross- product type correlation .
2030 natural gas spread represents the spread per million british thermal units of natural gas .
2030 oil spread represents the spread per barrel of oil and refined products .
range of significant unobservable inputs the following is information about the ranges of significant unobservable inputs used to value the firm 2019s level 3 derivative instruments : 2030 correlation .
ranges for correlation cover a variety of underliers both within one product type ( e.g. , equity index and equity single stock names ) and across product types ( e.g. , correlation of an interest rate and a currency ) , as well as across regions .
generally , cross-product type correlation inputs are used to value more complex instruments and are lower than correlation inputs on assets within the same derivative product type .
2030 volatility .
ranges for volatility cover numerous underliers across a variety of markets , maturities and strike prices .
for example , volatility of equity indices is generally lower than volatility of single stocks .
2030 credit spreads , upfront credit points and recovery rates .
the ranges for credit spreads , upfront credit points and recovery rates cover a variety of underliers ( index and single names ) , regions , sectors , maturities and credit qualities ( high-yield and investment-grade ) .
the broad range of this population gives rise to the width of the ranges of significant unobservable inputs .
130 goldman sachs 2017 form 10-k .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the change in amortized cost in 2009?

Dialogue history:
[Turn 1] what was the change in amortized cost in 2009?

Pre-table text:
impairment net unrealized losses on securities available for sale were as follows as of december 31: .

Table:
( in millions )	2009	2008
fair value	$ 72699	$ 54163
amortized cost	74843	60786
net unrealized loss pre-tax	$ -2144 ( 2144 )	$ -6623 ( 6623 )
net unrealized loss after-tax	$ -1316 ( 1316 )	$ -4057 ( 4057 )

Post-table text:
the above net unrealized loss amounts at december 31 , 2009 and december 31 , 2008 excluded the remaining net unrealized loss of $ 1.01 billion , or $ 635 million after-tax , and $ 2.27 billion , or $ 1.39 billion after- tax , respectively , related to reclassifications of securities available for sale to securities held to maturity .
these after-tax amounts are recorded in other comprehensive income .
the decline in the remaining after-tax unrealized loss amounts related to transferred securities resulted from amortization and from the recognition of losses from other-than-temporary impairment on certain of the securities .
we conduct periodic reviews of individual securities to assess whether other-than-temporary impairment exists .
to the extent that other-than-temporary impairment is identified , the impairment is broken into a credit component and a non-credit component .
the credit component is recognized in our consolidated statement of income , and the non-credit component is recognized in other comprehensive income to the extent that management does not intend to sell the security ( see note 3 of the notes to consolidated financial statements included under item 8 ) .
the assessment of other-than-temporary impairment involves an evaluation of economic and security- specific factors , which are more fully described in note 3 .
such factors are based upon estimates , derived by management , which contemplate current market conditions and security-specific performance .
to the extent that market conditions are worse than management 2019s expectations , other-than-temporary impairment could increase , in particular the credit component that would be recognized in our consolidated statement of income .
national housing prices , according to the case-shiller national hpi , have declined to date approximately 30% ( 30 % ) peak-to-current .
management currently estimates that national housing prices will continue to decline and bottom out during the second half of 2010 , consistent with a peak-to-trough housing price decline of approximately 37% ( 37 % ) .
as an indication of the sensitivity of our portfolio with respect to our more significant assumptions underlying our assessment of impairment , if we were to increase our default estimates to 110% ( 110 % ) of management 2019s current expectations with a corresponding slowing of prepayment speeds to 90% ( 90 % ) of management 2019s current expectations , credit-related other-than-temporary impairment could increase by approximately $ 120 million to $ 125 million , which impairment would be recorded in our consolidated statement of income .
excluding the securities for which other-than-temporary impairment was recorded , management considers the aggregate decline in fair value of the remaining securities and the resulting net unrealized losses to be temporary and not the result of any material changes in the credit characteristics of the securities .
additional information about our assessment of impairment is provided in note 3 of the notes to consolidated financial statements included under item 8. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
so what was the percentage change during this time?

Dialogue history:
[Turn 1] what was the change in amortized cost in 2009?
[Turn 2] so what was the percentage change during this time?

Pre-table text:
impairment net unrealized losses on securities available for sale were as follows as of december 31: .

Table:
( in millions )	2009	2008
fair value	$ 72699	$ 54163
amortized cost	74843	60786
net unrealized loss pre-tax	$ -2144 ( 2144 )	$ -6623 ( 6623 )
net unrealized loss after-tax	$ -1316 ( 1316 )	$ -4057 ( 4057 )

Post-table text:
the above net unrealized loss amounts at december 31 , 2009 and december 31 , 2008 excluded the remaining net unrealized loss of $ 1.01 billion , or $ 635 million after-tax , and $ 2.27 billion , or $ 1.39 billion after- tax , respectively , related to reclassifications of securities available for sale to securities held to maturity .
these after-tax amounts are recorded in other comprehensive income .
the decline in the remaining after-tax unrealized loss amounts related to transferred securities resulted from amortization and from the recognition of losses from other-than-temporary impairment on certain of the securities .
we conduct periodic reviews of individual securities to assess whether other-than-temporary impairment exists .
to the extent that other-than-temporary impairment is identified , the impairment is broken into a credit component and a non-credit component .
the credit component is recognized in our consolidated statement of income , and the non-credit component is recognized in other comprehensive income to the extent that management does not intend to sell the security ( see note 3 of the notes to consolidated financial statements included under item 8 ) .
the assessment of other-than-temporary impairment involves an evaluation of economic and security- specific factors , which are more fully described in note 3 .
such factors are based upon estimates , derived by management , which contemplate current market conditions and security-specific performance .
to the extent that market conditions are worse than management 2019s expectations , other-than-temporary impairment could increase , in particular the credit component that would be recognized in our consolidated statement of income .
national housing prices , according to the case-shiller national hpi , have declined to date approximately 30% ( 30 % ) peak-to-current .
management currently estimates that national housing prices will continue to decline and bottom out during the second half of 2010 , consistent with a peak-to-trough housing price decline of approximately 37% ( 37 % ) .
as an indication of the sensitivity of our portfolio with respect to our more significant assumptions underlying our assessment of impairment , if we were to increase our default estimates to 110% ( 110 % ) of management 2019s current expectations with a corresponding slowing of prepayment speeds to 90% ( 90 % ) of management 2019s current expectations , credit-related other-than-temporary impairment could increase by approximately $ 120 million to $ 125 million , which impairment would be recorded in our consolidated statement of income .
excluding the securities for which other-than-temporary impairment was recorded , management considers the aggregate decline in fair value of the remaining securities and the resulting net unrealized losses to be temporary and not the result of any material changes in the credit characteristics of the securities .
additional information about our assessment of impairment is provided in note 3 of the notes to consolidated financial statements included under item 8. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what is the amortized cost as a percentage of the fair value of securities in 2009?

Dialogue history:
[Turn 1] what was the change in amortized cost in 2009?
[Turn 2] so what was the percentage change during this time?
[Turn 3] what is the amortized cost as a percentage of the fair value of securities in 2009?

Pre-table text:
impairment net unrealized losses on securities available for sale were as follows as of december 31: .

Table:
( in millions )	2009	2008
fair value	$ 72699	$ 54163
amortized cost	74843	60786
net unrealized loss pre-tax	$ -2144 ( 2144 )	$ -6623 ( 6623 )
net unrealized loss after-tax	$ -1316 ( 1316 )	$ -4057 ( 4057 )

Post-table text:
the above net unrealized loss amounts at december 31 , 2009 and december 31 , 2008 excluded the remaining net unrealized loss of $ 1.01 billion , or $ 635 million after-tax , and $ 2.27 billion , or $ 1.39 billion after- tax , respectively , related to reclassifications of securities available for sale to securities held to maturity .
these after-tax amounts are recorded in other comprehensive income .
the decline in the remaining after-tax unrealized loss amounts related to transferred securities resulted from amortization and from the recognition of losses from other-than-temporary impairment on certain of the securities .
we conduct periodic reviews of individual securities to assess whether other-than-temporary impairment exists .
to the extent that other-than-temporary impairment is identified , the impairment is broken into a credit component and a non-credit component .
the credit component is recognized in our consolidated statement of income , and the non-credit component is recognized in other comprehensive income to the extent that management does not intend to sell the security ( see note 3 of the notes to consolidated financial statements included under item 8 ) .
the assessment of other-than-temporary impairment involves an evaluation of economic and security- specific factors , which are more fully described in note 3 .
such factors are based upon estimates , derived by management , which contemplate current market conditions and security-specific performance .
to the extent that market conditions are worse than management 2019s expectations , other-than-temporary impairment could increase , in particular the credit component that would be recognized in our consolidated statement of income .
national housing prices , according to the case-shiller national hpi , have declined to date approximately 30% ( 30 % ) peak-to-current .
management currently estimates that national housing prices will continue to decline and bottom out during the second half of 2010 , consistent with a peak-to-trough housing price decline of approximately 37% ( 37 % ) .
as an indication of the sensitivity of our portfolio with respect to our more significant assumptions underlying our assessment of impairment , if we were to increase our default estimates to 110% ( 110 % ) of management 2019s current expectations with a corresponding slowing of prepayment speeds to 90% ( 90 % ) of management 2019s current expectations , credit-related other-than-temporary impairment could increase by approximately $ 120 million to $ 125 million , which impairment would be recorded in our consolidated statement of income .
excluding the securities for which other-than-temporary impairment was recorded , management considers the aggregate decline in fair value of the remaining securities and the resulting net unrealized losses to be temporary and not the result of any material changes in the credit characteristics of the securities .
additional information about our assessment of impairment is provided in note 3 of the notes to consolidated financial statements included under item 8. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the net change in value of the amortization expense from 2016 to 2017?

Dialogue history:
[Turn 1] what was the net change in value of the amortization expense from 2016 to 2017?

Pre-table text:
table of contents the estimated amortization expense at september 26 , 2015 for each of the five succeeding fiscal years was as follows: .

Table:
fiscal 2016	$ 377.0
fiscal 2017	$ 365.6
fiscal 2018	$ 355.1
fiscal 2019	$ 343.5
fiscal 2020	$ 332.3

Post-table text:
goodwill in accordance with asc 350 , intangibles 2014goodwill and other ( asc 350 ) , the company tests goodwill for impairment at the reporting unit level on an annual basis and between annual tests if events and circumstances indicate it is more likely than not that the fair value of a reporting unit is less than its carrying value .
events that could indicate impairment and trigger an interim impairment assessment include , but are not limited to , current economic and market conditions , including a decline in market capitalization , a significant adverse change in legal factors , business climate , operational performance of the business or key personnel , and an adverse action or assessment by a regulator .
in performing the impairment test , the company utilizes the two-step approach prescribed under asc 350 .
the first step requires a comparison of the carrying value of each reporting unit to its estimated fair value .
to estimate the fair value of its reporting units for step 1 , the company primarily utilizes the income approach .
the income approach is based on a dcf analysis and calculates the fair value by estimating the after-tax cash flows attributable to a reporting unit and then discounting the after-tax cash flows to a present value using a risk-adjusted discount rate .
assumptions used in the dcf require significant judgment , including judgment about appropriate discount rates and terminal values , growth rates , and the amount and timing of expected future cash flows .
the forecasted cash flows are based on the company 2019s most recent budget and strategic plan and for years beyond this period , the company 2019s estimates are based on assumed growth rates expected as of the measurement date .
the company believes its assumptions are consistent with the plans and estimates used to manage the underlying businesses .
the discount rates used are intended to reflect the risks inherent in future cash flow projections and are based on estimates of the weighted-average cost of capital ( 201cwacc 201d ) of market participants relative to each respective reporting unit .
the market approach considers comparable market data based on multiples of revenue or earnings before interest , taxes , depreciation and amortization ( 201cebitda 201d ) and is primarily used as a corroborative analysis to the results of the dcf analysis .
the company believes its assumptions used to determine the fair value of its reporting units are reasonable .
if different assumptions were used , particularly with respect to forecasted cash flows , terminal values , waccs , or market multiples , different estimates of fair value may result and there could be the potential that an impairment charge could result .
actual operating results and the related cash flows of the reporting units could differ from the estimated operating results and related cash flows .
if the carrying value of a reporting unit exceeds its estimated fair value , the company is required to perform the second step of the goodwill impairment test to measure the amount of impairment loss , if any .
the second step of the goodwill impairment test compares the implied fair value of a reporting unit 2019s goodwill to its carrying value .
the implied fair value of goodwill is derived by performing a hypothetical purchase price allocation for each reporting unit as of the measurement date and allocating the reporting unit 2019s estimated fair value to its assets and liabilities .
the residual amount from performing this allocation represents the implied fair value of goodwill .
to the extent this amount is below the carrying value of goodwill , an impairment charge is recorded .
the company conducted its fiscal 2015 impairment test on the first day of the fourth quarter , and as noted above used dcf and market approaches to estimate the fair value of its reporting units as of june 28 , 2015 , and ultimately used the fair value determined by the dcf approach in making its impairment test conclusions .
the company believes it used reasonable estimates and assumptions about future revenue , cost projections , cash flows , market multiples and discount rates as of the measurement date .
as a result of completing step 1 , all of the company's reporting units had fair values exceeding their carrying values , and as such , step 2 of the impairment test was not required .
for illustrative purposes , had the fair value of each of the reporting units that passed step 1 been lower than 10% ( 10 % ) , all of the reporting units would still have passed step 1 of the goodwill impairment test .
at september 26 , 2015 , the company believes that each reporting unit , with goodwill aggregating 2.81 billion , was not at risk of failing step 1 of the goodwill impairment test based on the current forecasts .
the company conducted its fiscal 2014 annual impairment test on the first day of the fourth quarter , and as noted above used dcf and market approaches to estimate the fair value of its reporting units as of june 29 , 2014 , and ultimately used the fair value determined by the dcf approach in making its impairment test conclusions .
the company believes it used reasonable estimates and assumptions about future revenue , cost projections , cash flows , market multiples and discount rates as source : hologic inc , 10-k , november 19 , 2015 powered by morningstar ae document research 2120 the information contained herein may not be copied , adapted or distributed and is not warranted to be accurate , complete or timely .
the user assumes all risks for any damages or losses arising from any use of this information , except to the extent such damages or losses cannot be limited or excluded by applicable law .
past financial performance is no guarantee of future results. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.


[System] You are solving conversational numerical reasoning over financial reports. Use only the current question, dialogue history, pre-table text, table, and post-table text. Return ONLY a JSON object with exactly one key: "program_tokens". The value must be a list of FinQA-family DSL program tokens ending with "EOF". Do not return the final answer directly and do not include extra text.

[User]
Current question:
what was the value in 2016?

Dialogue history:
[Turn 1] what was the net change in value of the amortization expense from 2016 to 2017?
[Turn 2] what was the value in 2016?

Pre-table text:
table of contents the estimated amortization expense at september 26 , 2015 for each of the five succeeding fiscal years was as follows: .

Table:
fiscal 2016	$ 377.0
fiscal 2017	$ 365.6
fiscal 2018	$ 355.1
fiscal 2019	$ 343.5
fiscal 2020	$ 332.3

Post-table text:
goodwill in accordance with asc 350 , intangibles 2014goodwill and other ( asc 350 ) , the company tests goodwill for impairment at the reporting unit level on an annual basis and between annual tests if events and circumstances indicate it is more likely than not that the fair value of a reporting unit is less than its carrying value .
events that could indicate impairment and trigger an interim impairment assessment include , but are not limited to , current economic and market conditions , including a decline in market capitalization , a significant adverse change in legal factors , business climate , operational performance of the business or key personnel , and an adverse action or assessment by a regulator .
in performing the impairment test , the company utilizes the two-step approach prescribed under asc 350 .
the first step requires a comparison of the carrying value of each reporting unit to its estimated fair value .
to estimate the fair value of its reporting units for step 1 , the company primarily utilizes the income approach .
the income approach is based on a dcf analysis and calculates the fair value by estimating the after-tax cash flows attributable to a reporting unit and then discounting the after-tax cash flows to a present value using a risk-adjusted discount rate .
assumptions used in the dcf require significant judgment , including judgment about appropriate discount rates and terminal values , growth rates , and the amount and timing of expected future cash flows .
the forecasted cash flows are based on the company 2019s most recent budget and strategic plan and for years beyond this period , the company 2019s estimates are based on assumed growth rates expected as of the measurement date .
the company believes its assumptions are consistent with the plans and estimates used to manage the underlying businesses .
the discount rates used are intended to reflect the risks inherent in future cash flow projections and are based on estimates of the weighted-average cost of capital ( 201cwacc 201d ) of market participants relative to each respective reporting unit .
the market approach considers comparable market data based on multiples of revenue or earnings before interest , taxes , depreciation and amortization ( 201cebitda 201d ) and is primarily used as a corroborative analysis to the results of the dcf analysis .
the company believes its assumptions used to determine the fair value of its reporting units are reasonable .
if different assumptions were used , particularly with respect to forecasted cash flows , terminal values , waccs , or market multiples , different estimates of fair value may result and there could be the potential that an impairment charge could result .
actual operating results and the related cash flows of the reporting units could differ from the estimated operating results and related cash flows .
if the carrying value of a reporting unit exceeds its estimated fair value , the company is required to perform the second step of the goodwill impairment test to measure the amount of impairment loss , if any .
the second step of the goodwill impairment test compares the implied fair value of a reporting unit 2019s goodwill to its carrying value .
the implied fair value of goodwill is derived by performing a hypothetical purchase price allocation for each reporting unit as of the measurement date and allocating the reporting unit 2019s estimated fair value to its assets and liabilities .
the residual amount from performing this allocation represents the implied fair value of goodwill .
to the extent this amount is below the carrying value of goodwill , an impairment charge is recorded .
the company conducted its fiscal 2015 impairment test on the first day of the fourth quarter , and as noted above used dcf and market approaches to estimate the fair value of its reporting units as of june 28 , 2015 , and ultimately used the fair value determined by the dcf approach in making its impairment test conclusions .
the company believes it used reasonable estimates and assumptions about future revenue , cost projections , cash flows , market multiples and discount rates as of the measurement date .
as a result of completing step 1 , all of the company's reporting units had fair values exceeding their carrying values , and as such , step 2 of the impairment test was not required .
for illustrative purposes , had the fair value of each of the reporting units that passed step 1 been lower than 10% ( 10 % ) , all of the reporting units would still have passed step 1 of the goodwill impairment test .
at september 26 , 2015 , the company believes that each reporting unit , with goodwill aggregating 2.81 billion , was not at risk of failing step 1 of the goodwill impairment test based on the current forecasts .
the company conducted its fiscal 2014 annual impairment test on the first day of the fourth quarter , and as noted above used dcf and market approaches to estimate the fair value of its reporting units as of june 29 , 2014 , and ultimately used the fair value determined by the dcf approach in making its impairment test conclusions .
the company believes it used reasonable estimates and assumptions about future revenue , cost projections , cash flows , market multiples and discount rates as source : hologic inc , 10-k , november 19 , 2015 powered by morningstar ae document research 2120 the information contained herein may not be copied , adapted or distributed and is not warranted to be accurate , complete or timely .
the user assumes all risks for any damages or losses arising from any use of this information , except to the extent such damages or losses cannot be limited or excluded by applicable law .
past financial performance is no guarantee of future results. .

Allowed operations:
add, subtract, multiply, divide, greater, exp, table_max, table_min, table_sum, table_average

Return JSON only in this form:
{"program_tokens": ["subtract(", "5829", "5735", ")", "EOF"]}
For number_turn cases, a valid program can also be a single numeric token followed by EOF, for example {"program_tokens": ["206588", "EOF"]}.