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Financial Statement Analysis with Large Language Models Alex G. Kim1Maximilian Muhn2Valeri V. Nikolaev3 This draft: May 20, 2024 Abstract We investigate whether an LLM can successfully perform financial statement analy-sis in a way similar to a professional human analyst. We provide standardized and anonymous financial... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 1 1 Introduction Can large language models (LLMs) make informed financial decisions or are they simply a support tool? Their advanced capabilities to analyze, interpret, and generate text enable LLMs to excel across a wide range of tasks, including summarization o... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 2 them. In particular, based on the analysis of the two financial statements, the model must decide whether a firm's economic performance is sustainable and, more specifically, whether a company's earnings will grow or decline in the following period. We focus on ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 3 Our approach to testing an LLM's performance involves two steps. First, we anonymize and standardize corporate financial statements to prevent the potential memory of the com-pany by the language model. In particular, we omit company names from the balance sheet... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 4 may be sluggish to incorporate new information into their forecasts, we report them for comparison purposes. We start by analyzing GPT's performance compared to security analysts in predicting the direction of future earnings (Ou and Penman, 1989). At the outset... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 5 non-linearities and interactions among them. Third, to ensure consistency between GPT and ANN, we also use the ANN model trained on the same information set (the income statement and balance sheet) that we provide to GPT. Importantly, we train these models each ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 6 that the results generalize to other LLMs. In particular, Gemini Pro, recently released by Google, achieves a similar level of accuracy compared to GPT 4. Given the documented consistently impressive LLM's performance in fundamental anal-ysis, it is interesting ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 7 are the first to provide large-scale evidence on LLM's ability to analyze financial statements-a complex task that is traditionally performed by human analysts. We show that an LLM can generate state-of-the-art inferences about the direction of the company, outp... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 8 firms' earnings, which serves both as an input in their own stock market recommendations and an output that informs investors (Stickel, 1991; Brown et al., 2015). When making earnings forecasts, their work typically begins with a systematic analysis of financial... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 9 and relate it to future macroeconomic shocks. However, despite the successes of large language models in many tasks, they are pri-marily viewed as a support tool and their ability to act autonomously to perform financial statement analysis at a level of a human ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 10 3 Methodology and Data In this section, we outline how we approach the primary task of using an LLM to analyze and predict earnings changes. Earnings prediction is a complex task that combines qualita-tive and quantitative analyses and involves professional jud... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 11 problem into steps that parallel those followed by human analysts. This prompt effectively ingrains the methodology into the model, guiding it to mimic human-like reasoning in its analysis. We mostly focus on the results from this second prompt in our analysis.... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 12 the robustness of the model's performance outside GPT's training window. In particular, the GPT-4-Turbo preview's training window ends in April 2023, and the model cannot have seen the earnings data of 2023, which was released in late March 2024. Following prio... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 13 4 How Does an LLM Perform Compared to Financial Analysts? In this section, we evaluate the performance of a large language model in the analysis of financial statements aimed at predicting the direction of future earnings by using human analysts as a benchmark.... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 14 where TPis the number of true positive predictions, FPis the number of false positive predictions, and FNis the number of false negative predictions. 4. 2 Main Results Table 2 compares GPT's prediction accuracy with that achieved by financial analysts. Based on... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 15 generally, humans often rely on soft information not easily accessible to a machine (Costello et al., 2020; Liu, 2022), which puts humans at an informational advantage. We next explore the presence of complementarities and trade-offs related to LLM vs. human fo... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 16 GPT when a firm reports a loss and exhibits volatile earnings. These findings are the same for all analyst forecast measures as the magnitudes of the coefficients on Loss and Earn-ings Volatility in columns (2), (3), and (4) are consistently smaller than that o... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 17 our results in Table 3. Does GPT Do Well When Humans Struggle? To explore the relative advantage of an LLM compared to human analysts, we examine instances when human analysts are likely to struggle with accurately forecasting earnings. In particular, we identi... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 18 5. 1 Methodology Following Ou and Penman (1989) and Hunt et al. (2022), we focus on 59 financial vari-ables obtained from the Compustat Annual database to predict future earnings but exclude the price-to-earnings ratio for consistency reasons (stock price is no... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 19 ability of numerical information. Consistent with the results in the analyst sample, our Co T-based GPT predictions achieve an accuracy of 60. 31%, which is on par with the specialized ANN model. In fact, in terms of the F1-score, GPT achieves a value of 63. 45... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 20 Sources of Inaccuracy Next, we explore which firm characteristics are associated with the likelihood of making incorrect earnings predictions. Column (1) of Table 4 focuses on the accuracy of GPT's predictions and is consistent with our findings for the analyst... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 21 an alternative confidence score based on token-level logistic probability values, which we directly take from the probability vector provided by the model. Specifically, we average the logistic probability values across all output tokens to measure the overall ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 22 Results We present the results in Figure 5 and Table 5, columns (7) to (9). GPT 4 achieves the best performance, followed by Gemini 1. 5, and GPT 3. 5. Gemini 1. 5 achieves an overall accuracy of 59. 15%, which is close to that of GPT 4 (61. 05%) in the same 20... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 23 “guessing” the entity, we perform two formal analyses to further rule out this concern. 23 Can GPT Guess Firm Name and Year? In this set of tests, we instruct the model to make guesses about the firm or year based on the financial statements that we provide. Sp... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 24 of the model's training window. Open AI's GPT4-Turbo preview was trained on data up to April 2023, thereby significantly limiting the scope to conduct this analysis. Nevertheless, we use financial statement data from fiscal year 2022 (released in January-March ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 25 most commonly refers to the operating margin. In addition to the profitability information, the model also frequently computes efficiency (asset and inventory turnover) and liquidity (current ratio, current assets, and current liability). The model's rationale ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 26 generated by GPT contains a significant amount of information useful in predicting future earnings, i. e., it indeed represents narrative insights derived from numeric data based on the Co T prompt. This result suggests that the narrative insights serve as the ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 27 In particular, signals that are informative about future expected profits should exhibit a positive association with expected stock returns in the cross-section of firms (Fama and French, 2015). The asset pricing models typically use the current level of profit... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 28 year with the highest expected confidence. By doing so, we match the number of stocks to the number of stocks included in ANN or logit-based portfolios. 7. 2 Results Sharpe Ratios To compute Sharpe ratios, we form equal-weighted and value-weighted portfolios. F... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 29 and short portfolios separately. As expected, the long portfolio substantially outperforms the short portfolio. In the right panel, we plot the cumulative log returns for the long-short portfolio and compare them with the log market portfolio returns (dotted li... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 30 understanding and economic reasoning. Notably, the narrative financial statement analy-sis generated by the language model has substantial informational value in its own right. Building on these findings, we also present a profitable trading strategy based on G... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 31 References Abarbanell, J. S., Bernard, V. L., 1992. Tests of analysts' overreaction/underreaction to earnings information as an explanation for anomalous stock price behavior. The Journal of Finance 47, 1181-1207. Achiam, J., Adler, S., Agarwal, S., Ahmad, L., ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 32 learning and detailed financial data. Journal of Accounting Research 60, 467-515. Choi, G. Y., Kim, A., 2023. Economic footprints of tax audits: A generative ai-driven approach. Chicago Booth Research Paper. Choi, J. H., Hickman, K. E., Monahan, A. B., Schwarcz... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 33 Booth Research Paper. Kim, A. G., Nikolaev, V. V., 2023b. Profitability context and the cross-section of stock returns. Chicago Booth Research Paper. Kothari, S., So, E., Verdi, R., 2016. Analysts' forecasts and asset pricing: A survey. Annual Review of Financi... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 34 Appendix A. Time Series of GPT's Prediction Accuracy This table shows time-series prediction accuracy and F1 scores of GPT and ANN. The last two columns are the differences between the two models (GPT-ANN). Time trend is obtained by regressing accuracy metrics ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 35 Appendix B. Example Balance Sheet and Income Statement Panel A and Panel B show an example of standardized, anonymous balance sheet and income statement. We use Compustat's balancing formula and delete fiscal years. Panel A. Balance Sheet Panel B. Income Statem... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 36 Appendix C. Example Output We present one example output by GPT. GPT has rendered a prediction of “increase” with a moderate magnitude, and a prediction certainty of 0. 7. The correct prediction is “increase. ” Panel A shows the trend analysis results, Panel B ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 37 Appendix D. GPT's Guess About Fiscal Years In Table 6, we show that the accuracy of GPT's fiscal year guesses is 2. 95%. Our sample spans the period 1968-2021, and one might have concern that a pure random guess leads to a probability of 1. 85%, which is lower ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 38 Figure 1. GPT Processing Details This figure illustrates the structure of our experiment. Using raw data from Compustat annual, we construct standardized balance sheet and income statement using Compustat's balancing formulae. Then, we substitute fiscal years w... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 39 Figure 2. GPT vs. Human Analysts This figure compares the prediction performance of GPT and human analysts. Random Walk is based on the current earnings change compared to the previous earnings. Analyst 1m (3m, 6m) denotes the median analyst forecast issued one... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 40 Figure 3. GPT vs. Machine Learning Models This figure compares the prediction performance of GPT and quantitative models based on machine learning. Stepwise Logistic follows Ou and Penman (1989)'s structure with their 59 financial predictors. ANN is a three-lay... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 41 Figure 4. Time Trend in Prediction Accuracy This figure illustrates the time trend in GPT's prediction accuracy (left) and the difference in GPT and ANN's prediction accuracy (right). Left panel demonstrates annual accuracy of GPT's predictions. The dotted line... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 42 Figure 5. Different GPT Specifications This figure compares the model performance depending on several experimental settings. The first four bars are based on GPT's answers on its confidence and the averaged token-level log probabilities. The fifth and six bars... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 43 Figure 6. GPT's Memory This figure shows the experiment results to test GPT's memory. We ask GPT to produce ten most probable company names and the most probable fiscal year from the standardized, anonymous financial statements. Left panel shows the ten most fr... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 44 Figure 7. Sources of Prediction This figure shows descriptive bigram (monogram) frequency counts of GPT answers. Left panel shows the ten most frequently used bigrams in GPT's answers on the financial ratio analysis. Right panel shows the ten most frequently us... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 45 Figure 8. Equal-Weight Portfolio Cumulative Returns Over Time This figure shows cumulative log returns from 1968 to 2021 of the long-short strategies based on GPT predictions. We form equal-weight portfolios on June 30 of each year and hold them for one year. W... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 46 Table 1. Descriptive Statistics This table shows descriptive statistics of the variables used in analyses. Panel A uses the entire universe of Compustat and Panel B uses the intersection between I/B/E/S and Compustat. For Panel B, we require that each observati... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 47 Table 2. GPT vs. Human Analysts This table reports prediction performance of the random walk model, analysts' forecast issued one month after previous earnings release (Analyst 1m), three months after previous earnings release (Analyst 3m), and six months after... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 48 Table 3. Complementarities Between Human Analysts and GPT *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively. In Panel A, we investigate the determinants of incorrect predictions. I(Incorrect = 1), which is an indicator that ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 49 Table 4. Comparison with ML Benchmarks *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively. In Panel A, we compare the prediction performance of GPT and quantitative models based on machine learning. Stepwise Logistic follows ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 50 Table 5. Experimental Variations and GPT's Predictability We compare the predictive performance of the model based on several experimental settings. Conf Score is the confidence score (ranging from 0 to 1) that the model produces. Confidence score measures how ... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 51 Table 6. Memory of GPT In this table, we test GPT's memory. For Panel A and Panel B, we ask GPT to provide ten most probable names of the company and the most probable fiscal year, based on the standardized and anonymous financial statement information. In Pane... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 52 Table 7. Predictive Ability of GPT-Generated Texts We report the model performance of an ANN model based on text embedding. We use BERT-base-uncased model to extract contextualized embedding representation of the narrative financial statement analysis performed... | Financial Statement Analysis with Large Language Models .pdf |
Financial Statement Analysis with Large Language Models 53 Table 8. Asset Pricing Implications *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively. In this table, we show asset pricing implications of GPT's predictions. We form portfolios on June 30 of each year and hold the portfolio... | Financial Statement Analysis with Large Language Models .pdf |
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