---
description: >-
  Use this indicator to understand the trajectory of global risks as perceived
  by investors. Here we supply the raw data, you might find it more favorable to
  use the dashboard.
---

# 🏳️ Turing Risk Index

> **Data Notice**: This dataset provides academic research access with a 6-month data lag. 
> For real-time data access, please visit [sov.ai](https://sov.ai) to subscribe. 
> For market insights and additional subscription options, check out our newsletter at [blog.sov.ai](https://blog.sov.ai).

```python
from datasets import load_dataset
df_risks = load_dataset("sovai/risks", split="train").to_pandas().set_index(["date"])
```
    


Daily index arrive between 11 pm - 4 am before market open in the US.


`Tutorials` — [<mark style="color:blue;">`Business, Political, and Market Risk Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Turing%20Risk%20Index.ipynb)

<table data-column-title-hidden data-view="cards"><thead><tr><th>Category</th><th>Details</th></tr></thead><tbody><tr><td>Input Datasets</td><td>Hundreds of leading indicators.</td></tr><tr><td>Models Used</td><td>Imputation Models, Time Series Forecast Models</td></tr><tr><td>Model Outputs</td><td>Market, Business, and Political risk indicators,</td></tr></tbody></table>

## Description

This dataset provides a comprehensive Turing Risk Index, combining market, business, and political risk indicators. It offers daily updates on global risk perceptions, using leading indicators and advanced models to forecast various types of risk.

The data enables investors and analysts to assess and predict market volatility, recession probabilities, geopolitical tensions, and other key risk factors affecting global markets and economies.

## Data Access

#### Retrieving Data

```python
import sovai as sov 
df_risks = sov.data("risks")
```

<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/turing_risk_index_1 (2).png" alt=""><figcaption></figcaption></figure>

#### Market Risks

Isolating market risk indicators

```python
import sovai as sov
df_market = sov.data("risks/market")
```

#### Business Risks

Isolating business risk indicators

```python
import sovai as sov
df_business = sov.data("risks/business")
```

#### Business Risks

Isolating business risk indicators

```python
import sovai as sov
df_political = sov.data("risks/political")
```

### Data Dictionary

<table><thead><tr><th width="232">Column</th><th>Description</th></tr></thead><tbody><tr><td><code>TURING_RISK</code></td><td>Index combining Market, Business, and Political Risk using scholarly and research data.</td></tr><tr><td><code>MARKET_RISK</code></td><td>Global Value-at-Risk estimate for country indices using various models and an ensemble approach.</td></tr><tr><td><code>BUSINESS_RISK</code></td><td>Index tracking sentiment and conditions across business sectors using surveys, indicators, and news analysis.</td></tr><tr><td><code>POLITICAL_RISK</code></td><td>Index assessing domestic and global policy uncertainty using news, web search data, and reports.</td></tr><tr><td><code>HS</code></td><td>Historical Simulation method for VaR using the empirical distribution of past returns.</td></tr><tr><td><code>MA</code></td><td>Moving Average method for VaR assuming normally distributed returns.</td></tr><tr><td><code>EWMA</code></td><td>Volatility estimation method weighting recent observations more heavily.</td></tr><tr><td><code>GARCH</code></td><td>GJR-GARCH model estimating VaR incorporating responses to positive and negative shocks.</td></tr><tr><td><code>ENSEMBLE</code></td><td>Combined VaR estimate from multiple models to mitigate misspecification.</td></tr><tr><td><code>VIX</code></td><td>Index reflecting expected market volatility over the next 30 days.</td></tr><tr><td><code>SYSTEMIC</code></td><td>Measurement of global financial market interconnectedness using the absorption ratio.</td></tr><tr><td><code>TURBULENCE</code></td><td>Measure of financial turbulence calculated using the Mahalanobis Distance.</td></tr><tr><td><code>RECESSION_6</code></td><td>Six-month recession prediction probability using real-time data and gradient boosting models.</td></tr><tr><td><code>RECESSION_12</code></td><td>Twelve-month recession prediction probability with a proprietary diversification head.</td></tr><tr><td><code>RECESSION_24</code></td><td>Twenty-four-month recession prediction probability using economic and financial variables.</td></tr><tr><td><code>CAPE</code></td><td>Cyclically Adjusted Price-to-Earnings, a long-term stock valuation metric.</td></tr><tr><td><code>NAIIM_NEG</code></td><td>NAAIM Exposure Index reflecting active risk managers' equity market exposure.</td></tr><tr><td><code>AAII_NEG</code></td><td>AAII Sentiment Survey indicating individual investors' market direction opinions.</td></tr><tr><td><code>ADS_BUSINESS_NEG</code></td><td>ADS business conditions index tracking relative performance to average economic conditions.</td></tr><tr><td><code>NONMAN_OUTLOOK_NEG</code></td><td>Survey data on nonmanufacturing sector outlook from the Third Federal Reserve District.</td></tr><tr><td><code>MAN_PHIL_NEG</code></td><td>Monthly manufacturing survey from the Third Federal Reserve District assessing the sector's outlook.</td></tr><tr><td><code>MAN_TEX_NEG</code></td><td>Texas Manufacturing Outlook Survey tracking various sector indicators monthly.</td></tr><tr><td><code>MAN_NY_NEG</code></td><td>Monthly survey measuring manufacturing executives' perspectives in New York State.</td></tr><tr><td><code>CFNAI_FNEG</code></td><td>Composite index based on 85 monthly indicators of national economic activity.</td></tr><tr><td><code>ZEW_SENT_NEG</code></td><td>ZEW Economic Sentiment indicator measuring financial experts' economic outlook expectations.</td></tr><tr><td><code>ATLANTA_UNC</code></td><td>Survey on business uncertainty levels providing insights into economic conditions.</td></tr><tr><td><code>BUILDING_INDEX_NEG</code></td><td>Survey assessing sentiment in the building and construction industry.</td></tr><tr><td><code>CONSUMER_INDEX_NEG</code></td><td>Survey measuring consumer sentiment and perceptions in the economy.</td></tr><tr><td><code>INDUSTRY_INDEX_NEG</code></td><td>Survey capturing sentiment within the industrial sector.</td></tr><tr><td><code>MAIN_INDEX_NEG</code></td><td>Survey monitoring main economic indicators and business sentiment.</td></tr><tr><td><code>RETAIL_INDEX_NEG</code></td><td>Survey gauging sentiment within the retail sector.</td></tr><tr><td><code>SERVICES_INDEX_NEG</code></td><td>Survey measuring sentiment in the services sector.</td></tr><tr><td><code>MICS_ICS_NEG</code></td><td>Michigan series Monthly Indicator of Consumer Sentiment.</td></tr><tr><td><code>MICS_ICC_NEG</code></td><td>Michigan series Monthly Indicator of Consumer Confidence.</td></tr><tr><td><code>MICS_ICE_NEG</code></td><td>Michigan series Monthly Indicator of Consumer Expectations.</td></tr><tr><td><code>NEWS_SENT_NEG</code></td><td>Daily measure of economic sentiment from news articles.</td></tr><tr><td><code>TERM_SPREAD</code></td><td>Indicator representing the yield difference between long-term and short-term government bonds.</td></tr><tr><td><code>CREDIT_SPREAD</code></td><td>Measure of yield difference between below and above investment-grade bonds.</td></tr><tr><td><code>CORP_BOND_DISTRESS</code></td><td>Index quantifying distress in the corporate bond market.</td></tr><tr><td><code>MISERY_INDEX</code></td><td>Economic indicator combining unemployment and inflation rates.</td></tr><tr><td><code>HOUSING_AFFORD_NEG</code></td><td>Index measuring the affordability of housing.</td></tr><tr><td><code>NEW_TRUCKS</code></td><td>Sales data for heavy trucks above 14,000 pounds.</td></tr><tr><td><code>NEW_HOMES</code></td><td>Data on newly authorized housing units.</td></tr><tr><td><code>CFSEC_NEG</code></td><td>Diffusion indexes reflecting changes in organizations' operations and outlook.</td></tr><tr><td><code>US_POLICY_UNC_D</code></td><td>Daily Economic Policy Uncertainty Index based on American newspaper counts.</td></tr><tr><td><code>UK_POLICY_UNC_D</code></td><td>Daily Economic Policy Uncertainty Index based on British</td></tr><tr><td><code>CHINA_POLICY_UNC_M</code></td><td>Monthly index tracking economic policy uncertainty in China based on term frequency in Chinese newspapers.</td></tr><tr><td><code>US_MARKET_UNC_D</code></td><td>Daily index derived from term frequency analysis in American newspapers, focused on market uncertainty.</td></tr><tr><td><code>US_POLICY_VOL_M</code></td><td>Monthly tracker measuring market volatility through the lens of economic and stock market-related terms.</td></tr><tr><td><code>GLOBAL_POLICY_UNC_M</code></td><td>Monthly global economic policy uncertainty index, GDP-weighted, based on global newspaper term analysis.</td></tr><tr><td><code>US_SOVEREIGN_UNC_M</code></td><td>Categorical monthly data tracking US policy uncertainty across various domains using news article frequency.</td></tr><tr><td><code>GEO_UNC_D</code></td><td>Daily index quantifying geopolitical risk through newspaper coverage of geopolitical tensions.</td></tr><tr><td><code>GEO_UNC_M</code></td><td>Monthly index measuring geopolitical risk intensity based on media coverage of global tensions.</td></tr><tr><td><code>GEO_EQUAL_M</code></td><td>Monthly averaged geopolitical risk index across 22 countries, based on media coverage.</td></tr><tr><td><code>WEB_SEARCH_UNC_M</code></td><td>Monthly uncertainty index based on the intensity of web searches mimicking news-based approaches.</td></tr><tr><td><code>THINKTANK_UNC_M</code></td><td>Uncertainty index for 77 countries based on the frequency of 'uncertainty' in Economist Intelligence Unit reports.</td></tr></tbody></table>

### Computations

#### Custom Aggregates

We can use the inputs to come up with new aggregartes of the original input data, doing that we can come up with new indices. Here I have come up with a few new ones.

```python
df_risks_agg = sov.compute('risk-aggregates', df=df_risks)
```

<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/turing_risk_index_2 (2).png" alt=""><figcaption></figcaption></figure>

| Column                    | Description                                                                       |
| ------------------------- | --------------------------------------------------------------------------------- |
| VOLATILITY\_RISK          | A score calculated from market volatility indicators like ENSEMBLE and VIX.       |
| RECESSION\_PROBABILITY    | An average probability score of recession forecasted at 6, 12, and 24 months.     |
| GEOPOLITICAL\_RISK        | A score summarizing various geopolitical risk indicators.                         |
| DOMESTIC\_POLITICAL\_RISK | A composite score of US-specific political risk indicators.                       |
| BOND\_RISK                | An average score of bond market risks including credit and term spreads.          |
| ECONOMIC\_SENTIMENT       | A sentiment score based on economic indicators such as housing and vehicle sales. |
| INVESTOR\_SENTIMENT       | A score reflecting the sentiment of investors based on surveys.                   |
| CONSUMER\_SENTIMENT       | A score summarizing consumer confidence and economic outlook indicators.          |
| MANUFACTURING\_SENTIMENT  | A sentiment score derived from manufacturing sector surveys.                      |
| SERVICES\_SENTIMENT       | An index reflecting sentiment in the non-manufacturing industries.                |

#### Pandas Plots

```python
df_risks[["MARKET_RISK","BUSINESS_RISK","POLITICAL_RISK","TURING_RISK"]].tail(15400).plot()
```

## Use Cases

### Overview

The Risk Database is a sophisticated analytical tool designed to evaluate and forecast a wide range of risks across different sectors of the economy and financial markets. Utilizing an extensive collection of time-series data, the database aids investors in navigating the complex landscape of market, business, and political risks.

#### Potential Use Cases

1. **Strategic Investment Decisions**: Investors can use the database to understand the impact of various risks on asset classes, thereby tailoring their investment strategies to mitigate potential downsides or capitalize on emerging opportunities.
2. **Risk Management**: By quantifying and forecasting risk, the database serves as a critical component in the formulation of risk management policies for financial institutions.
3. **Economic Analysis**: Policymakers and economic analysts can leverage the insights to gauge economic health and prepare for potential market shifts caused by political or business developments.

### Key Components

#### Risk Indices

1. **Turing Risk Index**: A composite measure combining Market, Business, and Political Risks to provide an overarching view of the risk environment.
2. **Market Risk Score**: Evaluates the potential volatility and the downside risk within global markets using advanced statistical models.
3. **Business Risk Score**: Aggregates various measures of business conditions, sector sentiment, and economic indicators.
4. **Political Risk Score**: Measures the level of uncertainty in domestic and international policy-making spheres.

### Technical Use Cases

#### Market Dynamics

**Rolling Correlation**: Pinpoint emerging risks by monitoring the evolving correlations among key risk indices, useful for adjusting asset allocations.

#### Volatility Forecasting

**Rolling Standard Deviation**: Use trends in risk volatility to inform timing for investment decisions and risk hedging strategies.

#### Risk-Return Relationship

**Concurrent Correlation**: Apply insights from the risk-return interplay to refine predictive models for asset pricing and strategic investment planning.

#### Stock Risk Profiling

**Beta Distributions**: Leverage beta scores to align investment choices with risk profiles, potentially aiding in the construction of bespoke investment solutions.

#### Predictive Analytics

**Causal Analysis & Risk Forecasting**: Incorporate statistical significance testing and advanced forecasting methods to predict market movements and inform proactive risk management.

#### Trend Analysis

**Heatmap Visualization & Historical Comparison**: Utilize visual trend analysis and historical parallels to anticipate shifts in the risk environment and adapt investment strategies accordingly.

***