🏳️Turing Risk Index

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.

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

Tutorials — Business, Political, and Market Risk Tutorial

Input Datasets

Hundreds of leading indicators.

Models Used

Imputation Models, Time Series Forecast Models

Model Outputs

Market, Business, and Political risk indicators,

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

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

Market Risks

Isolating market risk indicators

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

Business Risks

Isolating business risk indicators

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

Business Risks

Isolating business risk indicators

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

Data Dictionary

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

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.

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

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

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.

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