Factor Signals

A financial factor dataset for in-depth company analysis and investment strategies.

Data is updated weekly as data arrives after market close US-EST time.

Tutorials are the best documentation — Factor Signals Tutorial

Input Datasets

Filings, Financial Data

Models Used

OLS Regression

Model Outputs

Factors, Coefficients, Standard Errors

Description

This dataset includes traditional accounting factors, alternative financial metrics, and advanced statistical analyses, enabling sophisticated financial modeling.

It could be used for bottom-up equity selection strategies and for the development of investment strategies.

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Data Access

Comprehensive Factors

Comprehensive Factors dataset is a merged set of both accounting and alternative financial metrics, providing a holistic view of a company's financial status.

import sovai as sov
df_factor_comp = sov.data("factors/comprehensive",tickers=["MSFT","TSLA"])

Accounting Factors

The Accounting Factors dataset includes key financial metrics related to accounting for various companies.

import sovai as sov
df_factor_actn = sov.data("factors/accounting",tickers=["MSFT","TSLA"])

Alternative Factors

This dataset contains alternative financial factors that are not typically found in standard financial statements.

import sovai as sov
df_factor_alt = sov.data("factors/alternative",tickers=["MSFT","TSLA"])

Coefficients Factors

The Coefficients Factors dataset includes various coefficients related to different financial metrics.

import sovai as sov
df_factor_coeff = sov.data("factors/coefficients",tickers=["MSFT","TSLA"])

Standard Errors Factors

This dataset provides standard errors for various financial metrics, useful for statistical analysis and modeling.

import sovai as sov
df_factor_std_err = get_data("factors/standard_errors",tickers=["MSFT","TSLA"])

T-Statistics Factors

The T-Statistics Factors dataset includes t-statistics for different financial metrics, offering insights into their significance.

import sovai as sov
df_factor_t_stat = get_data("factors/t_statistics",tickers=["MSFT","TSLA"])

Model Metrics

Model Metrics dataset includes various metrics such as R-squared, AIC, BIC, etc., that are crucial for evaluating the performance of financial models.

import sovai as sov
df_model_metrics = sov.data("factors/model_metrics",tickers=["MSFT","TSLA"])

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This documentation provides a clear guide on how to access each dataset, and can be easily extended or modified as needed for additional datasets or details.

Data Dictionary

Financial Factors Dataset

Name	Description
ticker	The unique identifier for a publicly traded company's stock.
date	The specific date for which the data is recorded.
profitability	A measure of a company's efficiency in generating profits.
value	Indicates the company's market value, often reflecting its perceived worth.
solvency	Reflects the company's ability to meet its long-term financial obligations.
cash_flow	Represents the amount of cash being transferred into and out of a business.
illiquidity	Measures the difficulty of converting assets into cash quickly without significant loss in value.
momentum_long_term	Indicates long-term trends in the company's stock price movements.
momentum_medium_term	Represents medium-term trends in stock price movements.
short_term_reversal	Reflects short-term price reversals in the stock market.
price_volatility	Measures the degree of variation in a company's stock price over time.
dividend_yield	The dividend per share, divided by the price per share, showing how much a company pays out in dividends each year relative to its stock price.
earnings_consistency	Indicates the stability and predictability of a company's earnings over time.
small_size	A factor indicating the company's size, with smaller companies potentially offering higher returns (albeit with higher risk).
low_growth	Reflects the company's lower-than-average growth prospects.
low_equity_issuance	Indicates a lower level of issuing new shares, which can be a sign of financial strength or limited growth prospects.
bounce_dip	Measures the tendency of a stock to recover quickly after a significant drop.
accrual_growth	Represents the growth rate in accruals, which are earnings not yet realized in cash.
low_depreciation_growth	Indicates lower growth in depreciation expenses, which might suggest more stable capital expenditures.
current_liquidity	A measure of a company's ability to pay off its short-term liabilities with its short-term assets.
low_rnd	Reflects lower expenditures on research and development, which could indicate less investment in future growth.
momentum	Overall momentum factor, representing the general trend in the stock price movements.
market_risk	Indicates the risk of an investment in a particular market relative to the entire market.
business_risk	Reflects the inherent risk associated with the specific business activities of a company.
political_risk	Measures the potential for losses due to political instability or changes in a country's political environment.
inflation_fluctuation	Indicates how sensitive the company is to fluctuations in inflation rates.
inflation_persistence	Measures the company's exposure to persistent inflation trends.
returns	Represents the financial returns generated by the company over a specified period.

ModelMetrics Dataset

Name	Description
ticker	The unique stock ticker symbol identifying the company.
date	The date for which the model metrics are calculated.
rsquared	The R-squared value, indicating the proportion of variance in the dependent variable that's predictable from the independent variables.
rsquared_adj	The adjusted R-squared value, accounting for the number of predictors in the model (provides a more accurate measure when dealing with multiple predictors).
fvalue	The F-statistic value, used to determine if the overall regression model is a good fit for the data.
aic	Akaike’s Information Criterion, a measure of the relative quality of statistical models for a given set of data. Lower AIC indicates a better model.
bic	Bayesian Information Criterion, similar to AIC but with a higher penalty for models with more parameters.
mse_resid	Mean Squared Error of the residuals, measuring the average of the squares of the errors, i.e., the average squared difference between the estimated values and the actual value.
mse_total	Total Mean Squared Error, measuring the total variance in the observed data.

In addition to the primary financial metrics and model metrics, our data suite includes three specialized datasets:

• Coefficients: This dataset provides regression coefficients for various financial factors. These coefficients offer insights into the relative importance and impact of each factor in financial models.

• Standard Errors: Accompanying the coefficients, this dataset provides the standard error for each coefficient. The standard errors are crucial for understanding the precision and reliability of the coefficients in the model.

• T-Statistics: This dataset contains the t-statistic for each coefficient, a key metric for determining the statistical significance of each financial factor. It helps in evaluating the robustness of the coefficients' impact in the model.

These datasets form a comprehensive toolkit for financial analysis, enabling detailed regression analysis and statistical evaluation of financial factors.

Factor Analysis Datasets

Our suite of Factor Analysis datasets offers a rich and comprehensive resource for investors seeking to deepen their understanding of market dynamics and enhance their investment strategies. Here's an overview of each dataset and its potential use cases:

Comprehensive Financial Metrics

1. Accounting Factors (FactorsAccounting): This dataset includes core financial metrics like profitability, solvency, and cash flow. It's invaluable for fundamental analysis, enabling investors to assess a company's financial health and operational efficiency.

2. Alternative Factors (FactorsAlternative): Focusing on non-traditional financial metrics such as market risk, business risk, and political risk, this dataset helps in evaluating external factors that could impact a company's performance.

3. Comprehensive Factors (FactorsComprehensive): A merged set of accounting and alternative factors providing a holistic view of a company's status. This dataset is perfect for a comprehensive financial analysis, blending traditional and modern financial metrics.

Advanced Statistical Analysis

1. Coefficients (FactorsCoefficients): Reveals the weight or importance of each financial factor in a statistical model. Investors can use this to identify which factors are most influential in predicting stock performance.

2. Standard Errors (FactorsStandardErrors): Provides precision levels of the coefficients. This is crucial for investors in assessing the reliability of the coefficients in predictive models.

3. T-Statistics (FactorsTStatistics): Offers insights into the statistical significance of each factor. Investors can use this to gauge the robustness and credibility of the factors in their investment models.

4. Model Metrics (ModelMetrics): Includes advanced metrics like R-squared, AIC, and BIC. This dataset is essential for evaluating the effectiveness of financial models, helping investors to choose the most reliable models for their investment decisions.

Potential Use Cases

• Portfolio Construction and Optimization: By understanding the importance and impact of various financial factors, investors can construct and optimize their portfolios to maximize returns and minimize risks.

• Risk Assessment and Management: Alternative factors, along with risk-related metrics from other datasets, enable investors to conduct thorough risk assessments, leading to better risk management strategies.

• Market Trend Analysis: Long-term and medium-term momentum factors can be used for identifying prevailing market trends, aiding in strategic investment decisions.

• Statistical Model Validation: Investors can validate their financial models using model metrics and statistical datasets (Standard Errors and T-Statistics), ensuring robustness and reliability in their analysis.

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