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A financial factor dataset for in-depth company analysis and investment
strategies.
Factor Signals
Data Notice: This dataset provides academic research access with a 6-month data lag. For real-time data access, please visit sov.ai to subscribe. For market insights and additional subscription options, check out our newsletter at blog.sov.ai.
from datasets import load_dataset
df_factor_comp = load_dataset("sovai/factor_signals", split="train").to_pandas().set_index(["ticker","date"])
Data is updated weekly as data arrives after market close US-EST time.
Tutorials are the best documentation — Factor Signals Tutorial
| Category | Details |
|---|---|
| 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.
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"])
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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"])
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
- 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. - 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. - 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
- 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. - Standard Errors (
FactorsStandardErrors): Provides precision levels of the coefficients. This is crucial for investors in assessing the reliability of the coefficients in predictive models. - 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. - 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.