sovai/factor_signals

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 — <mark style="color:blue;">`Factor Signals Tutorial`</mark>

<table data-column-title-hidden data-view="cards"><thead><tr><th>Category</th><th>Details</th></tr></thead><tbody><tr><td><strong>Input Datasets</strong></td><td>Filings, Financial Data</td></tr><tr><td><strong>Models Used</strong></td><td>OLS Regression</td></tr><tr><td><strong>Model Outputs</strong></td><td>Factors, Coefficients, Standard Errors</td></tr></tbody></table>

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"])

<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/factorsignals1 (2).png" alt=""><figcaption></figcaption></figure>

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.

<pre class="language-python"><code class="lang-python">import sovai as sov <strong>dffactorcoeff = sov.data("factors/coefficients",tickers=["MSFT","TSLA"]) </strong></code></pre>

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

<table><thead><tr><th width="286">Name</th><th>Description</th></tr></thead><tbody><tr><td><code>ticker</code></td><td>The unique identifier for a publicly traded company's stock.</td></tr><tr><td><code>date</code></td><td>The specific date for which the data is recorded.</td></tr><tr><td><code>profitability</code></td><td>A measure of a company's efficiency in generating profits.</td></tr><tr><td><code>value</code></td><td>Indicates the company's market value, often reflecting its perceived worth.</td></tr><tr><td><code>solvency</code></td><td>Reflects the company's ability to meet its long-term financial obligations.</td></tr><tr><td><code>cashflow</code></td><td>Represents the amount of cash being transferred into and out of a business.</td></tr><tr><td><code>illiquidity</code></td><td>Measures the difficulty of converting assets into cash quickly without significant loss in value.</td></tr><tr><td><code>momentumlongterm</code></td><td>Indicates long-term trends in the company's stock price movements.</td></tr><tr><td><code>momentummediumterm</code></td><td>Represents medium-term trends in stock price movements.</td></tr><tr><td><code>shorttermreversal</code></td><td>Reflects short-term price reversals in the stock market.</td></tr><tr><td><code>pricevolatility</code></td><td>Measures the degree of variation in a company's stock price over time.</td></tr><tr><td><code>dividendyield</code></td><td>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.</td></tr><tr><td><code>earningsconsistency</code></td><td>Indicates the stability and predictability of a company's earnings over time.</td></tr><tr><td><code>smallsize</code></td><td>A factor indicating the company's size, with smaller companies potentially offering higher returns (albeit with higher risk).</td></tr><tr><td><code>lowgrowth</code></td><td>Reflects the company's lower-than-average growth prospects.</td></tr><tr><td><code>lowequityissuance</code></td><td>Indicates a lower level of issuing new shares, which can be a sign of financial strength or limited growth prospects.</td></tr><tr><td><code>bouncedip</code></td><td>Measures the tendency of a stock to recover quickly after a significant drop.</td></tr><tr><td><code>accrualgrowth</code></td><td>Represents the growth rate in accruals, which are earnings not yet realized in cash.</td></tr><tr><td><code>lowdepreciationgrowth</code></td><td>Indicates lower growth in depreciation expenses, which might suggest more stable capital expenditures.</td></tr><tr><td><code>currentliquidity</code></td><td>A measure of a company's ability to pay off its short-term liabilities with its short-term assets.</td></tr><tr><td><code>lowrnd</code></td><td>Reflects lower expenditures on research and development, which could indicate less investment in future growth.</td></tr><tr><td><code>momentum</code></td><td>Overall momentum factor, representing the general trend in the stock price movements.</td></tr><tr><td><code>marketrisk</code></td><td>Indicates the risk of an investment in a particular market relative to the entire market.</td></tr><tr><td><code>businessrisk</code></td><td>Reflects the inherent risk associated with the specific business activities of a company.</td></tr><tr><td><code>politicalrisk</code></td><td>Measures the potential for losses due to political instability or changes in a country's political environment.</td></tr><tr><td><code>inflationfluctuation</code></td><td>Indicates how sensitive the company is to fluctuations in inflation rates.</td></tr><tr><td><code>inflation_persistence</code></td><td>Measures the company's exposure to persistent inflation trends.</td></tr><tr><td><code>returns</code></td><td>Represents the financial returns generated by the company over a specified period.</td></tr></tbody></table>

ModelMetrics Dataset

<table><thead><tr><th width="267">Name</th><th>Description</th></tr></thead><tbody><tr><td><code>ticker</code></td><td>The unique stock ticker symbol identifying the company.</td></tr><tr><td><code>date</code></td><td>The date for which the model metrics are calculated.</td></tr><tr><td><code>rsquared</code></td><td>The R-squared value, indicating the proportion of variance in the dependent variable that's predictable from the independent variables.</td></tr><tr><td><code>rsquaredadj</code></td><td>The adjusted R-squared value, accounting for the number of predictors in the model (provides a more accurate measure when dealing with multiple predictors).</td></tr><tr><td><code>fvalue</code></td><td>The F-statistic value, used to determine if the overall regression model is a good fit for the data.</td></tr><tr><td><code>aic</code></td><td>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.</td></tr><tr><td><code>bic</code></td><td>Bayesian Information Criterion, similar to AIC but with a higher penalty for models with more parameters.</td></tr><tr><td><code>mseresid</code></td><td>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.</td></tr><tr><td><code>mse_total</code></td><td>Total Mean Squared Error, measuring the total variance in the observed data.</td></tr></tbody></table>

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. 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. 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. 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. 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. 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. 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. 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.

###