Linear Regression for Financial Investment Analysis Using SQL: A Practical Guide to Modeling Asset Returns

This article demonstrates how to use SQL to perform linear regression for financial investment analysis, enabling investors and analysts to rigorously quantify asset risk and performance, support portfolio decision-making, and drive transparent, data-driven strategies.

Article Outline:

1. Introduction

   • The critical role of quantitative modeling in financial investment

   • Why linear regression is a cornerstone for analyzing asset returns and risk

   • How SQL supports scalable, transparent financial analysis in modern data environments

2. Understanding Linear Regression in Finance

   • The linear regression model: alpha (intercept), beta (slope), and their financial interpretation

   • Applications in investment analysis:

     • Quantifying asset sensitivity to the market (beta)

     • Attributing performance and identifying excess returns (alpha)

     • Risk management and portfolio construction

3. Structuring Financial Data in SQL

   • Creating a SQL table for asset and market returns

   • Data preparation: aligning dates, calculating returns, ensuring data quality

   • Using SQL queries for data exploration and cleaning

4. Calculating Linear Regression Coefficients with SQL

   • Computing means, variances, and covariances in SQL

   • Deriving alpha, beta, fitted values, and residuals using SQL queries

   • Calculating R-squared and interpreting model fit

5. Interpreting Regression Output for Investment Decisions

   • Understanding the economic significance of alpha, beta, and R-squared

   • Using results to inform portfolio allocation and risk management

   • Communicating insights to stakeholders

6. Forecasting and Scenario Analysis in SQL

   • Predicting asset returns for different market scenarios using the regression equation

   • Building scenario tables for investment planning and stress testing

   • Integrating regression output into investment dashboards and workflows

7. Best Practices, Limitations, and Extensions

   • Ensuring regression assumptions: linearity, homoscedasticity, independence

   • Handling limitations in financial time series and SQL environments

   • Extending to multi-factor regression and connecting SQL to advanced analytics tools

8. Conclusion

   • The enduring value of linear regression for transparent, data-driven financial investment

   • The strengths of SQL for repeatable, large-scale investment modeling

   • Next steps: multi-factor models, time series analysis, and integrating SQL analytics into enterprise solutions

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