Multiple Linear Regression in Financial Investment Analysis Using SQL: Modeling Asset Returns with Market and Economic Factors

This article explains how investors and analysts can leverage SQL-driven multiple linear regression to model asset returns, quantify factor exposures, and support rigorous, data-driven investment decisions.

Article Outline:

1. Introduction

   • The central role of data-driven modeling in modern financial investment

   • Why multiple linear regression is crucial for understanding and predicting asset returns

   • The benefits of using SQL for large-scale, auditable investment analytics

2. Multiple Linear Regression in Financial Investment

   • The mathematical model and interpretation of regression coefficients

   • Key applications in finance:

     • Modeling asset returns as a function of market indices, rates, and economic variables

     • Performance attribution and multi-factor risk modeling

     • Stress testing and scenario analysis

   • Comparison with single-factor and alternative regression approaches

3. Preparing Financial Investment Data in SQL

   • Structuring historical returns, benchmarks, and economic factors in SQL tables

   • Cleaning, aligning, and transforming data for regression analysis

   • Exploratory queries for summary statistics and initial data validation

4. Implementing Multiple Linear Regression with SQL Queries

   • Calculating means, variances, and covariances for regression

   • Deriving the coefficients for multiple predictors using SQL

   • Calculating fitted values, residuals, and R-squared for model diagnostics

   • Outputting results for further analysis

5. Interpreting Regression Results for Investment Decisions

   • Understanding beta exposures, intercept (alpha), and significance of each factor

   • Using regression output for portfolio allocation, sensitivity analysis, and risk management

   • Residual analysis for uncovering market inefficiencies or anomalies

6. Scenario Analysis and Forecasting Using SQL

   • Applying the regression model to new or hypothetical market and economic scenarios

   • Stress-testing portfolio outcomes under changing factor values

   • Integrating SQL-based regression with BI dashboards and investment workflows

7. Best Practices, Limitations, and Extensions

   • Ensuring model assumptions and data integrity

   • Recognizing the limitations of linear regression with financial data (autocorrelation, regime changes, non-normality)

   • Extending SQL analytics to advanced factor models and machine learning integration

8. Conclusion

   • The practical value of multiple linear regression in investment analysis

   • SQL’s strengths for transparent, scalable, and auditable modeling

   • Pathways to more advanced investment analytics and automation

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