AutoML Asset Pricing Pipeline: Automated Dow Jones Index Return Prediction

An automated machine learning pipeline combining economic data and computing technology for predicting the daily logarithmic returns of the Dow Jones Industrial Average, featuring data preprocessing, statistical validation, and model interpretability functions.

• Original Author/Maintainer: FuturoBlack
• Source Platform: GitHub
• Original Title: AutoML-Asset-Pricing-Pipeline
• Original Link: https://github.com/FuturoBlack/AutoML-Asset-Pricing-Pipeline
• Publication Date: June 15, 2026

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In financial markets, predicting asset prices has always been a core challenge in the field of quantitative investment. Traditional prediction methods often rely on complex mathematical models and extensive manual parameter tuning, while the rise of machine learning has brought new possibilities to this field. However, the high noise, non-stationarity, and complex dynamic characteristics of financial data make the construction and optimization of machine learning models extremely difficult.

The AutoML-Asset-Pricing-Pipeline project is designed to address this pain point. It provides a complete automated machine learning pipeline specifically for predicting the daily logarithmic returns of the Dow Jones Industrial Average. This project combines economic data analysis with advanced computing technology, simplifying the entire process from data preprocessing to model deployment through automation.

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The quality of financial data directly affects the predictive ability of the model. This project uses a variety of statistical techniques to ensure the reliability of input data:

Winsorization: This is a method for handling outliers by limiting extreme values to a certain percentile range, reducing the impact of abnormal data on the model while preserving the overall distribution characteristics of the data.

Augmented Dickey-Fuller (ADF) Test: Used to detect the stationarity of time series. Financial time series often have unit root characteristics, and the ADF test can help identify whether the data needs to be differenced to meet the assumptions of many statistical models.

The automated execution of these preprocessing steps ensures that the data has reached a high quality standard before entering the model training phase.

Model evaluation is a key link in quantitative investment. This project introduces the Diebold-Mariano test, a statistical test method for comparing the prediction accuracy of two forecasting models.

Unlike the traditional simple comparison of Mean Squared Error (MSE), the Diebold-Mariano test considers the serial correlation of prediction errors and can more accurately determine whether one model is significantly better than another. This is particularly important for financial forecasting, as the prediction errors of financial time series often have autocorrelation characteristics.

In today's increasingly strict financial regulation, model interpretability is becoming more and more important. This project integrates SHAP (SHapley Additive exPlanations) value analysis, a model interpretation method based on game theory.

SHAP values can quantify the contribution of each feature to the model's prediction, helping users understand:

• Which factors have the greatest impact on the prediction results
• The interaction effects between features
• The decision basis for individual prediction instances

This is of great significance for supporting risk management and investment decisions.

The project provides an intuitive user interface, allowing users without programming backgrounds to use this powerful analysis tool. This design concept lowers the technical threshold for quantitative analysis, enabling more financial practitioners to benefit from machine learning technology.

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The project is built based on the H2O AutoML framework, an industry-leading automated machine learning platform. H2O AutoML can automatically handle:

• Feature engineering and data encoding
• Automatic training and parameter tuning of multiple algorithms
• Model integration and stacking
• Performance evaluation and model selection

This automated approach greatly reduces the need for manual intervention, and through systematic search strategies, it can often discover excellent model configurations that human experts may overlook.