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Stock price prediction using principal components.

Mahsa Ghorbani¹, Edwin K P Chong²

¹PhD Student, Department of Systems Engineering, Colorado State University, Fort Collins, Colorado, United States of America.

|March 21, 2020

Summary

This summary is machine-generated.

This study introduces a novel stock price prediction method using time-varying covariance and principal component analysis (PCA). The approach effectively reduces dimensionality for improved financial time series analysis and prediction accuracy.

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Area of Science:

Quantitative Finance
Financial Econometrics
Data Science

Background:

Stock price movements are predictable using historical data.
Principal Component Analysis (PCA) is a key technique for dimensionality reduction and data analysis.
Financial time series exhibit time-varying characteristics that pose challenges for prediction.

Purpose of the Study:

To develop a general stock price prediction method leveraging time-varying covariance.
To incorporate PCA for dimensionality reduction in financial data.
To enhance prediction accuracy and manage risk in stock market forecasting.

Main Methods:

Utilizing time-varying covariance with exponentially weighted price data to prioritize recent information.
Applying Principal Component Analysis (PCA) for dimension reduction, projecting data onto a principal subspace.
Comparing the proposed method against Gauss-Bayes and moving average models.

Main Results:

The proposed PCA-based method demonstrates effective stock price prediction.
Performance evaluation using mean squared error and directional change statistics indicates competitive results.
Analysis of prediction volatility provides insights into associated risks.

Conclusions:

The developed method offers a robust approach to stock price prediction by addressing time-varying financial data characteristics.
PCA-based dimension reduction enhances the conditioning of prediction problems.
The method shows promise for both performance and risk management in financial forecasting.