Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns

TL;DR

This paper combines principal component analysis and hidden Markov models to forecast stock returns, demonstrating improved trading strategies over traditional buy-and-hold approaches.

Contribution

The paper introduces a novel approach integrating PCA and HMM for stock return prediction, with empirical validation on S&P 500 data.

Findings

Model outperforms buy-and-hold in Sharpe ratio

Effective use of PCA and HMM for return forecasting

Hyperparameter tuning improves prediction accuracy

Abstract

This paper presents a method for predicting stock returns using principal component analysis (PCA) and the hidden Markov model (HMM) and tests the results of trading stocks based on this approach. Principal component analysis is applied to the covariance matrix of stock returns for companies listed in the S&P 500 index, and interpreting principal components as factor returns, we apply the HMM model on them. Then we use the transition probability matrix and state conditional means to forecast the factors returns. Reverting the factor returns forecasts to stock returns using eigenvectors, we obtain forecasts for the stock returns. We find that, with the right hyperparameters, our model yields a strategy that outperforms the buy-and-hold strategy in terms of the annualized Sharpe ratio.