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Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns

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  • Eugene W. Park

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.

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  • Eugene W. Park, 2023. "Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns," Papers 2307.00459, arXiv.org.
    • Handle: RePEc:arx:papers:2307.00459
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    References listed on IDEAS

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    1. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    2. G. Kavitha & A. Udhayakumar & D. Nagarajan, 2013. "Stock Market Trend Analysis Using Hidden Markov Models," Papers 1311.4771, arXiv.org.
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