Report
Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns
| العنوان: | Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns |
|---|---|
| المؤلفون: | Park, Eugene W. |
| سنة النشر: | 2023 |
| المجموعة: | Mathematics Quantitative Finance |
| مصطلحات موضوعية: | Quantitative Finance - Statistical Finance, Mathematics - Probability, Quantitative Finance - Portfolio Management |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2307.00459 |
| رقم الانضمام: | edsarx.2307.00459 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |