Hidden Markov Model for Stock Selection
The hidden Markov model (HMM) is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for sto...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-10-01
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| Series: | Risks |
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| Online Access: | http://www.mdpi.com/2227-9091/3/4/455 |
| _version_ | 1828807926921297920 |
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| author | Nguyet Nguyen Dung Nguyen |
| author_facet | Nguyet Nguyen Dung Nguyen |
| author_sort | Nguyet Nguyen |
| collection | DOAJ |
| description | The hidden Markov model (HMM) is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI)), industrial production index (INDPRO), stock market index (S&P 500) and market volatility (VIX). At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500. |
| first_indexed | 2024-12-12T08:33:07Z |
| format | Article |
| id | doaj.art-3b8aebc43a1a430f96e2cc8b9fbca492 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-12-12T08:33:07Z |
| publishDate | 2015-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-3b8aebc43a1a430f96e2cc8b9fbca4922022-12-22T00:31:02ZengMDPI AGRisks2227-90912015-10-013445547310.3390/risks3040455risks3040455Hidden Markov Model for Stock SelectionNguyet Nguyen0Dung Nguyen1Faculty of Mathematics and Statistics, Youngstown State University, 1 University Plaza, Youngstown, OH 44555, USAQuantitative Researcher, Ned Davis Research Group, 600 Bird Bay Drive West, Venice, FL 34285, USAThe hidden Markov model (HMM) is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI)), industrial production index (INDPRO), stock market index (S&P 500) and market volatility (VIX). At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.http://www.mdpi.com/2227-9091/3/4/455hidden Markov modeleconomicsobservationsregimespredictionstocksscoresrankingMLE |
| spellingShingle | Nguyet Nguyen Dung Nguyen Hidden Markov Model for Stock Selection Risks hidden Markov model economics observations regimes prediction stocks scores ranking MLE |
| title | Hidden Markov Model for Stock Selection |
| title_full | Hidden Markov Model for Stock Selection |
| title_fullStr | Hidden Markov Model for Stock Selection |
| title_full_unstemmed | Hidden Markov Model for Stock Selection |
| title_short | Hidden Markov Model for Stock Selection |
| title_sort | hidden markov model for stock selection |
| topic | hidden Markov model economics observations regimes prediction stocks scores ranking MLE |
| url | http://www.mdpi.com/2227-9091/3/4/455 |
| work_keys_str_mv | AT nguyetnguyen hiddenmarkovmodelforstockselection AT dungnguyen hiddenmarkovmodelforstockselection |