Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model

Majumder, Reetam; Ji, Qing; Neerchal, Nagaraj K.

doi:10.1007/s13571-022-00290-5

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…It is especially crucial for precipitation modeling due to its less smooth spatial distribution compared to temperature. Majumder et al [31] used copula to estimate spatial correlations in HMMs, but this was conducted within the framework of maximum likelihood estimation using the Baum-Welch algorithm rather than in a Bayesian context. We would also like to use the reduced climate model output as a covariate of the model, as Robertson et al [6] did.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Precipitation Generation for the Xilingol League Using Hidden Markov Models with Variational Bayes Parameter Estimation

Zhang,

Tuerde,

2023

Water

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Precipitation modeling holds significant importance in various fields such as agriculture, animal husbandry, weather derivatives, hydrology, and risk and disaster preparedness. Stochastic precipitation generators (SPGs) represent a class of statistical models designed to generate synthetic data capable of simulating dry and wet precipitation stretches for a long duration. The construction of Hidden Markov Models (HMMs), which treat latent meteorological circumstances as hidden states, is an efficient technique for simulating precipitation. Considering that there are many choices of emission distributions used to generate positive precipitation, the characteristics of different distributions for simulating positive precipitation have not been fully explored. The paper includes a simulation study that demonstrates how the Pareto distribution, when used as the distribution for generating positive precipitation, addresses the limitations of the exponential and gamma distributions in predicting heavy precipitation events. Additionally, the Pareto distribution offers flexibility through adjustable parameters, making it a promising option for precipitation modeling. We can estimate parameters in HMMs using forward–backward algorithms, Variational Bayes Expectation-Maximization (VBEM), and Stochastic Variational Bayes (SVB). In the Xilingol League, located in the central part of the Inner Mongolia Autonomous Region, China, our study involved data analysis to identify crucial locations demonstrating a robust correlation and notable partial correlation between the Normalized Difference Vegetation Index (NDVI) and annual precipitation. We performed fitting of monthly dry days ratios and monthly precipitation using seasonal precipitation and year-round precipitation data at these crucial locations. Subsequently, we conducted precipitation predictions for the daily, monthly, and annual time frames using the new test dataset observations. The study concludes that the SPG fits the monthly dry-day ratio better for annual daily precipitation data than for seasonal daily precipitation data. The fitting error for the monthly dry day ratio corresponding to annual daily precipitation data is 0.053 (exponential distribution) and 0.066 (Pareto distribution), while for seasonal daily precipitation data, the fitting error is 0.14 (exponential distribution) and 0.15 (Pareto distribution). The exponential distribution exhibits the poorest performance as a model for predicting future precipitation, with average errors of 2.49 (daily precipitation), 40.62 (monthly precipitation), and 130.40 (annual precipitation). On the other hand, the Pareto distribution demonstrates the best overall predictive performance, with average errors of 0.69 (daily precipitation), 34.69 (monthly precipitation), and 66.42 (annual precipitation). The results of this paper can provide decision support for future grazing strategies in the Xilingol League.

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Section: Discussionmentioning

confidence: 99%

Stochastic Precipitation Generation for the Xilingol League Using Hidden Markov Models with Variational Bayes Parameter Estimation

Zhang,

Tuerde,

2023

Water

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“…They managed to highlight that a strategy calibrated based on HMM methodology works well even in periods of extreme volatility such as the one generated in 2020 by COVID-19 pandemic. Majumder & Neerchal (2022) has shown that the underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take into account these market trends; however, the bull and bear market states tend to be unobserved and can only be assigned retrospectively.…”

Section: 4mentioning

confidence: 99%

Stock Selection through Hidden Markov Model: A Case of Pakistan Stock Exchange

Iqbal

Mirza

Mehmood

et al. 2022

REAL

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To foresee hidden regimes inside the data, Hidden Markov Model is widely used. Many researchers had used various data mining methods to predict stock market prices. This research will describe usage of Hidden Markov Model (HMM) via MATLAB to forecast stock prices for stock’ selection and portfolios development. In this study listed companies of PSX (Pakistan Stock Exchange) are explored specifically KSE-100 Index companies. From January 2012 to June 2022 monthly closing stock prices are used in this study. Many studies based on the historical data have been conducted in developed markets, but we managed the contextual study of the Pakistan Stock Exchange to recognize the stream and variations happening for individual stocks in stock market of Pakistan. Outcomes of the research suggested a strong relationship between stock prices as projected by the Model.

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Application of Informer model to CSI300 and BTC prediction

Niu

2024

Proceedings of the International Conference on Digital Economy, Blockchain and Artificial Intelligence

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Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model

Cited by 3 publications

References 25 publications

Stochastic Precipitation Generation for the Xilingol League Using Hidden Markov Models with Variational Bayes Parameter Estimation

Stochastic Precipitation Generation for the Xilingol League Using Hidden Markov Models with Variational Bayes Parameter Estimation

Stock Selection through Hidden Markov Model: A Case of Pakistan Stock Exchange

Application of Informer model to CSI300 and BTC prediction

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