Analysis of Gold Price Trend Using the Hidden Markov Model


Year : 2024 | Volume : 11 | Issue : 02 | Page : 7-16
    By

    Sarode Rekha,

  • Thirupathi Rao Padi,

  • Raj Kumar Sahoo,

  1. Visiting Scientist, (Short-Term Post Doctoral Fellow), Indian Statistical Institute, Theoretical Statistics and Mathematics Unit,, New Delhi, India
  2. Professor, Department of Statistics, Pondicherry University, Tamil Nadu, India
  3. Research Scholar, Department. of Statistics, Utkal University, Odisha, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_181928’);});Edit Abstract & Keyword

This study aims to analyze the behavior of gold prices in India through a two-state Hidden Markov Model (HMM). We first formulated crucial parameters, such as the Transition Probability Matrix (TPM), Initial Probability Vector (IPV), and Emission Probability Matrix (EPM). Subsequently, we constructed a hidden Markov probability distribution and evaluated Pearson’s coefficients to gauge the correlations separately for each state. The goodness of fit of the developed model was assessed using the chi-square test. Real-time data from the internet, particularly from the Bombay Stock Exchange (BSE) for hidden states and gold prices in India for emission states, were utilized for the model application. We categorized states as incremental or decrement based on the positivity of returns for both the BSE and gold prices. Utilizing the model, we computed probability distributions and statistical measures and validated its performance by gathering future data from various online sources. This study employs an HMM to comprehend the dynamics of gold prices in India, establish meaningful relationships, and validate predictive capabilities using real-time data. In saummary, this study utilizes an HMM to explore the dynamics of gold prices in India. By identifying significant relationships and validating the model’s predictions using real-time data, this study offers valuable insights into market behavior and presents a reliable tool for forecasting future gold price trends.

Keywords: Bombay stock exchange, inflation rate, investment policy, hidden markov model, gold price

[This article belongs to Research & Reviews: Discrete Mathematical Structures ]

How to cite this article:
Sarode Rekha, Thirupathi Rao Padi, Raj Kumar Sahoo. Analysis of Gold Price Trend Using the Hidden Markov Model. Research & Reviews: Discrete Mathematical Structures. 2024; 11(02):7-16.
How to cite this URL:
Sarode Rekha, Thirupathi Rao Padi, Raj Kumar Sahoo. Analysis of Gold Price Trend Using the Hidden Markov Model. Research & Reviews: Discrete Mathematical Structures. 2024; 11(02):7-16. Available from: https://journals.stmjournals.com/rrdms/article=2024/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_181928’);});Edit

References

  1. Li B. Hidden Markov model-based stock price prediction: A financial research report based on big data technology. SSRN Electron J. 2022. DOI: 10.2139/ssrn.4622722
  2. Sopipan N, Sattayatham P, Premanode B. Forecasting volatility of gold price using Markov regime switching and trading strategy. J Math Fin. 2012;2:121–31. DOI: 10.4236/jmf.2012.21014.
  3. Kılıç SB. Predicting the direction of gold price returns: Integrating composite artificial neural network models by Markov chain process. Çukurova Univ İktisadi İdari Bil Fak Derg. 2013;17:
    15–28.
  4. Klongdee W, Sous S, Thongjunthug T. Gold price forecasting based on the improved GM (1,1) model with Markov chain by average of middle points. Sci J KKU. 2014;42(3):693–9.
  5. Akgül I, Bildirici M, Özdemir S. Evaluating the nonlinear linkage between gold prices and stock market index using Markov-Switching Bayesian VAR models. Procedia – Social and Behavioral Sciences. 2015;210:408–15. DOI: 10.1016/j.sbspro.2015.11.388.
  6. Jayasree M, Jyothi P. Gold prices & regime shifts with Markov model: A study in the Indian context. Int J Recent Trends Bus Tourism. 2019;3:92–5.
  7. Shen L, Shen K, Yi C, Chen Y. Regression and hidden Markov models for gold price prediction. IEEE Int Conf Big Data (Big Data). 2020;5451–6. DOI: 10.1109/BigData50022.2020.9378468.
  8. Tiwari AK, Aye GC, Gupta R, Gkillas K. Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model. Energy Econ. 2020;88:104748. DOI: 10.1016/j.eneco.2020.104748.
  9. Patalay S, Bandlamudi MR. Gold price prediction using machine learning model trees. In: International Conference on Changing Business Paradigm, Murshidabad; 2021. p. 154–88.
  10. Bidin J, Syed Abas SF, Sharif N, Che Muhammad Fahimi CAA, Ku Akil KA. Comparative study between Holt’s double exponential smoothing and fuzzy time series Markov chain in gold price forecasting. J Comput Res Innov. 2022;7:283–93. DOI: 10.24191/jcrinn.v7i2.320.
  11. Qasim TB, Iqbal GZ, Hassan MU, Ali H. Application of Markov regime switching autoregressive model to gold prices in Pakistan. Rev Econ Dev Stud. 2021;7:309–23. DOI: 10.47067/reads.v7i3.

Regular Issue Subscription Original Research
Volume 11
Issue 02
Received 30/07/2024
Accepted 01/08/2024
Published 10/08/2024
Publication Time 11 Days

async function fetchCitationCount(doi) {
let apiUrl = `https://api.crossref.org/works/${doi}`;
try {
let response = await fetch(apiUrl);
let data = await response.json();
let citationCount = data.message[“is-referenced-by-count”];
document.getElementById(“citation-count”).innerText = `Citations: ${citationCount}`;
} catch (error) {
console.error(“Error fetching citation count:”, error);
document.getElementById(“citation-count”).innerText = “Citations: Data unavailable”;
}
}
fetchCitationCount(“10.37591/RRDMS.v11i02.0”);

Loading citations…

PlumX Metrics