Application of Markov Process for Prediction of Stock Market Performance
Lakshmi G1, Jyothi Manoj2
1Lakshmi G, Student, Department of Mathematics, Amrita School of Arts and Science, Amrita Viswa Vidyapeetham, Amritapuri, Kollam, India.
2Jyothi Manoj, Associate Professor, Department of Statistics, Kristu Jayanti College (Autonomus), Bengaluru, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1516-1519 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7784038620/2020©BEIESP | DOI: 10.35940/ijrte.F7784.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of stock market performance is a challenging problem. There are numerous methods which are tried by various researchers in this regard. The memoryless property of Markov process seems to be more relevant when stock market prices are analysed for futuristic prediction. It is a stochastic process where the future probabilities are determined by the immediate present and not past values. This is suitable for the random nature of stock market fluctuations. The present study adopts this property to compare the performance of five prominent stocks in Oil and Gas Sector in India. The analysis is carried out based on past three years data of 5 prominent stocks in Oil and gas sector. The findings suggest that Bharat Petroleum, Reliance and Hindustan Petroleum are having high probability of increase in its value while Indian Oil corporation (IOC) and Oil India exhibits a higher chance of being stable with no significant increase or decrease.
Keywords: Markov Chain, Memoryless Property, Steady State Probability, State Transition Probability, Stock Performance
Scope of the Article: Network Performance; Protocols; Sensors.