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Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India

This research examines whether stock prices in the Indian stock markets follow a Geometric Brownian Motion (GBM). This study is keen on knowing if one can predict the simulated stock prices accurately against the actual stock prices. One-year, three-year, and five-year data of the historical stock p...

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Main Authors: Prasad, Krishna, Prabhu, Bhuvana, Pereira, Lionel, Prabhu, Nandan, S, Pavithra
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/21289/1/Effectiveness%20of%20Geometric%20Brownian%20Motion%20Method%20in%20Predicting%20Stock%20Prices%20Evidence%20from%20India.pdf
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spelling my-ukm.journal.212892023-03-06T02:34:37Z http://journalarticle.ukm.my/21289/ Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India Prasad, Krishna Prabhu, Bhuvana Pereira, Lionel Prabhu, Nandan S, Pavithra This research examines whether stock prices in the Indian stock markets follow a Geometric Brownian Motion (GBM). This study is keen on knowing if one can predict the simulated stock prices accurately against the actual stock prices. One-year, three-year, and five-year data of the historical stock prices of 50 stocks listed on the S&P BSE (Bombay Stock Exchange) Sensex 50 Index were employed as the base data to predict stock prices using the Monte Carlo simulation’s GBM method. This study investigates whether there are statistically significant differences between the actual stock prices for three months and the simulated prices of the same period. This research has found that the GBM Monte Carlo simulation effectively predicts future stock prices for three months based on the historical data of stock prices of the past year. This study did not find significant differences between the actual and predicted stock prices when the simulation used the past one year’s data. This research is original in the Indian context, as it situates the GBM method of Monte Carlo simulation in the premise of bounded rationality and efficient market hypothesis theories. There is thus the empirical evidence for bounded rationality and that the stock markets are not efficient. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/21289/1/Effectiveness%20of%20Geometric%20Brownian%20Motion%20Method%20in%20Predicting%20Stock%20Prices%20Evidence%20from%20India.pdf Prasad, Krishna and Prabhu, Bhuvana and Pereira, Lionel and Prabhu, Nandan and S, Pavithra (2022) Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India. Geografia : Malaysian Journal of Society and Space, 18 (4). pp. 121-134. ISSN 2180-2491 https://ejournal.ukm.my/gmjss/index
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description This research examines whether stock prices in the Indian stock markets follow a Geometric Brownian Motion (GBM). This study is keen on knowing if one can predict the simulated stock prices accurately against the actual stock prices. One-year, three-year, and five-year data of the historical stock prices of 50 stocks listed on the S&P BSE (Bombay Stock Exchange) Sensex 50 Index were employed as the base data to predict stock prices using the Monte Carlo simulation’s GBM method. This study investigates whether there are statistically significant differences between the actual stock prices for three months and the simulated prices of the same period. This research has found that the GBM Monte Carlo simulation effectively predicts future stock prices for three months based on the historical data of stock prices of the past year. This study did not find significant differences between the actual and predicted stock prices when the simulation used the past one year’s data. This research is original in the Indian context, as it situates the GBM method of Monte Carlo simulation in the premise of bounded rationality and efficient market hypothesis theories. There is thus the empirical evidence for bounded rationality and that the stock markets are not efficient.
format Article
author Prasad, Krishna
Prabhu, Bhuvana
Pereira, Lionel
Prabhu, Nandan
S, Pavithra
spellingShingle Prasad, Krishna
Prabhu, Bhuvana
Pereira, Lionel
Prabhu, Nandan
S, Pavithra
Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India
author_facet Prasad, Krishna
Prabhu, Bhuvana
Pereira, Lionel
Prabhu, Nandan
S, Pavithra
author_sort Prasad, Krishna
title Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India
title_short Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India
title_full Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India
title_fullStr Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India
title_full_unstemmed Effectiveness of geometric brownian motion method in predicting stock prices: evidence from India
title_sort effectiveness of geometric brownian motion method in predicting stock prices: evidence from india
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2022
url http://journalarticle.ukm.my/21289/1/Effectiveness%20of%20Geometric%20Brownian%20Motion%20Method%20in%20Predicting%20Stock%20Prices%20Evidence%20from%20India.pdf
http://journalarticle.ukm.my/21289/
https://ejournal.ukm.my/gmjss/index
_version_ 1759690445206585344
score 13.211869

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