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A Comparative Study on Efficient Market Hypothesis and Adaptive Market Hypothesis in Indian Stock Market
Subject area: Management and Commerce · Area of research: Finance
DOI: https://doi.org/10.64388/IREV10I2-1722151
Abstract
Efficient Market Hypothesis (EMH) has always been considered a theory that best explains the working of the stock market on the grounds of stock prices being perfectly reflecting all information available. However, market anomalies, financial crises, and behavioural biases raised questions against this theory, resulting in the formulation of the Adaptive Market Hypothesis (AMH). This hypothesis states that market efficiency keeps changing with changing market conditions. Therefore, this study attempts to make a comparison between the two hypotheses with regard to the Indian stock market based on the analysis of NIFTY 50 Index. Quantitative approach is taken up for this study using secondary data collected from the National Stock Exchange (NSE) over the period from April 2011 to March 2026. Efficiency of the market is determined with the help of various statistical tests that include Runs Test, Autocorrelation Test, Variance Ratio Test, and rolling window test. It was found that there is a mixed result with respect to market efficiency. While the results from Runs Test and Autocorrelation Test suggest the existence of serial dependency and periods of inefficiency, the results from Variance Ratio Test suggest random walk over the entire time horizon. Moreover, the results of rolling window test show that market efficiency changes with changing economic and financial scenarios over time. It can thus be concluded that Adaptive Market Hypothesis has a better application in the case of Indian stock market due to changing economic conditions and behaviour of investors.
Keywords
Efficient Market Hypothesis, Adaptive Market Hypothesis, Indian stock market, Random walk, NIFTY 50, Behavioural Finance
How to cite this paper
@article{1722151,
author = {Shilpa Shekhargouda Dadmi, Prof. Maruthi V},
title = {A Comparative Study on Efficient Market Hypothesis and Adaptive Market Hypothesis in Indian Stock Market},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {648-652},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722151.pdf},
abstract = {Efficient Market Hypothesis (EMH) has always been considered a theory that best explains the working of the stock market on the grounds of stock prices being perfectly reflecting all information available. However, market anomalies, financial crises, and behavioural biases raised questions against this theory, resulting in the formulation of the Adaptive Market Hypothesis (AMH). This hypothesis states that market efficiency keeps changing with changing market conditions. Therefore, this study attempts to make a comparison between the two hypotheses with regard to the Indian stock market based on the analysis of NIFTY 50 Index. Quantitative approach is taken up for this study using secondary data collected from the National Stock Exchange (NSE) over the period from April 2011 to March 2026. Efficiency of the market is determined with the help of various statistical tests that include Runs Test, Autocorrelation Test, Variance Ratio Test, and rolling window test. It was found that there is a mixed result with respect to market efficiency. While the results from Runs Test and Autocorrelation Test suggest the existence of serial dependency and periods of inefficiency, the results from Variance Ratio Test suggest random walk over the entire time horizon. Moreover, the results of rolling window test show that market efficiency changes with changing economic and financial scenarios over time. It can thus be concluded that Adaptive Market Hypothesis has a better application in the case of Indian stock market due to changing economic conditions and behaviour of investors.},
keywords = {Efficient Market Hypothesis, Adaptive Market Hypothesis, Indian stock market, Random walk, NIFTY 50, Behavioural Finance },
month = {August},
doi = {https://doi.org/10.64388/IREV10I2-1722151}
}