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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
Modelling Stock Price Predictability: Insights from Random Walk and Autoregressive Simulations
Authors
Paramita Karmakar
Keywords
Random Walk, Autoregressive, Simulation, Efficient Market Hypothesis
Abstract
Predicting stock prices is a central challenge in finance. This paper examines stock price behavior through two fundamental models: the Random Walk and the Autoregressive model of order one (AR(1)). Using Monte Carlo simulations, we generate 50 independent price paths over 100 time periods, all starting at the same initial price. The Random Walk paths diverge rapidly, demonstrating unpredictability and randomness, while the AR(1) paths exhibit smoother, persistent trends, indicating some short-term predictability. Through these visual and statistical comparisons, the study highlights how memory in price dynamics influences predictability and provides a hands-on illustration of these competing views on financial market behaviour.
IJCRT's Publication Details
Unique Identification Number - IJCRT2510258
Paper ID - 294090
Page Number(s) - c150-c155
Pubished in - Volume 13 | Issue 10 | October 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Paramita Karmakar, "Modelling Stock Price Predictability: Insights from Random Walk and Autoregressive Simulations", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 10, pp.c150-c155, October 2025, Available at :http://www.ijcrt.org/papers/IJCRT2510258.pdf
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Volume 14 | Issue 2
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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