Time-Varying Approach-Based Indian Stock Price Prediction Using ARIMA and Monte Carlo Simulation

Authors: T P Sameerapallavi

DOI: https://doi.org/10.37082/IJIRMPS.v11.i2.233237

Short DOI: https://doi.org/hcqxd6

Country: India

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Abstract: The stock market is influenced by several factors such as economic and business policies, events that affect the economy of a country, natural disasters, climatic conditions, and also the psychological states of investors. As a result, the market is dynamic, volatile, and complex. The choice of predictor variables determines the interpretation of results and the level of forecast performance. There are generally four categories of information for stock market prediction: basic information, technical information, behavioural information, and information on the history of the stock return series. This paper proposes a stock prediction approach using commodities. Crude oil is considered to be a major commodity in India. In the proposed model, the Autoregressive Integrated Moving Average (ARIMA) model is used to forecast the stock price of companies based on international crude oil prices. The ARIMA parameters, such as the degree of integration, autoregressive order, and moving average order, have been tuned to predict the stock values. In order to tune these parameters, exploratory data analysis has been performed, and the data has been converted into stationary data. Monte-Carlo simulation is used to forecast the stock price based on the results of ARIMA. The experimental analysis proves that the proposed algorithm has predicted the stock values very efficiently.

Keywords: Autoregressive Integrated Moving Average, Monte-Carlo simulation, Stock price, Crude oil.

Paper Id: 233237

Published On: 2023-03-04

Published In: Volume 11, Issue 2, March-April 2023

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