Analysing Stock Prices of Manufacturing Companies through Support Vector Regressor and Random Forest Algorithm

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Year	2025
Volume/Issue/Review Month	Volume-XVIII \| Issue-I \| Jan.-Jun.
Title	Analysing Stock Prices of Manufacturing Companies through Support Vector Regressor and Random Forest Algorithm
Authors	Chandan Kumar Bal, Dr. Rohita Kumar Mishra
Broad area	Finance
Abstract	Abstract: Stock market prediction is a challenging task. The purpose of the study is to analyze and predict stock prices of five companies using machine learning. These companies include three automotive manufacturers, one steel producer, and one aluminum manufacturer. Ratios have been used to analyze the stock for the given period and two machine learning algorithms named support vector machine and the random forest are used to predict the closing stock prices. The smoothed independent variables are feed to the models for prediction. The variables are smoothed using Hendrick-Prescott filter. The result of both the support vector regression and random forest are significant, performed well. Both Models has performed better than the hit ratio. Both models can be considered for strategy making but random forest Regressor surpassed another model in accuracy. A limited variable is taken for a short period of time. Data can be pre-processed more precisely to make the study more accurate by using feature engineering. The predicted results from the models along with financial performance analysis can be used for taking decisions on buying, selling and holding stocks, diversification of fund to different assets and maximize return with minimal risk. The paper tried to analyze the financial performance of five companies in India with the help of Support Vector Regressor-Hendrick-Prescott filter and Random Forest- Hendrick-Prescott filter to understand the nature of the stock data and efficient prediction of stocks.
DOI	https://doi.org/10.63340/samt/1002
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