Predicting Stock Market Trends using Random Forest: A Comparative Analysis

Lavingia, Kruti; Khanpara, Pimal; Mehta, Rupa G.; Patel, Karan A.; Kothari, Niket

doi:10.1109/icces54183.2022.9835876

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Random Forest has proven to be aeffective tool for investors and traders alike (Park et al, 2019) (Abraham et al, 2022) (Maini and Govinda, 2017). As input features, the algorithm makes use of past trading data and a variety of technical indicators (Lavingia et al, 2022) (Yin et al, 2023). It adjusts learning parameters during the training process to optimize accuracy (Park et al, 2019).…”

Section: Review Of Literaturementioning

confidence: 99%

“…Combining Random Forest with other AI technologies has also helped with stock price analysis and investment decision-making (Du et al, 2022) (Dhanalakshmi et al, 2023). The algorithm has produced good results when applied to various stock markets, such as the NASDAQ, BSE, NSE, and SSE A-share (Lavingia et al, 2022) (Qiu and Jia, 2022). (Sadorsky, 2022) research utilizes machine learning algorithms that are structured like trees to predict the future value of solar stocks, paying close attention to technical indications, silver prices, volatility in silver prices, and volatility in oil prices.…”

Section: Review Of Literaturementioning

confidence: 99%

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Effectiveness of Random Forest Model in Predicting Stock Prices of Solar Energy Companies in India

Meher,

Singh,

Birau

et al. 2024

IJEEP

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The solar energy industry's positive impact on India's GDP is perceptible through increased investments, innovation, and enhanced energy security. As the nation continues to prioritize clean energy solutions, the solar sector stands as a key player driving both economic prosperity and environmental sustainability, aligning India with world wide determinations to battle climate change and encourage a greener future. As the Indian government continues to champion initiatives promoting renewable energy, Solar Energy Companies have seen unprecedented growth and have become increasingly attractive to investors seeking long-term, sustainable returns. This influx of interest, however, brings with it the challenge of navigating the volatile and dynamic nature of the stock market. In this context, forecasting the stock prices of solar energy corporations in India becomes a pivotal aspect of investment strategy for both institutional and retail investors. This paper targets to add to the prevailing body of knowledge by evaluating the efficacy of the Random Forest model, a machine learning technique known for its versatility and robustness, in forecasting the stock prices of top four Solar Energy Companies in India on the basis of market capitalization, by using the daily opening, high, low and closing stock prices ranging from 1st October, 2019 to 30th September, 2023 i.e. 4 years. The findings reveal that high Coefficient of Determination (R2) values for all companies, ranging from 0.9928 to 0.9939 is a clear indication of the model's ability to predict a substantial portion of the variance in each company's stock prices. But in case of Adani Green Energy Ltd a notably higher MSE and RMSE are exhibited, implying a greater degree of fluctuation in prediction accuracy compared to the other companies. On the other hand, all the selected solar energy companies display lower MAE values, indicating tightly clustered predictions around actual values.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Effectiveness of Random Forest Model in Predicting Stock Prices of Solar Energy Companies in India

Meher,

Singh,

Birau

et al. 2024

IJEEP

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“…Technical analysts or chartists are not concerned with any of the company's fundamentals. They seek to determine the future price of a stock based solely on the (potential) trends of the past price (a form of time series analysis) [5]. Numerous patterns are employed such as the head and shoulders or cup and saucer.…”

Section: Technical Analysismentioning

confidence: 99%

“…Beyond the allure of profits, the prediction of stock market direction plays a vital role. It can function as an early recommendation system for short-term investors and an early warning system for long-term shareholders in financial distress [5]. While numerous stock prediction studies focus on using macroeconomic indicators like the Consumer Price Index (CPI) and Gross Domestic Product (GDP) to train prediction models, obtaining daily data for these indicators can be a daunting task [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Stock Market Trends: A Bivariate Regression Analysis of Trading Volume Impact

Adeduro,

Adedeji,

Ajao

2023

CTICTR

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Stock market prediction presents a perennial challenge for investors attempting to forecast the future value of traded stocks at the exchange. The allure of successful predictions, which can yield substantial profits, has driven this pursuit since the inception of stock markets. The 2008 financial crisis highlighted the complexity of market behaviour, emphasizing the need for a deeper understanding of stock market prediction. Regression analysis, a quantitative research method, quantifies relationships between dependent and independent variables. This study conducted a bivariate regression analysis to assess the predictive potential of traded volume on equity price movements within the Nigerian Exchange Limited (NGX or The Exchange), using Guaranty Trust Holding Company Plc (GTCO) historical data. The results revealed a statistically significant correlation (r=0.59) between volume (X) and price (Y). The regression equation (Y = 44.164 + 0.179X) demonstrated that 34.5% of price variance can be predicted from traded volume, indicating a reasonably strong relationship. These findings suggest a moderately robust association between trading volume and stock price movements. This implies that, to some extent, trading volume can guide predictions of stock prices. As investors seek to outperform the market, using linear Regression for stock price prediction remains an engaging area for further research, aiming to enhance return on investment. In conclusion, our linear regression model can generate more precise stock price predictions with a more extensive dataset. Keywords: Efficient market hypothesis, linear Regression, Price prediction, trading volume, Stock market.

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“…Machine learning adalah bagian dari kecerdasan buatan yang dapat digunakan untuk peramalan harga saham. Metode machine learning yang dapat digunakan untuk peramalan harga saham antara lain multi-layer perceptron (Azizah et al, 2020;Namdari & Durrani, 2021;Sundar & Satyanarayana, 2019), linear regression (Chauhan & Sharma, 2023;Deshmukh et al, 2023;Meghana & Arumugam, 2023), support vector regression (Azimifar et al, 2020;Fang, 2020;Huang et al, 2022), dan random forest regression (Ghosh et al, 2022;Illa et al, 2022;Lavingia et al, 2022). Penelitian Xia dkk (2013) Peramalan harga saham menggunakan data transaksi harian historis dan indikator teknikal (Albahli et al, 2022).…”

Section: Introductionunclassified

Impurity-Based Important Features for feature selection in Recursive Feature Elimination for Stock Price Forecasting

Priyatno,

Sudirman,

Musridho

et al. 2023

JUTIN

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Stock investors perform stock price forecasting based on technical indicators and historical stock prices. The large number of technical indicators and historical data often leads to overfitting and ambiguity in forecasting using machine learning. In this paper, we proposed a feature selection approach using impurity-based important features in recursive feature elimination for stock price forecasting. The data utilized includes historical data and various moving averages. Feature selection is employed to reduce the number of features and obtain important and relevant features. The recursive feature elimination with impurity-based important features is utilized as the feature selection method. The machine learning methods employed are linear regression, support vector regression, multi-layer perceptron regression, and random forest regression. The measurement results of mean squared error (mse), root mean squared error (rmse), mean absolute error (mae), and mean absolute percentage error (mape) show that the optimal feature selection and machine learning method is achieved with six features and linear regression. The average mse, rmse, mae, and mape values are 0.000279, 0.016577, 0.012843, and 1.42236%, respectively. These results validate the effectiveness of impurity-based important features for feature selection in recursive feature elimination using historical data and various moving averages in stock price forecasting.

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Predicting Stock Market Trends using Random Forest: A Comparative Analysis

Cited by 5 publications

References 21 publications

Effectiveness of Random Forest Model in Predicting Stock Prices of Solar Energy Companies in India

Effectiveness of Random Forest Model in Predicting Stock Prices of Solar Energy Companies in India

Predicting Stock Market Trends: A Bivariate Regression Analysis of Trading Volume Impact

Impurity-Based Important Features for feature selection in Recursive Feature Elimination for Stock Price Forecasting

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