Multivariate random forest-based stock trading signal classification for the Indian equity market

Abstrak / Ringkasan

Stock markets and stocks have been around for centuries. For investors, forecasting changes in the price of stocks has long been an aim. Using computer tools for information systems can lead to better and more accurate results. Machine learning uses machines to mimic human thinking and habits. This helps in making predictions and decisions based on data. Today, machine learning is common in areas like face recognition, investment advice, and natural language processing. This study proposes a robust framework for stock trading signal classification within the Indian equity market using a Multivariate Random Forest (MRF) approach. Separate Random Forest classifiers are designed to capture the unique market traits of each company. Bootstrap sampling and random feature selection help the ensemble model to find intricate relationships among various technical variables. This method reduces the variance that comes with individual decision trees. By leveraging the ensemble learning capabilities of Random Forest, the model effectively mitigates the risks of overfitting while managing the high-dimensional feature space typical of financial time series. The Multivariate Random Forest-based stock market model offers a more reliable and practical way to classify stock market signals. The model aids in making smarter, data-driven choices for portfolio management and key market drivers.

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