USE OF MACHINE INTELLIGENCE IN OVER THE COUNTER PRODUCTS IN STOCK MARKET
Abstract
In this study, machine intelligence was applied in predicting stock prices of over the counter (OTC) products and comparing the traditional machine learning models' (Logistic Regression, Random Forest, Support Vector Machine) and the deep models' (LSTM) performance. The research tries to find the effectiveness of these models in the prediction of stock price and the influence in the profitability, trading volume, and market sentiment. Models inferences were evaluated by combining historical stock price data, sentiment analysis and profitability metrics. It turns out that LSTM surpassed traditional models on accuracy, precision, recall, and profitability, attaining a 9.5% average monthly return and a Sharpe ratio of 2.1. Moreover, the introduction of machine intelligence models increased trading volume, and it showed their effect on trading activity. Both sentiment analysis and stock price movements had strong correlation. In general, the results indicate that LSTM based deep learning models outperformed in terms of prediction and profitability by OTC stock markets with important implications for trading strategies.
Keyword : Machine Intelligence, Stock Price Prediction, OTC Products, Sentiment Analysis, LSTM.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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