An Empirical Study on Implementation of AI & ML in Stock Market Prediction

Abstract

The introduction of Artificial Intelligence (AI) and Machine Learning (ML) has transformed numerous fields, including agriculture, industry, economy, and medicine, with significant advancements in automation and decision-making processes. Today, AI and ML have also made notable strides in financial markets, particularly in stock and foreign exchange (Forex) forecasting, where complex algorithms are used to predict market movements and assist in decision-making. This paper examines such applications, particularly on using AI and ML techniques in the stock trading and market prediction. Specifically for this paper, the general approach to the application of AI and more concretely the ML in the trading of stocks is examined in terms of learning processes and the algorithms which are used to make predictions. The paper examines data extraction techniques, which are crucial for identifying such patterns as historical stock prices and volumes of trading. These patterns are utilized in the determination of the future market tendencies hence of more utility in exploring the elusive tendencies of the financial markets. It will also be seen that the usage of deep learning models, as well as neural networks, is very helpful in discovering as well as addressing these patterns. A considerable part of the study is devoted to the presentation of AI-based models implemented in R programming for stock price prediction. Two primary models are examined: the Artificial Neural Network (ANN) and the time series model by employing Auto-Regressive Integrated Moving Average (ARIMA). Another kind of deep learning model is known as the ANN which is a kind of artificial neural network or a computer model of the brain derived after researching the way the human brain processes information, which efficiently learns and identifies patterns in large data presumed to make future predictions on these patterns. On the other hand, the ARIMA is a model developed to handle time series data, because through the exploration of the data used in the analysis when developing a model for the estimation of the future stock price. Thus, the use of ANN and ARIMA models presents a complete solution for forecasting the stock market. Indeed, the use of the ANN model to analyze data demonstrates its strength in finding patterns that could not be easily discernable using standard approaches, in contrast, the ARIMA model is the most effective for short-term forecasting utilizing trends that have already been observed and set. Taken together, this study seeks to improve the reliability of the models used in predicting stock price fluctuations and to contribute to effective investment decision-making. Last but not least, this study intends to raise awareness about how AI and ML perform better in stock market business to negotiate the challenges that are related to market volatility and the unpredictability of data. The realizations produced through these models not only equip the investors with a strategic guide on where to invest but also offer a more technical and rational means of decision-making compared to applying the 'gut feel' in the financial markets. Whereas R programming makes it easier to apply both the ANN and the ARIMA models, the research shows how AI and ML can be employed to control for risks and maximize returns and thus raise the efficiency of the trading models. This research adds to the current knowledge on the trends of applying AI and ML to financial markets since the technology has massive potential to provide additional and advanced tools for traders and investors.