Artificial Intelligence in Stock Trading and Analysis

Raziya Khatoon, Dr. Satish N. Gujjar

Abstract: The stock market is a challenging environment for predicting price changes because prices continuously fluctuate. Additionally, many internal and external elements impact stock prices, including economic conditions, market performance, and investor behaviour. This research aims to examine historical stock market price data using study examines stock market data collected over different time periods to understand changes in stock prices. Previous market records are analysed to identify trends and patterns related to market behaviour. Different statistical techniques are used to study earlier price movements and estimate possible future changes in stock values. Moving averages and the ARIMA model are used to study market trends and support forecasting. The prediction results are checked using common accuracy measures to compare predicted prices with actual market values. According to the findings from the model analysed, they were able to demonstrate that they have the ability to identify general patterns or effectiveness of stock prices (in addition to making reliable predictions) when there was stability with respect to market conditions. However, it has proven very difficult to predict sudden movements in stock prices caused by unexpected events (e.g., catastrophic natural disasters, terrorist attacks, etc). The study concludes with an emphasis on the efficacy of 'simple' and 'interpretable' models for the evaluation and prediction of the financial and stock markets -and subsequently their ease of application as a credible basis for making decisions.

Keywords: Artificial Intelligence, Stock Market Forecasting, Machine Learning Techniques, Financial Data Analysis, Historical Market Data, Data Mining Techniques, Time Series Forecasting, Investment Decision-Making Process, Market Trend Analysis, Predictive Modelling