Machine learning predictions of stock market pattern using Econophysics approach

A financial market collapse is the failure of a financial organisation, whereas a stock market crash is usually characterised as a sudden, large decrease in the price of stocks or stock market indexes. A speculative bubble, in which prices increase unreasonably before overvaluation causes a fall, may set this off. A stock market collapse is specifically caused by the panic that hits the market as a result of an excessive number of sell orders executed at once. According to Econophysics, interrelated behaviours and newly forming patterns in the market system are what propel market crashes, which are seen as a component of a bigger cycle of stability and instability. This research outlines the study of stock market patterns using the Econophysics approach. There are various techniques for identifying and observing the stock market patterns, one of the techniques is to use Python programming to evaluate and possibly forecast stock market behaviour through predictive modelling, combining both machine learning and Econophysics insights. Hence, this research will be using Monte Carlo Simulation and identify which machine learning algorithm is suitable for predicting stock market patterns. By leveraging machine learning algorithms, such as Long Short-Term Memory (LSTM), the predictions generated closely follow the actual stock price movements for Inari Amertron Berhad. The predicted stock prices correctly reflect whether the market is moving upward or downward and they correspond with actual market trends. Furthermore, there is a strong correlation between the model’s buy or sell recommendations and the actual and anticipated price trends. In conclusion, the study of Econophysics principles with Python programming and machine learning algorithms has indicates that the predictive framework is reliable and effective in capturing stock price fluctuations, enhancing decision-making for investors based on data-driven insights.