A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction

Forecasting the future price trend of a stock traded on financial exchange is aim market prediction. In recent decades, prediction has been fascinating topic in domain Data Science and Finance. reality, movement ambiguous chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At same time, accurate enough forecasting leads substantial benefits for investors. This paper provides comprehensive review application comparison Machine Learning (ML) algorithms Econometric Models The mentioned models are categorized into (i) ML algorithms, including Linear Regression (LR), K-nearest neighbors (KNN), Support Vector (SVM), Long Short-Term Memory (LSTM). (ii) Models, Autoregressive Integrated Moving Average (ARIMA) Model, Capital Asset Pricing Model (CAPM), Fama-French (FF) Factor Model.