2020, Vol. 2, Issue 2, Part A

This research presents an innovative approach to Stock Market Analysis and Prediction utilizing Machine Learning (ML) techniques. The volatile nature of financial markets poses a significant challenge for investors and traders seeking to make informed decisions. Traditional methods often fall short in capturing the complexity and dynamic patterns inherent in stock market movements. In response to this, our study employs advanced ML algorithms to analyze historical market data, extract meaningful patterns, and develop predictive models.

The primary objectives of this research include data preprocessing, feature engineering, and the implementation of ML algorithms to discern patterns in stock price movements. Historical stock market data, including price, volume, and relevant financial indicators, are leveraged to train and validate the ML models. Various algorithms, such as Support Vector Machines (SVM), Random Forests, and Neural Networks, are employed to capture both linear and non-linear relationships within the data. The outcomes of this research not only contribute to the field of financial forecasting but also provide valuable insights for investors, financial analysts, and policymakers. The application of ML techniques in stock market analysis holds the potential to enhance decision-making processes, mitigate risks, and improve overall market efficiency. As financial markets continue to evolve, the integration of advanced technologies becomes imperative for staying ahead in the complex and dynamic landscape of the stock market.