Machine Learning-Based Predictive Stock Market Analyses: Analyzing the Use of Machine Learning Algorithms to Predict Financial Trends and Patterns and Improve Investment Decision-Making - Akcay, Tolga (Digital Repository of Theses - SSBM Geneva, 2024)
This research proposal aims to examine the utilization of Machine Learning (ML) methodologies to forecast stock market patterns. The equity market is a multifaceted and ever-changing structure subject to many variables, rendering precise prognostications arduous. Conventional techniques frequently need to be revised to capture the complex patterns and interrelationships within the data comprehensively. Thus, the utilization of ML algorithms can augment the precision of predictions. The principal aim of this investigation is to construct a resilient and precise prognostic framework capable of anticipating stock market patterns with notable accuracy. To attain the desired objective, the research will concentrate on four crucial elements, namely data collection, feature engineering, model selection, and performance evaluation. The methodology entails the acquisition of an extensive dataset encompassing past stock market information, encompassing price fluctuations, trading volumes, and pertinent financial metrics. Diverse feature engineering methodologies shall derive significant and predictive features from the unprocessed data. Various ML algorithms, including regression models, decision trees, and neural networks, will be examined to construct predictive models. The models will be evaluated by utilizing suitable metrics such as accuracy, precision, recall, and F1-score. Furthermore, the study aims to examine the effects of various data preprocessing approaches, feature selection methodologies, and model hyperparameter tuning on predictive accuracy. The anticipated results of this study involve the creation of a dependable and precise prognostic framework for stock market patterns. The study's results will enhance the current knowledge base by showcasing the efficacy of employing ML methodologies for forecasting stock market trends. Moreover, the study's findings will hold pragmatic ramifications for investors, financial establishments, and policymakers, furnishing significant discernments for decision-making and risk mitigation tactics.