Predictive Modeling of Stock Prices Using Time Series Analysis

Thesis Abstract

Abstract
This thesis investigates the application of time series analysis in predicting stock prices, aiming to enhance the accuracy and efficiency of stock market forecasting. The research utilizes historical stock price data to develop predictive models that can forecast future stock prices. The study focuses on the analysis of various time series models, including autoregressive integrated moving average (ARIMA), exponential smoothing, and machine learning algorithms, such as random forest and support vector machines. Chapter One provides an introduction to the research topic, discussing the background of the study, the problem statement, research objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes definitions of key terms relevant to the research. Chapter Two presents a comprehensive literature review, covering ten key aspects related to predictive modeling of stock prices using time series analysis. The review includes discussions on previous studies, methodologies, and findings in the field of stock market forecasting. Chapter Three details the research methodology employed in the study, including data collection, preprocessing, model selection, validation techniques, and performance evaluation metrics. The chapter outlines the step-by-step process of building and testing time series models for predicting stock prices accurately. Chapter Four offers an in-depth discussion of the findings obtained from the application of various time series models to predict stock prices. The chapter analyzes the performance of different models, compares their accuracy, and discusses the implications of the results for stock market forecasting. Chapter Five presents the conclusion and summary of the thesis, highlighting the key findings, contributions, limitations, and future research directions. The chapter summarizes the research outcomes and recommendations for improving the predictive modeling of stock prices using time series analysis. Overall, this thesis contributes to the field of stock market forecasting by demonstrating the effectiveness of time series analysis in predicting stock prices accurately. The research findings provide valuable insights for investors, financial analysts, and policymakers to make informed decisions based on reliable stock price forecasts.