phd thesis on stock market prediction

thesis was to investigate into the impact on machine learning-based stock price forecasting by using various inputs (technical, fundamental, and combined) and also by accounting for the states of stock market.

This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that

This thesis focuses on two fields of machine learning in quantitative trading. The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. The second (Chapter 4) applies machine learning to optimize decision-making for pairs trading.

The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature.

In this research, the forefront of predicting stock market prices by integrating the strengths of statistical analysis, machine learning models, deep learning neural networks, transformers with transfer learning techniques are explored.

STOCK MARKET PREDICTIONS USING MACHINE LEARNING In this thesis, an attempt is made to try and establish the impact of news articles and correlated stocks on any one stock. Stock prices are dependent on many factors, some of which are common for most stocks, and some are specific to a type of company. For instance, a product-based com-

The first part of the tested hypothesis states that deep-layered feedforward artificial neural networks are able to learn complex time-shifted correlations between step-wise trends of a large number of noisy time series, using only the preceding time steps’ gradients as inputs.

With the help of today's technology we can aim to predict the stock market for the future value of stocks. To make informed predictions, time series analysis is used by most stock brokers around the world. This paper explains and analyzes the prediction of a stock by using machine learning.

This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus.

One focusing on day trading and using technical analysis of the markets to predict the immediate value, and the other focusing on the stocks as long-time investments and using fundamental analysis to predict the future value of the stock in the long run.