HARNESSING MICROBLOGGING DATA FOR FINANCIAL FORECASTING: XGBOOST MODEL FOR ALPHA AND SIGNAL PREDICTION

Abstract

One of the most important factors to consider when evaluating the performance of a financial asset in comparison to the general market benchmark is the presence of alpha signals, which are also known as excess returns. The capability to properly and rapidly identify alpha signals is of tremendous benefit to investors and financial analysts. This is because it has the potential to significantly impact decisions about portfolio optimization and risk management with significant implications. Traditional techniques of alpha signal prediction, on the other hand, which mainly rely on previous financial data, have inherent limits when it comes to catching real-time market sentiments and movements. In order to circumvent these restrictions, academics have begun investigating other data sources, notably data from social media platforms, with the goal of gaining more profound insights into the feelings of the market and improving their ability to forecast alpha signals. Understanding investor mood, market views, and collective behavior may be accomplished through the utilization of a novel technique that is presented by the incorporation of data from social media platforms into financial analysis. Twitter and StockTwits are two examples of microblogging systems that provide as rich sources of real-time information. These platforms represent thoughts and reactions to financial events as they occur. The utilization of such data for the purpose of alpha signal prediction has the potential to supplement and reinforce conventional techniques of financial analysis, which would ultimately result in forecasts that are more accurate and reliable. As a result of this, the primary objective of this research is to make use of the data associated with microblogging on social media platforms in order to forecast alpha signals in the financial markets. The solution that was selected makes use of the XGBoost model, which is a strong machine learning algorithm that is well-known for its capacity to manage complicated and unstructured data that comprises a high number of dimensions. In order to evaluate the predicted performance and accuracy of the model, it is first trained using historical data and then evaluated with data that is not selected from the main sample. The purpose of this proposed effort is to contribute to the progress of alpha signal prediction techniques and to strengthen decision-making processes in the financial arena. This will be accomplished by utilizing the real-time and sentiment-rich information that is captured from social media.