Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network

Abstract

This study shows importance hierarchy of financial factors of corporations’ Goodwill and tries to foresee with popular machine learning and deep learning models. Financial engineering is using mathematical model to study financial behavior. Financial engineers are hired by investment banks, commercial banks, hedge funds, insurance companies, corporate treasuries, and regulatory agencies. It is vital for each of them to asses a company’s sustainability before any sort of investment. However, predicting sustainability is not deterministic. Therefore, corporate sustainability has become a mainstream business goal for stakeholders. Whether Quantitative finance impacts goodwill or has implicit insight can be a machine learning problem. Deep learning and machine learning are rapidly changing the financial services industry. Business leaders can now transform vast amounts of financial data into insightful predictions with the help of data science, creating significant savings in the bottom line. This thesis is concerned with investigating financial factors of a company’s Goodwill and also fits popular machine learning and deep learning models and evaluate goodness of fit. To aid the research, a comparison between the proposed models-XGboost and Deep LSTM are conducted.