Mr Eghbal Rahimikia

Overview

Eghbal obtained his undergraduate degree in Economics from the University of Tehran (first-class honours) and his master degree in Industrial Engineering from the Iran University of Science and Technology (distinction). In the master’s degree, he focused on the theory and application of machine learning in bankruptcy prediction of companies. During that time, the Iranian National Tax Administration offered him a project related to tax evasion detection. He worked for more than two years in this project as a project manager and developer, and he developed a comprehensive software with more than 10,000 lines of written code incorporating different machine learning techniques such as Artificial Neural Network and Support Vector Machine. After this, he did different projects using Factor Analysis and Data Envelopment Analysis related to the calculation of efficiency of tax sectors and banking industry. Furthermore, he published the results of his research and projects in different journals. He is currently a Ph.D. student at Alliance Manchester Business School (AMBS) under the supervision of Prof. Ser-Huang Poon and Dr. Yoichi Otsubo working on the theory and application of machine learning and deep learning in finance with a focus on volatility forecasting in the ultra-high-frequency and high-dimensional environment (Big Data). The main focus of my research is using more in-depth information from the market (order book and message file) and also news data and sentiment behind the news to enrich the models for Realized Volatility (RV) forecasting. Also, I focus on deep learning models and notably different variations of LSTM (such as attention LSTM, CNN-LSTM, etc.) for this task and analyze the behaviour of these models in detail for practical RV forecasting.