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 Lectuter in Finance and PhD candidate at Alliance Manchester Business School (AMBS) under the supervision of Ser-Huang Poon working on the theory and application of machine learning and deep learning in finance. He finished working on three papers entitled Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News, Machine learning for Realised Volatility Forecasting, and his job market paper Realised Volatility Forecasting: Machine Learning via Financial Word Embedding.
NEWS: FinText, a purpose-built financial word embedding for financial textual analysis, is available now for download. This is the outcome of a collaboration with the Oxford-Man Institute of Quantitative Finance, the University of Oxford under the supervision of Stefan Zohren. For more information, please visit Personal Page.