Eghbal Rahimikia_
Lecturer in Finance, Alliance Manchester Business School (AMBS)
PhD candidate in Finance/ML, Alliance Manchester Business School (AMBS)
Eghbal Rahimikia_
Lecturer in Finance, Alliance Manchester Business School (AMBS)
PhD candidate in Finance/ML, Alliance Manchester Business School (AMBS)
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I'm a Lecturer in Finance and PhD candidate in finance/machine learning(ML) at Alliance Manchester Business School (AMBS), the University of Manchester under the supervision of
Ser-Huang Poon working on the theory and application of machine learning and deep learning in finance.
I 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 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.
University of Manchester, UK
Alliance Manchester Business School (AMBS)
2018 - Present
Ph.D. in Finance/Machine Learning.
Supervisor: Prof. Ser-Huang Poon.
GPA: Distinction.
Iran University of Science and Technology, Iran
2012 - 2014
M.Sc. in Industrial Engineering.
Major: Socio-Economical Systems Engineering.
Thesis: Bankruptcy prediction of Iranian companies based on hybrid intelligent systems.
GPA: 18.33/20, Thesis grade: 20/20 (distinction).
University of Tehran, Iran
2009 - 2012
B.A. in Economics.
GPA: 18.42/20 (first-class honours).
Lecturer in Finance
University of Manchester, UK
Sep 2021 - Cur.
Research Assistant Intern
University of Oxford, UK
Jan 2021 - Apr 2021
Graduate Teaching Assistant
University of Manchester, UK
Sep 2019 - Sep 2021
2020-2021 - Semester 1, Statistics and Machine Learning (M.Sc.).
School of Social Sciences.
2019-2021 - Semester 1, Financial Derivatives.
Alliance Manchester Business School (AMBS).
209-2020 - Semester 2, Advanced Statistics.
Economics Department.
CEO Consultant
Omid Investment Management Group Co, Iran
Jul 2017 - Dec 2017
Project manager/Software developer
Iranian National Tax Administration, Iran
Jul 2014 - Jul 2017
The study determines if information extracted from a big data set that includes limit order book (LOB) and Dow Jones corporate news can help to improve realised volatility forecasting for 23 NASDAQ tickers over the sample from 28 June 2007 to 17 November 2016.
Keywords:
https://dx.doi.org/10.2139/ssrn.3684040
This paper examines, for the first time, the performance of machine learning models in realised volatility forecasting using big data sets such as LOBSTER limit order books and news stories from 'Dow Jones News Wires' for 28 NASDAQ stocks over a sample period of June 28, 2007, to November 17, 2016.
Keywords:
http://dx.doi.org/10.2139/ssrn.3707796
We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database.
Keywords:
http://dx.doi.org/10.2139/ssrn.3895272Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
Rahimikia, Eghbal, Stefan Zohren, and Ser-Huang Poon
Available at SSRN 3895272 (2021). 2021
Machine Learning for Realised Volatility Forecasting
Rahimikia, Eghbal and Ser-Huang Poon
Available at SSRN 3707796 (2020). 2020
Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book
Rahimikia, Eghbal and Ser-Huang Poon
Available at SSRN 3684040 (2020). 2020
Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran
Rahimikia, Eghbal, Shapour Mohammadi, Teymur Rahmani, and Mehdi Ghazanfari
International Journal of Accounting Information Systems 25 (2017) pp. 1–17. Elsevier, 2017
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