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Eghbal Rahimikia_

PhD candidate in Finance/ML, Alliance Manchester Business School (AMBS)

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I'm a PhD candidate in finance/machine learning(ML) 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. I finished working on three papers entitled Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News (under review, JBF), Machine learning for Realised Volatility Forecasting (under review, RFS), and my job market paper Realised Volatility Forecasting: Machine Learning via Financial Word Embedding (under review, JFEC).

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.


Education_

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).

Experience_

Research Assistant Intern

University of Oxford, UK

Jan 2021 - Apr 2021

Graduate Teaching Assistant

University of Manchester, UK

Sep 2019 - Present

2021-2022 - Semester 1, Advanced Mathematics.

Economics Department.

2020-2022 - Semester 1, Statistics and Machine Learning (M.Sc.).

School of Social Sciences.

2019-2021 - Semester 1, Financial Derivatives.

Alliance Manchester Business School (AMBS).

2020-2022 - 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

Financial Software Developer

Hekmat Iranian Bank, Iran

Sep 2015 - May 2017

project-img

Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News

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:

  • Realised Volatility Forecasting
  • Heterogeneous Auto-regressive (HAR) models
  • Limit Order Book (LOB) Data
  • Dow Jones Corporate News
  • Big Data
https://dx.doi.org/10.2139/ssrn.3684040
project-img

Machine Learning for Realised Volatility Forecasting

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:

  • Realised Volatility Forecasting
  • Machine Learning
  • Long Short-Term Memory
  • Heterogeneous AutoRegressive (HAR) Models
  • Limit Order Book (LOB) Data
  • Dow Jones Corporate News
  • Big Data
http://dx.doi.org/10.2139/ssrn.3707796
project-img

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database.

Keywords:

  • Realised Volatility Forecasting
  • Machine Learning
  • Natural Language Processing
  • Word Embedding
  • Explainable AI
  • Dow Jones Newswires
  • Big Data
http://dx.doi.org/10.2139/ssrn.3895272

Publications_

Realised 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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