avatar

Eghbal Rahimikia_

Assistant Professor in Financial Technology, Alliance Manchester Business School (AMBS)

  Download CV

I obtained my undergraduate degree in Economics from the University of Tehran (first-class honours), my master's degree in Industrial Engineering from the Iran University of Science and Technology (distinction) and my PhD degree in Finance from the Alliance Manchester Business School (AMBS), the University of Manchester. I am currently an Assistant Professor (Lecturer) in Financial Technology (FinTech) at the Alliance Manchester Business School, working on the theory and application of machine learning (ML) and natural language processing (NLP) in Finance. My current project focuses on developing the first comprehensive financial NLP repository, FinText, in collaboration with researchers and industry partners. This repository covers a variety of NLP models (from Word2Vec to GPT) and benchmarks, incorporating different big financial textual datasets.

NEWS:

  1. • (January 2023) - FinText Gold-Standard Financial Benchmark is available to download now!

  2. • (November 2022) - FinText has been visited and downloaded over 1500 times during the past 12 months. We are happy to announce that it is renaming as a brand new repository of NLP models in finance. Updates are on the way by introducing new models and benchmarks in this area.

  3. • (July 2021) - The first group of purpose-built financial word embedding for financial textual analysis is available now for download under the FinText repository.


Education_

University of Manchester, UK

Alliance Manchester Business School (AMBS)

2018 - 2023 (March)

Ph.D. in Finance.

Supervisor: Ser-Huang Poon.

Examination Committee: Stephen Roberts (University of Oxford), Stuart Hyde (University of Manchester).

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_

Assistant Professor in Financial Technology (FinTech)

University of Manchester, UK

Agusut 2023 - Cur.

Consultant

Hull Tactical Asset Allocation, US

Agusut 2023 - Cur.

Lecturer in Finance

University of Manchester, UK

Sep 2021 - Agusut 2023

Research Assistant Intern

University of Oxford, UK

Jan 2021 - Apr 2021

Graduate Teaching Assistant

University of Manchester, UK

Sep 2019 - Sep 2021

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

project-img

Alternative Data for Realised Volatility Forecasting: Limit Order Book and News Stories

This paper tests if the limit order book (LOB) and news stories from 27 July 2007 to 27 January 2022 can help forecast realised volatility (RV) for stocks.

Keywords:

  • Realised Volatility Forecasting
  • Heterogeneous AutoRegressive models
  • Limit Order Book Data
  • News Stories
  • Sentiment Measures
https://dx.doi.org/10.2139/ssrn.3684040
project-img

Machine Learning for Realised Volatility Forecasting

his paper compares machine learning (ML) and HAR class of models for forecasting realised volatility using 147 input variables extracted from limit order book (LOB), and stock-specific news stories for the period from 27 July 2007 to 27 January 2022.

Keywords:

  • Realised Volatility Forecasting
  • Machine Learning
  • Long Short-Term Memory
  • Heterogeneous AutoRegressive models
  • Explainable AI
  • Limit Order Book
  • News Stories
http://dx.doi.org/10.2139/ssrn.3707796
project-img

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

This study develops FinText, a financial word embedding compiled from 15 years of business news archives. The results show that FinText produces substantially more accurate results than general word embeddings based on the gold-standard financial benchmark we introduced.

Keywords:

  • Realised Volatility Forecasting
  • Machine Learning
  • Natural Language Processing
  • Word Embedding
  • Explainable AI
  • 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

Alternative Data for Realised Volatility Forecasting: Limit Order Book and News Stories

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

Get in touch_