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Academic profile

Amin Aminimehr

Ph.D. Student in Business Analytics with research interests in empirical asset pricing, machine learning, and financial markets.

I study how modern statistical and machine learning methods can be used to understand asset prices, market efficiency, and financial prediction problems. My work connects finance theory, empirical design, and interpretable predictive modeling.
Affiliation
Ph.D. Student in Business Analytics, University of Cincinnati
Research
Empirical asset pricing, machine learning, market efficiency, model interpretation

About Me

Currently I am a Ph.D. student in Business Analytics at the University of Cincinnati, Lindner College of Business. For my undergraduate degree, I pursued Financial Management at the University of Tehran, Faculty of Management. My research focus is on empirical asset pricing using machine learning. My research also involves theoretical and empirical properties of machine learning and deep learning methods, as well as the advantages and disadvantages of ML compared to conventional statistical methods.

In addition to my research, I have been also involved in teaching and mentoring students since 2021 with more than 2K in-person and online students in Iran and United States in total. I have more than 100 hours of recorded class videos in this area (See teaching section). I enjoy explaining quantitative methods in economics, econometrics, and finance, with an emphasis on building intuition behind formal models and bridging theory with practical applications using Python programming language.

Also, I have a long-standing interest in chess, which has shaped my analytical thinking, and I value discipline and consistency through regular physical training.

Research interests

  • Empirical Asset Pricing
  • Market Efficiency
  • Explainable AI
  • Model Interpretation
  • Time Series Analysis

Publications

Selected peer-reviewed and working research in finance, economics, and machine learning.

  1. 2022

    A comprehensive study of market prediction from efficient market hypothesis up to late intelligent market prediction approaches

    A Aminimehr, A Raoofi, A Aminimehr, A Aminimehr

    Computational Economics, 60 (2), 781-815. Citations: 24

  2. 2021

    ParsBERT post-training for sentiment analysis of tweets concerning stock market

    M Pouromid, A Yekkehkhani, MA Oskoei, A Aminimehr

    2021 26th International Computer Conference, Computer Society of Iran (CSICC). Citations: 22

  3. 2020

    The role of feature engineering in prediction of tehran stock exchange index based on LSTM

    A Aminimehr, A Raoofi, A Aminimehr, A Aminimehr

    Iranian Journal of Economic Studies, 9 (2), 527-548. Citations: 8

  4. 2021

    A study on the characteristics of TSE index return data and introducing a regime switching prediction method based on neural networks

    A Aminimehr, S Bajalan, H Hekmat

    Journal of Financial Management Perspective, 11 (34), 145-171. Citations: 6

  5. 2023

    The strength of convolutional neural network in financial distress prediction

    A Aminimehr, H Hekmat

    Financial Management Strategy, 11 (2), 77-96. Citations: 2

  6. 2024

    Stock market dynamics through deep learning context

    A Aminimehr, A Aminimehr, HM Kamali, S Eetemadi, S Hoseinzade

    arXiv preprint arXiv:2405.09932. Citations: 5

  7. 2024

Teaching Experience

Aug 2025 - Present

Graduate Teaching Assistant & Independent Instructor

UC

I teach core business analytics courses to undergraduate students at Lindner College of Business, University of Cincinnati.

Courses as Independent Instructor:

Summer 2026 Business Analytics II

Courses as Graduate Teaching Assistant:

Spring 2026 Business Analytics II By Dr. Iman Attari
Fall 2025 Management of Operations By Professor Rao Uday
Fall 2025 Operations Planning and Scheduling By Professor Rao Uday
Sep 2024 - May 2025

Graduate Teaching Assistant

UWM

At the University of Wisconsin-Milwaukee Department of Economics, I conducted weekly review sessions, summarized class materials, and solved problems for undergraduate macroeconomics courses. I also held regular office hours and contributed exam questions.

Spring 2025 Principles of Macroeconomics By Professor Mohsen Bahmani-Oskooee
Fall 2024 Principles of Macroeconomics By Doctor Antu Murshid
Fall 2024 Principles of Macroeconomics By Professor Rebecca Neumann
2022 and 2023

Course designer and instructor

Alzahra University

I designed and taught courses in introductory Python programming and financial time series analysis for students from non-engineering backgrounds. More than 40 graduate students from Finance, Accounting, and Economics enrolled at Alzahra University.
Jan 2022 - Feb 2022

Course designer and instructor

University of Tehran

I designed a customized course featuring finance-related examples with NumPy and pandas. The course introduced key documentation practices and applied Python tools to solve finance problems and perform financial calculations. It has received more than 7K views and more than 500 enrollments.

Industry Experience

Aug 2022 - Jul 2024

Senior Data Scientist/ML Engineer

Iran's National Traffic and Transportation Association

I worked as a data scientist and machine learning pipeline engineer, designing fully automated prediction pipelines for traffic flow on critical highways in the Tehran metropolitan area. I also collaborated on image processing projects, including YOLO-based object detection to count vehicles in parking lots using drone-captured videos.
Sep 2020 - Dec 2021

Research and Development Engineer

Mofid Securities

At Mofid, I collaborated on research and development for prediction projects. My responsibilities included

  • Testing neural network architectures to achieve the highest prediction accuracy.
  • Denoising high-frequency data related to stock prices and returns.
  • Backtesting prediction models to evaluate their performance in real-world scenarios.

Education

Master of Financial Engineering and Risk Management

Ershad Damavand Institute of Higher Education

2018 - 2022

Research Code

Applied research projects and reproducible code connected to asset pricing, forecasting, and data science.

Stock Characteristics

A research project focused on building and analyzing stock-level characteristics for asset pricing applications. The project organizes market and firm-level signals into a reproducible data workflow for studying cross-sectional return patterns and machine learning models in empirical asset pricing.

  • Asset Pricing
  • Stock Data
  • Python

Traffic Flow Prediction - LSTM Encoder-Decoder

Developed an encoder-decoder architecture based on Long Short-Term Memory (LSTM) networks to forecast short-term traffic flow across three major metropolitan highways in Tehran. The model leverages sequential time-series data to capture complex temporal dependencies and generate multi-step ahead predictions, supporting more efficient traffic management and congestion analysis.

  • LSTM
  • Encoder-Decoder
  • Python

Drone Video Object Detection - YOLOv8

Applied the YOLOv8 framework to drone-captured video data to detect and count vehicles in large-scale parking lots. The system processes aerial footage in real time, leveraging deep learning-based object detection to accurately localize and track cars across frames, enabling scalable parking utilization analysis and monitoring.

  • YOLO 8
  • Python

Contact

I welcome research conversations, teaching inquiries, and collaboration related to empirical finance, business analytics, and machine learning.
Email