Merger & Acquisition Outcome Prediction (GY - ONLY)

  • Exploring and discovering new methods for merger and acquisition outcome prediction using artificial intelligence and machine learning techniques

Merger & Acquisitions Outcome Prediction. Picture of one black and one white bull intertwined.

This VIP project is intended to explore and discover new methods for merger and acquisition outcome prediction using artificial intelligence and machine learning techniques such as hidden Markov models, Kalman filters, random forests, genetic programming, deep learning, reinforcement learning, etc. This project is an opportunity to apply the ‘learning by doing’ methodology that starts with research and continues with engineering, hence simulating a research and development (R&D) environment. Students will continue working on a project developed by previous VIP groups. One of the central components of a VIP is that the project work builds beyond the participation of any one student, and students participate in something that could someday be very substantial.

As this VIP focuses on the development and the application of machine learning models in finance, students are required to have taken at least one machine learning or database systems course or be able to justify the mastery of one of these subjects prior to enrolling in the VIP project.

This is a 1.5 credits course (or optionally a 0 credit course) for graduate students only (MS and PhDs). This VIP project simulates the work of a quant researcher or a R&D engineer in finance and tech companies. Students are expected to deliver working code, and actively participate in the team effort. 

Students will be required to:

  1. Pair with a teammate to give a presentation on a topic of their choice related to the VIP theme.
  2. Contribute to the team implementation of a research paper and improve upon it along with the associated data science, modeling, and backtesting. 
  3. Contribute to the team summary report.

This opens the possibility for interested students to participate in competitions (e.g. the International Association for Quantitative Finance (IAQF) Student Competition, the Rotman International Trading Competition (RITC), etc.), present their work in workshops and conferences, and publish a research paper.

VIP M&A Project

This project is intended to predict the success of an M&A action by Machine Learning. The research team will analyze the M&A process and build machine learning models to predict the success of the action. The data science team will collect appropriate data and build the machine learning HPC pipeline. The goal of this project is to build a successful pipeline that can be utilized by companies who are looking for M&A opportunities using publicly released data to evaluate their outcome.

Research, Design, or Technical Issues Involved or Addressed

  • Research and Design Issues

    • M&A features to use and the source of data
    • Appropriate models to use and compare their result
    • Evaluating the model performance and the associated risk
    • Building the model prototype
  • Technical Issues 
    • Exploratory data analysis
    • Data fetching and management
    • Machine learning pipeline building
    • Cloud platform application building
    • UI design


This team will have two subteams.

  • Research Subteam (student’s second semester of VIP): 

    • Machine learning model development
    • Machine learning results analysis, comparison, and presentation.
  • Data Science / Data Engineering Subteam (student’s first semester of VIP):
    • In charge of the EDA process
    • In charge of building the pipeline onto the application platform, debugging the system
    • Design the UI that can present the application


  • Research
  • Data Science / Data Engineering

Majors and Areas of Interest

  • Financial Engineering
  • Mathematical Finance
  • Finance
  • Data Science
  • Software Engineering
  • Merger and Acquisition
  • Cloud Application Development
  • UI Design

Methods and Technologies

  • Data Science
  • Statistical Learning
  • Optimal Control
  • Machine Learning
  • Deep Learning
  • Reinforcement Learning
  • UI Design


  • NYU Tandon School of Engineering

Primary Instructors