DeepAI AI Chat
Log In Sign Up

Predicting Mergers and Acquisitions using Graph-based Deep Learning

04/05/2021
by   Keenan Venuti, et al.
0

The graph data structure is a staple in mathematics, yet graph-based machine learning is a relatively green field within the domain of data science. Recent advances in graph-based ML and open source implementations of relevant algorithms are allowing researchers to apply methods created in academia to real-world datasets. The goal of this project was to utilize a popular graph machine learning framework, GraphSAGE, to predict mergers and acquisitions (M A) of enterprise companies. The results were promising, as the model predicted with 81.79 data sources and algorithmic decision making within financial data science, graph-based machine learning offers a performant, yet non-traditional approach to generating alpha.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/15/2022

Data Science in Perspective

Data and Science has stood out in the generation of results, whether in ...
06/16/2020

NodeNet: A Graph Regularised Neural Network for Node Classification

Real-world events exhibit a high degree of interdependence and connectio...
07/23/2018

Data Science with Vadalog: Bridging Machine Learning and Reasoning

Following the recent successful examples of large technology companies, ...
12/07/2018

The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning

We present a pedagogical introduction to the recent advances in the comp...
01/30/2022

Similarity Search on Computational Notebooks

Computational notebook software such as Jupyter Notebook is popular for ...
09/11/2020

Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA

The research on mortality is an active area of research for any country ...
04/19/2021

Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks

Even though machine learning algorithms already play a significant role ...