Machine Learning in Finance
In this video series, learn how to use MATLAB® to develop and deploy various machine learning in finance applications, including algorithmic trading, asset allocation, sentiment analysis, credit analytics, fraud detection, and hedging. Also, you can use the Classification Learner app and the Regression Learner app to solve risk management problems such as credit risk, market risk, and operational risk.
Fraud Detection Using Machine Learning
Learn how to use machine learning to detect fraudulent activities like credit card fraud.
Classifying Credit Card Default Using the Classification Learner App
Use the Classification Learner app and simplified datasets to classify and predict credit card default.
Machine Learning for Algorithmic Trading
Using MATLAB and machine learning for algo trading.
Asset Allocation - Hierarchical Risk Parity
This example will walk you through the steps to build an asset allocation strategy based on Hierarchical Risk Parity (HRP).
Asset Allocation, Machine Learning, and High-Performance Computing
Aberdeen Standard discusses the use of MATLAB for machine learning to analyze financial market trends and testing on Microsoft Azure.
Model Interpretability in MATLAB
Machine learning models are known as “black box” because their representations of knowledge and decision-making aren’t intuitive. See how interpretability algorithms overcome the black-box nature of machine learning and how to apply them in MATLAB.
Reinforcement Learning for Trading
An agent hedges a European call option contract using reinforcement learning.
News Sentiment Analysis Using MATLAB and RavenPack
Use MATLAB to analyze news sentiment with data from RavenPack .
Forecast Electrical Load Using the Regression Learner App
Learn how to use the Regression Learner app to predict the amount of electricity required to support an electric grid.
