Quantitative Finance and Risk Management

 

MATLAB for Machine Learning in Finance

Uncover hidden patterns and create predictive models with financial and alternative data

Quants and financial data scientists use MATLAB to develop and deploy various machine learning applications in finance, including algorithmic trading, asset allocation, sentiment analysis, credit analytics, and fraud detection. MATLAB makes machine learning easy with:

  • Point-and-click apps for training and comparing models
  • Automatic hyperparameter tuning and feature selection to optimize model performance
  • The ability to use the same code to scale processing to big data and clusters
  • Automated generation of C/C++or GPU code for embedded and high-performance applications
  • All popular classification, regression, and clustering algorithms for supervised and unsupervised learning
  • Faster execution than Python® and R on most statistical and machine learning benchmarks

Customers' Choice

MathWorks Named a May 2019 Gartner Peer Insights Customers’ Choice for Data Science and Machine Learning Platforms

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Deep Learning and Traditional Machine Learning: Choosing the Right Approach

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Machine Learning Applications in Finance

Asset Allocation
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.

Algorithmic Trading
Ebook: Machine Learning with MATLAB
This short ebook is your guide to the basic techniques. You’ll see that machine learning is within your grasp—you don’t need to be an expert to get started.

Risk Management
Machine Learning Applications in Risk Management (2 videos)
Learn how to apply machine learning techniques to risk management, including market risk, credit risk, and operational risk.

Exploratory Data Analysis

Spend less time preprocessing data. From financial time series to text, MATLAB datatypes significantly reduce the time required to preprocess data. High-level functions make it easy to synchronize disparate time series, replace outliers with interpolated values, filter anomalies, split raw text into words, and much more. Quickly visualize your data to understand trends and identify data quality issues with plots and the Live Editor.

Exploratory Data Analysis

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Applied Machine Learning

Applied Machine Learning

Find the best machine learning models. Whether you’re a beginner looking for some help getting started with machine learning or an expert looking to assess many different types of models, apps for classification and regression provide quick results. Choose from a wide variety of the most popular classification and regression algorithms, compare models based on standard metrics, and export promising models for further analysis and integration. If writing code is more your style, you can use hyperparameter optimization, which is built into model training functions, to find the best parameters to tune your model.

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Multi-Platform Deployment

Deploy machine learning models anywhere, including C/C++ code, CUDA® code, enterprise IT systems, or the cloud. When performance matters, you can generate standalone C code from your MATLAB code to create deployable models with high-performance prediction speed and small memory footprint. You can also deploy machine learning models to MATLAB Production Server for integration with web, database, and enterprise applications.

Multi-Platform Deployment

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Computational Finance Suite

Computational Finance Suite

The MATLAB Computational Finance Suite is a set of 12 essential products that enables you to develop quantitative applications for risk management, investment management, econometrics, pricing and valuation, insurance, and algorithmic trading.

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Free eBook

Modeling Financial Risk with MATLAB

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