# Financial Data Science

18 Oct 2017

## MATLAB in the Finance Industry

The video presents an overview of how MATLAB can add value to the Finance Industry as an all-in-one platform that supports the entire computational finance workflow.

05 Mar 2019

## AI in Finance – FOREX: Algorithmic Trading

Learn how easy Machine Learning in MATLAB is for FOREX Algorithmic Trading. Read how supervised machine learning can be used to identify trading signals. Download the code and get started creating your own models right away.

27 Mar 2019

## AI in Finance – Risk Management: Classifying Credit Card Default

AI For Risk Management: Classifying Credit Card Default

In this article, we explore machine learning models using MATLAB which aim to classify whether or not somebody will default on their credit card payment in the next 30 days. We develop models which are instinctive to understand and able to be quickly prototyped and improved. We provide a live script for you to get started immediately with model development.

13 May 2019

## AI in Finance – Trading: Clustering

Pairs trading is a popular trading strategy. However, it can be shrouded in statistics. By leveraging the strengths of MATLAB’s machine learning and statistics capabilities, you too can formulate your own pairs trading strategies.  In this example, we examine 300 stocks from the S&P 500 and group them into clusters of stocks such that each […]

30 May 2019

## AI in Finance: Constructing a Trading Strategy Using Deep Learning

In this article, you will note the ease with which you can train and validate a neural network, and then see how this is applied by means of a trading strategy. By simple manipulations of historical data, we can leverage the flexibility of deep learning in MATLAB to generate future predictions of time series data, with reasonable precision.

17 Jul 2019

## Optimisation in Finance: Building the Optimal Trading Strategy

In a previous example, it was shown how MATLAB can be used to backtest a simple trading strategy in 8 lines of code, where the trading strategy was developed based on the Relative Strength (RS) index. In this article, I take Kawee’s work a step further and investigate if we can optimise the buy and sell RS index values to maximise our returns. The buy and sell signals are originally chosen at an RS Index of 40 and 70 respectively. Over a six-year period, this gave approximately 18% return-on-equity. Can this be improved?

26 Nov 2019

## AI in Finance: Using Deep Learning for RegTech

Regulatory Technology (RegTech) refers to the use of new technology such as Artificial Intelligence to improve how companies adhere to regulatory requirements. The RegTech app, developed in MATLAB, aims to accelerate and automate tedious regulatory data extraction processes. Data capturers at financial institutions constantly capture data from IDs, payslips and proofs-of-address. By using deep learning […]

26 Nov 2019

## Constructing the Optimal Portfolio with MATLAB and Smart Beta

Verushen Coopoo, Application Engineer Opti-Num Solutions (2019) In this script, we demonstrate the strength of MATLAB’s optimisation capabilities to construct a portfolio driven by smart beta investment techniques. We do not advocate for the use of any particular investment or trading strategy, but rather demonstrate how to import your own data, implement a custom-made optimisation […]

26 Nov 2019

## Accessing Equity Data using a MATLAB Web App

Timely and convenient access to data is crucial in the world of finance to enable rapid and informed decision-making.  To address this need, we have developed a web app in MATLAB that demonstrates how data access and custom analytics can be made accessible across an organisation. The app shows a very simplistic workflow that allows […]

26 Nov 2019

## Conifers for Crypto – Bitcoin Volatility Forecasting Using Machine Learning

Verushen Coopoo, Application Engineer Opti-Num Solutions (2019) In this script, we demonstrate MATLAB’s machine learning abilities to build a regression tree that will be used to forecast the 10-day volatility of Bitcoin. We show that in one environment, you can download up-to-date Bitcoin data, rapidly prototype a machine learning model, and then test and evaluate […]