Introduction to Neural Networks for Finance

Machine Learning and deep learning have become new and effective strategies commonly used by quantitative hedge funds to maximize their profits. As an AI and finance enthusiast myself, this is exciting news as it combines two of my areas of interest.

This article will be an introduction on how to use neural networks to predict the stock market, in particular, the price of a stock (or index). This post is based on my project IntroNeuralNetworks (you can check out my GitHub for the same). The goal of this project is to predict the stock prices of a chosen company using methods from machine learning and neural networks.

For the second, more advanced implementation of neural networks for stock prediction, do check out my next article, or visit this GitHub repo. For more content like this, check my page: Engineer Quant.

The need for Neural Networks in Finance

Finance is highly nonlinear and sometimes stock price data can even seem completely random. Traditional time series methods such as ARIMA and GARCH models are effective only when the series is stationary, which is a restricting assumption that requires the series to be preprocessed by taking log returns (or other transforms). However, the main issue arises in implementing these models in a live trading system, as…