Machine Learning for Finance

Machine learning is being rapidly adopted for a range of applications in the financial services industry. The adoption of machine learning in financial services has been driven by both supply factors, such as technological advances in data storage, algorithms, and computing infrastructure, and by demand factors, such as profitability needs, competition with other firms, and supervisory and regulatory requirements. Machine learning in finance includes algorithmic trading, portfolio management, insurance underwriting, and fraud detection, just to name a few subject areas.

There are several types of machine learning algorithms, but the two main ones that you will commonly come across in machine learning literature are supervised and unsupervised machine learning. Our discussion in this chapter focuses on supervised learning. Supervised machine learning...

Introduction to machine learning

Before machine learning algorithms became mature, many software application decisions were rule-based, consisting of a bunch of if and else statements to generate the appropriate response in exchange to some input data. A commonly cited example is a spam filter function in email inboxes. A mailbox may contain blacklisted words defined by a mail server administrator or owner. Incoming emails have their contents scanned against blacklisted words, and should the blacklist condition hold true, the mail is marked as spammed and sent to the Junk folder. As the nature of unwanted emails continues to evolve to avoid detection, spam filter mechanisms must also continuously update themselves to keep up with doing a better job. However, with machine learning, spam filters can automatically learn from past email data and, given an incoming email, calculate...

Predicting prices with a single-asset regression model

Pairs trading is a common statistical arbitrage trading strategy employed by traders using a pair of co-integrated and highly positively correlated assets, though negatively correlated pairs can also be considered.

In this section, we will use machine learning to train regression-based models using the historical prices of a pair of securities that might be used in pairs trading. Given the current price of one security for a particular day, we predict the other security's price on a daily basis. The following examples uses the historical daily prices of Goldman Sachs (GS) and J.P. Morgan (JPM) traded on the New York Stock Exchange (NYSE). We will be predicting prices of JPM's stock price for the year 2018.

Linear regression...

Predicting returns with a cross-asset momentum model

In this section, we will create a cross-asset momentum model by having the prices of four diversified assets predict the returns of JPM on a daily basis for the year of 2018. The prior 1-month, 3-month, 6-month, and 1-year of lagged returns of the S&P 500 stock index, 10-year treasury bond index, US dollar index, and gold prices will be used for fitting our model. This gives us a total of 16 features. Let's begin by preparing our datasets for developing our models.

Preparing the independent variables

We will use Alpha Vantage again as our data provider. As this free service does not provide all of the dataset required for our investigation, we shall consider other...

Predicting trends with classification-based machine learning

Classification-based machine learning is a supervised machine learning approach in which a model learns from given input data and classifies it according to new observations. Classification may be bi-class, such as identifying whether an option should be exercised or not, or multi-class, such as the direction of a price change, which can be either up, down, or unchanging.

In this section, we will look again at creating cross-asset momentum models by having the prices of four diversified assets predict the daily trend of JPM on a daily basis for the year of 2018. The prior 1-month and 3-month lagged returns of the S&P 500 stock index, the 10-year treasury bond index, the US dollar index, and gold prices will be used to fit the model for prediction. Our target variables consist of Boolean indicators, where a True value...

Conclusion on the use of machine learning algorithms

You may have observed that predicted values from our models are far off from actual values. This chapter aims to demonstrate the best of the machine learning features that scikit-learn offers, which may possibly be used to predict time series data. No studies to date have shown that machine learning algorithms can predict prices even close to 100% of the time. A lot more effort goes into building and running machine learning systems effectively.

Summary

In this chapter, we have been introduced to machine learning in the context of finance. We discussed how AI and machine learning is transforming the financial sector. Machine learning can be supervised or unsupervised, and supervised algorithms can be regression-based and classification-based. The scikit-learn Python library provides various machine learning algorithms and risk metrics.

We discussed the use of regression-based machine learning models such as OLS regression, ridge regression, LASSO regression, and elastic net regularization in predicting continuous values such as security prices. An ensemble of decision trees was also discussed, such as the bagging regressor, gradient tree boosting, and random forests. To measure the performance of regression models, we visited the MSE, MAE, explained variance score, and R² score.

Classification-based machine learning classifies...