Machine Learning is the new buzz word in the quantitative finance space. The use of computer algorithms to generate buy/sell signals (also known as Algorithmic Trading) has been been prevalent for quite some time now, and is no longer considered as the new age technology. There has been tremendous improvement in electronic trading space in last few years which includes Artificial intelligence, Machine learning and High frequency trading. While HFT is just a subset of Algo Trading, Machine learning and Artificial intelligence are totally novel concepts. In this post, we would take a closer look at Machine learning algorithms for trading and the benefits associated with it.
What is Machine Learning?
In layman terms, Machine Learning is the ability of computers or any electronic devices to learn without being manually programmed. In order to ‘learn’, these systems are exposed to Gigabytes of data using which it adapts and changes. Another way to think about machine learning is that it is “pattern recognition” – the act of teaching a program to react to or recognize patterns.
Facebook has a machine learning module through which it feeds the data into users’ timeline. The algorithm continuously monitors user interaction and activities and changes its feed logic accordingly. For example: if you frequently browse pictures of your favorite friend, the machine learner module would identify this and would send any notification of that friend towards the top of your news feed.
Machine Learning in Trading
Machine Learning is one step above Algorithmic trading. While Algorithmic trading involves feeding the buy/sell rules to the computer, Machine learning is the ability to change those rules according to the market conditions. Machine learning algorithms for trading continuously monitor the price charts, patterns, or any fundamental factors and adjust the rules accordingly. Definitely some rules need to be fed into these systems initially but those are flexible to be modified by the program itself.
Ever since their introduction, Machine Learning Algorithms for trading have revolutionized stock market operations because they made it easier to react faster to specific events that occurred on stock markets. Their main function was to enable analysts to generate models to predict future stock prices and these new models were developed on the availability of historical data.
There are countless possible trading strategies that could utilize machine learning libraries, models & algorithms in some way. Support vector machines, neural networks, decision trees, random forests, gradient boosting machines, the list goes on and on. Well-implemented machine learning constructs allow us to make inference from data, and in the financial markets there’s no shortage of high-quality data sources.
Using machine learning you could make trades off social media signals, or use computational linguistics to ascertain sentiment for breaking news tickers. You could classify securities into various groups based on numerous factors, & weigh portfolio allocations towards the best-performing groups on a moving timeframe. The possibilities are endless.
Word of Caution!
It needs to be borne in mind that Machine Learning Algorithms for trading are not absolutely foolproof and mistakes do happen. In other words, it can never be said that such algorithms are the last and final predictors of future stock prices in any market anywhere in the world, particularly Indian markets that record huge trade volumes every day. Overall, however, these are reliable for making fairly accurate predictions on equity price movements in the long run. And definitely these are the future of Stock markets!