Financial markets never sleep. The trade in financial instruments has long been a global marketplace, operating round the clock.
But increasingly, market continuity is maintained not only by human intervention, but also by algorithmic tools; the world of finance deals with statistical data and quantitative figures that make it a perfect area for machine learning (ML). As such, financial services benefit a lot from deploying “intelligent” computer systems.
In addition, the trading world requires fast and sometimes immediate actions. Speed is vital for a trader to become a competent player in the market. Machine learning algorithms can accelerate data processing and provide a solution for managing vast amounts of data. Also, a machine learning system can easily spot complex patterns; if a substantial amount of past data is properly recorded and analyzed, then a model can be built to predict future events (at some confidence).
Another problem that traders often face, is when the market conditions change over time and they have to adapt to those changing conditions quickly. Traditional trading approaches, used up until a few years ago, tend to be based on a static set of parameters that are generated and validated via some sort of historical back-testing process. Though they can work well in isolated environments, if there is some sort of shift in circumstances they may become less effective.
On the contrary, ML trading systems can adapt continuously so as to spot new opportunities, tune strategies to these opportunities and make new recommendations by ‘learning’. They can also potentially tell the trader when these opportunities are becoming less effective or need fine-tuning.
Today, we can observe a substantial growth of the number of machine learning solutions applied to trading needs; there are different algorithm-optimization strategies like deep learning, neural networks, linear regressions and much more to be implemented as state-of-the-art trading tools. Sentiment analysis is another recent application of machine learning in algorithmic trading, which is highly estimated by numerous hedge funds.
Trading companies have hundreds of ML algorithms at their disposal and can use each of them for specific job. The range of purposes includes trade predictions, determination of strategy parameters, analysis of market behavior, signal providing etc. For example, building a model of decisions requires a decision tree algorithm; regression algorithms can be applied to solve regression problems and shape relationship between variables. Quants opt for ML and AI systems as a helpful tool for versatile purposes involved in estimation of investments and risks.
Another benefit of using artificial intelligence in finance is its ‘unbiased nature’. You’ll never find a computer with bad intentions which means that you can trust machine learning algorithms for trading financial instruments.
Unlike humans, who are to make errors when dealing with repetitive tasks, well-tailored algorithms provide a lower risk of mistakes. As such, accuracy is the third-important major benefit of machine learning.
All the above has brought with it huge client benefits: executing trades at an unprecedented pace and volume, lowering costs, increasing accuracy, and removing human fallibility and emotional biases.
In short, algorithms are helping to deliver financial services better and faster, providing a huge boost to the wider economy.
However, it is a common view that the human trader is still a critical piece of the puzzle; although algorithmic trading is a form of automated trading, it hasn’t reached the point where humans can be totally disengaged.
On the contrary, the more we rely on systems to make trading decisions, the more human oversight is needed to make sure they’re performing as intended, as the widely-publicized trading glitches of the past years have made clear.
The consensus view is that although machines can adapt, and can be useful as trading assistants, we aren’t still near the point where they will displace human traders, chiefly because they lack the intuition and insight that humans possess.
Also, past experience has shown that there are many risks associated with algorithmic trading. Threats could arise from mis-specifications of models (due to flawed assumptions, for example), coding errors, or the misuse of such models.
Equally, such risks could be a result of a failure in the development, testing, or deployment of the IT systems used for algorithms trading, or due to the risk management team not fully understanding complex algorithmic models and failing to identify potential conduct or concentration risks.
As a conclusion we could say that the future is already upon us, and implementing machine learning in financial field bears witness to this statement.
However, as algorithms and machine learning drive more decisions, traders need to assume a more active supervisor role of algo-driven activities and eventually evolve further so as to become the evaluator of the ML strategies they use.