International financial markets are becoming increasingly dynamic and complex. The exponential growth of available data (Big Data) and computing power further accelerates this process. Human analysts can only process a fraction of all available information and emotions distort the analysis process. The solution to these problems are automated forecast models. Such models are nothing new per se. They exist since the 1980s and are established in the field of algorithmic trading. Today, a large part of the trading volume on international financial markets is already traded by algorithms. However, industry-standard forecast models have weaknesses that now can be efficiently solved by AI. In this article we discuss the strengths and weaknesses of algorithmic trading and show you why AI represents the next generation in the automation process of asset management.
Strengths of Algorithmic Trading
On the one hand, algorithmic trading is not characterized by subjective perceptions and emotions, but follows empirical findings. Hypotheses about existing cause-effect relationships are developed, which can be validated by extensive testing. On the other hand, it is possible to perform much more detailed analyses. Many tasks can be parallelized and scaled with sufficient computing power. Thus it is possible to observe considerably more stocks and markets at the same time. A further advantage of algorithmic trading is the systematic decision making, which allows a higher transparency and analytical optimization afterwards.
Weaknesses of Algorithmic Trading
The industry-standard forecast models have weaknesses, because they are rule-based. Both the data and the rules by which the data is processed are fed into the model. The algorithm executes the instructions step by step and finally comes to a result. This always follows a static if-then scheme. If X (Input) is given, then do Y (Output). However, these rules are constantly changing in financial markets. This means that the relationship between X (Input) and Y (Output) changes over time, which is called concept drift. In rule-based models, this concept drift leads to poor results in the long run. They are not able to adapt to changing market conditions and the prediction quality decreases over time. Furthermore, the input must be in a structured form to be processed by the algorithm.
Machine Learning, a sub-area of artificial intelligence, can now efficiently solve these problems. An introduction to the topic of artificial intelligence can be found in our blog post Artificial Intelligence for Beginners.
Concept Drift Handling
A major advantage of artificial intelligence in asset management is that the model learns the rules autonomously. The input consists of data and results. On this basis, the algorithm learns the rules that lead to the desired results. Continuously repetitive training can thus ensure that the model adapts constantly to changing market conditions.
Since the model learns the rules on its own, there is no need to make assumptions about cause-effect relationships in advance. This enables a hypothesis-free analysis of the data sets and the detection of highly complex, non-linear relationships. The result is a better understanding of the dependencies between influencing factors.
Another crucial advantage is the multitude of influencing factors that can be analyzed. AI enables the analysis of structured data sets such as key financial data, but also unstructured data sets such as text data. Especially since the emergence of social media, this has gained enormously in importance. Using artificial intelligence, it is for instance possible to carry out sentiment analyses on the basis of thousands of text messages and use them to predict the price movements of stocks.
Artificial Intelligence will fundamentally change asset management. Automated forecast models are nothing new per se, but the industry-standard models are rule-based. The achievements in the field of artificial intelligence and the computing power and amount of data (Big Data) available today now allows the efficient application of self-learning models. Such models offer elementary advantages over industry-standard forecast models.