Throughout the history of modern business, investing has developed to be a nuanced science. And as every science, it now has many different schools of thought, methodologies, and strategies. A major group of such strategies is quantitative investing. With the advancements of computer technology and its growing applications in finance and investing, quantitative investing methods are becoming more and more important. Thus, it is high time to get acquainted with the usefulness and influence of quantitative investing.
The development of investing strategies
Every experienced investor will tell you that investing requires more than just common sense or gut feeling. To do investing as a sustainable means of profit one has to approach the subject methodically, not unlike a scientist would.
Thus, many strategies, rules, and principles have been developed and tested to ensure the highest possible overall returns of investments. Since the legendary Benjamin Graham, the idea that investor has to be intelligent and capable of understanding different methods has been planted deep into our common consciousnesses.
The complete history and taxonomy of investment methods are well beyond the scope of this article. So we concentrate on the group of strategies that are extremely important in the age of always developing computer technologies – quantitative methods.
Quantitative investing has already been around when computer technology was not yet developed enough to provide much assistance. But with the rise of the capabilities of computers, quantitative investing has flourished.
This is due to the fact that quantitative investment is based on data analysis. And as is hinted in the name of the method, the quantity of data matters. The more information is analyzed and accounted for, the better the more accurate will the determinations of risk and predictions of the return of investment be. Of course, there are limits to the number of data humans are able to handle manually. However, there are no such limits to artificial intelligence.
Thus, in general, quantitative investing strategies are data-driven investing models. They are methods unified by the utilization of AI tools to generate investment strategies and enhance decisions.
Best quantitative investing methods
Many mathematical investing models can be categorized as quantitative. Depending on the factors that the models take into account and the principles that govern the analysis, these models can either be considered as independent or different versions of the same model.
These models are usually created by professionals known as quantitative analysts or quants. Each quant can be said to create its own model thus it is hardly possible to cover all the peculiarities of every model.
However, there are quantitative investing methods that have already proved their worth in the industry. Here are 5 of the methods that have been established as the most beneficial.
- Smart beta. First developed through Modern Portfolio Theory by Nobel Prize in Economic Sciences laureate Harry M. Markowitz, smart beta strategy is, like many things in investing, all about striking the right balance between risk and returns. This strategy tries to achieve it by portfolio diversification, combining passive and active investing.
- Long–short risk premia. Long-only risk premia strategy is often considered a form of smart beta. Long-short risk premia differs in that while taking long positions in undervalued stocks it would also short sell the most expensive. This provides the advantage of returns being generated independently of market conditions.
- Risk management strategies. In this case risk management refers to the reduction of impact of the left-tail events. By definition, such events are hard to predict, thus these strategies try to mitigate their effects through systematic continuous risk management based on historic data.
- AI and big data based strategies. This is a broad group of mathematical models that aim to take into account as much as possible in making predictions. Such models also utilize machine learning and state-of-the-art AI technologies to improve the results.
- Quantitative factor–based investing. Generally, factor-based models refer to strategies of recognizing the stocks that possess one or more features that has historically led to outperformance. Quantitative models add to that the ranking of stocks based on stock-scoring by their characteristics relevant to outperformance.
The science of investing
Each of the methods above actually refers to a broader group or blueprints of quantitative investing methods. When building an original model, quants would usually take an established method and put a new spin on it to solve a particular problem of forecasting or try to create an overall more reliable strategy.
This brings quantitative investing to the way science is usually done, where scientists would adapt established methodologies to the particularities of their research, thus creating a new method within a broader methodological approach. And it goes to show that quantitative investing is a driving force behind the general trend of modern investing becoming more and more like science.
As such, investing today is an interdisciplinary science, as it is dependent on the advancements being made in many fields. Among the most important of the disciplines related to the science of investing are economics, data science, and artificial intelligence studies. While the former has always been a companion to investing, the importance of the latter two have only become apparent later on.
But now it is clear that data science and AI are two fields that modern designers of investment strategies have to follow closely. Analysis of empirical data has shown to be the most reliable method of managing risks and enhancing decisions both in investing and in business. And AI is what provides tools to efficiently carry out such analysis.
Thus, further developments in combining AI and data science, such as machine learning, will in many ways determine the future of investing.
It is likely that soon all investing methods will at least in part need to be considered quantitative, as mathematical models are becoming a necessity in handling the variables of modern data-rich markets. But, as we have seen, there is a great variety in quantitative investing methods and a lot of them are built on traditional investing principles and strategies. And there is still a lot to do for investors in combining and adopting various methods.