What is quantitative investing?
Quantitative investing, also known as systematic investing, is an investment strategy that uses mathematical modeling, computer systems and data analysis to calculatethe optimal probability of executing a profitabletrade. They are usually carried out by highly qualified teams, usually by quantitative analysts. As a rule, quantitative analysts combine the skills of investment analysts, statisticians and programmers. Due to the complexity of the mathematical and statistical models, it is common to have a university degree and a doctorate in finance, economics, mathematics and engineering. There are now even programs that implement the models. Quantitative models work well for backtesting (analyzing the success of an investment in advance), but their actual application and success rate is controversial. Normally, the markets can be modeled well in good times. However, during crises or other extraordinary events that affect the market, the implementation of quantitative investing becomes more difficult. The reason for this is that mathematical models are usually based on assumptions that cannot really be made in times of crisis.
One of the founding fathers of quantitative finance theory was Robert Merton. At that time, the implementation of quantitative investing was very complex, as computers were not yet in use. Some of the first quantitative studies gave rise to other theories in finance, including the basis of portfolio diversification and the foundation of modern portfolio theory. The application of quantitative finance and infinitesimal calculus led to many other common tools, including one of the most famous, the Black-Scholes option pricing formula, which not only helps investors price options and develop strategies, but also helps provide liquidity to the markets.
Quantitative strategies are now accepted in the investment community and are used by investment funds, hedge fundsand institutional investors. They are usually referred to as alpha generators or alpha gens.
Advantages of quant strategies
Even if it is very difficult to predict the success of an investment, models can be very helpful in estimating success. This is of course based on the assumption that the model is correct. In general, the advantage of quant strategies is that very fast computers can be used. This speed allows inefficiencies in the markets to be exploited on the basis of quantitative data. Successful strategies can identify trends at an early stage as the computers are constantly running scenarios to spot inefficiencies before others do. The models are able to analyze a large group of assets simultaneously, whereas a traditional analyst might only look at a few at a time. The models themselves can be based on a few financialmetrics such as price-earnings ratio, leverage and earnings growth, or use thousands of inputs interacting simultaneously.
Quant models also allow variations of strategies such as long, short and long/short. Good quantitative funds pay very close attention to risk control. Diversification can be very well controlled by using sector and industry weightings, for example. Quant funds tend to be cheaper as they require fewer traditional analysts and portfolio managers to run them through the use of computers.
Another advantage is that the analysis is not based on the subjective assessment of one person. Subjective assessments are often made with a certain degree of emotion. In contrast, a model is based on data and research and cannot be influenced by emotions. In addition, a computer can incorporate many different scenarios, possibilities and amounts of data into the decision-making process due to its computing power. Over the years, it has been shown that many actively managed funds cannot beat the benchmark in the long term. In summary, quantitative investing enables the rapid and simultaneous evaluation of large amounts of data using decisions based on research rather than subjective judgment. It offers a systematic approach to portfolio management.
Disadvantages of quant strategies
A model is only useful if it is correct. However, models are based on the past, which cannot always predict the future. While a strong quant team will constantly add new aspects to the models to estimate future events, it is impossible to predict the future correctly every time. If certain assumptions are not correct, the model may produce incorrect results. An example of this is the quant hedge fund Long-Term Capital Management (LTCM), which was managed by Myron S. Scholes and Robert C. Merton, among others. The company’s models did not take into account that the Russian government might not be able to repay part of its debt. The occurrence of this event therefore had a major impact. LTCM was heavily involved in other investment businesses, so its collapse affected world markets.
Quant strategies also require a lot of data in order to model the return distribution correctly, which can lead to dilution. In addition, the success of these strategies often requires a certain investment horizon. They often cannot beat the market in the short term.
Quantitative vs. fundamental investment
Fundamental analysis relies on an in-depth analysis of a company’s business, management team and market opportunities to determine the attractiveness of a stock. It draws on the investment manager’s expertise to make informed decisions about which stocks to buy and which to sell. Fundamental investments are based on key business figures, for example. These are analyzed to find out whether the current stock market pricecorresponds to the true value or whether a company is currently over- or undervalued. Key figures such as the price/earnings ratio, the equity ratio, the price/sales ratio and the debt/equity ratio are used for this purpose. Qualitative values are also included in the fundamental analysis. This includes, for example, the quality of management.
The quantitative approach uses data-driven analysis to evaluate a broad universe of stocks. It relies on factors that have been identified over time by portfolio managers and academics to construct portfolios of stocks with attractive characteristics. These factors have outperformed in the past and should therefore be used. Both approaches aim to get clients to the same financial destination. They also seek to beat a market benchmark.
For example, two equity funds – one traditional and one quantitative – are both managed against the S&P 500® Index (a common US benchmark). Although they have the same general investment objective, the strategies and tools used by the portfolio managers are different. In general, quantitative/systematic equity managers view investing as a science, eliminating emotional biases and buying stocks with certain characteristics. Fundamental/traditional managers view investing more as an art and rely on their judgment and experience. It can be beneficial to view the two approaches as complementary rather than contradictory.