Share this Article & Support our Mission Alpha for Impact
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.

How quants can realise their full potential.

Post by 
Text Link
Quant teams are poorly positioned at many asset managers. However, this is not so much due to the fundamental "ills" they have to deal with in the market, but rather to unfavourable organisational structures. A viable solution is needed here.In September, Professor Marcos López de Prado gave an interesting keynote presentation at the "AI & Data Science" conference, which he also published. [1] Based on the papers, we summarise his assessments and show a solution approach.

The five "evils"

First, de Prado summarises the five major problems that quants face. There is no definitive solution to these, nor can one avoid them. Instead, one must learn to cope with them in the best possible way.

1) Barriers to experimentation:

A fundamental principle of science is empirical falsification. In the natural sciences, this is made possible by controlled experiments. In the financial industry, however, this is rarely possible. This is because we cannot repeat the flash crash of 2010, for example, and in doing so fade out the influence of certain groups of market participants, because they interact. Therefore, it is very difficult to determine clear connections between cause and effect.

2) Non-stationarity of financial markets:

On the one hand, there are structural breaks caused by regulatory changes or unforeseeable events (black swans). On the other, parameters that affect the basic process for generating price data can be subject to drift, for example due to competition between companies. So even if correlations between cause and effect are established, their intensity can change significantly over time.

nonstationarity of financial markets
Figure 1) Non-stationarity
In this example, a structural break occurs after 250 observations with a probability of 50 percent. Subsequently, either the green or the red course result. The problem is to detect the break as quickly as possible, but also as reliably as possible.
Source: de Prado, M. L. (2021), Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential, p. 9.

3) Rigorous competition (zero-sum game):

Fierce competition among market participants worsens the ratio of actual signals to mere noise. The probability of finding profitable strategies is therefore extremely low. And if one does discover alpha, it is in most cases only scalable to a limited extent and/or quickly regressive. This is also evident in the fact that the publication of research findings affects their future validity.

Figure 2) False discoveries dominate
Suppose that the probability of a tested strategy to be profitable is one per cent. With the usual threshold of five per cent used in significance tests and an (optimistic) statistical power of 80 per cent, one would expect to make a total of 58 discoveries in 1000 trials. Of these, eight would be true positives and 50 false positives. In other words, about 86 per cent would be false!
Source: de Prado, M. L. (2021), Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential, p. 10

4) Complexity of the system:

Econometrics was developed to model relatively simple processes. It assumes that one knows the predictive variables and the functional form including all interaction effects. If this is not the case or if the function is incorrect, true predictive variables can be rejected as false negatives and false ones accepted as false positives. The (alleged) knowledge of the function should therefore not be assumed. For this may in fact be highly dimensional - or even impossible to determine.

5) Small samples:

Financial data sets are often short time series with few variables and high mutual, as well as, serial dependencies. Therefore, inferences are often drawn from small samples, which involves heavily overfitting of training and test data. This means that statistical tests at the standard confidence levels used have a low significance, a high proportion of false-negative results as well as many false discoveries. As an example of this, capital market professional Mark Rzepczynski cites the countless business cycle analyses that are largely based on only eight such cycles since 1970. How significant can these studies really be? [2]

Problematic structures

Traditional asset managers often rely on small, independent quant teams that do not collaborate but compete for the allocation of capital. The goal here is to achieve diversification through competition. However, this structure suffers from several problems:

• lack of research depth, as teams are mostly made up of generalists

• knowledge that cannot be reused if the intellectual property belongs to the teams

• no mechanisms for self-correction due to lack of collaboration

• incentives for false positives as a result of competition.

The conclusion is that classical silo structures or platforms for scientific talent are not optimal as they limit the potential of quant teams.

Sisyphean work

As a result, such teams may end up performing repetitive, pointless tasks such as backtesting, leading to an endless cycle of false discoveries. A good comparison to this is Sisyphus' work, which comes from a legend in Greek mythology. This refers to work that is not only fruitless and difficult, but will also never be done, as one has to start all over again. [3]

According to de Prado, to best deal with the five evils of financial market research, co-specialised teams are needed. A division of labour is essential to implement the control mechanisms anchored in scientific methods, such as peer review and empirical falsification. So what is his proposed solution?

To best deal with the five evils of financial market research, co-specialised teams are needed. A division of labour is essential to implement the control mechanisms anchored in scientific methods, such as peer review and empirical falsification.

The assembly line principle

De Prado advocates the tried and tested assembly line principle. He cites Prof. Ernest Lawrence, who founded Berkeley Lab as a research factory in 1931, as a model. There, interdisciplinary teams of scientists were to solve problems that could not be solved at universities. The idea led from the Manhattan Project (development of the first nuclear weapons during World War II) to Big Science (industrially driven science) to the network of National Laboratories of the Department of Energy in the USA with a record number of more than 100 Nobel Prizes.

The process, adapted for the quant field, can be summarised in three steps:

1) The research team defines a strategy based on a falsifiable investment hypothesis.

2) The test team performs independent back-calculations of the strategy and checks whether the proposed arguments are viable

3) The production team builds the code, calculates the allocations of the strategy, monitors the performance and executes the daily trades

Figure 3) Structure of an "investment assembly line".
The investment assembly line strikes a balance between creativity and predictability, which encourages breakthroughs at a predictable pace and constant renewal of existing strategies. The principle also enables scalability, independent validation and knowledge reuse.
Source: de Prado, M. L. (2021), Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential, p. 19.

In the assembly line paradigm, the crucial property of co-specialisation can be illustrated: Individual participants have deep technical skills, rather than mastering a strategy or an entire asset class, and are part of an interdisciplinary team. Scientific protocols ensure a clear separation between research and peer review, avoiding over-optimisation of backtests. Hierarchies are flat and there are numerous checks, balances and self-correction mechanisms at each station.

Conclusion

The idea of developing a systematic strategy, verifying it with a backtest and then applying it permanently is no longer up-to-date. Today's markets are far too dynamic for that - the rules of the game are constantly changing and nothing seems to be really stable over time. This is in the nature of things: prices on the markets are based on discounted expectations that are influenced by a multitude of factors, which in turn are constantly changing.

Against this background, interdisciplinary teams that continuously work on new ideas and adaptations of existing strategies are a promising approach to survive in this dynamic, non-linear environment in the long run.

[1] de Prado, M. L. (2021), Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential, School of Operations Research & Information Engineering (ORIE), Cornell University.
[2] Rzepczynski, M. (2021), Five Financial Research Curses - They Cannot Be Avoided, https://mrzepczynski.blogspot.com/2021/09/five-financial-research-curses-they.html, accessed 04.11.2021
[3] Wikipedia (2021), Sisyphus, https://de.wikipedia.org/wiki/Sisyphos, accessed 05.11.2021

Would you like to use this article - in full or in part - for your purposes? Then please consider the following Creative Commons-Lizenz.