Written By: Thomas Kieselstein
Thomas Kieselstein of Quoniam Asset Management emphasises the importance for quantitative asset management of being at the forefront of the race to understand and implement the increasing volume of new technologies
After years of extreme outperformance by growth and technology stocks, investors are focusing more on classic business models and fundamental factors. The quality of a company and its valuation allow more conclusions to be drawn about future share price development, which was not the case for most of 2020 or even last year. Overall, the decline in excessive liquidity provided by central banks for many years has meant that the most recent period of capital market excess seems to be coming to an end.
Active quantitative asset management should benefit from this development. The systematic processing of information and model-based analysis typically show their strengths in more rational markets. Despite the positive backdrop for fundamental analysis, the large index and ETF providers continue to attract investors with a diverse selection of semi-active strategies. A certain saturation seems to have been reached, which is evident from the fact that there are now significantly more equity indices than equities.
The consequence is not only growing confusion regarding products, but also an increasing grey area between active and passive investment approaches. With certain indices, such as highly concentrated sustainability indices, investors will have a strategic allocation, for example to technology stocks, even though the index is sold as passive. This is a fallacy. Each of these “passive” strategies is based at its core on active decisions, such as the exact formulation of the thematic focus or the selection of the rules for weighting. However, the decision-maker usually remains in the background and a fund manager who is accountable does not for the most part exist.
Boundaries are becoming blurred
The differences between index or ETF providers and active quantitative asset managers are becoming smaller and the boundaries between quantitative and traditional portfolio management are becoming blurred. Fundamental providers are also making intensive use of quant techniques such as factor models. At the same time, quant investors are also increasingly adopting the views of fundamental managers in the further development of their investment processes and models. Examples include business cycle analysis or macro risk management.
Furthermore, it is foreseeable that assets previously considered illiquid, such as private debt or private equity, can be analysed and traded automatically. This will be possible primarily through the tokenisation of these assets and blockchain-based smart contracts, which create fungibility. The field of non-fungible tokens (NFT) has shown how quickly digital development can progress. However, because issues such as standardisation and custody have not yet been clarified, institutional investors are reluctant to invest in this area. Regardless of the asset class, a high degree of diversification as well as optimal risk/return management in cost-optimised solutions will be necessary in the future.
Diversity of business models
On the provider side, we also expect an increasing diversity of business models. The trend towards customisation and tailor-made investment solutions is unmistakable. Forecasting return and risk will remain at the forefront alongside other dimensions, in particular sustainability. Active quant approaches benefit from this development. Investors differ in their sustainability goals. Quant managers can handle the breadth and depth of available data and can adapt their investment processes efficiently and cost-effectively to the respective client needs.
Success factors for active quantitative management
In the future, the mastery and efficient implementation of new technologies will determine the success of quantitative investment strategies. These allow access to ever larger and newer amounts of data, which can be used for strategies that go beyond classic risk premiums. At the same time, the barriers to accessing technology and data, for example via cloud platforms, have also become lower. It is important to be at the forefront of this race. In the past, it was important to access the data. The challenge in the future will be to be able to use new technologies to process the growing data volumes to make reliable investment decisions. This is especially true for unstructured data such as text, audio or video information.
Four “big Vs” of importance
The “big Vs” of big data remain a challenge: The amount of data (volume), the speed with which new information arrives (velocity) and the variety of data sources and formats (variety) and the reliability of the information (verocity) will grow in importance – the figures in a company balance sheet are usually reliable, but exceptions prove the rule, for example, Wirecard¹. A tweet, on the other hand, can be manipulated by a bot, and this rate is well over 30% according to estimates.
To be able to efficiently process the huge amounts of data into signals, a corresponding infrastructure and computing power is a prerequisite. But it also requires a high level of competence in data management, data engineering and data science. For quant managers, this means that in the future they will face stronger competition with other companies, but also with data providers in terms of technical solutions and talent.
In addition to the technical setup, however, capital market expertise is also indispensable. Unstructured data and the corresponding factor signals require financial market-related interpretation as well as strong risk management know-how.
The market will continue to shift
One thing is clear: the market will continue to shift. There is not one future for quant investing, but many areas that will be occupied by specialists. A certain consolidation is also likely, especially in niches, because it is becoming more and more technically complex to offer everything from a single source. The need to concentrate on one business model is therefore likely to increase. Ultimately, however, the same will be true for the future: Not the amount of data per se, but clever modelling, and not the computer power, but its clever use will make up the decisive added value.
1. A series of corrupt business practices and fraudulent financial reporting led to the insolvency of Wirecard, a payment processor and financial services provider, headquartered in Munich, Germany