Although often tarred with a ‘black-box’ brush, rules-based investing can be more transparent than conventional fundamental investing, and quant-based methodologies are playing an increasing role in portfolio construction.
Quants are often seen as solely focused on databases and rules-based models, and therefore detached from the market’s daily ups and downs. But in fact, effective quant managers closely follow market developments and their influence on investment portfolios.
Regardless of investment style, it’s important to be able to explain performance in a logical and sensible way. This is possible when factor-based stock selection models are transparent – and not complicated black-box processes.
Within portfolio management there is a clear divide between researchers and portfolio managers (PMs). Researchers tend to be more detached from the daily ups and downs. Ultimately, this is a good thing – they should focus on developing and enhancing models that work in the long-term. But PMs, in contrast, need to be able to explain what’s happening in client portfolios, something that is easier for those with a long history in fundamental investing.
Ignoring the noise
An advantage of rules-based investing is that you don’t act on noise; it enables the manager to keep calm in volatile periods and not fall for one of the many behavioural pitfalls.
In one sense, quants are very different from fundamental investors. Because the process is rules-based, it allows for stock selection in an emotion-free way. That’s very different from fundamental investing, where human judgement is the main decision driver.
In another sense, quants aren’t so different. Stock selection is based on factors that many fundamental investors use, such as balance sheet leverage, downside risk, dividend stability, valuation measures like price-to-earnings and price-to-book, and earnings revisions. They just use these easily understandable factors in a rules-based way. Mind you, there are many different quant approaches from Robeco’s factor-based stock selection to very complicated mathematical optimisation processes.
In that sense, Robeco’s quant strategy is closer to fundamental approaches. Our human overview allows us to overrule models when it makes sense, for example when we spot data issues, M&A activity, suspected fraud, etc.
For many years quant investing remained niche but in recent times it has become much more mainstream. During my time in the asset allocation team, I witnessed how pension funds started to incorporate a strategic allocation to factors, especially after the financial crisis. Nowadays, pension funds around the world allocate to quant funds, and it’s all about the differences between quant managers, uniqueness of their approach, fee levels and long-term performance.
The perception of quant being an obscure approach has certainly gone. Clients realise that there are many different ways to apply quant strategies. At Robeco we apply long-term rules-based investing, instead of ‘quant’. That is, applying understandable factors based on long-term holding periods and long-term datasets.
Nevertheless, there are some unmistakable challenges for the development of quant investing. Firstly, too much money is chasing the same factors and the same stocks, particularly passive factor indices that have a low capacity which can’t be controlled.
Successful quant investing monitors factor indices constituents and excludes stocks from the investment universe if they are overcrowded, carefully considering the investable market cap and liquidity of a stock. In this sense, the human overview is becoming more important.
Other challenges include too many asset managers jumping on the bandwagon, and overreliance on short-term simulations. Also, some managers aren’t putting enough resources into quant investing processes, preferring to overspend on marketing.
If some strategies fail, quant managers generally could become guilty by association. But those who succeed will do so because of prudency, long-term simulations and an economic rationale of the existence of factor premiums.
Quant investors like to analyse very long datasets. But, without the context of when the data were collected, the numbers are not enough. So history matters to quants. We want to know and understand what was going on and how markets worked at the time.
Ultimately, quant investors want to exploit human biases like greed, fear, overconfidence, underreaction and overreaction. The first book on stock market investing from 1688 is full of great examples of these biases and is structured as a lively theatre play, making it very human, not some theoretical piece.
Lots of books and plays about investment behaviour popped up around the 1720 market bubbles, many of which are well-worth reading today.
As a conservative quant investor, the 1830’s Prudent Man Rule resonates. It states that investors should focus on capital protection, a high and stable income, without speculation, hence investing for the long term. Interestingly, the first mutual funds, dating back to the late 18th century in Europe, and subsequently in the UK and the US, had the same objectives.
Nowadays, many fund managers focus on short-term performance, tracking errors and active portfolio changes to prove their ‘worth’ to clients. We like to think the other way around: we have a long-term investment objective, aren’t constrained by any tracking error and we trade infrequently, just like the first fund managers. Stay cool, and don’t microscope daily noise and short-term performance.
Jan Sytze Mosselaar is senior portfolio manager in Robeco’s Quantitative Equities team
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