In our previous note, we looked at Active Quantitative Investing and understood that it is a strategy that uses mathematical models and algorithms to analyze financial data and make investment decisions. Also, this approach has become increasingly popular in recent years, with many investors turning to quantitative methods to gain an edge in the market. However, like any investment strategy, quantitative investing has its advantages and challenges.
Advantages of Quantitative Investing:
- Objectivity: One of the key advantages of quantitative investing is its objectivity. By using quantitative models and algorithms, investment decisions are based on data and objective criteria, rather than subjective opinions and emotions. This also ensures that a given strategy or model will always react the same way to a given set of input data and factors, hence making it more deterministic.
- Efficiency: Quantitative investing allows investors to analyze large amounts of data quickly and efficiently, which can be a significant advantage in fast-moving markets where time is of the essence.
- Scalability: Since most of the data analysis is carried out by computers, the same model or strategy can be simultaneously run across multiple securities or even markets, hence increasing scalability as well as improving portability across geographies and markets.
- Back-testing: Quantitative investing allows researchers and traders to backtest their strategies to see how they would have performed in the past, which can help them identify potential weaknesses and make adjustments before putting the strategy into practice. This also helps in setting a median expectation and defining bands of performance tolerance.
- Portfolio construction: Another aspect of investing that is often done quantitatively is the overall construction of the portfolio or the allocation of capital across selected securities. There are multiple well defined quantitative approaches to construct a portfolio within a defined objective and constraints.
Challenges of Quantitative Investing:
- Overfitting: One of the biggest challenges of this style, specifically around back-testing, is the risk of overfitting. This occurs when a model is too closely fit to the data it has been trained on and performs poorly on new data. There are various mitigating techniques used to reduce this risk and a good quantitative portfolio manager will ensure that these are carried out before allocating capital to a particular strategy.
- Lack of Flexibility: Quantitative models can be affected by market conditions and events that were not taken into account during the development of the model, which can lead to inaccurate predictions. This lack of flexibility can be a disadvantage in markets where things have undergone a structural or regulatory change.
- Computational Complexity: Quantitative analysis can be computationally complex, which can make it difficult for some investors to implement. It also requires a deep understanding of systematic models and algorithms, which can be a barrier to entry for some investors.
- Difficulty in attribution: As this style relies heavily on data and statistical models, at times it could lead to outcomes that are not intuitive and therefore it can be difficult to analyze and attribute the drivers of outperformance (or underperformance). This is one of the reasons that a lot of investors think of quantitative products as ‘black-boxes’ as they are not aware of the exact logic used to arrive at a decision.
In conclusion, as with any other style of investing, there are a fair share of pros and cons for quantitative investing and the debate whether models can outperform discretionary managers will continue for a long time. However, this is a style that has increasingly scaled with improving computational power and is now an unavoidable tool for any portfolio manager.
