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By – Bijon Pani is the Chief Investment Officer of NJ Asset Management Private Limited
Factor-based investing is a rule-based method of choosing stocks or any other asset class based on certain specific characteristics. For equity stocks, the most common factors are value, quality, low volatility, momentum, and size. There are parameters in addition to these factors as well, but the effectiveness of these factors has been proved by both academic research and investment experience.
Each factor offers a distinct source of risk-adjusted returns which can be harvested by investing over a long period of time. However, each of these has some cyclical characteristics over shorter time periods. Each of these also has a different economic rationale for their use and a unique risk and reward signature. Factor premiums tend to also be uncorrelated (or low correlated) to each other which is why combining them together in a multi-factor portfolio provides diversification. Multi-factor portfolios often provide a better risk-adjusted return compared to single-factor portfolios.
However, when putting various factor parameters together, as the saying goes, keep it simple but not too simple. As such, factor models should be frugal in the number of factors used. Adding too many factor parameters may also add noise, which reduces the efficacy of any model. Often, different parameters of the same factor are highly correlated, and merely adding a lot of parameters may not show any improvement in risk-adjusted returns. Care must be taken that every parameter adds to the robustness of the model and not be made overtly complex without reason.
In India, additional challenges of a multi-factor model lie in its building blocks i.e. the factor parameters. Constructing robust factor parameters is a nuanced science, especially in India where the market microstructure is very different from the developed world, where most factor research comes from.
Factor parameter construction involves back-testing on a large sample of stocks over a substantial period of time to judge its efficiency over various macro cycles and determine the consistency of its premium. One of the challenges of using factor-based strategies in Indian markets is the absence of long data history and relatively lower-quality datasets. Combined with some very unique market characteristics, these challenges make the blind copying of developed market parameters fraught with risk. Considerable effort and dedicated manpower is needed to construct a robust dataset before any research can be done.
Another challenge relates to liquidity. Unlike developed markets, only a small portion of the Indian market has sizable liquidity for deploying these strategies. Academic calculations may not translate into an easily investable strategy so they often need to be further enhanced. It is important to understand whether the factor premium that is measured academically can be extracted effectively in a real-world application. This makes it equally crucial to measure how liquidity risk premium and size premium influence backtesting results.
Lastly, factor investing needs active research to stay ahead of the curve. Many anomalies lose their performance as they get crowded with more participants and higher assets under management (AUM) over time.
All these challenges make passive factor investing an inherently risky proposition in India. Most factor indexes copy western factor parameters without adapting them to Indian realities. Also, the performance of these indexes isn’t subject to any liquidity constraint as they enter and exit stock positions seamlessly with zero impact cost regardless of the size of the company. In addition, these stands to lose whatever factor premium can be captured by following them if they are successful and followed by many passive strategies. This can result in a concentration of assets making them susceptible to higher slippage at time of rebalancing. So, merely identifying some rules is not enough, they need to be modified, actively monitored, and managed for the best outcome.
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