Over the past few decades, volumes have risen to prominence in all aspects of finance and investment due to the availability of large data sets and exponential computing power. However, the area that has attracted maximum investor attention is quantitative investment strategies.
Quant Strategies analyze and generate historical data Investment Signals using a strictly rule-based framework. Hence, these strategies are free from human biases. As straightforward as Nifty 50 ETF is, it is also a quantitative strategy. The investment signal is based on only one metric – the market capitalization rank of the company. However, some strategies use over 100 different metrics to make buy/sell decisions. Broadly speaking, quant strategies can be bucketed under the following heads:
Factor investment strategies: These strategies use companies’ fundamental and price data, combined with statistical analysis, to identify specific characteristics that may have led to the company’s historical outperformance. Then, they invest in companies that rank best based on these characteristics. For example, characteristics such as company valuation (P/E), quality (ROE) and momentum (past 1-year returns) are often used. Funds that focus on only one type of characteristic are called single-factor funds – and are usually available in index fund/ETF wrappers. However, single-factor funds are often cyclical in performance; Hence, some fund houses combine multiple factors to create a more stable return profile. Such funds are called multi-factor funds and are generally available in the form of mutual funds or PMS.
These funds are primarily ‘long-only’ in nature, which means they benefit only when the underlying portfolio appreciates. However, factor strategies are probably more effective in the ‘long-short’ way, which involves buying a portfolio of stocks with the best characteristics while, at the same time, short-selling those with the worst characteristics. Therefore, a value-long short fund typically buys companies that are most undervalued, while simultaneously short-selling the most overvalued stocks.
Enhanced Long Equity Strategies: These are long-short strategies that seek to generate near-equity returns at much lower volatility levels than traditional long equities. For example, when investing 100, such a fund can invest 50-60 in long-only equity strategies and the rest in some safe investments like liquid funds. In addition, the fund can also take additional exposure 30 in the long-short portfolio (which means buying the portfolio for 30 as well as short selling a portfolio 30). Futures are commonly used in long-short portfolios.
Equity Market Neutral Strategies: These are long-short strategies that seek to generate slightly better returns than debt without taking on equity risk. To illustrate, out of an initial corpus of 100, such a fund can invest Take additional exposure of 70-80 more in safe equipment 20-30 in long-short portfolio.
Quantitative Multi-Asset Strategies: As the name suggests, these strategies use quantitative models based on macroeconomic data, valuations and trends in taking long or short positions on different asset classes. For example, based on their model, these strategies may buy equities and gold while at the same time short selling government bonds and soybeans. Managed futures are a specialized multi-asset strategy that relies only on the price trends of different asset classes, buying trending ones and short selling those that are not.
Statistical Arbitrage Strategies: These strategies use advanced mathematical models to detect patterns in the prices of various tradable instruments. An example of statistical-arbitrage is pairs trading. It is believed that stocks belonging to the same sector/business move together. For example, if Stock A goes up significantly compared to Stock B (both stocks from similar sectors/businesses), one may short A and long B in anticipation of a price reversal. Trades made in statistical arbitrage funds are typically short term and largely intra-day.
Since the investment decisions of quantitative funds are backed by empirical evidence, results are expected to be more predictable. However, a quant strategy cannot guarantee outperformance. It may also go through extended periods of underperformance. Additionally, because of the wide variety of quant funds that exist to serve different objectives, investors should look under the hood before deciding on the allocation of these funds.
Sankaranarayanan Krishnan is the Quant Fund Manager (PMS and AIF) at Motilal Oswal Asset Management Company. This article is for informational purposes and should not be construed as investment advice.
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