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Quantitative strategies for a brave new world

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Over the past few decades, quants have grown in prominence in all aspects of finance and investing due to the availability of large data sets and exponential computing power. However, the area that has caught the maximum investor attention is quantitative investment strategies.

Quant strategies analyse historical data and generate investment signals using a strictly rules-based framework. Hence, these strategies are free from human biases. Something as straightforward as the Nifty 50 ETF is also a quantitative strategy. The investment signal is based only on one metric—the company’s market capitalization rank. However, some strategies use over 100 different metrics to form buy/sell decisions. Broadly, quant strategies can be bucketed under the following heads:

Factor Investing Strategies: These strategies use fundamental and price data of companies, combined with statistical analysis, to identify specific characteristics that may have led to a company’s historical outperformance. Then, they invest in companies that rank the best based on these characteristics. For instance, characteristics such as a company’s valuation (P/E), quality (ROE) and momentum (past 1Y returns) are often used. Funds which 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, few fund houses combine multiple factors to generate a more stable return profile. Such funds are called multi-factor funds and are usually available as mutual funds or PMSs.

These funds are primarily ‘long-only’ in nature, which means they only benefit when the underlying portfolio appreciates. However, factor strategies are perhaps more effective in a ‘long-short’ manner, which involves buying a portfolio of stocks having the best characteristics while, at the same time, short-selling those with the worst characteristics. So, a value-long short fund would typically buy the companies that are the most undervalued while simultaneously short-selling the most overvalued stocks.

Enhanced Long Equity Strategies: These are long-short strategies that seek to generate close-to-equity returns at much lower volatility levels than traditional long equity. For example, while investing 100, such a fund may invest 50-60 in a long-only equity strategy and the rest in some safe investment like a liquid fund. In addition, the fund may also take an additional exposure of 30 in a long-short portfolio (which means buying a portfolio for 30 and simultaneously short-selling a portfolio of 30). The long-short portfolio is typically traded using futures.

Equity Market Neutral Strategies: These are long-short strategies that seek to generate slightly better returns than debt without taking equity risk. To illustrate, out of a starting corpus of 100, such a fund may invest 70-80 in safe instruments and take an additional exposure of 20-30 in a long-short portfolio.

Quantitative Multi-Asset Strategies: As the name suggests, these strategies utilize quantitative models based on macroeconomic data, valuation, and trends to take long or short positions on various asset classes. For instance, based on their model, these strategies may buy equity and gold while at the same time short-selling government bonds and soybean. Managed futures is a specialised multi-asset strategy that only relies on the price trend of different asset classes, buying the trending ones and short-selling the ones that are not.

Statistical Arbitrage Strategies: These strategies use advanced mathematical models to detect patterns in the prices of different tradable instruments. One example of Statistical-arbitrage is pairs trading. Stocks that belong to the same sector/business are believed to move in tandem. For instance, if stock A rises considerably compared to stock B (both stocks from same sectors/businesses), one might go short A and long B in anticipation of a reversion of prices. The trades taken in statistical arbitrage funds are characteristically of brief duration and largely tend to be intra-day.

Since investment decisions of quantitative funds are backed by empirical evidence, outcomes are expected to be more predictable. However, a quant strategy cannot guarantee outperformance. It can even go through extended periods of underperformance. Additionally, due to the wide variety of quant funds that exist to serve different objectives, investors must take a look under the hood before deciding on their allocation to these funds.

Sankaranarayanan Krishnan is Quant Fund Manager (PMS & AIF) at Motilal Oswal Asset Management Co. The article is for information purpose and should not be construed as investment advice.

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