Quantitative Investing: From Smart Beta to Long-Short Risk Premia

Quantitative investment strategies, which are guided by sets of rules, aim to exploit market abnormalities in order to improve returns, control risk or diversify portfolios.

Quant strategies, as they are usually known, are built to identify and target the underlying factors responsible for the outperformance of some financial assets over others.

This is done by formulating models which explain factors, back-testing models to identify those that work, and finally implementing strategies based on a set of rules which identify and screen assets to be included in a portfolio.

This process, which is far more complex than can be explained in a short paragraph, is driven by highly-skilled quants (quantitative analysts). In short, quants aim to identify factors and design strategies that best extract them. For that reason, this approach is also called factor investing.

Based on wide-reaching analysis, quant strategies use computers to formulate, test and implement strategies. This, and the fact that they are rules-based, means they are relatively objective in their pursuit of alpha returns.

In the pre-computer world, quant strategies were difficult to implement given the huge amounts of information and data involved throughout the process.

In many ways, quant strategies deviate from the efficient-market theory on which they are based. Pricing models like the capital asset pricing model (CAPM) say that the expected return from an investment is dependent on its relationship to the market and the market alone, since it is assumed under financial theory that investors price securities appropriately.

The standard CAPM model fails to explain why stocks with certain characteristics outperform others, which is why expanded versions of CAPM now include a stock’s exposure to different factors in order to gauge expected returns.

What are factors?

A factor is a characteristic inherent in groups of financial assets which explain why these investments have different risk and return metrics from the market.

Among the most common factors that investors target are value, low size, low volatility, quality, high yield, liquidity and momentum. These factors have historically earned a long-term risk premium and many can be found across several sectors and asset classes – including equity, bond, commodities and currency markets.

The value factor refers to the tendency of undervalued stocks to outperform expensive ones, while the size factor explains why small-cap stocks outperform large caps over the long term (one explanation is the relative lack of information about small stocks available to traders).

Low-volatility stocks, meanwhile, are often used to control risks, though many investors argue that they have shown a tendency to yield higher returns, especially in financial market downturns. The momentum factor explains why stocks with momentum tend to maintain their upward trajectory, at least over the short term.

Other factors include quality and high yield, though ongoing analysis by quants continues to reveal more.

Important to note is that not all factors earn a risk premium over the long term. The risk premium associated with momentum and growth stocks, for instance, tends to be relatively short-lived.

Investors can target single factors or build multi-factor portfolios. Many investors are beginning to allocate assets across factors rather than the traditional approach of diversifying across asset classes.

This is because different asset classes are more closely correlated than previously thought, while some factors are uncorrelated from others and offer better diversification benefits, at least in theory.

Types of quant strategies

Quant strategies can be packaged in a number of ways, each with different mechanisms to extract the factor risk premium.

The most common quant strategies are smart beta and risk premia. Smart beta is a long-only strategy based on alternatively-constructed indices which have a tilt towards one or more factor.

This can either be done by reweighting benchmark indices like the S&P 500 Index, Russell 2000 or MSCI index, which weight stocks by their size. A smart beta version of the index might reweight the benchmark to shift the bias towards low-volatility stocks, with the aim of generating better risk-adjusted returns than the benchmark.

The benchmark index, which acts as a proxy for equity market exposure, captures the equity risk premium (or excess return over risk-free assets like government bonds) in a cheap and passive way. The corresponding smart beta fund captures most of the equity market risk premium as well as the risk premium attached to the factor it is targeting.

Sometimes known as custom indices, smart beta funds can also be constructed from the bottom up by selecting a basket of high-yielding or quality assets, for example. Stocks are selected in accordance with the rules of the strategy, meaning that smart beta funds are transparent and strictly rules-based.

These passive indices, which have an element of active management, offer cheap exposure to risk factors and are becoming widely-adopted additions and alternatives to mutual funds.

Smart beta funds have a strong beta element, meaning they are closely correlated to the market and their performance depends largely on the movements of the broad market.

Risk premia strategies, meanwhile, target factors through long-short trades that aim to generate absolute returns. This means they are able to strip out much of the beta element and can offer positive returns even when markets are in decline.

Like hedge funds, risk premia strategies can also make use of tools such as leverage and derivatives in order to amplify returns or hedge against certain risks.

A long-short value strategy could involve taking long positions in the most undervalued stocks in a portfolio while short selling the stocks which are most expensive (on a price-to-book value basis).

This means there is greater opportunity to capture alpha returns since a long-only smart beta fund can only target excess returns from buying undervalued stocks. Risk premia can benefit from short selling overvalued stocks as well, capturing the risk premium from both sides of the same coin.

Risk premia can also to a large extent eliminate the risks associated with market exposure, unlike smart beta funds, which are heavily influenced by market movements.

One problem with shorting though is that this involves relatively high transaction costs, partly because short selling involves borrowing assets. These costs increase the longer a short position is held open. Additionally, costs are relatively higher when shorting small cap stocks, which means the benefits from the size factor are somewhat diluted when targeted through a long-short risk premia approach.

Accessibility of quant strategies

While large institutional clients have a greater number of options, smart beta indices are readily available to most investors – including individual retail investors – largely because they can be offered as exchange-traded funds (ETFs).

Risk premia strategies are less accessible than smart beta funds, though they are more easily accessed than hedge funds. So far, the main market for risk premia is the typical hedge fund client, who is looking for cheaper and more transparent ways to generate absolute returns from factors.

Long-short risk premia products cannot be offered as ETFs, which means retail investors are excluded from directly participating in these products – at least for now.

Professional services firm PwC said in a 2020 outlook report that factor investing will shift from active managers through sophisticated institutional passive investors and into the mass-market retail space. Factor investing would also be the driving force behind the growth of passive strategies.

The general consensus among asset managers appears to be that quant strategies can play a complementary role alongside traditional fund management models, rather than replacing them outright.

FTSE Russell said in a 2016 report that nearly half of the asset owners it had surveyed said they were now looking to combinations of factor strategies for future asset allocation objectives.