Henry Zhang breaks down smart beta to explain what these investment strategies are made of.
Last year, I was in an investment trivia night with my colleague when a question came up on who established the first index equity fund. Only two of us knew the answer. Why? We are both interested in smart beta.
So what is this “smart beta”? Put it in simple language, it is a technical term which refers to getting market based returns (beta) but in a smarter way than is the case with traditional passive management.
Smart beta strategies generally possess the following characteristics:
- non-market capitalisation based;
- systematic or rule based;
- low cost;
- transparent; and
I know you other actuaries probably enjoy the equation more than me, so here is an overview that uses equations, on the history of (smart) beta.
In recent years, the introduction of smart beta along with the expansion of Fama-French’s multi-factor model has shaped how alpha (skill) should be assessed across the industry. In many circumstances, the active manager tends to employ certain styles or biases throughout their active stock selection process. These could be value, quality, high yield, etc. As such, the permanent style bias can be largely replicated systematically through smart beta strategy. The remaining ‘True’ Alpha (that a high quality active manager possesses) can be attributed to superior skill in fundamental stock analysis and/or portfolio management capability.
What is smart beta made of?
Smart beta can be categorised into three segments: Style (or risk factor) based, risk based and diversification based.
Style based smart beta
Style (or risk factor) based smart betas are probably the most commonly known, with the first two risk factors first established by Fama and French being value and size. Since then, we have been experiencing proliferation of this type of smart beta. Though there are a wide range of definitions of style based smart beta, I generally classify them into following types:
- Value: The value style strategy tends to construct the portfolio through focusing on the value metrics and weights the stocks accordingly. As a result, it generally has low Price to Book ratio and/or low Price to Earnings ratio.
- Size: The size factor generally segments the broad market based on the market cap of the constituents then overweights smaller segments.
- Quality: This strategy focuses on assessing the quality of the companies. The measure of quality tends to be multi-faceted. Commonly, the strategy is designed to weight more heavily towards companies that produce a higher profit margin, stable earnings, low leverage etc.
- Income: The primary focus of this strategy is on the company’s ability for shareholder wealth creation through redistribution of the company’s profit through dividends or buybacks. For example, the strategy would have higher weight to companies that pay high and stable dividends.
- Momentum: This strategy follows the winners by overweighting the stocks which have stronger positive momentum in price movement.
- Growth: This strategy is designed to capture stocks which are in their growth cycle. It tends to overweight the stocks which have higher earnings/revenue growth.
- Sustainability: Usually the strategy screens the stock universe using a commonly used Environmental, Social or Governance (ESG) measure, produces a score for each stock and allocates a heavier weight to the stocks that screen positively and score highly.
Risk based smart beta
The idea that a portfolio focused on the left hand side of Markowitz’s Efficient Frontier delivers a superior risk adjusted return was first introduced by Professor Haugen back in 1972. This came to be known as the “low volatility anomaly”. Put another way, the risk-adjusted returns from lower risk portfolios are greater than you would expect. This idea sowed the seed for risk based smart beta, of which there are a few commonly known strategies.
- Inverse Volatility: This process assigns the largest weight to the stock with the lowest volatility, and the smallest weight to the stock with the highest return volatility. The weight of stock is calculated by dividing the inverse of its return standard deviation by the total inverted return standard deviation.
- Risk Parity: This uses measures of past stock return volatilities and correlations to choose weights such that the contribution of each stock to the risk of the overall portfolio is equal.
- Risk clustering: This strategy equally weights “risk clusters” of stocks. Risk clusters comprise market value weighted constituents that have similar risk characteristics.
- Min Volatility: The weight of stock is optimised so that the expected risk of the portfolio is as low as possible.
- Risk efficiency: The weight of stock is proportional to the downside deviation of the stock return.
Diversification based smart beta
This category of smart beta tends to focus on adding value through constructing a portfolio with a high level of diversification. The most popular strategies are:
- Max deconcentration (Equal Weighted): Each stock is given an equal weight so that the overall portfolio is evenly distributed across all the constituents.
- Diversity Weight: This construction combines features of both market cap weighting approach and the equal weighing approach. It sets a cap on the market value of any particular stock in a market cap weighted index and redistributes the weight above the ceiling among the remaining index constituents.
- Max Decorrelation: This method weights the constituents in the index by the pairwise correlation and aims to achieve a portfolio with minimum correlation.
- Max diversification ratio: This method utilises the concept of a diversification index as a measure of diversification within the portfolio to construct a portfolio which maximises the diversification ratio.
Smart beta performance
I selected a few typical smart beta based indices from MSCI and, interestingly enough, they all outperformed the market cap weighted index.
Furthermore, some excellent work from Andrew Clare and his colleagues at Cass Business School entitled “Monkeys beat market cap weighted indices” found that while most smart beta strategies outperformed the market-cap weighted benchmark, so did most randomly created portfolios.
Discussion of the economic rationale for superior risk-adjusted returns from a smart beta strategy is beyond the scope of this article. However, I would like to give a little flavour on some considerations when implementing a smart beta strategy:
- A smart beta strategy does not require fundamental stock analysis, instead it is rule based. Therefore managing idiosyncratic risk is necessary. It should be implemented over a broad universe.
- Though I classified the smart beta strategy into 3 different categories, smart beta strategies are highly customisable, and the final portfolio could be a blend of multiple smart beta strategies depending on the needs of different investors.
- Smart beta can be used to replicate some elements of traditional alpha sourced from active managers at lower cost and higher capacity. This however does not rule out including some quality active managers.
- Smart beta strategies are deliberately designed to explore certain risk premia across the market, therefore a high tracking error to a market cap weighted index is not unusual.
- Smart beta is a useful tool to manage the exposure within the portfolio dynamically depending on the investor’s profile.
- It is important to understand what investment universe is used in the smart beta strategy and benchmark it appropriately.
I hope you enjoyed this light touch on the smart beta idea, if you are interested in further discussion, please watch this space or email me.
1The MSCI index used for each type of smart beta strategy.
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