In the real world, some investors are prohibited from using leverage, and other investors’ leverage is limited by margin requirements. Therefore, they over-weigh risky stocks instead of using leverage, which makes these stocks more expensive. This behavior suggests that high-beta (risky) stocks should deliver lower risk-adjusted returns than low-beta stocks. Investors not limited in leverage (arbitrageurs) could exploit this inefficiency by “betting against beta”, i.e., by going long on a portfolio of low-beta stocks, leveraged to a beta of 1, and short on a portfolio of high-beta stocks, de-leveraged to a beta of 1.
This long-short portfolio delivers substantial risk-adjusted returns and works well for the US and Global equities. Still, nevertheless, caution is needed in implementing this strategy (costs, slippage, etc.) as research also suggests that this effect is the strongest in small-cap stocks. Research also shows that the effect isn’t limited to stocks but also works well in other asset classes (even between asset classes).
The reason for the anomaly functionality was already stated in the short description – a lot of the investors are prohibited from using leverage, and their only way to achieve higher returns is to buy more risky stocks, which is the main cause for their overvaluation. Investors not facing these constraints could earn above-average returns by exploiting this phenomenon.
Backtest period from source paper
Confidence in anomaly's validity
Notes to Confidence in Anomaly's Validity
OOS back-test shows slightly negative performance. It looks, that strategy’s alpha is deteriorating in the out-of-sample period.
Notes to Indicative Performance
per annum, annualized monthly return (geometrically) of 0.71% (from table III – return for long short portfolio)
Period of Rebalancing
Notes to Period of Rebalancing
Notes to Estimated Volatility
Number of Traded Instruments
Notes to Number of Traded Instruments
more or less, it depends on investor’s need for diversification
Notes to Maximum drawdown
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of all stocks from the CRSP database. The beta for each stock is calculated with respect to the MSCI US Equity Index using a 1-year rolling window. Stocks are then ranked in ascending order on the basis of their estimated beta. The ranked stocks are assigned to one of two portfolios: low beta and high beta. Securities are weighted by the ranked betas, and portfolios are rebalanced every calendar month. Both portfolios are rescaled to have a beta of one at portfolio formation. The “Betting-Against-Beta” is the zero-cost zero-beta portfolio that is long on the low-beta portfolio and short on the high-beta portfolio. There are a lot of simple modifications (like going long on the bottom beta decile and short on the top beta decile), which could probably improve the strategy’s performance.
Hedge for stocks during bear markets
Partially - Low beta stocks (low-risk stocks) are usually safer during turmoil, and Beta Factor in a long-short variant can be used as a portfolio hedge against equity risk. However, caution should be used as the popularity of betting-against-beta investing could move valuation (measured by common valuation ratios like P/E, P/B, P/CF, etc.) of low beta stocks into excessive-high (compared to neutral market valuation). This popularity of betting-against-beta factor investing and high valuation of low beta stocks can be then detrimental to their performance during market stress.
Out-of-sample strategy's implementation/validation in QuantConnect's framework