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Realized/Implied Return Variance Ratio as a Trading Signal

Posted in Momentum Investing, Volatility Effects

Is it possible to predict serial correlation (autocorrelation) of stock returns, and thereby enhance reversal and momentum strategies. In the January 2014 version of his paper entitled “The Information Content of Option Prices Regarding Future Stock Return Serial Correlation”, Scott Murray investigates the relationship between the variance ratio (the ratio of realized to implied stock return variance, a measure of the variance risk premium) to stock return serial correlation. He calculates realized variance at the end of each month from daily log stock returns over the prior three months. He calculates implied variance at the end of each month as the square of the volatility implied by at-the-money 0.5 delta call and put options one month from expiration. He first measures the power of the variance ratio to predict stock return serial correlation. He then tests the effectiveness of this predictive power to enhance reversal and momentum stock trading. Using the specified return and option data for all U.S. stocks with listed options during January 1996 through December 2012, he finds that:

  • There is a strong negative relationship between the variance ratio and future stock return serial correlation. An extremely high (low) variance ratio relates to strongly negative (positive) future serial correlation of returns.
  • This predictive power supports a daily trading strategy that exploits reversal (momentum) for stocks expected to exhibit strong negative (positive) serial correlation.
    • Within the fifth of stocks predicted to exhibit the lowest serial correlations based on the variance ratio at the end of last month, during this month the strategy takes a long (short) position at the end of each trading day in stocks with a same-day loss (gain).
    • Conversely, within the fifth of stocks predicted to exhibit the highest serial correlations based on the variance ratio at the end of last month, during this month the strategy takes a long (short) position at the end of each trading day in stocks with a same-day gain (loss).
    • For equal weighting and daily rebalancing, this strategy generates an average gross monthly return of 0.53% (6.6% per year) and a four-factor (market, size, book-to-market, momentum) risk-adjusted average gross monthly alpha of 0.59%.
  • Results are similar (see the chart below) when constraining the portfolio: to have equal long and short sides (dollar-neutral); to have no overall exposure to the market factor (beta-neutral); or, to include only the most liquid stocks. Results are also robust to different definitions for the variance ratio and to controlling for historical serial correlation.

The following chart, taken from the paper, shows gross cumulative log returns for several variations of the reversal-momentum enhancement strategy described above, as follows:

  • Daily – the basic strategy with daily rebalancing.
  • Zero-Cost – the basic strategy plus daily long-short balancing to achieve a dollar-neutral portfolio.
  • Beta – the basic strategy plus daily market beta hedging to achieve a beta-neutral portfolio.
  • Beta $X – the beta-neutral strategy limited to stocks whose trading-day dollar volume is greater than X million dollars.

In general, the beta-neutral versions perform best on a gross basis, regardless of liquidity restrictions. However, performance of these best-performing versions concentrates in the late 1990s. Costs of daily rebalancing and daily hedging may substantially offset gains.

enhanced-gross-momentum-reversal-cumulatives

In summary, evidence suggests that stock traders may be able to enhance short-term reversal and momentum trades by incorporating a realized/implied variance ratio signal to identify stocks most likely to exhibit return reversal and continuation.

Cautions regarding findings include:

  • Reported results are gross, not net. As noted, the daily signaling/rebalancing/hedging specifications would likely generate very high trading frictions, potentially invalidating exploitability.
  • The study does not account for the cost/feasibility of shorting. Liquidity restrictions do not ensure availability of shares for shorting.
  • As noted, much of the gains of the best-performing strategies concentrate in the late 1990s, early in the sample period.
  • The test strategy is elaborate, involving potentially substantial data acquisition and processing costs (or fees if delegated). There may be simpler ways to exploit variance risk premium signals.

For a much simpler, somewhat related strategy applied to a stock market index, see “Volatility Risk Premium an Exploitable Stock Market Predictor?”.

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