Earnings announcement periods are unique periods in the life of each stock. Stocks are under more scrutiny, and investors and traders react more actively to all news related to them. And they respond to this activity with higher prices around the dates of the earnings announcements. This premium (the earnings announcement premium) is a substantial anomaly with a monthly strategy earning excess returns of between 7% and 18% per year (with Sharpe ratios larger than other popular anomalies). The return on earnings announcements is essentially unrelated to market risk or any of the other factors commonly used in asset pricing. The premium is strong in large-capitalization stocks, is not only confined to a three-day window around the announcement (but for the whole month), and has appeared consistently since 1927.

The effect could be enhanced by using past volume as a secondary indicator. Stocks with a predictably high announcement volume have a premium of 1.5% per month, which allows one to construct a long/short portfolio generating an 18% return per year.

Fundamental reason

Research hypothesizes that the predictable rise in stock price is driven by the predictable rise in volume around the earnings announcements.

The earnings announcement premium is strongly related to the concentration of past trading activity around earnings announcement dates. In particular, stocks with a high volume around earnings announcements subsequently have both high premiums and high imputed buying by individual investors. This finding suggests that prices for some stocks are pushed higher around announcement dates by buying pressure from individuals.

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Markets Traded
equities

Financial instruments
stocks

Confidence in anomaly's validity
Strong

Backtest period from source paper
1973-2004

Notes to Confidence in Anomaly's Validity

Indicative Performance
18.36%

Period of Rebalancing
Monthly

Notes to Indicative Performance

per annum, annualized monthly return (arithmetically) of 1.53%, return from table VIII for long-short portfolio of high volume stocks


Notes to Period of Rebalancing

Estimated Volatility
16.12%

Number of Traded Instruments
1000

Notes to Estimated Volatility

estimated from t-statistic (6,34) from table VIII


Notes to Number of Traded Instruments

more or less, it depends on investor’s need for diversification


Maximum Drawdown
0%

Complexity Evaluation
Complex strategy

Notes to Maximum drawdown

not stated


Notes to Complexity Evaluation

Sharpe Ratio
0.89

Simple trading strategy

The investment universe consists of all stocks from the CRSP database. At the beginning of every calendar month, stocks are ranked in ascending order on the basis of the volume concentration ratio, which is defined as the volume of the previous 16 announcement months divided by the total volume in the previous 48 months. The ranked stocks are assigned to one of 5 quintile portfolios. Within each quintile, stocks are assigned to one of two portfolios (expected announcers and expected non-announcers) using the predicted announcement based on the previous year. All stocks are value-weighted within a given portfolio, and portfolios are rebalanced every calendar month to maintain value weights. The investor invests in a long-short portfolio, which is a zero-cost portfolio that holds the portfolio of high volume expected announcers and sells short the portfolio of high volume expected non-announcers.

Hedge for stocks during bear markets

Not known - Source and related research papers don’t offer insight into the correlation structure of the proposed trading strategy to equity market risk; therefore, we do not know if this strategy can be used as a hedge/diversification during the time of market crisis. The strategy is built as a long-short, but it can be split into two parts. The long leg of the strategy is surely strongly correlated to the equity market; however, the short-only leg can be maybe used as a hedge during bad times. Rigorous backtest is, however, needed to determine return/risk characteristics and correlation.

Source paper
Strategy's implementation in QuantConnect's framework (chart+statistics+code)
Other papers

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