The “52-week high effect” states that stocks with prices close to the 52-week highs have better subsequent returns than stocks with prices far from the 52-week highs. Investors use the 52-week high as an “anchor” against which they value stocks. When stock prices are near the 52-week high, investors are unwilling to bid the price all the way to the fundamental value. As a result, investors under-react when stock prices approach the 52-week high, and this creates a 52-week high effect.
This effect could be enhanced with a strategy using a narrower investment universe and buying stocks in industries in which stock prices are close to 52-week highs. The strategy is related to the momentum effect, but research shows it is independent of it. The strategy should not be implemented in January as it has negative results in this month (like momentum). Most of the gains for the long-short version are from long positions, which makes the “52-week high effect” easier to implement in real-world trading.
Academics speculate that this effect is connected to “adjustment and anchoring bias”. Anchoring is a psychological bias that says that people start with an implicitly suggested reference point (the “anchor” -> 52-week high in our example) and then make incremental adjustments based on additional information. The financial paper says that traders use the 52-week high as a reference point in which they evaluate the potential impact of news. When good news has pushed a stock’s price near or to a new 52-week high, traders are reluctant to bid the price of the stock higher even if the information warrants it. The information eventually prevails, and the price moves up, resulting in a continuation. It works similarly for 52-week lows.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, annualized (geometrically) monthly return 0,93% from table 11 for long-short portfolio and 3-month holding period
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
estimated from t-statistic 6,85 from table 11
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 NYSE, AMEX, and NASDAQ (the research paper used the CRSP database for backtesting). The ratio between the current price and 52-week high is calculated for each stock at the end of each month (PRILAG i,t = Price i,t / 52-Week High i,t). Every month, the investor then calculates the weighted average of ratios (PRILAG i,t) from all firms in each industry (20 industries are used), where the weight is the market capitalization of the stock at the end of the month t. The winners (losers) are stocks in the six industries with the highest (lowest) weighted averages of PRILAGi,t. The investor buys stocks in the winner portfolio and shorts stocks in the loser portfolio and holds them for three months. Stocks are weighted equally, and the portfolio is rebalanced monthly (which means that 1/3 of the portfolio is rebalanced each month).
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.
Hong, Jordan, Liu: Industry Information and the 52-Week High Effect
We find that the 52-week high effect (George and Hwang, 2004) cannot be explained by risk factors. Instead, it is more consistent with investor under-reaction caused by anchoring bias: the presumably more sophisticated institutional investors suffer less from this bias and buy (sell) stocks close to (far from) their 52-week highs. Further, the effect is mainly driven by investor under-reaction to industry instead of firm-specific information. The extent of under-reaction is more for positive than for negative industry information. A strategy that buys stocks in industries in which stock prices are close to 52-week highs and shorts stocks in industries in which stock prices are far from 52-week highs generates a monthly return of 0.60% from 1963 to 2009, roughly 50% higher than the profit from the individual 52-week high strategy in the same period. The 52-week high strategy works best among stocks with high R-squares and high industry betas (i.e., stocks whose values are more affected by industry factors and less affected by firm-specific information). Our results hold even after controlling for both individual and industry return momentum effects.
Hwang, George: The 52-Week High and Momentum Investing
Whencoupled with a stock’s current price, a readily available piece of information—the 52-week high price-explains a large portion of the profits from momentum investing. Nearness to the 52-week high dominates and improves upon the forecasting power of past returns (both individual and industry returns) for future returns. Future returns forecast using the 52-week high do not reverse in the long run. These results indicate that short-term momentum and long-term reversals are largely separate phenomena, which presents a challenge to current theory that models these aspects of security returns as integrated components of the market’s response to news.
Liu, Liu, Ma: The 52-Week High Momentum Strategy in International Stock Markets
We study the 52-week high momentum strategy in international stock markets proposed by George and Hwang (2004). This strategy produces profits in 18 of the 20 markets studied, and the profits are significant in 10 markets. The 52-week high momentum profits still exist conditional on past individual and industry returns, and are independent from the profits from the Jegadeesh and Titman (1993) momentum strategy. These profits are robust when we control for the Fama-French three factors and they do not show reversals in the long run. We find that the 52-week high is a better predictor of future returns than macroeconomic risk factors or the acquisition price. The individualism index has no explanatory power to the variations of the 52-week high momentum profits across different markets. However, the profits are no longer significant in most markets once transaction costs are taken into account.