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.

Fundamental reason

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.

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Markets Traded
equities

Financial instruments
stocks

Confidence in anomaly's validity
Strong

Backtest period from source paper
1963-2009

Notes to Confidence in Anomaly's Validity

Indicative Performance
11.75%

Period of Rebalancing
Monthly

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

Estimated Volatility
11%

Number of Traded Instruments
1000

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


Maximum Drawdown
0%

Complexity Evaluation
Complex strategy

Notes to Maximum drawdown

not stated


Notes to Complexity Evaluation

Sharpe Ratio
0.7

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.

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

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