The 52-week high is one of the strangest reference points in markets because, economically, there is nothing particularly special about it. A stock trading at $80 does not become more valuable because it traded at $100 sometime in the previous year. Yet investors clearly pay attention to old highs, and a large literature suggests that this simple reference point affects trading behavior and subsequent returns.

A recent paper by Qingzhong Ma, David Whidbee and Wei Zhang pushes the idea in a slightly different direction. In “Anchoring and Stock Return Distributions“, they aren’t mainly asking whether distance from the 52-week high predicts the average future return. They ask whether anchoring changes the entire shape of the return distribution. “Their answer is ‘yes.’” Stocks trading further below their 52-week highs subsequently have higher idiosyncratic volatility, more positive skewness, greater kurtosis, larger maximum daily gains and more extreme minimum daily returns.

That is much more interesting than another return anomaly.

The authors measure a stock’s position relative to its 52-week high using a reference price ratio: current price divided by the previous 52-week high. A stock trading at $98 after reaching $100 has a high ratio. A stock trading at $60 after previously reaching $100 has a low one. The lower this ratio, the more dramatic the subsequent return distribution appears to become.

So, the stock far below its high doesn’t merely have a somewhat different expected return. Its future behavior looks different. It experiences bigger moves in both directions, fatter tails and greater positive skew. In other words, distance from the high seems related to the probability of unusually large outcomes.

That immediately makes the result relevant in a way that a cross-sectional return predictor isn’t. If some variable forecasts expected return, a stock trader cares. If it forecasts volatility, skewness and tail behavior as well, an options trader should probably care too.

The proposed explanation is still anchoring. Investors use the old high as a psychological reference point even though it should have little fundamental significance. A stock that has fallen from $100 to $60 doesn’t simply trade at $60 in investors’ heads. It trades at “$60, down from $100.”

That framing changes behavior.

Some investors may see the old high as evidence that the stock can get back there. Others may be reluctant to sell because selling at $60 crystallizes a loss relative to the price they remember. The same historical number can therefore affect buyers, sellers and expectations even though nothing in valuation theory says it should.

The result can be disagreement.

And disagreement is a useful way to think about why the distribution might become wider rather than merely shift upward or downward. If everyone agrees that a company’s fair value has fallen from $100 to $60, the old high should cease to matter. But if $100 remains psychologically important, investors may disagree violently about what $60 means. One group sees a broken company. Another sees a stock trading at a 40% discount to where it “should” be.

That kind of disagreement is exactly the environment where you might expect larger moves.

It also helps explain the paper’s skewness result. Stocks far below their highs subsequently show more positive skewness and larger maximum daily returns. A beaten-down stock has room for a dramatic comeback narrative. A moderately good earnings announcement, takeover rumor or change in sentiment can suddenly become evidence that the stock is “heading back to $100.”

There is an obvious resemblance here to lottery-like stocks. Investors often seem willing to pay for a small probability of a spectacular positive outcome. A stock trading far below a salient historical high provides a particularly easy story to attach to that possibility. You don’t need a complicated valuation model to imagine the upside. You just draw a line back to where the stock used to trade (it doesn’t even need to be a straight line).

If the stock is at $60 and the old high is $100, the upside is sitting there on the chart.

This doesn’t mean the stock will return to $100, of course. The paper also finds that stocks further below their highs subsequently experience more extreme negative daily returns. The interesting result is not that these stocks are safe bargains. Quite the opposite. Their return distributions become more extreme in both directions.

That distinction matters because investors often think about signals almost entirely in terms of expected return. We ask whether low price-to-book stocks outperform high price-to-book stocks, whether momentum predicts future returns or whether earnings surprises generate drift. But expected return is only one feature of a distribution.

For many strategies, it isn’t even the most important one.

An option price is, at least implicitly, driven by volatility, skewness and tails. A short-volatility strategy can have almost no opinion about a stock’s average return and still be destroyed because the distribution is wider or more skewed than expected. Conversely, a long-option strategy can make money from the shape of the distribution even if its directional forecast is poor.

This paper therefore suggests an interesting empirical question that goes beyond what the authors directly test: does the implied volatility surface fully reflect distance from the 52-week high?

Suppose two otherwise similar stocks both have 30% implied volatility. One is trading 2% below its 52-week high and the other 40% below it. If the second stock subsequently produces systematically greater realized volatility, fatter tails and more positive skewness, perhaps those options should not be priced identically.

The skew result is particularly intriguing. If stocks far below their highs have more positively skewed future returns, you might expect upside options to deserve relatively more value. Whether option markets already incorporate this is an empirical question, but it is exactly the sort of question a trader should ask after reading a behavioral-finance paper like this.

There is also a broader lesson about technical indicators.

Finance academics and traders sometimes talk past each other about chart-based reference points. A trader might say that an old high is resistance, while an academic might reasonably ask why a price printed nine months ago should affect fundamental value today.

But that is the wrong argument.

The old high doesn’t need to affect fundamental value. It only needs to affect people’s decisions.

If enough investors use the same reference point, the reference point can influence order flow, expectations and positioning. Once that happens, something economically arbitrary can have real market consequences. The number matters because people believe it matters.

This is the essence of anchoring. The interesting question isn’t whether $100 is objectively important. It isn’t. The interesting question is whether investors behave differently because they remember $100.

Ma, Whidbee and Zhang’s evidence suggests that they do, and that the consequence goes beyond a small change in average return. The relationships appear across portfolio sorts and regression specifications and are especially evident among S&P 500 stocks.

As always, this is a working paper, and the jump from a cross-sectional empirical relationship to a profitable strategy is a large one. But the idea is worth taking seriously because it expands what behavioral finance should try to explain. Psychological biases don’t necessarily just push the mean return up or down. They can affect disagreement, trading intensity and investor demand for particular types of payoff. That means they can change variance, skewness and tails as well.

And once you start thinking in distributions rather than point forecasts, that arbitrary line on the chart suddenly becomes a lot more interesting.

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