The January Effect: An Evidence-Based Perspective

/The January Effect: An Evidence-Based Perspective

The January Effect: An Evidence-Based Perspective

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(Last Updated On: August 18, 2017)

January is here again and market commentators are already telling stories about the so-called January Effect. Some articles (examples here and here) are saying the effect is an illusion, while others are claiming the effect can help you make some profits (examples here and here).

Before we dig into the academic research on the subject, let’s first understand the January Effect. Put simply, the January Effect is a term for the observation by some researchers that certain types of stocks (e.g., value and/or small size) earn abnormally high returns in January. A good example of this research is from a 1996 FAJ article by Bob Haugen (rest in peace!) and Philippe Jorion.

Source: FAJ 1996

Source: FAJ 1996. Note the strong performance of smaller stocks in the month of January (highlighted via the red circle). The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

But is the January Effect Real?

As we pointed out last year, researchers are still debating the merits of the January Effect. In this piece(1), we’ll step back and reflect on the extensive academic research published on the January Effect. I’ll end with a recommendation for investors entertaining the idea of using the January Effect as part of their investment process.

The research on stock return seasonality likely extends back to when stock markets were invented. Nonetheless, one early piece by the Harvard Committee on Economic Research (published in 1919) shows that there are no seasonal effects in stock returns from 1897 to 1914. Sidney B. Wachtel, who published a paper in 1942, found something different: Professor Wachtel identified that tax considerations can lead to seasonality in stock returns and do generate a January Effect.

Next up in the January Effect debate were Michael S. Rozeff and William R. Kinney, Jr., a group of economists who published a more comprehensive empirical investigation of Wachtel’s initial ideas in 1976. Rozeff and Kinney examine stock returns from 1904 to 1974 and confirm Wachtel’s finding — the January Effect is real!

But the debate raged on. Early skeptics of the January Effect include Richard Roll, Don Keim, and Marc Reinganum, all of whom published papers in 1983 (here, here, and here, respectively). The collective work of these researchers showed that January returns are limited to smaller firms, implying that the January Effect was not as pervasive as previously thought. Of course, more recent studies in 2004, by Honghui Chen and Vijay Singal, and another by Mark Grinblatt and Tobias J. Moskowitz, leverage better analytical techniques and 1) confirm the existence of a January Effect and 2) tie it to year-end tax-loss selling.

Is Tax-Loss Selling Driving the January Effect?

But if taxes are driving the January Effect, can we find evidence for this hypothesis? Jay Ritter digs a bit further into this question and examines the buying and selling of individual investors near the turn of the year. By measuring the ratio of buys and sells of individual investors, he finds that individual investors sell more near the end of the year, and buy more in the beginning of the year—so a seasonal pattern exists for individual investors, who tend to hold smaller stocks. In two different academic papers, James Poterba and Scott Weisbenner and Richard Sias and Laura Starks, confirm the findings from Jay Ritter: individual investors engaged in tax-loss selling contribute to the January Effect.

But the tax-selling hypothesis isn’t a done deal. Early research by Philip Brown, Donald Keim, Allan Kleidon, and Terry Marsh examine the tax-induced January Effect hypothesis in the Australian equity markets. At the time, Australia had similar tax laws to the US, but a June-July tax year. In theory, if tax incentives were the cause of the January Effect, the authors should find a “July Effect.” But that wasn’t the case. The evidence shows that Australian equity returns have the same January effect documented in U.S. markets. The analysis in this paper raises a few possibilities: 1) The January Effect is an artifact of data-mining, or 2) researchers still don’t understand why the January Effect exists.

The Reality of the January Effect: Who Knows

Clearly, there has been a lot of research on the January Effect (and we’ve only touched the wave tops!). On net, the research suggests the January Effect may have some mojo, but there are plenty of questions on the topic.

Bottomline: Most investors are better off not paying attention to the January Effect.

Instead, investors should focus on what they can control: 1) Identify their goals, 2) maintain a long-term horizon, and 3) keep things simple.

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References   [ + ]

1. This post is similar to a discussion on seasonality outlined in our new book, Quantitative Momentum

About the Author:

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes,, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.
  • evo34

    Whoa. Invoking studies from 1988? I’d have to think behaviors change quite a bit in 30 years — esp. when market access for indivduals has gone from near-zero to full.

  • Sam Zaydel

    Is there any research about how presidential and congressional changes affect the January effect? It seems like we find it irresistible as people to spot patterns in almost anything. Probably our greatest undoing…

  • We cited papers from 1919…

  • Not sure.
    I recommend searching around on google scholar or SSRN:

  • Sam Zaydel

    I am planning on it. It seems like an interesting topic if nothing else. I did a couple of quick searches and seems there is something out there. At least some people figured it was worth investigating. 🙂

  • Great.
    Please share anything you find in the comments — love to see what you dig up

  • evo34

    Well, that’s even more absurd. I doubt studying the horse and buggy patterns from the early 1900s would do a good job predicting 2017 traffic in NYC.

  • Depends if you believe we are studying technology or human behavior. If we are studying human behavior, indirectly via stock markets, I think older studies are valid

  • Sam Zaydel

    There are more than these pieces, but I am not strongly compelled by them to share links. Most seemingly studied market effects during presidential cycles, so much longer periods. Some of this may be a result of doing too much digging and wishful thinking. I suppose it will be interesting to see more studies in the coming years, which I am sure are going to happen, in particular with a new normal like president-elect Trump.

  • Sam Zaydel

    Arguably, learning from human behavior is as valid using data from 1000 years ago, subject to quality of said data as it is using data from yesterday. The evolutionary machine is not all that rapid, at least not in humans.

  • evo34

    Do you have any proof than maket trends from even 20 years ago are at all predictive today?

  • Sam Zaydel

    A lot of what I read suggests there are many long-term trends, whether or not they are enough to base your investment strategy around, I am not sure. I would not really bet on it. After all, when your dataset is sufficiently small, just a few data points in one direction or anther may cause your less than robust results to suggest that there are tangible trends, where realistically you just don’t have robust enough, or large enough dataset. I remain skeptical.