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You flip a coin and get heads ten times in a row. Which best describes your opinion?
- Odds are your next flip will be a head.
- Odds are the next flip will be tail.
- Odds are 50/50 it will be a head or tail.
- It's time to take a close look at the coin and make sure it doesn't have heads on both sides, or is not a normal balanced coin.
The January Effect
Stocks in general and small stocks in particular have historically generated abnormally high returns during the month of January. According to Robert Haugen and Philippe Jorion, "The January effect is, perhaps the best-known example of anomalous behavior in security markets throughout the world."1 The January Effect initially persisted long after its initial discovery, but some argue it has diminished over time. Theoretically an anomaly should disappear as traders attempt to take advantage of it in advance. Additionally, many have argued that some of the other anomalies occur primarily or entirely during the month of January. The bottom line is that January has historically been the best month to be invested in stocks.
The effect is often attributed to small stocks rebounding following year-end tax selling. Individual stocks depressed near year-end are more likely to be sold for tax-loss recognition, while stocks that have run up are often held until after a new year. Some people have suggested that the January effect had moved into November and December as a result of mutual funds being required to report holdings at the end of October, and from investors buying in anticipation of gains in January. Some have argued the effect stopped working long ago (for instance, William Bernstein wrote in 1999 the effect was primarily in one or both of the value or small cap effect, the latter being debatable because of bid/ask spread costs).2
Some studies of foreign countries have found that returns in January were greater than the average return for the whole year. Interestingly, the January effect had also been observed in many foreign countries including some (Great Britain and Australia) that don't use December 31 as the tax year-end, which implies that there is more to the January effect than just tax effects.“Analysis of broad samples of value-weighted and equal-weighted returns of U.S. equities documents that abnormally high rates of return on small-capitalization stocks continue to be observed during the month of January. This January effect in small-cap stock returns is remarkably consistent over time ... After a generation of intensive study, the January effect continues to present a serious challenge to the efficient market hypothesis."Mark Haug and Mark Hirschey in “The January Effect”3
January is also watched closely by many because strong market performance in January has historically indicated strong performance for the rest of the year. Some researchers have suggested being long stocks after January when the market has been up, but switching to treasury bills the years when January returns were negative.4
Turn of the Month Effect
Stocks historically show higher returns around the turn of the month. Josef Lakonishok and Seymour Smidt coined the phrase5 and later Frank Russell examined returns of the S&P 500 over a 65 year period in finding that U.S. large-cap stocks consistently show higher returns at the turn of the month. Chris Hensel and William Ziemba presented the theory that the effect results from cash flows at the end of the month (salaries, interest payments, etc.). They found returns for the turn of the month were significantly above average from 1928 through 1993 and "that the total return from the S&P 500 over this sixty-five-year period was received mostly during the turn of the month." The study implied that investors making regular purchases may benefit by scheduling to make those purchases prior to the turn of the month.6
In “Equity Returns at the Turn of the Month” John McConnell and Wei Xu studied CRSP daily returns for the 80-year period between 1926 and 2005. Specifically, "turn-of-the-month is defined as beginning with the last trading day of the month and ending with the third trading day of the following month." They found that the turn-of-the-month effect was pronounced over the prior two decades such that, when they combined their findings with those of Lakonishok and Smidt, the result was that over the 109-year interval of 1897-2005, on average, all of the positive return to equities occurred during the turn-of-the-month interval. They also concluded that it was not confined to small and low-price stocks, calendar year-ends or calendar quarter-ends, to the U.S., and was not due to a buying of shares at the turn-of-the-month since trading volume wasn't higher and the net flows of funds to equity funds was not systematically higher. They concluded that the turn-of-the-month effect in equity returns poses a challenge to both “rational” and “behavioral” models of security pricing and it continues to be a puzzle in search of a solution.7
The Monday Effect
Monday tends to be the worst day to be invested in stocks. The first study documenting a weekend effect was by M. J. Fields in 1931 in the Journal of Business at a time when stocks traded on Saturdays. Fields had also found in a 1934 study that the DJIA commonly advanced the day before holidays. Several other studies have shown that returns on Monday are worse than other days of the week. Larry Harris studied intraday trading and found that the weekend effect tended to occur in the first 45 minutes of trading as prices fall, but on all other days prices rise during the first 45 minutes of trading. This anomaly presents the interesting question: Could the effect be caused by the moods of market participants? People are generally in better moods on Fridays and before holidays, but are generally grumpy on Mondays (in fact, suicides are more common on Sundays and Mondays than other days of the week8). Investors should however, keep in mind that the difference is small and could be difficult to take advantage of because of trading costs.9
The question of whether past prices and charts can be used to predict future prices has been subject to extensive research and debate. "Technical Analysis" is a general term for a number of investing techniques that attempt to forecast future securities prices by studying past prices and related statistics. Common techniques include strategies based on relative strength, moving averages, as well as support and resistance. The majority of researchers that have tested technical trading systems (and the weak-form efficient market hypothesis) have found that prices adjust rapidly to stock market information and that technical analysis techniques are not likely to provide any advantage to investors who use them."The central proposition of charting is absolutely false, and investors who follow its precepts will accomplish nothing but increasing substantially the brokerage charges they pay. There has been a remarkable uniformity in the conclusions of studies done on all forms of technical analysis. Not one has consistently outperformed the placebo of a buy-and-hold strategy."
Burton Malkiel has been one of the most public critics of technical analysis and the excerpt above is from his best seller A Random Walk Down Wall Street. I’ve included some other quotes from those that criticize technical analysis at the end of this chapter. However, there have been studies that suggest there may be some validity to some forms of technical analysis.
A 1992 study by William Brock, Josef Lakonishok, and Blake LeBaron (BLL) analyzed moving averages and trading range breaks on the Dow Jones Industrial Index from 1897 to 1985. They found that moving averages and trading range breaks (using 50, 150, and 200 days) did have some statistically significant value. They concluded with the following. "Our results are consistent with technical rules having predictive power. However, transactions costs should be carefully considered before such strategies can be implemented."10
“Data-Snooping, Technical Trading Rule Performance, and the Bootstrap” was an article that revisited the BLL paper and later appeared in the October 1999 edition of the Journal of Finance. In the article, Ryan Sullivan, Allan Timmermann, and Halbert White (STW) attempted to determine the effect of Data-Snooping on the BLL results. They also used data collected from the period following the original study in order to provide an out of sample test. Adding the recent years provided a full 100 years of data. STW calculated a break even transaction cost level of 0.27% percent per trade for the best performing trading rule for the full period. STW found "that the results of BLL appear to be robust to data-snooping . . . However, we also find that the superior performance of the best trading rule is not repeated in the out-of-sample experiment covering the period 1987-1996" and "there is scant evidence that technical trading rules were of any economic value during the period 1987-1996."
Another technical analysis debate is whether strong performance from one period continues (or reverses) in future periods. Some studies have concluded that positive correlation exists (winners repeat) in the short term (weeks and months) while negative autocorrelation exists over longer periods of time.
“Momentum Strategies” was a review and analysis of the subject in the December 1996 issue of the Journal of Finance.11 The authors noted that any excess returns from "momentum strategies" may be outweighed by trading costs (particularly with smaller issues). K. Geert Rouwenhorst later published a paper titled “International Momentum Strategies” in The Journal of Finance that documented momentum strategies in 12 European markets from 1980-1995.
One of the best examples of an individual that both publishes about factors and invests using them is Clifford Asness and his team at AQR Capital Management. The firm’s web site lists several papers authored by the firm’s staff about momentum strategies, including “Fact, Fiction and Momentum Investing” which was published in the Fall 2014 issue of the Journal of Portfolio Management.12 See also “Can Momentum Investing Be Saved?” (coauthored by Rob Arnott) for a good summary of explanations for the momentum effect.13
Dimensional Funds was one of the original factor based investing firms and they have suggested that most managers using momentum benefit from the factor, but trading costs wipe out most or all of that advantage.14
Regarding the question I asked at the start of the chapter, with a normal coin, every flip should have an equal chance of coming up heads or tails, regardless of prior flips (believers in market efficiency and random walk theory would tend to agree). Believers in technical analysis I suspect would predict the series would continue, while believers in value investing might predict the series is more likely to end the longer it lasts. But, if the series is unusually long and improbable, you have to consider the question of whether the coin is normal.
The only thing we know for certain about technical analysis is that it's possible to make a living publishing a newsletter on the subject.
Martin Fridson, Investment IllusionsTechnical analysts are the witch doctors of our business. By deciphering stock price movement patterns and volume changes, these Merlins believe they can forecast the future.
William Gross, Everything You've Heard About Investing is Wrong!The one principal that applies to nearly all these so-called "technical approaches" is that one should buy because a stock or the market has gone up and one should sell because it has declined. This is the exact opposite of sound business sense everywhere else, and it is most unlikely that it can lead to lasting success in Wall Street. In our own stock-market experience and observation, extending over 50 years, we have not known a single person who has consistently or lastingly made money by thus "following the market." We do not hesitate to declare that this approach is as fallacious as it is popular.
Benjamin Graham, The Intelligent InvestorTechnical analysis is doomed to fail by the statistical fact that stock prices are nearly random; the market's patterns from the past provide no clue about its future. Not surprisingly, studies conducted by academicians at universities like MIT, Chicago, and Stanford dating as far back as the 1960s have found that the technical theories do not beat the market, especially after deducting transaction fees. It is amazing that technical analysis still exists on Wall Street. One cynical view is that technicians generate higher commissions for brokers because they recommend frequent movement in and out of the market.
William Sherden, The Fortune Sellers: The Big Business of Selling and Buying Predictions
1. Robert Haugen and Philippe Jorion, The January Effect: Still There after All These Years Financial Analysts Journal, January-February 1996
2. William Bernstein, The Incredible Shrinking January Effect, http://www.efficientfrontier.com/ef/799/january.htm Robert Haugen and Philippe Jorion, The January Effect: Still There after All These Years Financial Analysts Journal, January-February 1996
3. Mark Haug and Mark Hirschey, “The January Effect,” Financial Analysts Journal September/October 2006
4. Michael Cooper, John McConnell, and Alexei Ovtchinnikov, “What’s the Best Way to Trade Using the January Barometer?” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1436516 July 20, 2009 (later published in the Journal of Investment Management, 2010)
5. Josef Lakonishok and Seymour Smidt, 1988, Are seasonal anomalies real? A ninety-year perspective Review of Financial Studies 1988 1(4), 403-425
6. Chris R. Hensel and William T. Ziemba, "Investment Results from Exploiting Turn-of-the-Month Effects," Journal of Portfolio Management, Spring 1996
7. John McConnell and Wei Xu, Equity Returns at the Turn of the Month, Financial Analysts Journal, March/April 2008
9. Lawrence Harris, "A Transaction Data Study of Weekly and Intradaily Patterns in Stock Returns," Journal of Financial Economics, June 1986. See also http://calendar-effects.behaviouralfinance.net/weekend-effect/
10. William Brock, Josef Lakonishok, and Blake LeBaron, Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, The Journal of Finance, December 1992
11. Louis K. C. Chan, Narasimhan Jegadeesh, and Josef Lakonishok, Momentum Strategies, The Journal of Finance, December 1996.
13. Rob Arnott, Vitali Kalesnik, Engin Kose, and Lillian Wu “Can Momentum Investing Be Saved?” https://www.researchaffiliates.com/en_us/publications/articles/637-can-momentum-investing-be-saved.html October 2017
14. Julie Segal, The Momentum Factor Is Real. Too Bad It Doesn't Work, November 21, 2018 https://www.institutionalinvestor.com/article/b1bxr4ppygmdth/The-Momentum-Factor-Is-Real-Too-Bad-It-Doesn-t-Work
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