In this article, we will take a closer look at the "January Barometer" hypothesis. This term should not be confused with the "January Effect" hypothesis.
- The "January Effect" hypothesis: The January effect is a hypothesis that there is a seasonal anomaly in the financial market where securities' prices increase in the month of January more than in any other month. This calendar effect would create an opportunity for investors to buy stocks for lower prices before January and sell them after their value increases.
- The "January Barometer" hypothesis: The January barometer is the hypothesis that stock market performance in January (particularly in the U.S.) predicts its performance for the rest of the year. So if the stock market rises in January, it is likely to continue to rise by the end of December.
First of all, why might calendar effects occur at all in the stock market? This can be explained by individual investors selling their stocks at the year end for tax reasons (for example, to claim a capital loss) and reinvesting their money again at the beginning of the year (increasing the demand for stocks). Beside these fiscal reasons, a number of individuals also receive their bonus payments at the end of the year, which can then be reinvested in the stock market in January again.
The January Barometer was originally devised by Yale Hirsch in 1972. and in the previous decades, the results seemed very impressive:
(Source: Stock Trader's Almanac)
In the previous years, the January Barometer effect became a rather controversial barometer. As we can see in the table above, there certainly were years where performance during the January month would have served as a barometer for the stock market performance for the rest of the trading year, but in the latter years, more and more researchers and analysts have questioned its validity. In a recent Wall Street Journal article, a comparison is made with a simple rule that would have advised you to go long the stock market each year. This rule would have been correct for 76% of the time, while the January Barometer would have been correct only for 64% of the cases over the same time period.
Here we will investigate whether the price performance of stocks in January can help to predict performance for the whole year. We will first do this for the stock market in general by testing the hypothesis on the SPDR S&P 500 Trust ETF (NYSEARCA:SPY). We will then do the same for 9 ETFs which track the performance of a specific stock market sector:
- Consumer Discretionary Select Sector SPDR ETF (NYSEARCA:XLY)
- Consumer Staples Select Sector SPDR ETF (NYSEARCA:XLP)
- Energy Select Sector SPDR ETF (NYSEARCA:XLE)
- Financial Select Sector SPDR (NYSEARCA:XLF)
- Health Care Select Sect SPDR ETF (NYSEARCA:XLV)
- Industrial Select Sector SPDR ETF (NYSEARCA:XLI)
- Materials Select Sector SPDR ETF (NYSEARCA:XLB)
- Technology Select Sector SPDR ETF (NYSEARCA:XLK)
- Utilities Select Sector SPDR ETF (NYSEARCA:XLU)
I have used the historical adjusted closing prices from Yahoo to calculate the price returns. The advantage of these adjusted closing prices is the fact that they are adjusted for the historical dividends which have been paid out, which means we will base our calculations on the total return and not only on the price return of the sector ETFs.
The total returns are calculated as follows:
January return = Adjusted Cl. Price of January / Adjusted Cl. Price of December of the previous year
Year return = Adjusted Cl. Price of December / Adjusted Cl. Price of December of the previous year
All the data is from Yahoo Finance. I have used as much data as was available to base my backtest on for the sector ETFs. While the data goes back 16 years in time, please keep in mind that the results hereunder are calculated only on 2 prices for each year (the adjusted closing price for January and the adjusted closing price for December of the same year). To validate whether the correlation coefficients mentioned below are statistically significant, we would ideally use a larger sample size.
In the table below, we demonstrate the price performance for January:
In the table below, we demonstrate the price performance for the whole year:
Based on these results, we make the following observations:
- For SPY, the performance in January has become a poor indicator for forecasting the performance of the whole year. Especially for the previous 3 years, we can see this effect clearly. This also been mentioned by other analysts describing the January Effect (here and here).
- We can calculate the correlation coefficient for each ETF to validate whether the price performance in January is correlated with the price performance for the whole trading year:
As these correlation coefficients vary highly for each ETF, we cannot draw a general conclusion as to whether the month January works as a barometer to predict the performance for the whole year. For certain ETFs, the correlation seems very high (XLU, XLV, XLE), while for others, it is even negative (XLY, XLF, XLB, XLK).
When we look at the price performance of XLU, we can see the stock price has been in an uptrend in previous years without significant price corrections. Thus, we'd need a larger sample size to safely conclude whether the price performance of January can actually forecast future price performance.
While the January month calendar effect might have worked in the past, it seems to have lost its predicting power to forecast the price performance for the whole of the year. In this article, we have tried to find whether this effect exists for stock sector ETFs. While we observe a high correlation coefficient for certain ETFs, we cannot make a general conclusion based on these results. Therefore, we would advise not to use the price performance in January as an indicator of future price performance.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.