# The January Barometer

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The January Barometer, sometimes also referred to as January effect (although the January effect is a different thing), is the theory according to which market performances during the month of January can be used to predict the trend for the rest of the year. The January Barometer theory is often summarized by the saying: “As goes January, so goes the year”. The practical implications are rather straightforward: if the asset class under examination has a positive return in January, the theory suggests that it will be a bullish year while a negative performance should predict a bearish trend for the upcoming months. The January Barometer theory is based on the assumption that many fund managers and institutional investors, particularly those who are interested in medium–to–long term investments, at the beginning of each new year, will tend to place their positions already discounting their view on the next 6 to 12 months. Consequentially, the theory suggests that, should the view be negative, portfolio managers will position themselves on the short side of the market at the beginning of January. Otherwise, should they think the price action will go up in the next 6 to 12 months, they will go long. Hence, the theory is based on the fact that the buying or selling pressure generated by the amount of money allocated in the market by big players in January should move the price in the direction of their forecasts. This is the theory but as Albert Einstein stated: “In theory, theory and practice are the same. In practice, they are not”. The present **HyperVolatility** research aims to investigate and study the reliability of the January Barometer theory in order to assess, under a probabilistic point of view, what are the chances to actually earn consistent profits if applied to financial markets. The research has been carried out on major equity indices in the world that have been, in turn, subdivided for geographical location: North America (S&P500, NASDAQ Composite), Europe (DAX30, FTSE100), AustralAsia (Nikkei225, ASX200) and Emerging Markets (Hang Seng, Bovespa, BSE Sensex). The dataset that has been used consists of index prices ranging from January 2004 until December 2014 implying 11 years worth of data. The back testing analyses that will be exposed and commented are simply aimed to understand if January’s market returns matched, if not in size at least in sign, with yearly ones. Hence, the combination negative return in January/negative yearly return as well as the combination positive return in January/positive yearly return will all be calculated as success cases. On the other hand, all non–matching returns will be treated as a failure of the theory. The results of the analyses will be presented and explained by geographical location, hence, the first equity indices that will be analyzed are the North American ones:

The above reported table shows, in the first column, January’s lognormal returns calculated for the S&P500 while the second one lists the total return for each year. The right hand side of the table provides the same calculations but based on the NASDAQ Composite index. First of all, it is worth noting that, over the last 11 years, the average return for the S&P500 index has been +4.89% while the NASDAQ Composite yielded an average of +6.78%. Secondly, both equity indices have experienced similar fluctuations during the crises but the NASDAQ has consistently outperformed the S&P500 since the 2012 so far. The following chart plots the success/failure rates of the January Barometer theory applied to the aforementioned asset classes over the last 11 years:

The success rate for both S&P500 and NASDAQ Composite is 54.5% while the failure rate is 45.5%. These numbers simply mean that the JB theory held true for 6 years but it unsuccessfully predicted yearly returns in 5 occasions. Nevertheless, the 54.5% success rate definitely does not fall within the category “reliable strategies” but different results could potentially come from different markets. The next table presents the results of our analyses on the German DAX30 and the British FTSE100:

The tables evidently show that during the financial crises in 2008, the selected European markets performed as poorly as their American counterparties (although the British FTSE100 did not violate the –40% threshold). The last 3 years have gone quite well as far as returns are concerned although in the 2014 the British FTSE100 (–2.62%) underperformed the DAX30 (+3.06%). On average, the German index, over the last 11 years, has yielded a +7.26% return while long term investors in the British market managed to gain only +2.34%. The next graph plots the success/failure rates of the January Barometer theory applied on the aforementioned European asset classes:

The results are very different from the ones observed for the overseas equity indices. First of all, the JB theory applied to the DAX30 has higher failures (54.5%) than successes (45.5%). In particular, the January Barometer strategy has successfully predicted future returns 5 times but it has failed 6 times implying that even in this market it led to poor results. On the other hand, the JB applied to the British FTSE100 shows a 63.6% winning chance while the losing probability is only 36.4%. In this case, the returns registered in the first month of the year since 2004 proved to be good forecasters for yearly returns. Numerically, the JB strategy would have been profitable in 7 cases while it would have failed only in 4 occasions. The next table lists the historical returns for the AustralAsian region:

The equity indices that have been used as a proxy for the AustralAsian geographic region are the Japanese Nikkei225 and the Australian ASX200. The 2008 was a very negative year also in AustrlAsia, in fact, both yearly returns are very close to the –50% threshold. However, also the European credit crisis in 2011 has dramatically influenced the aforementioned indices: –20.58% for the Nikkei225 and –13.16% for the ASX200. The data for the average returns, calculated over the last 11 years, show that the Nikkei225 yielded a +2.89% while the Australian equity index returned an average of +3.57% to potential long term investors. Did the January Barometer strategy provide any extra gains? The next chart will attempt to answer this question:

The Japanese index displays the usual 54.5% / 45.5% split between success and failure rates implying that the JB strategy proved profitable only 6 times and failed in 5 cases. Conversely, the success rate for the ASX200 is 63.6% (7 successes) while the failure rate is only 36.4% (4 failures) implying that the JB strategy was definitely more profitable when applied to the Australian index than to its Japanese equivalent. The last table of the present research focuses on the past performances of a geo–economic, rather than merely geographic, area: emerging markets.

The equity indices that have been selected for this section are the Hang Seng (Hong Kong), the Bovespa (Brazil) and the BSE Sensex (India). The first thing to notice is that during the crisis the losses incurred by these markets have been larger than the ones observed for the indices we have mentioned so far. In fact during the credit crunch, with the exclusion of the Brazilian Bovespa that in 2008 had a negative return of “only” 46.25%, the Hang Seng yielded a –55.43% while the Indian return for the same year was a remarkable –70.64%. However, the scenario considerably changes if the 2011–2014 time interval is taken into account. In fact, the Indian equity index, even including the drop in the 2011, has consistently outperformed the other two. As far as long term average returns are concerned, since the 2004 so far, the Hong Kong’s Hang Seng yielded a +5.43%, the Brazilian Bovespa returned a +5.81% while the Indian BSE Sensex averaged an impressive +13.68%. Emerging markets average returns are clearly very high but the BSE managed to outperform all other equity indices considered in the present research (the German DAX30 ranks second with a +7.26% and it is immediately followed by the NASDAQ Composite index that yielded an average return of +6.78%). The next chart plots the success/failure rates obtained by running the January Barometer strategy on the Hang Seng, Bovespa and BSE Sensex:

The success rates for the BSE Sensex (54.5%) and Bovespa (45.5%) do not really seem to provide a hedge (although the strategy proved more profitable on the Indian equity index than on the Brazilian one). The most significant information that can be extracted from this analysis is the high failure rate on the Hong Kong’s index (63.6%). In particular, the January Barometer strategy has consistently failed to predict yearly returns on the Hang Seng index for 7 years while it proved successful only in 4 cases.

All in all, the analyses just conducted on the 9 equity indices considered in the study seems to point out that the JB strategy does not to provide any particular hedge for investors. Nevertheless while wrangling data some interesting patterns have emerged. The patterns that have been detected can be grouped in two categories: the winning pattern and the losing pattern.

*Winning Pattern*

The January Barometer strategy can only yield two mutually exclusive outcomes: it is either profitable or not (there is a chance that the yearly return could eventually be 0% but the associated probability, over 252 trading days, is so low that we have voluntarily excluded it from the scenario analysis). The winning pattern has been extracted by simply running a frequency analysis on the times the JB strategy proved to be profitable. The JB strategy basically states that January’s return should match the yearly one, however, it makes no difference between negative or positive returns. Consequentially, as stated at the beginning of the research, a positive January’s return in a positive year would be counted as a success but a negative January’s return in a negative year would be also counted as a success. The most important thing for the strategy to work is a match between returns. The questions this section is trying to give an answer to are: is there a pattern among success cases? Do success cases have anything in common? Do bullish success cases outnumber bearish ones or vice versa? Before showing the results, it is worth reminding that 11 years worth of data on 9 different asset classes have been filtered implying a total of 99 observations. 52 out of 99 observations fall into the winning pattern category while the remaining 47 are axiomatically assigned into the losing pattern one. The following pie chart attempts to summarize the results obtained from data mining the data associated to the first pattern:

The success cases, for the combination January’s positive return in a positive yielding year (green area called BULL), are 34 while the success cases, for the combination January’s negative return in a bearish year (red area called BEAR), are only 18. Clearly, the outcome of this frequency analysis is a consequence of the overall trend in each year but it seems that the JB strategy, when profitable, works best in positive performing years rather than in negative ones (winning pattern). In order to understand why this is the cases we have to proceed and mine the data for failure cases.

*Losing Pattern*

In the previous section it has been observed that, numerically speaking, the majority of success cases happened during the combination January’s positive return in a positive yielding year. It has also been stated that the JB strategy “works best in positive performing years rather than in negative ones”. However, in order to understand why this is case, the present section will analyze all failure cases. If the JB strategy did not work, it axiomatically implies that January’s return did not match the sign of the yearly one. Consequentially, there are only two scenarios to consider when analyzing failure cases: the return in January was positive but the yearly one turned out to be negative or the year started by yielding a negative return in January but it ended with a positive performance. The next pie chart plots all the failure cases and groups them into two categories: BEAR TO BULL (negative return in January but positive on a yearly basis) and BULL TO BEAR (positive return in January but negative on a yearly basis):

The above reported chart is evidently displaying that, among failures, there is a very frequent pattern: the vast majority of failures in the JB strategy are due to years starting out with a negative return but that subsequently yield a positive one (losing pattern). Numerically speaking, the BEAR TO BULL cases counted 39 observations while the BULL TO BEAR ones are only 8.

*Conclusion*

- The January Barometer strategy does not seem to provide consistent profits at least for the considered asset classes and the selected time frame
- The January Barometer strategy successfully predicted yearly returns in 52 cases (52.5% of the total observations)
- 34 of the 52 success cases come from the combination January’s positive return in a positive yielding year while only 18 come from the combination January’s negative return in a negative yielding year
- The January Barometer strategy failed to predict yearly returns in 47 cases (47.5% of the total observations)
- 39 of the 47 failure cases fall within the BEAR TO BULL category where January yielded a negative return but the year ended up with a positive performance
- Only 8 failure cases fall within the BULL TO BEAR category where January yielded a positive return but the year ended up with a negative performance

The present research can be expanded in many ways. In fact, potential research developments could come from increasing the datasets in order to allow 20 or 30 years worth of observations, from including more equity indices or by expanding the analysis to different types of asset classes such as treasury bonds, commodities and currencies.

Here are other quantitative researches you may find interesting:

**“Commodities and Currencies: Inter-Market Analysis”**

**“Commodity Volatility Indices: OVX and GVZ”**

**“Trading Gold & Silver: A Realized Volatility Approach”**

Visit **HyperVolatility** for more quant researches

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