A deep dive into the January barometer
So as it's still a rather fresh 2011, and January so far is looking a little mixed, I thought it would be fun to do a deep dive into a calender effect called the January Barometer. Many people seem to confuse this with the January Effect, but they are different things.
The January barometer states that the direction (of the S&P 500 at least) during January can be viewed as an indicator for the following 11 months. So in other words, if the S&P 500 ends up this January, that would be an indication of a good coming year overall and vice versa.
There's no specific reasoning given for why it should be like this. One could theorize that there might be some form of structural break as we enter a new year with new budgets and everything. So say the general economy was heading up, then more would be invested, starting in January and then continuing.
But why just theorize? There's easily available data we could download and look at. And that's just what I did.
With Yahoo as my source I downloaded all their data on the S&P 500 index. That's data from the start of 1950 up till today. I feel it's important to check if the effect is equally reliable for both up and down moves as there's 43 positive years and only 18 negative. So ignoring the negative predictability would be rather stupid with such a positive bias. Then again I guess one could argue there's not enough data on the negative side, but it's all we've got.
Next step is to organize the data. I took the returns of each month and then grouped them into two columns: The first column contains January, with the second containing the compounded returns of the following 11 months. Looking at data from 1950 till 2010 that gave me the following results:
Positive predictability result: 70%
Negative predictability result: 73%
Overall predictability result: 71%
That doesn't look too bad. Both the positive and negative predictability power is even and at around 70%. 50% would be like flipping a coin, and anything consistent above 60% wouldn't be too bad.
But what about the other months? Could market trends be so slow in general that we would see similar results for the other months? According to the EMH there shouldn't be consistent arbitrage opportunities like these present. In other words, there shouldn't be any consistent structural breaks available for exploitation and I should be free to analyze the other months similarly.
By taking the data and shifting it one month forward (in addition to cutting it a year at the end to maintain balance), I performed the same analysis for the other 11 months. So, does February predict the returns for the next 11 months, etc.. The results are shown below.
There seems to be a clear reductional trend in predictive power as we go further out, with diverging behavior at the end. But besides from that last part, this is what I guess we could expect from the January barometer. January results contain predictive properties for both positive and negative directions while the other months show weaker or no predictive power.
But is this the complete story? I'm not so sure, as there's been people talking about a loss in this predictive power over time. So let's check the data ones more, but now by splitting it into two periods: One from 1950 till 1984, the other from 1985 till 2010.
It might not be a clear cut, but this is what I would conclude when comparing the latter part (1985 - 2010) to the first (1950 - 1984):
- The latter part shows far greater (random) variations over time, with the trend no longer being as clear
- The latter part shows far greater difference between positive and negativ predictive power over time
- Negative predictive power is at only 50% for January (no predictive power)
My portfolio performance
About three years ago I created a low frequency trading system. The only reason it's low frequency was due to market data access and available resources, as there's nothing much frequency specific about the strategies I've developed. I applied my strategies so that their performance could be compared to the OBX index, in other words the 25 most liquid stock on the Oslo Stock Exchange.
So it's been a few years now, and I think it's time to do a review of the performance. The strategies reviewed here have remained unchanged for this period, and the portfolio consists of in total 9 different stocks, all part of the OBX index. There are several distinct strategies, and based on back-test performance (three years ago) a stock/strategy combination was selected so that of the 9 distinct stocks my portfolio would consists of 20 separate stock/strategy combinations.
OBX performance
The benchmark we're comparing against is the OBX index, and the time period is roughly three years, in other words from the beginning of 2008 till now (almost end of 2010, 8. of December to be accurate). A buy-and-hold strategy would not have performed very well, ending at about the same value as we started on three years ago. Not an impressive performance by any means.
Individual stock/strategy performance
Below I've put in a graph showing each stock/strategy performance. All of the strategies are allowed to go both long and short, and some of them also stay out of the market if they so chose to do. Now remember that this performance is not based on over-fitted back-testing. This is based on signals given by the strategies that I could, and indeed did trade on. The signals (long/short/close) are given after market close, and acted on the next trading day at opening prices.
I wouldn't comment too much on each of them, other than saying the vast majority have given a profit over the given period. The Y-axis represents stock/strategy value with an investment of 1 NOK at T = 0, while the X-axis shows the number of days since T = 0.
Portfolio performance
There's quite a bit of work that I could have put into selecting the optimal portfolio, doing continuous rebalancing etc. But I made it simple by doing an equally weighted portfolio without rebalancing. The system focus has been on individual stock/strategy performance and not portfolio performance, and at the moment I have a few exams to prepare for as well.
So in the graph below I've plotted the portfolio performance against the OBX performance. No rebalancing is done at any moment, so each strategy starts out with 1/20th (since there's 20 stock/strategy combinations) of the total portfolio size at T = 0. In our example this is represented by a 1 NOK total investment. This could of course be any amount, but that's not the point.
The red dots represents the daily value of my portfolio, while the blue dots represents the OBX index. It's not a bad performance at all, with a fourfold increase compared to the initial investment.
Long only performance
Maybe it's unfair to compare a long/short strategy to a buy-and-hold strategy. So what happens if I limit all strategies to being long/close only. In other words, if they wish to go short, I change the signal to close instead, forcing it to go out of the market. This isn't optimal as all strategies are run and monitored as if they could go short.
Still very good, with almost a twofold increase compared to the initial investment.
Final details
Using an equally weighted portfolio probably isn't optimal, so if there was one thing to look at it would be the portfolio construction and rebalancing routines. Also, I should include the risk free rate for investments that are taken out of the market (close signal), but it wouldn't change my results too much.
None of the results for my portfolio presented here has used any form of leverage. For those of you curious about transaction costs I haven't done any analysis on this but can say that a total of 434 long signals, 408 short signals, and 415 close signals was given over this three year period. That should be about 2 trades each day for all the 20 stock/strategy combinations combined, if my calculations are correct.