Dominant Strategy

Background

In finance, there exists certain pricing anomalies that disappear as soon as they become known. For instance, stock price returns on Monday are expected to be lower than the rest of the week. The exact cause of these anomalies are difficult to find out, but likely have to do with investor behavior rather than theories of asset pricing. To read more about pricing, check out the Wikipedia page.

These anomalies can be valuable for investors because they allow for a market edge. In fact, if one identifies a pricing anomaly and keeps it to oneself, that person has generated alpha for their portfolio.

Living in Japan, I was amazed at the amount of work holidays there were. In 2019 alone, there were 25 public holidays (although 2019 was an anomaly itself, since an extremely rare 10 day holiday was held for Japan’s Emperor abdication). On these days, the banks are closed. The bank’s frequent holiday closure begs the question: is there an anomaly in the USD/Yen exchange rates on these days?

Hypothesis

My hypothesis is that there is an abnormality about exchange rates on Japanese public holiday dates. The reasoning behind this guess is that most days, Western markets react to news in Japan that happened while they were asleep. However, on holidays, the Japanese markets are the ones to react. To put in more concrete terms, I predict that the mean daily USD/Yen exchange rate on Japanese holidays is statistically different than the mean daily USD/Yen exchange rate on days before and after a holiday.

Methods

To go about testing my prediction, I first downloaded 30 years of historical USD/Yen data from FRED. Then, I used the holidays library in Python to download the past 30 years of Japanese holiday dates. Then, in a Jupyter notebook, I used Pandas and Matplotlab to manipulate and visualize the data.

To test my hypothesis, I conducted a two-sample t-test. My null hypotheses I wanted to test were: $\bar{x}_{holiday} = \bar{x}_{holiday-1}$ and $\bar{x}_{holiday} = \bar{x}_{holiday+1}$ . These tests were straightforward to run using the ttest_ind package from the SciPy library.

Results

Null Hypothesis	T-Test Statistic	P-Value
$\bar{x}_{holiday} = \bar{x}_{holiday+1}$	-0.571	0.568
$\bar{x}_{holiday} = \bar{x}_{holiday-1}$	-0.134	0.893

The first test yielded a t-test statistic of -0.57 and a p-value of 0.56. Therefore, the averages are not different at any significance level.

The second test yielded a t-test statistic of -0.134 and a p-value of 0.893. Similar to the previous test, the averages are not different at any significance level.

Conclusion

Taken together, these results suggest that there are no exchange rate anomalies during Japanese holidays. Indeed, the daily percent change of the USD/Yen exchange rate is no higher on a holiday than the day preceding or following a holiday.

Graph 1 illuminates this point well. Even at a cursory glance, it is apparent that the daily percent change in exchange rate is scattered randomly, instead of mostly falling below the 30 day moving average, or mostly above the 30 day moving average.

Pricing anomalies do exist, but this analysis shows that there is none on a Japanese holiday. When a holiday rolls around, traders are better off relaxing than trying to exploit a (nonexistent) exchange rate anomaly.

Links

Github:

I recently started a masters in finance degree at Washington University. While finance and economics share a lot in common, I’m starting to realize how different the types of people are in each field.

Economics is a research discipline. Things are changing slightly, but for the most part, economists primarily work for universities and research institutions, not Wall Street. Because of this, they tend to be more left-leaning and less worried about excessive government regulation and more worried about human “irrationality” and breakdowns of markets.

A classic example of this is when economists talk of externalities. When a market produces too little or too much of something, economics teaches that subsidies or taxes should be introduced bring markets closer to the socially optimal level of production. For example, too many people smoking cigarettes might led to higher insurance premiums for everyone, so a cigarette tax should be implemented to bring down the amount of cigarette smokers.

Finance types, on the other hand, don’t have much patience for government regulation and (in general) believe that markets (i.e. prices) are free of error. Because people who studied finance in college tend to go on to work for Wall Street, it’s pretty logical that they would have this opinion: Who wants to admit they’re bad at something? Isn’t it easier to blame someone else (i.e. government regulation)?

Today in one of my finance classes, as an example of how crowds tend to converge to be correct, the professor passed around a jar full of jelly beans. He claimed that the class average of all our guesses would be very close to the actual number, which shows that a large group of people are better at figuring out what’s best rather than one person. This exercise might work for a jar of jelly beans, but it has shortfalls in the real world.

For example, there might be a new financial instrument that very few people understand how to accurately price. If everyone is allowed to take their best shot at pricing this instrument, the “market” might get it terribly wrong. If there is incentive for financial companies to collude and price things higher than their true value, the price will not be the “true” price.

I’m not totally sure where I fall on the economic-finance spectrum. But, It’s always important to keep a nuanced view of the world, and I think economics does more to explain those nuances.