Quant Research

Is seeing believing? Pairs trading on International ETFs

I recently came across a paper titled “Pairs Trading on International ETFs” on quantpedia.com. A Sharpe Ratio of 1.66 and an “indicative performance” of 20.6%, as reported by quantpedia, seemed amazing. So, I decided to explore the strategy and accompanying research paper myself.


[Wow, I’ve never seen such a good looking equity curve before… except maybe Madoff’s… ]

The basics of the strategy are simple to understand. The research paper uses a metric that gauges the historical average distance between cumulative returns of two ETFs, and picks pairs that have the highest historical average distance to trade. Once the current “return difference” of two ETFs exceeds the pair’s historical average distance by a certain threshold, we enter the pair trade and exit in 20 days or when the pair converges, which ever comes first. 22 country ETFs are used, including countries like Italy, Germany, Australia, and Canada.

The biggest concern I had when reading the paper was that it just assumes convergence for country ETFs–there is no measure of price co-movement between a pair of country ETFs. The only way it “measures” past price movement between a pair is with the historical average described above. Especially with the recent events in Europe, assuming that a pair with a high historical average distance will converge in the near future seems dangerous. 

A backtester (using Python and the immensely helpful pandas library) coded with the entry and exit conditions specified in the paper produced very different results: a 1.5% CAGR, with a  -20% max drawdown. Granted I used data from June 2001 to today, and the paper used data from April 1996 (before some of the 22 ETFs in the basket even existed…) through 2009, but the vastly different slopes in the equity curves speak for themselves.


[What happened to the pair trading strategy’s “returns”?]

Something seems off: maybe there’s a bug in my code (in the process of checking). Nonetheless, through this exercise, I’ve learned that verifying a strategy’s returns for yourself is always a wise idea.

Happy new year by the way. Remember this, whenever you’re starting something new:


Quant Research

Looking at the turn of the month effect in equity indices

After reading papers about the consistency of the turn of the month effect (Lakonishok and Schmidt (1988), Xu and McConnell (2006)) and hearing about its success from several trader friends (it’s been covered several times in the blogosphere too, e.g. at MarketSci) I decided to explore it a bit.

Basically, returns at the turn of every month (last day of current month, through the 3rd day of the next month) are positive and significantly different from market returns.

Below is a PDF detailing my findings/research replication, tables and graphs included. All the analysis was done during my free time over the summer and I just got around to writing it up. I decided to do the writeup in Latex because I think Latex documents are pretty. LyX, the latex wysiwyg processor, was used.

To test the profitability of the turn of the month effect in real time, four months ago I started trading a strategy that I developed based on the effect. The strategy enters two new positions at the beginning of every month and gets out a few days later. I have only entered six positions so far (five have been profitable), two are missing because I decided not to enter any positions at the end of July due to the debt ceiling debate (good thing too because both positions would’ve been losers).

Here’s the writeup:

An exploration: the turn of the month effect in equities from 1926 through 2010

Quant Research

ETFRot performance update

I posted backtested performance figures for an ETF rotational trading system back in November 2010. Here’s the forward tested performance (orange representing the start of the forward testing period):


Ouch. For a refresher on how the system works, see the old post. It looks like momentum failed to anticipate the precipitous drop in the markets during August 2011. Which makes sense, as the market truly seemed to be in panic mode during that time.

Lesson: don’t put all your eggs in one basket (in this case, in one ETF), no matter how good you think your “timing” is. Because anything can happen that throws your model out the window. 

August 2011 was just another example of asset class correlations all going towards 1 during a panic selloff; historical correlations were not a good predictor of future correlations. Then I wonder if historical correlations conditional on whether the market is in crisis mode or not is predictive (eg if these two asset classes weren’t correlated in the past during market crises, does that mean that they have a good chance of remaining uncorrelated in future market crises?). Perhaps in “most cases”, but then again, if you have a chance of being completely wrong and losing all your money, do “most cases” even matter? 

So the search for uncorrelated returns continues… reminds me of AQR’s new reinsurance group.


Quant Research

Accruals anomaly

Troy Shu Accrual Presentation  

This was my final presentation on the accrual anomaly.

At the very basics, remember from accounting 101 that earnings = cash flow from operations + accruals (derived from the indirect method of calculating cash flow from ops). For example, you make a sale, which goes to revenue and earnings, the left side of the equation. For the RHS: that sale could’ve been paid in cash, in which it counts CFO; if it was made on account, it counts as an accounts receivable, an accrual. Essentially, accruals are a measure of earnings quality.

Intuitively, an investor would want to invest in a company with relatively low accruals, which means that the company generates lots of cash to pay expenses, to use to invest, etc. There are countless papers that have shown that trading a portfolio that goes long the stocks with the lowest accruals and shorts the stocks with the highest accruals is profitable, even after size and value adjusting returns. 

Rough outline of presentation:

  • What is an accrual: earnings = CFO + accrual
  • How it’s measured: usually signal = accrual/average assets
  • Three papers: trading a hedged portfolio based on accruals produces roughly 10% size and value adjusted annual returns. 
  • Improvement: percent accruals (signal=accrual/abs(earnings)) seems to be a more profitable measure of accruals
  • My replication: despite data and time constraints, ordinality of returns by deciles is still present
  • Interesting topics for future research: the death of the accrual anomaly (or what happened to performance post 2007?), scaling quarterly accruals by earnings instead of assets
Quant Research

Upcoming posts

Back at school. Upcoming posts: more turn of month effect, accruals, and recent performance of ETFRot. What I’ve learned: Cliff Asness of AQR, and I’m sure many others, are right, “diversification is the only free lunch in finance”. Diversification across asset classes, and across time as well? More to come…

Quant Research

Socially generated financial data the next big thing?


A ton of articles/papers released recently on this topic:

Social media seems like the best way to gauge sentiment without actually asking the person. Bloomberg has already included Twitter feeds on their platform, Yahoo Finance has StockTwits feeds integrated on their stock pages. StockTwits hasn’t even released their API yet. With the rise of social media, will we see the rise of socially generated financial data?

All the above algorithms that trade Twitter sentiment are very short term. This could be due to the fleeting nature of Twitter posts, thus relevant financial tweets are dominated by those from short term traders with short term outlooks. Social media gauges short term sentiment, but its hard to tell whether it can gauge what I call longer term “behavioral biases” such as price momentum (“let’s jump on the bandwagon”) and post earnings announcement drift (people being slow to realize and price in the better prospects suggested by a positive earnings surprise).