Another one fresh off the pre-printing press at arXiv. Having skimmed the paper, this looks like a serious treatment of a very serious problem – reconstructing the coefficient on the correlation term of models when returns are sampled asynchronously, as is almost always the case when using tick data.  On a related note, Section 2 is the best presentation of the Epps effect in this context I’ve seen.

Abstract: A detailed analysis of correlation between stock returns at high frequency is compared with simple models of random walks. We focus in particular on the dependence of correlations on time scales – the so-called Epps e ect. This provides a characterization of stochastic models of stock price returns which is appropriate at very high frequency.

I. Mastromatteo, M. Marsili, P. Zoi. Financial correlations at ultra-high frequency: theoretical models and empirical estimation. arXiv:1011.1011

I’m going to assume that you’ve heard that the number is $600B in expansion, putting the total amount of purchases including reinvestment at just shy of $1T. Here are some excerpts from the official statement that are more interesting, as well as my emphasis added in bold:

Purchases associated with balance sheet expansion and those associated with principal reinvestments will be consolidated into one set of operations to be announced under the current monthly cycle. On or around the eighth business day of each month, the Desk will publish a tentative schedule of purchase operations expected to take place through the middle of the following month, as well as the anticipated total amount of purchases to be conducted over that period. The schedule will include a list of operation dates, settlement dates, security types to be purchased (nominal coupons or TIPS), the maturity date range of eligible issues, and an expected range for the size of each operation.

The Desk expects to conduct the November 4 and November 8 purchase operations that were announced on October 13, and it plans to publish its first consolidated monthly schedule on November 10 at 2:00 p.m.

Purchases will be conducted with the Federal Reserve’s primary dealers through a series of competitive auctions operated through the Desk’s FedTrade system. Consistent with current practices, the results of each operation will be published on the Federal Reserve Bank of New York’s website shortly after each purchase operation has concluded. In order to ensure the transparency of our purchase operations, the Desk will also begin to publish information on the prices paid in individual operations at the end of each monthly calendar period, coinciding with the release of the next period’s schedule.

Note that this means that much more out-of-sample prediction may be possible in the future for POMO, both due to better prospective data release and higher detail in released training data.

Looks like Didier Sornette has a new pre-print out on the arXiv. I’ve only had a minute or two to scan the paper, but it looks like they’ve slightly modified their JLS model to fit to the repo market to measure the “bubblieness” of leverage. They claim this allows them to some successful prediction, and make sure the reader connects this to the recent chatter at the Reserve and in Dodd-Frank on “detecting” bubbles or crises.

Abstract: Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) market size is a very important element in calculating the overall leverage in a financial market. Therefore, studying the behavior of repos market size can help to understand a process that can contribute to the birth of a financial crisis. We hypothesize that herding behavior among large investors led to massive over-leveraging through the use of repos, resulting in a bubble (built up over the previous years) and subsequent crash in this market in early 2008. We use the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles and behavioral finance to study the dynamics of the repo market that led to the crash. The JLS model qualifies a bubble by the presence of characteristic patterns in the price dynamics, called log-periodic power law (LPPL) behavior. We show that there was significant LPPL behavior in the market before that crash and that the predicted range of times predicted by the model for the end of the bubble is consistent with the observations.

Citation: W. Yan, R. Woodard, D. Sornette. Leverage Bubble. arXiv:1011.0458.

I also noticed that two of the EPS figures didn’t make it through arXiv’s compilation, so I’ve uploaded them here.

I’m attaching a copy of the bargaining platform that was circulated by the leaders of the University of Michigan graduate student union. I don’t want to go into too much detail on my opinion of my fellow doctoral students, but I’d like to highlight how out of touch with reality these bargaining demands are in light of the current Michigan labor market:

  • Full child-care subsidy.
  • 3%, 3%, and 6% year-on-year wage increases for 2010, 2011, 2012.
  • No cap on mental health care visits.
  • Two pairs of glasses per year.
  • Prevent student instructors from being removed for lack of English language proficiency.
  • 401K with employer matching.

In case you didn’t know, graduate student instructors and research assistants (myself included) already get the following compensation:

  • Full tuition waivers, which are worth between $20K (in-state) to $40K (out-of-state) after-tax per year.
  • Monthly stipends ranging from $1000 to $2000 per month, some of which are tax-free.
  • Healthcare benefits that exceed average private sector benefits.
  • Access to a wide range of other University services.

That’s right, the demands above are in addition to this compensation that we already receive.

Go ahead and read the document itself below.  Make sure to soak these demands in while looking at the unemployment rate and per-capita income for Michigan.


Since I’m sick of hearing ZeroHege purposefully misstating the empirical relationship between POMO and the equity market, I decided to put up this little figure below. This figure demonstrates the performance of the S&P 500 (SPY) in solid black compared to two POMO strategies in dashed black and red (close-close and open-close, respectively).

Note that only holding the market on POMO days has not returned more than the buy-and-hold S&P 500 strategy year-to-date. The S&P 500 has returned 3.62% YTD (close-close, not including dividend, which puts the buy-hold strategy even further ahead), whereas the open-close and close-close strategies have returned -2.63 and 0.79% respectively. These strategies do not even outperform the S&P 500 on a risk-adjusted basis (Sharpe). Furthermore, none of the regressions that were significant (p=0.05) in the 2005-2010 dataset are significant (p=0.1) in the 10 months through this year. In other words, though a relationship between the accepted-submitted proportion and return magnitude exists in the dataset as a whole, this relationship appears to have disappeared on the daily timescale.  Sorry, Tyler(s).

Floppy disk jokes are always funny, especially when they’re about 5.25″s.  This ad also happens to be one of the first photographs of robo-finance.


I saw that this paper on SSRN, High Frequency Trading and its Impact on Market Quality, was updated a week or so ago (courtesy of Alea) and I thought it would be worth posting.   The abstract of the paper below:

This paper examines the impact of high frequency trading (HFT) on the U.S. equities market. I analyze a unique dataset to study the strategies utilized by high frequency traders (HFTs), their profitability, and their relationship with characteristics of the overall market, including liquidity, price discovery, and volatility. The 26 high frequency trading firms in the dataset participate in 73.7\% of all trades. I find the following key results: (1) HFTs tend to follow a price reversal strategy driven by order imbalances, (2) HFTs earn gross trading profits of approximately \$2.8 billion annually, (3) HFTs do not seem to systematically engage in a non-HFTr anticipatory trading strategy, (4) HFTs’ strategies are more correlated with each other than are non-HFTs’, (5) HFTs’ trading level changes only moderately as volatility increases, (6) HFTs add substantially to the price discovery process, (7) HFTs provide the best bid and offer quotes for a significant portion of the trading day and do so strategically so as to avoid informed traders, but provide only one-fourth of the book depth as do non-HFTs, and (8) HFTs may dampen intraday volatility. These findings suggest that HFTs’ activities are not detrimental to non-HFTs and that HFT tends to improve market quality.

J. Brogaard. High Frequency Trading and its Impact on Market Quality. Available at SSRN.

My gut instinct is that there are a few issues with the paper:

  • As Jonathan acknowledges, he does not address order book dynamics or, more importantly, other identified malicious HFT practices (e.g., stuffing).  Since this is a job paper and therefore has to be approachable and “bite-sized,” this in itself is fine.  However, the normative claim that “HFTs are not bad” may be too strong in the absence of this analysis.
  • Without a better idea of where HFTs trade, it is hard to extrapolate from the sample of 120 stocks.  The exact selection method is not explicit in the paper, but it seems that the stocks were chosen from the NASDAQ to include a range of market caps.  Without a better idea of where HFTs trade, it is quite possible that this selection undersamples the stocks that most HFT transactions occur on.  In the absence of industry-wide data, this is a necessary research choice, but its limiting nature on the conclusions should be better stated.
  • On a somewhat related note, the estimation of profitability and risk-adjusted return is quite daring given the above two constraints.

Despite these few notes, the paper should definitely get Jonathan a great job and I’m interested to see more work on his dataset.  He’s also got a great advisor, Thomas Brennan, who I met at the Midwest Law & Economics Conference.  Thomas has more industry experience than 90% of all academics and his papers also make for great reads (some on SSRN, most in law reviews or finance journals).

Since October has apparently been National Bash “Nobelist” Paul Krugman Month and I only have one more day left to get in on the action, here are my two cents on his  column today, Accounting Identities.

OK, so here’s the bit:

To avoid all this, we’d need policies to encourage more spending. Fiscal stimulus on the part of financially strong governments would do it; quantitative easing can help, but only to the extent that it encourages spending by the financially sound, and it’s a little unclear what the process there is supposed to be.

Oh, and widespread debt forgiveness (or inflating away some of the debt) would solve the problem.

But what we actually have is a climate in which it’s considered sensible to demand fiscal austerity from everyone; to reject unconventional monetary policy as unsound; and of course to denounce any help for debtors as morally reprehensible. So we’re in a world in which Very Serious People demand that debtors spend less than their income, but that nobody else spend more than their income.

My understanding of this passage is that Krugman is arguing that we probably can’t avoid fundamental national accounting identities with austerity (UK) or indirect measures (QE1/2).  However, his flippant suggestion in paragraph two above is that forgiving  debt (of consumer debtors, I assume) would solve the problem by freeing up these actors to spend.  Many countries around the world, ours included, have demonstrated that property rights and contract enforcement are sometimes “flexible” in times of crisis.  Debt forgiveness, or a debt-to-equity swap more generally, can be reasonable tools if the rules for these credit events are determined a priori in a way that creditors can model.

However, for someone like Krugman to suggest widespread debt forgiveness as an ex-post government policy seems like an incredible affront to property rights and contract enforcement, the two basic legal principles that have been empirically demonstrated to produce real growth around the world (on both sides of the autocratic/democratic scale, by the way).   Krugman may have named his column “the conscience of a liberal,” but I’d love to see what he thinks wealth demographics would look like after consumer credit markets disappear.