Trade Frequencies, Part 2

One of the main challenges in high-frequency finance is the irregular arrival of prices. Once you get down to the tick by tick prices, you can't count on normal interval time series. There are autoregressive models which support irregular arrival of prices. Engle and Russell's autoregressive conditional duration (ACD) model comes to mind (described in Econometric Modeling of Multivariate Irregularly-Spaced High-Frequency Data which extends the Engle-Russell Autoregressive Conditional Duration (ACD) model for the multivariate case).

Net Worth, Income, and Media

In New York, everywhere you look is CNBC and Bloomberg. Both tout how their viewership has high net worth and income. With all the captive audiences on trading floors around the world, that isn't a far-fetched claim. For the most part, though, all I ever see on those two channels were GEICO commercials. The interesting part is that as a percentage of viewership, seekingalpha.com does better than both CNBC and Bloomberg at attracting people with household incomes 100k+ (44% versus 40% and 38% respectively). One can do better than that if you can convince kitcometals.com, Neiman-Marcus, or ThinkOrSwim.com to let you advertise on their sites which attract 56%, 41%, and 52% respectively of 100k+ household income viewership as a percentage of total viewership.

I would have included MoneyScience.com in this comparison but the data source I used was quite US-centric.

Trade Frequencies, Part 1

Typically what people refer to as high-frequency trading occurs on the stock markets. However, firms have also branched out to a whole assortment of other markets in attempt to reap the alpha from high-frequency strategies. Moreover, data such as futures prices sometimes serve as a signal for equity trading. Different kinds of markets, however, have considerably different characteristics. Moreover, the volume and trade frequency varies among each individual security. On one side, you have the SPY ETF which is easily among the most actively traded securities in the stock market with daily volumes in the hundreds of millions. During August 2011, SPY's volume even spiked to as much as 717 million, though 100-200 million is more typical. To put that into perspective, the total daily volume of the NYSE is about a couple billion in recent times. In contrast, some securities are barely traded at all.

More on all this after the jump...

How long does it take to compile a web browser, Part 1

In today's brave new world, people can build and deploy a web app very quickly. However, on the other side of the equation, systems software such as web browsers are not getting any simpler. The cost of the rich media experience in modern web apps quite frequently borne by systems software including browsers, servers, and plugins. Just eyeballing, in Chrome (default multi-process mode), each tab take 30-100 MB of RAM. Luckily, I am not one to keep many tabs open concurrently, but many, many people do. A rich web experience can quickly gobble up whatever amount of memory you can throw at it. There are many people who have compared memory usage for Chrome, Firefox, Safari, and Internet Explorer (TechPP, CNet, Tom's Hardware, DotNetPerls, and many more I am sure), but, here, I would like to take a look at another facet of web browser complexity: source code size, time to build, and size of debug executable. This post follows and expands upon the type of study I did on open source finance software.

Share Buybacks, Part 4

Taking a sampling of the 41 PKW Buyback Achievers I could find XBRL annual reports on, there are a number of interesting points. The mean buyback rate for 2010 was 7.3% of common stock shares outstanding (DEI). Given this data point, IBM's rate of buyback seems only a middling one. I would caution generalization from a single year's worth of data, but it does provide a little perspective. A particular high or low buyback rate does not mean much by itself. One has to consider where the money for the buybacks is coming from and how is the rest of the business doing.

Top 5 Open Source Quant Finance Projects

In the recent months, I have found the data on open source development projects to be quite fascinating. It turns out that most of the activity in open source quant finance code happens on Sourceforge. I pulled some of that data and charted them.

Correlations

Looks like the stock market broke down a bit and is hanging outside the previous trading range. I put together a script to calculate correlation coefficients to see where things stand.

	spy	sdy	tlt	zroz	uup	gld	xle	xlf
spy	1.00	0.93	-0.68	-0.64	-0.34	-0.57	0.95	0.94
sdy	0.93	1.00	-0.41	-0.36	-0.15	-0.43	0.83	0.77
tlt	-0.68	-0.41	1.00	1.00	0.69	0.51	-0.77	-0.87
zroz	-0.64	-0.36	1.00	1.00	0.72	0.45	-0.75	-0.84
uup	-0.34	-0.15	0.69	0.72	1.00	-0.08	-0.53	-0.51
gld	-0.57	-0.43	0.51	0.45	-0.08	1.00	-0.42	-0.61
xle	0.95	0.83	-0.77	-0.75	-0.53	-0.42	1.00	0.95
xlf	0.94	0.77	-0.87	-0.84	-0.51	-0.61	0.95	1.00

The correlation coefficients are computed for the past 100-days of returns calculated from adjusted closing prices for each asset. There is a little something for everyone here. Equities (SPY) and dividend-paying equities (SDY) have strong correlations. Despite appreciating crude prices, energy stocks (XLE) are still strongly tied with equities as a whole. To get negative correlation with equities, one may look to treasuries (TLT and ZROZ [Pimco Long Dated Zeros]), the dollar, and gold. TLT and ZROZ similar but in terms of correlations with other assets, it happens to be TLT that exhibits the slightly more pronounced negative correlations despite ZROZ being more of a pure play. Surprisingly, the least correlated assets in this study are gold (GLD) and dollar (UUP).

Cash Secured Puts

Cash secured put writing is a venerable strategy where an investor sells puts while simultaneously keeping enough cash for the duration of the put contracts to cover possible exercise. Note that although puts tend to be exercised upon expiration, there is a significant probability of early exercise. This strategy is typically considered a conservative strategy, in the realm of options strategies at least, because an investor will not have to fork over any additional money to cover margin calls so max loss is capped at the exercise value of the put minus premium from the put writing. Typically, this strategy is utilized when an investor expects the underlying instrument to stay largely flat (i.e., appreciate or depreciate no more than the premium generated from writing the put contract). With an options qualified brokerage account, an investor can easily execute his own cash secured put writing strategy. Some ETF managers (e.g., WisdomTree), however, have also introduced the strategy as executed on broad equity indices in the form of an ETF.

Seeing that this morning was a benign CPI day with no earth-moving news from Europe, I thought that it would be an interesting exercise to look at a volatility-selling options strategy. The strategy is a fairly conservative one, cash secured put writing. My sample is the 341 component subset of the S&P 500 with options activity today (i.e., there are bids and asks). I will be only looking at the nearest to the money strikes to the last traded price of the underlying. The puts expire in less than 3 days (Friday, 11/19/11) so there really is not much time premium left. The returns are computed as put premium (midpoint of the bid and ask) over the strike times the 100 multiplier (i.e., the amount of cash needed in case of assignment). The VIX today (the last time I looked) remains elevated over 30.

The above is a histogram of the cash secured put returns. The horizontal axis marks the returns (put premium/ (100*strike)) and the vertical the number of securities that have that return. The median, mean, and standard deviation for the returns are 0.000675, 0.00101, and 0.00111 respectively. The max is 0.01 for TSS.