Link: C.L. Peterson. “Two Faces: Demystifying the Mortgage Electronic Registration System’s Land Title Theory”

For anyone interested in the legal background on MERS, the Mortgage Electronic Registration System, I strongly recommend this very accessible law review by Christopher Peterson.  There are two take-aways that I will emphasize.  First, a substantial body of case law stands behind the principle that  ”[a] note and mortgage are inseparable…, the assignment of the note carries with it the mortgage, while an assignment of the latter alone is a nullity” (id, pg. 7).  Second, municipalities may have ground to claim reimbursement for unpaid mortgage assignments over the past 15 years.  These unpaid county recorder fees may also constitute tax fraud, and penalties and interest may therefore apply to the total amounts.

Below is the abstract and citation:

Hundreds of thousands of home foreclosure lawsuits have focused judicial scrutiny on the Mortgage Electronic Registration System (“MERS”). This Article updates and expands upon an earlier piece by exploring the implications of state Supreme Court decisions holding that MERS is not a mortgagee in security agreements that list it as such. In particular this Article looks at: (1) the consequences on land title records of recording mortgages in the name of a purported mortgagee that is not actually mortgagee as a matter of law; (2) whether a security agreement that fails to name an actual mortgagee can successfully convey a property interest; and (3) whether county governments may be entitled to reimbursement of recording fees avoided through the use of false statements associated with the MERS system. This Article concludes with a discussion of steps needed to rebuild trustworthy real property ownership records.

Peterson, Christopher Lewis, Two Faces: Demystifying the Mortgage Electronic Registration System’s Land Title Theory (September 19, 2010). Real Property, Probate and Trust Law Journal, Forthcoming. Available at SSRN.

Though the article does take a normative (rather than objective) stance on some points, the article text provides an excellent summary of issues with MERS and its footnotes list many additional sources of information.

October 24, 2010 | | Tags: | No Comments »

Best and Worst Performing ETFs, Week of Oct. 18, 2010

Another week in the black for the market, though only barely for most indices. The list of big movers this week is an interesting one.

In the green, we have exchange-traded funds for cotton (BAL), 2x inverse Brazil (BZQ), coffee (JO), 3x real estate (DRN), and 2x inverse silver (ZSL).  Interestingly, cotton, Brazil, coffee, and silver are all commodity or export-based assets that are strongly affected by the dollar.  However, the direction of their movement varied (remember, ZSL is inverse silver).

The red side of the table features 3x inverse real estate (DRV), 2x BRICs (BRIL), 2x Brazil (UBR), natural gas (GAZ), and short-term VIX (VXX).  DRV, BRIL, and UBR all correspond to funds on the green side of the table.  However, the natural gas ETF (GAZ) and VIX ETF (VXX) do not have corresponding products in the green.

VXX especially has been labeled a “bad” product (see agwarner’s latest in his ongoing crusade against VXX).  I would tend to agree with Adam, as a number of VXX calls I recently held expired OTM despite what would have been a profitable trade on the underlying futures.

Symbol Return $ Volume
BAL 9.7% 15M
BZQ 9.4% 12M
JO 6.3% 7M
DRN 6.3% 284M
ZSL 6.3% 86M
DRV -7.5% 140M
BRIL -7.6% 1M
UBR -10.3% 4M
GAZ -11.2% 7M
VXX -12.7% 2.6B

Since we’re in for a few weeks driven by domestic data and possible market exogeneities (e.g., G-20, France, U.S. politics), look for these ETFs to continue their dramatic moves.

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Animation: Holders of Treasury Securities, 2004-2010

Around a year ago, Dan and I put up an animation of the major foreign holders of Treasury securities from 2002 to 2009 at my other blog, Computational Legal Studies.  At the time, the conversation was driven by China surpassing Japan as the largest foreign holder.

Since then, there’s been quite a bit of speculation as to when the Federal Reserve would surpass these largest foreign holders.  The Fed has been acquiring these securities through its various Open Market Operations (OMO).  However, I think focusing on just this Fed-vs.-China benchmark may be a bit misleading.

The animation below shows the proportion of Treasury securities held by the Federal Reserve, Japan, China, and all other foreign holders of Treasury securities between 2004 and 2010.  The Federal Reserve holdings are based on the second column of the Fed’s latest H.4.1 and are current up to October 21st.  The Treasury’s TIC data (historical here) is significantly lagged, however, and only current as of the end of August.  Therefore, I’ve held the values constant from August for foreign holders, though the Fed’s slice does change based on real data.

I’ll let the video mostly speak for itself, but note that the increase in the Fed’s holdings are relatively dwarfed by the increase in total foreign holdings.

N.B.: It’s HD, make sure the watch the video in fullscreen!

Holders of Treasury Securities, 2004-2010 from Computational Legal Studies on Vimeo.

October 22, 2010 | | Tags: , | No Comments »

Link: ‘Something exceptional’ is happening in volatility, correlation

Here’s the beginning of an FT Alphaville article from this morning:

Whilst much has been written about the rise in correlation recently — what’s been less frequently observed is the strange disconnection that’s occurring between correlation and volatility.

The two traditionally move together. That is, correlation tends to rise and fall with volatility.

Yet as FT Alphaville discovered — whilst working on a special report on the subject of how increasing correlation is impacting banks’ structured products desks — what’s really puzzling at the moment is why correlation is refusing to budge lower as volatility has fallen.

Read the rest here – `Something exceptional’ is happening in volatility, correlation

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S&P 500, Gold, Dollar Index, and the Probability of Republican-controlled House

There's been a lot of talk lately about what factors might be driving the market.  The three factors I've seen suggested recently are the dollar, gold, and political expectations.  I thought I'd take a few moments to offer a very simple picture of the relationship since September.  The chart below shows the S&P 500 (SPX), the Dollar Index Futures (DXY), NYMEX Gold (XAU), and the InTrade probability that Republicans will control the House after the midterm.

I've taken two very simple approaches to assessing the relationships between these factors and the S&P 500.  They should be viewed as "approximations" at best.

The first approach is to calculate the correlation between the S&P 500 and these factors since September, both with and without a one-day lag.  

Without a lag, the correlation between the log-return of the S&P 500 and the dollar, gold, and political environment are -0.54, 0.37, and 0.17 respectively.  These coefficients can be interpreted as indicating that, within a given day, the S&P tends to move the opposite direction as the dollar and the same direction as gold and the probability of a Republican-controlled House.  Of these, the magnitude of the coefficient is largest on the dollar index, indicating that this relationship is likely strongest.

When we consider a lagged correlation between the S&P 500 and these factors, we obtain a different picture.  In this case, the correlation between the S&P 500's return tomorrow and today's return in the dollar, gold, and the probability of Republican House control are 0.55, -0.29, and -0.39 respectively.  These coefficients suggest the opposite of the within-day coefficients above, though the magnitude of the dollar correlation remains strongest.  This reverse relationship is likely due to the significant negative autocorrelation in the S&P 500 over the sample.

The second approach to assessing this relationship is to fit a GLM to the data to predict tomorrow's S&P 500 return from today's return in the factors.  In this case, I fit a simple normal model and obtain values of \beta of 0.79, 0.11, and -0.068 for the dollar index, gold, and probability of Republican-controlled House.  However, the t-statistic is only greater than 2 for the dollar index coefficient.

There are some measurement issues to address.  First, we're comparing the spot equity market to futures markets for gold and the dollar index.  Second, InTrade's probability of a Republican-controlled House is a very imperfect proxy for political environment. Not only does this probability ignore both the Senate and the Executive branch, but it also assumes that the House and Republican policy is capable of improving business climate.

However, taking this naive analysis at face value, it appears that the dollar does appear to be driving the S&P 500 more than gold or expectations of political environment.

October 21, 2010 | | Tags: , , | No Comments »

Update: J. Bollen, H. Mao, X.-J. Zeng. Twitter mood predicts the stock market.

Update: Johan wrote me back this afternoon and confirmed that the z-scores are not used in assessing predictive power.  I think future drafts of the paper will be much more clear on this point, as apparently similar concerns had been raised by others.  Based on Johan's statements, I'd like to emphasize that this paper does rigorously support the claim that Twitter can be used to help predict the direction of the Dow for their sample.  Though directional prediction does not necessarily equate to profitable strategy, this is an exciting conclusion.  I think the paper would benefit most from a portfolio backtesting instead of just directional prediction, and perhaps an extensions to either interest rates or the something like the VIX.  All in all, I'm excited to see future research from their group.

As I noted when I first linked to this paper on arXiv, I think there may be an issue with the claim of prediction.  Here is the portion of text that raises some serious questions in my mind.  Emphasis is mine.

 

 

Note then that the assessment of predictive power later uses these z-scores, which are clearly not out-of-sample since they incorporate $k$ periods of future knowledge. Figure 3 and its caption below drive this point home, as they clearly indicate that $Z_t$ is used here.

 

 

The remainder of the text is somewhat ambiguous.

I've emailed the authors twice over the last week, and despite the fact that they visited my personal homepage through the email, I've received no response.  In the meantime, I think the jury is out on whether Twitter can actually be used to rigorously, out-of-sample predict the stock market.

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Link: Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws

Another paper of note on q-fin last night. Though this one isn't completely new, I thought it was worth noting since H. Eugene Stanley has received quite a bit of press with his "financial earthquake" research.

We study the cascading dynamics immediately before and immediately after 219 market shocks. We define the time of a market shock T_{c} to be the time for which the market volatility V(T_{c}) has a peak that exceeds a predetermined threshold. The cascade of high volatility "aftershocks" triggered by the "main shock" is quantitatively similar to earthquakes and solar flares, which have been described by three empirical laws — the Omori law, the productivity law, and the Bath law. We analyze the most traded 531 stocks in U.S. markets during the two-year period 2001-2002 at the 1-minute time resolution. We find quantitative relations between (i) the "main shock" magnitude M \equiv \log V(T_{c}) occurring at the time T_{c} of each of the 219 "volatility quakes" analyzed, and (ii) the parameters quantifying the decay of volatility aftershocks as well as the volatility preshocks. We also find that stocks with larger trading activity react more strongly and more quickly to market shocks than stocks with smaller trading activity. Our findings characterize the typical volatility response conditional on M, both at the market and the individual stock scale. We argue that there is potential utility in these three statistical quantitative relations with applications in option pricing and volatility trading.

Alexander M. Petersen, Fengzhong Wang, Shlomo Havlin, H. Eugene Stanley. Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws. 82 Phys. Rev. E., forthcoming (2010). http://arxiv.org/abs/1006.1882

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ETF Central on Twitter

If you're interested in smaller, bite-sized thoughts and less of the post-length stuff, feel free to follow ETF Central on Twitter.  I'll often post headlines as they hit the newswire with short thoughts and follow-up links.  (If you're so inclined, you can also follow me as a person.)

October 20, 2010 | | No Comments »
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