The Unanticipated Effects of Insider Trading Regulation

Using a sample of 2,827 firms from 21 countries we examine whether insider trading laws achieve the primary objective for which they are introduced – protecting uninformed investors from private information-based trading. We find that when control is concentrated in the hands of a large shareholder, insider trading regulation is less effective in reducing private information-based trading if investor protection is poor. We suggest that controlling shareholders who are banned from trading may resort to covert expropriation of firm resources, creating more information asymmetry and thereby encouraging private information trading by informed outsiders. Consistent with this, we find evidence that when the rights of controlling shareholders are high, insider trading restrictions are associated with greater earnings opacity.http://deepblue.lib.umich.edu/bitstream/2027.42/40081/3/wp695.pd

Correct ordering in the Zipf-Poisson ensemble

We consider a Zipf--Poisson ensemble in which X_i\sim\poi(Ni^{-\alpha}) for $\alpha>1$ and $N>0$ and integers $i\ge 1$. As $N\to\infty$ the first $n'(N)$ random variables have their proper order $X_1>X_2>...>X_{n'}$ relative to each other, with probability tending to 1 for $n'$ up to $(AN/\log(N))^{1/(\alpha+2)}$ for an explicit constant $A(\alpha)\ge 3/4$. The rate $N^{1/(\alpha+2)}$ cannot be achieved. The ordering of the first $n'(N)$ entities does not preclude $X_m>X_{n'}$ for some interloping $m>n'$. The first $n"$ random variables are correctly ordered exclusive of any interlopers, with probability tending to 1 if $n"\le (BN/\log(N))^{1/(\alpha+2)}$ for $B<A$. For a Zipf--Poisson model of the British National Corpus, which has a total word count of $100{,}000{,}000$, our result estimates that the 72 words with the highest counts are properly ordered

Empirical stationary correlations for semi-supervised learning on graphs

In semi-supervised learning on graphs, response variables observed at one node are used to estimate missing values at other nodes. The methods exploit correlations between nearby nodes in the graph. In this paper we prove that many such proposals are equivalent to kriging predictors based on a fixed covariance matrix driven by the link structure of the graph. We then propose a data-driven estimator of the correlation structure that exploits patterns among the observed response values. By incorporating even a small fraction of observed covariation into the predictions, we are able to obtain much improved prediction on two graph data sets.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS293 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Extension of Gutenberg-Richter Distribution to Mw -1.3, No Lower Limit in Sight

With twelve years of seismic data from TauTona Gold Mine, South Africa, we show that mining-induced earthquakes follow the Gutenberg-Richter relation with no scale break down to the completeness level of the catalog, at moment magnitude MW −1.3. Events recorded during relatively quiet hours in 2006 indicate that catalog detection limitations, not earthquake source physics, controlled the previously reported minimum magnitude in this mine. Within the Natural Earthquake Laboratory in South African Mines (NELSAM) experiment\u27s dense seismic array, earthquakes that exhibit shear failure at magnitudes as small as MW −3.9 are observed, but we find no evidence that MW −3.9 represents the minimum magnitude. In contrast to previous work, our results imply small nucleation zones and that earthquake processes in the mine can readily be scaled to those in either laboratory experiments or natural faults

The Unanticipated Effects of Insider Trading Regulation

Using a sample of 2,827 firms from 21 countries we examine whether insider trading laws achieve the primary objective for which they are introduced – protecting uninformed investors from private information-based trading. We find that when control is concentrated in the hands of a large shareholder, insider trading regulation is less effective in reducing private information-based trading if investor protection is poor. We suggest that controlling shareholders who are banned from trading may resort to covert expropriation of firm resources, creating more information asymmetry and thereby encouraging private information trading by informed outsiders. Consistent with this, we find evidence that when the rights of controlling shareholders are high, insider trading restrictions are associated with greater earnings opacity.Insider Trading Regulation, Ownership, Private Information Trading, Earnings Opacity

Study on the feasibility of a tool to measure the macroeconomic impact of structural reforms

The main aim of this study is to assess the feasibility of empirical tools to study the impact of structural reforms on the macroeconomic performance in the member countries of the European Union (EU). This report presents the results of the project "Study on the feasibility of a tool to measure the macroeconomic impact of structural reforms" (ECFIN-E/2005/001) and amalgamates the findings of the two previous interim reports and the main conclusions of the workshop held in Brussels on May 11th. The main goal of the project is to determine the most reliable and robust methods to investigate the impacts of economy-wide structural reforms as well as reforms in individual markets or sectors, and to make suggestions as to how they best to implement them and possible improvements of the institutional dataset. In addition, a roadmap has been created which includes the main steps in the model-developing process, and solutions feasible even in the short term are discussed.The most relevant conclusion to be drawn from the study is that the most appropriate tool that can be developed in the short term is the integration of a DSGE model (preferably QUEST due to its in-house availability) with different satellite models, to be developed.structural reforms, product markets, labour markets, financial markets, Dreger, Art�s, Moreno, Ramos, Suri�ach

Outlier Detection Using Nonconvex Penalized Regression

This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the $n$ data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual $L_1$ penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The $L_1$ penalty corresponds to soft thresholding. We introduce a thresholding (denoted by $\Theta$) based iterative procedure for outlier detection ($\Theta$-IPOD). A version based on hard thresholding correctly identifies outliers on some hard test problems. We find that $\Theta$-IPOD is much faster than iteratively reweighted least squares for large data because each iteration costs at most $O(np)$ (and sometimes much less) avoiding an $O(np^2)$ least squares estimate. We describe the connection between $\Theta$-IPOD and $M$-estimators. Our proposed method has one tuning parameter with which to both identify outliers and estimate regression coefficients. A data-dependent choice can be made based on BIC. The tuned $\Theta$-IPOD shows outstanding performance in identifying outliers in various situations in comparison to other existing approaches. This methodology extends to high-dimensional modeling with $p\gg n$, if both the coefficient vector and the outlier pattern are sparse

Broadband Records of Earthquakes in Deep Gold Mines and a Comparison with Results from SAFOD, California

For one week during September 2007, we deployed a temporary network of field recorders and accelerometers at four sites within two deep, seismically active mines. The ground-motion data, recorded at 200 samples/sec, are well suited to determining source and ground-motion parameters for the mining-induced earthquakes within and adjacent to our network. Four earthquakes with magnitudes close to 2 were recorded with high signal/noise at all four sites. Analysis of seismic moments and peak velocities, in conjunction with the results of laboratory stick-slip friction experiments, were used to estimate source processes that are key to understanding source physics and to assessing underground seismic hazard. The maximum displacements on the rupture surfaces can be estimated from the parameter Rv, where v is the peak ground velocity at a given recording site, and R is the hypocentral distance. For each earthquake, the maximum slip and seismic moment can be combined with results from laboratory friction experiments to estimate the maximum slip rate within the rupture zone. Analysis of the four M 2 earthquakes recorded during our deployment and one of special interest recorded by the in-mine seismic network in 2004 revealed maximum slips ranging from 4 to 27 mm and maximum slip rates from 1.1 to 6:3 m=sec. Applying the same analyses to an M 2.1 earthquake within a cluster of repeating earthquakes near the San Andreas Fault Observatory at Depth site, California, yielded similar results for maximum slip and slip rate, 14 mm and 4:0 m=sec