A Constrained EM Algorithm for Independent Component Analysis

We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm. The source distributions are modeled as D one-dimensional mixtures of gaussians. The observed data are modeled as linear mixtures of the sources with additive, isotropic noise. This generative model is fit to the data using constrained EM. The simpler “soft-switching” approach is introduced, which uses only one parameter to decide on the sub- or supergaussian nature of the sources. We explain how our approach relates to independent factor analysis

Behavioral Finance

Behavioral finance as a subdiscipline of behavioral economics is finance incorporating findings from psychology and sociology into its theories. Behavioral finance models are usually developed to explain investor behavior or market anomalies when rational models provide no sufficient explanations. To understand the research agenda, methodology, and contributions, this survey reviews traditional finance theory first. Then, this survey shows how modifications (e.g. incorporating market frictions) can rationally explain observed individual or market behavior. In the second section, the survey will explain the behavioral finance research methodology -how biases are modeled, incorporated into traditional finance theories, and tested empirically and experimentally- using one specific subset of the behavioral finance literature, the overconfidence literature.

Determination of the Local Dark Matter Density in our Galaxy

The rotation curve, the total mass and the gravitational potential of the Galaxy are sensitive measurements of the dark matter halo profile. In this publication cuspy and cored DM halo profiles are analysed with respect to recent astronomical constraints in order to constrain the shape of the Galactic DM halo and the local DM density. All Galactic density components (luminous matter and DM) are parametrized. Then the total density distribution is constrained by astronomical observations: 1) the total mass of the Galaxy, 2) the total matter density at the position of the Sun, 3) the surface density of the visible matter, 4) the surface density of the total matter in the vicinity of the Sun, 5) the rotation speed of the Sun and 6) the shape of the velocity distribution within and above the Galactic disc. The mass model of the Galaxy is mainly constrained by the local matter density (Oort limit), the rotation speed of the Sun and the total mass of the Galaxy from tracer stars in the halo. It is shown from a statistical chi^2 fit to all data that the local DM density is strongly positively (negatively) correlated with the scale length of the DM halo (baryonic disc). Since these scale lengths are poorly constrained the local DM density can vary from 0.2 to 0.4 GeV/cm^3 (0.005 - 0.01 M_sun/pc^3) for a spherical DM halo profile and allowing total Galaxy masses up to 2 * 10^12 M_sun. For oblate DM halos and dark matter discs, as predicted in recent N-body simulations, the local DM density can be increased significantly.Comment: 10 pages, 8 figure

Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.Overconfidence; Differences of opinion; Trading volume; Individual investors; Investor behavior; Correlation of economic and psychological variables; Combination of psychometric measures of judgment biases and field data

Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, the better than average effect, illusion of control, unrealistic optimism). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. The results hold even when we control for several other determinants of trading volume in a cross-sectional regression analysis. In connection with other recent findings, we conclude that the usual way of motivating and modelling overconfidence which is mainly based on the calibration literature has to be treated with caution. We argue that our findings present a psychological foundation for the ``differences of opinion'' explanation of high levels of trading volume. In addition, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena drive economic behavior.

Which Past Returns Affect Trading Volume?

Anecdotal evidence and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this prediction using linear panel regressions as well as negative binomial panel regressions and Logit panel regressions. We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the probability to trade stocks in a given month) and are thus able to confirm predictions of overconfidence models. However, contrary to intuition, the effect of market returns on subsequent trading volume is stronger for the whole group of investors. Using survey data of our investor sample, we present evidence that individual investors, on average, are unable to give a correct estimate of their own past realized stock portfolio performance. The correlation between return estimates and past realized returns is insignificant. For the subgroup of respondents, we are able to analyze the link between the ability to correctly estimate the past realized stock portfolio performance on the one hand and the dependence of trading volume on past returns on the other hand. We find that for the subgroup of investors that is better able to estimate the own past realized stock portfolio performance, the effect of past portfolio returns on trading volume is stronger. We argue that this finding might explain our results concerning the relation between past returns and subsequent trading volume.Individual investors; Investor behavior; Trading volume; Stock returns and Trading Volume; Overconfidence; Discount broker; Online broker; Online banks; Panel data; Count data

Nonlinear optics with full three-dimensional illumination

We investigate the nonlinear optical process of third-harmonic generation in the thus far unexplored regime of focusing the pump light from a full solid angle, where the nonlinear process is dominantly driven by a standing dipole-wave. We elucidate the influence of the focal volume and the pump intensity on the number of frequency-tripled photons by varying the solid angle from which the pump light is focused, finding good agreement between the experiments and numerical calculations. As a consequence of focusing the pump light to volumes much smaller than a wavelength cubed the Gouy phase does not limit the yield of frequency-converted photons. This is in stark contrast to the paraxial regime. We believe that our findings are generic to many other nonlinear optical processes when the pump light is focused from a full solid angle.Comment: 6 pages main text + 4 pages appendix, modified abstract and introduction + some other minor change

Measuring the temperature and heating rate of a single ion by imaging

We present a technique based on high resolution imaging to measure the absolute temperature and the heating rate of a single ion trapped at the focus of a deep parabolic mirror. We collect the fluorescence light scattered by the ion during laser cooling and image it onto a camera. Accounting for the size of the point-spread function and the magnification of the imaging system, we determine the spatial extent of the ion, from which we infer the mean phonon occupation number in the trap. Repeating such measurements and varying the power or the detuning of the cooling laser, we determine the anomalous heating rate. In contrast to other established schemes for measuring the heating rate, one does not have to switch off the cooling but the ion is always maintained in a state of thermal equilibrium at temperatures close to the Doppler limit