Bear Stearns

Abstract: This paper provides an in depth analysis of an investor's reluctance to realize losses and his propensity to realize gains - a behavior known as the disposition effect. Together, sophistication (static differences across investors) and trading experience (evolving behavior of a single investor) eliminate the reluctance to realize losses. However, an asymmetry exists as sophistication and trading experience reduce the propensity to realize gains by 37% (but fail to eliminate this part of the behavior.) Our research design allows us to follow an individual's behavior from the start of his investing life/career. This ability makes it possible to track the evolution of the disposition effect as it is reduced and/or disappears. Our results are robust to alternative explanations including feedback trading, calendar effects, and frequency of observation.

Abstract: Methods and systems for initiating trading, and more particularly, to pairs trading across multiple exchanges by allowing a trader to monitor, trade, and hedge related securities that span across various markets (50, 52, 54). In certain embodiments of the invention, one or more processors (20) are configured to receive spread parameters pertaining to a relationship between two or more securities in one or more markets, receive and process market data relating to the two or more securities, determine whether the market data falls within the spread parameters, initiate a first order for a first security in the spread in a foreign currency, when the market data falls within the spread parameters, initiate a second order for a second security in the spread, when the market data falls within the spread parameters, whereby the second order is in conjunction with the first order at a selected ratio to reduce the risk of adverse price movements in the first security, and initiate an FX Order (30) to offset foreign exchange exposure resulting from the first order.

Abstract: We investigate Grossman and Laroque's (1990) conjecture that costs of adjusting consumption can account, in part, for the empirical failure of the consumptionbased capital asset pricing model (CCAPM). We incorporate small fixed costs of consumption adjustment into a CCAPM with heterogeneous agents. We find that undetectably small consumption adjustment costs can account for much of the discrepancy between the observed variance of nondurable aggregate consumption growth and the predictions of the CCAPM, and can partially reconcile nondurable consumption data with the observed equity premium. We conclude that the CCAPM's implications are nonrobust to extremely small adjustment costs. THE CONSUMPTION-BASED CAPITAL ASSET PRICING MODELS (CCAPMs)1 of Lucas (1978), Breeden (1979), and Grossman and Shiller (1982) have difficulty matching the high volatility of equity returns and the high mean equity premium found in U.S. data. First, the variance of the CCAPM asset pricing kernel is too low to generate plausible equity-return volatility. More precisely, Hansen and Jagannathan (1991) and Cochrane and Hansen (1992) show that, for any conjectured pricing kernel mean, the variance of the kernel is too low to satisfy the Hansen-Jagannathan bounds without implausibly high risk aversion or substantial habit formation.2 Second, the covariance between the CCAPM asset pricing kernel and excess equity returns is too low to generate a plausible equity premium. More precisely, let us define EP' as follows:

Abstract: Computationally efficient algorithms for computing minimal-volume ellipsoidal bounds on the state of a linear, discrete-time dynamical system are presented. Ellipsoidally bounded noise is considered, rather than the scalar-noise case usually treated; the latter is covered as a degenerate special case of the former. The algorithms have differing accuracy and computational requirements, resulting from differing specifications of the bounds. The paper compares these alternative approaches to computing ellipsoidal bounds on state, giving simulation results to illustrate the performance of the algorithms. Copyright © 2002 John Wiley & Sons, Ltd.