A Nonlinear Super-Exponential Rational Model of Speculative Financial Bubbles

Keeping a basic tenet of economic theory, rational expectations, we model the nonlinear positive feedback between agents in the stock market as an interplay between nonlinearity and multiplicative noise. The derived hyperbolic stochastic finite-time singularity formula transforms a Gaussian white noise into a rich time series possessing all the stylized facts of empirical prices, as well as accelerated speculative bubbles preceding crashes. We use the formula to invert the two years of price history prior to the recent crash on the Nasdaq (april 2000) and prior to the crash in the Hong Kong market associated with the Asian crisis in early 1994. These complex price dynamics are captured using only one exponent controlling the explosion, the variance and mean of the underlying random walk. This offers a new and powerful detection tool of speculative bubbles and herding behavior.Comment: Latex document of 24 pages including 5 eps figure

Activity Overinvestment: The Case of R&D

The literature on corporate diversification has often argued for and established the case that companies often overdiversify in a product market sense – i.e. enter into unrelated product markets where they may not fully cover their cost of capital. Yet, even without engaging in unrelated diversification, managers need to make resource allocation decisions to a variety of activities that a company conducts to consummate its business. In this article we focus on Research and Development (R&D) activity and we discuss the effects that the uncertainty, boundary ambiguity, feedback latency, R&D lumpiness and legitimacy that characterize technological contexts can have in making overinvestment in R&D likely. Specifically, in this article we a) draw attention to the construct of activity overinvestment, and specifically R&D overinvestment, b) use the received literature to argue that there exists a prima facie case for examining this construct and its antecedents in order to evaluate the extent and implications of R&D overinvestment, and c) make the more general case that the resource allocation literature needs to study the issue of activity overinvestment systematically

Institutional Herding in Financial Markets: New Evidence Through the Lens of a Simulated Model

Due to data limitations and the absence of testable, model-based predictions, theory and evidence on herd behavior are only loosely connected. This paper contributes towards closing this gap in the herding literature. We use numerical simulations of a herd model to derive new, theory-based predictions for aggregate herding intensity. Using high-frequency, investor-specific trading data we confirm the predicted impact of information risk on herding. In contrast, the increase in buy herding measured for the financial crisis period cannot be explained by the herd model

Redirecting research efforts on the diversification-performance linkage: The search for synergy

We review the literature on the diversification-performance (D-P) relationship to a) propose that the time is ripe for a renewed attack on understanding the relationship between diversification and firm performance, and b) outline a new approach to attacking the question. Our paper makes four main contributions. First, through a review of the literature we establish the inherent complexities in the D-P relationship and the methodological challenges confronted by the literature in reaching its current conclusion of a non-linear relationship between diversification and performance. Second, we argue that to better guide managers the literature needs to develop along a complementary path – whereas past research has often focused on answering the big question of does diversification affect firm performance, this second path would focus more on identifying the precise micro-mechanisms through which diversification adds or subtracts value. Third, we outline a new approach to the investigation of this topic, based on (a) identifying the precise underlying mechanisms through which diversification affects performance; (b) identifying performance outcomes that are “proximate” to the mechanism that the researcher is studying, and (c) identifying an appropriate research design that can enable a causal claim. Finally, we outline a set of directions for future research

Partial Independence in Nonseparable Models

We analyze identification of nonseparable models under three kinds of exogeneity assumptions weaker than full statistical independence. The first is based on quantile independence. Selection on unobservables drives deviations from full independence. We show that such deviations based on quantile independence require non-monotonic and oscillatory propensity scores. Our second and third approaches are based on a distance-from-independence metric, using either a conditional cdf or a propensity score. Under all three approaches we obtain simple analytical characterizations of identified sets for various parameters of interest. We do this in three models: the exogenous regressor model of Matzkin (2003), the instrumental variable model of Chernozhukov and Hansen (2005), and the binary choice model with nonparametric latent utility of Matzkin (1992)