We claim that the stock market encourages business creation, innovation, and growth by allowing the recycling of “informed capital”. Due to incentive and information problems, start-ups face larger costs of going public than mature firms. Sustaining a tight relationship with a monitor (bank, venture capitalist) allows them to finance their operations without going public until profitability prospects are clearer or incentive problems are less severe. However, the earlier young firms go public, the quicker monitors' informed capital is redirected towards new start-ups. Hence, when informed capital is in limited supply, factors that lower the costs for start-ups to go public encourage business creation. Technological spill-overs associated with business creation and thick market externalities in the young firms segment of the stock market provide prima facie cases for encouraging young firms to go public

The literature offers many explanations for why the IPO market cycles from hot to cold. These include theories in which hot markets represent clusters of IPOs in a new industry, and signaling models that predict that hot markets draw in better quality firms. Others suggest hot market IPOs' stock returns reflect their poor quality. We compare IPOs over cycles during 1975–2000 and find that hot and cold IPO markets do not differ so much in the characteristics of the firms that go public as in the quantity of firms that go public. Both hot and cold IPOs are largely concentrated in the same narrow set of industries and they have few distinctions in profits, age, or growth potential. Our results suggest that hot markets are not driven primarily by changes in adverse selection costs, managerial opportunism, or technological innovations, but more likely reflect greater investor optimism

I analyse social interactions that stem from the successive endeavours of new cohorts of heterogeneous decision makers to learn from the experiences of past cohorts. A dynamic process of information accumulation and decision making occurs as the members of each cohort observe the experiences of earlier ones, and then make choices that yield experiences observable by future cohorts. Decision makers face the "selection problem" as they seek to learn from observation of past actions and outcomes, while not observing the counterfactual outcomes that would have occurred had other actions been chosen. Assuming that all cohorts face the same outcome distributions, I show that social learning is a process of sequential reduction in ambiguity. The specific nature of this process, and its terminal state, depend critically on how decision makers make choices under ambiguity. I use the problem of learning about innovations to illustrate. Copyright The Review of Economic Studies Limited, 2004

We study a general equilibrium model in which entrepreneurs finance investment with optimal financial contracts. Because of enforceability problems, contracts are constrained efficient. We show that limited enforceability amplifies the impact of technological innovations on aggregate output. This implies that economies with lower enforceability of contracts are characterized by greater macroeconomic volatility. A key assumption for the amplification result is that defaulting entrepreneurs are not excluded from the market

Recent theoretical work has shown the importance of measuring microeconomic uncertainty for models of both general and partial equilibrium under imperfect insurance. In this paper the assumption of i.i.d. income innovations used in previous empirical studies is removed and the focus of the analysis placed on models for the conditional variance of income shocks, which is related to the measure of risk emphasized by the theory. We first discriminate amongst various models of earnings determination that separate income shocks into idiosyncratic transitory and permanent components. We allow for education- and time-specific differences in the stochastic process for earnings and for measurement error. The conditional variance of the income shocks is modelled as a parsimonious ARCH process with both observable and unobserved heterogeneity. The empirical analysis is conducted on data drawn from the 1967-1992 Panel Study of Income Dynamics. We find strong evidence of sizeable ARCH effects as well as evidence of unobserved heterogeneity in the variances

The role of technology in the transition from premodem, Malthusian to modem economies in late 18thand 19th-century Europe is among the major questions in economic history, but it is still poorly understood. In particular, the view that premodern societies experienced low labor productivity and stagnant living standards, and that technological change before ca. 1800 was close to zero due to pervasive guild rent-seeking and poorly specified property rights to knowledge (Douglass C. North, 1981; Joel Mokyr, 2002), is hard to square with the fact that the surge of technological innovation in the 18th century occurred within institutional frameworks not too dissimilar to those of 1300

Economists have invested a great deal of effort in trying to understand the motivation for family transfers, yet recent empirical work testing the seemingly appealing models of altruism and exchange has led to decidedly mixed results. A major stumbling block has been the lack of adequate data. We take a fresh look at the issue using responses to an innovative survey question that directly asks mother about the planned division of their estates. We find that both altruism and exchange are frequently offered as explanations of behavior and are of nearly equal importance. Furthermore, the explanations are consistent with observable characteristics of the mother, lending support to the validity of the question. We also find that among step or adopted families, genetic ties play an important role. Because motivating factors appear to differ across families the lack of a consensus among previous researchers about motives ought not to be surprising

The objective of this paper is to uncover the determinants of trading intensity in futures markets. In particular, the time between adjacent transactions (referred to as transaction duration) on the FTSE 100 index futures market is modeled using various augmentations of the basic autoregressive conditional duration (ACD) model introduced by Engle and Russell [Econometrica 66 (1998) 1127]. The definition of transaction duration used in this paper is an important variable as it represents the inverse of instantaneous conditional return volatility. As such, this paper can also be viewed as an investigation into the determinants of (the inverse of) instantaneous conditional return volatility. The estimated parameters from various ACD models form the basis of the hypothesis tests carried out in the paper. As predicted by various market microstructure theories, we find that bid–ask spread and transaction volume have a significant impact upon subsequent trading intensity. However, the major innovation of this paper is the finding that large (small) differences between the market price and the theoretical price of the futures contract (referred to as pricing error) lead to high (low) levels of trading intensity in the subsequent period. Moreover, the functional dependence between pricing error and transaction duration appears to be non-linear in nature. Such dependence is implied by the presence of arbitragers facing non-zero transaction costs. Finally, a comparison of the forecasting ability of the various estimated models shows that a threshold ACD model provides the best out-of-sample performance

Over the past three decades the literature on financial distress prediction has largely been confined to simple multiple discriminant analysis, binary logistic or probit analysis, or rudimentary multinomial logit models (MNL). There has been a conspicuous absence of modeling innovation in this literature as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. In particular, there has been no recognition of major advances in discrete choice modeling over the last 15 years, which has increasingly relaxed behaviorally questionable assumptions associated with the independently and identically distributed errors (IID) condition and allowed for observed and unobserved heterogeneity. In contrast to standard logit, the mixed logit model fulfils this purpose and provides a superior framework for explanation and prediction. We explain the theoretical and econometric underpinnings of mixed logit and demonstrate its empirical usefulness in the context of a specific but topical area of accounting research: financial distress prediction. Comparisons of model-fits and out-of-sample forecasts indicate that mixed logit outperforms standard logit by significant margins. While mixed logit has valuable applications in financial distress research, its potential usefulness in other areas of accounting research should not be overlooked