Labor market equilibrium under nonsequential search

This study presents a full model of the labor market under imperfect information. Workers bear the burden of search and are assumed to use a stochastic variant of an optimal sample size search strategy. The existence and uniqueness of a nondegenerate equilibrium distribution of wage offers is established. Infficiencies which lead to underinvestment in search are uncovered and analyzed.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

Intraday Volatility in Interest‐Rate and Foreign‐Exchange Markets: ARCH,Announcement, and Seasonality Effects

We explore the determinants of intraday volatility in interest‐rate and foreign‐exchange markets, focusing on the importance and interaction of three types of information in predicting intraday volatility: (a) knowledge of recent past volatilities (i.e., ARCH or Autoregressive Conditional Heteroskedasticity effects); (b) prior knowledge of when major scheduled macroeconomic announcements, such as the employment report or Producer Price Index, will be released; and (c) knowledge of seasonality patterns. We find that all three information sets have significant incremental predictive power, but macroeconomic announcements are the most important determinants of periods of very high intraday volatility (particularly in the interest‐rate markets). We show that because the three information sets are not independent, it is necessary to simultaneously consider all three to accurately measure intraday volatility patterns. For instance, we find that most of the previously documented time‐of‐day and day‐of‐the‐week volatility patterns in these markets are due to the tendency for macroeconomic announcements to occur on particular days and at particular times. Indeed, the familiar U‐shape completely disappears in the foreign‐exchange market. We also find that estimates of ARCH effects are considerably altered when we account for announcement effects and return periodicity; specifically, estimates of volatility persistence are sharply reduced. Separately, our results show that high volatility persists longer after shocks due to unscheduled announcements than after equivalent shocks due to scheduled announcements, indicating that market participants digest information much more quickly if they are prepared to receive it. However, contrary to results from equity markets, we find no evidence of a meaningful difference in volatility persistence after positive or negative price shocks. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21: 517–552, 2001     

Managing The Volatility Of Gaap Earnings

Abstract

The adoption of Statement of Financial Accounting Standards No. 97 (SFAS 97) eliminated the “lock-in” concept introduced in SFAS 60. Since many of the actuarial assumptions used in the calculation of the deferred acquisition cost (DAC) asset are difficult to predict over an extended period of time, “dynamic unlocking” was a sensible solution. Although this “dynamic unlocking” keeps the assumptions in line with recent experience, it comes at a cost—increased volatility of GAAP earnings. Some of the causes of this volatility are warranted since it accentuates the effects on earnings due to certain changes in the underlying experience. Other causes of this volatility may be unwarranted because of a misapplication of the principles underlying SFAS 97 and SFAS 120 or the manner in which changes in experience were reflected. In addition, most analysts expect the amortization of deferred acquisition costs to increase when earnings are better than expected. Conversely, analysts expect the amortization of deferred acquisition costs to decrease when earnings are worse than expected. Often the amortization of deferred acquisition costs behaves in a manner contrary to their expectations. This article analyzes what causes this volatility, explains why the amortization can behave contrary to expectations, and suggests several techniques for minimizing these unwarranted results.     

On the utility premium of Friedman and Savage

We re-examine the utility premium of Friedman–Savage [Friedman, Milton and Savage, Leonard J., “The Utility Analysis of Choices Involving Risk.” Journal of Political Economy 56, 1948, pp. 279–304.]. This measure is useful in understanding risky choices. For instance its reaction to an increased wealth equates to a precautionary demand for saving. We also analyze its two components.     

Technological Scanning by Small Canadian Manufacturers

Given that in many industries new production and information technologies have fundamentally changed the way in which firms must operate and compete, the technological aspect of environmental scanning has become a critical success factor for many small and medium-sized manufacturing enterprises. As little is presently known about how technological scanning manifests itself in these organizations and about what determines the nature and level of this activity, a survey study of 324 Canadian firms was done. Testing a research model resulted in identifying four interrelated dimensions of scanning activity, namely scanning objectives, type of information, information sources, and management practices. Key determinants of this activity were also identified, including the firms' strategy, environmental uncertainty, production technology, level of R&D, information networks, and the owner-manager's education level.