On the estimation of stock-market reaction to corporate layoff announcements

This study investigates the stock-market reaction to layoff announcements where more than 1000 workers are affected. We employ a dummy variable regression (DVR) version of the market model and compare the results obtained using ordinary least squares (OLS) versus exponential GARCH (EGARCH), and value-weighted (VW) versus equally weighted (EW) market index. We find that the stock market responds negatively to layoffs attributed to low demand. We also find that contrary to prior research, the market reacts positively to restructuring-related layoffs on the announcement date. This pattern of market reaction is observed regardless of the market index used or the parameter estimation methods employed, although the empirical results indicate that using EGARCH/VW market index tends to generate fewer statistically significant test results and smaller (in the absolute size of the cumulative) abnormal returns (ARs). Taken together, our study provides additional support for the claim that studies of stock-market reaction to corporate events must account for the time variation in return volatility. Ignoring these could result in erroneous inferences.     

Using Market Expectations to Test for Speculative Bubbles in the Crude Oil Market

The wide fluctuations of oil prices from 2003 to 2008 have attracted the interest of academics and policymakers. A popular view is that these fluctuations were caused by speculative bubbles due to the increased financialization of oil futures markets. This hypothesis, however, is difficult to examine since the fundamental price of oil is unobservable and, therefore, econometric evidence in favor of bubbles may actually be due to misspecified market fundamentals. In this paper, we extend two recently proposed methodologies for bubble detection that alleviate this problem by using market expectations of future prices. Both methodologies provide no evidence of speculative bubbles.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

A logistic regression point of view toward loss given default distribution estimation

We propose a new procedure to estimate the loss given default (LGD) distribution. Owing to the complicated shape of the LGD distribution, using a smooth density function as a driver to estimate it may result in a decline in model fit. To overcome this problem, we first apply the logistic regression to estimate the LGD cumulative distribution function. Then, we convert the result into the LGD distribution estimate. To implement the newly proposed estimation procedure, we collect a sample of 5269 defaulted debts from Moody’s Default and Recovery Database. A performance study is performed using 2000 pairs of in-sample and out-of-sample data-sets with different sizes that are randomly selected from the entire sample. Our results show that the newly proposed procedure has better and more robust performance than its alternatives, in the sense of yielding more accurate in-sample and out-of-sample LGD distribution estimates. Thus, it is useful for studying the LGD distribution.     

Performance of Moving Average Trading Rules in a Volatile Stock Market: The Russian Evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Russian stock market over the 2003–12 period. It contributes to the existing technical analysis (TA) literature by testing, for the first time, the applicability of ordered weighted moving averages (OWMA) as an alternative calculation basis for determining DMACs. In addition, this article provides the first comprehensive performance comparison of DMAC trading rules in the stock market that is known as one of the most volatile markets in the world. The results show that the best trading strategies of the in-sample period can also outperform their benchmark portfolio during the subsequent out-of-sample period. Moreover, the outperformance of the best DMAC strategies is mostly attributable to their superior performance during bearish periods and, particularly, during stock market crashes.     

Effects of Label on Consumer Preferences: Focus on Hawaiian Avocado Industry

This study examines market challenges facing a local agriculture industry in a small island setting. Examination of the Hawai‘i local avocado industry indicates labeling can address the product information gap between consumers and producers, improve market share, and increase import substitution by local products. Currently, local avocados occupy only 33% of market share, compared with 67% captured by imports. Consumer preferences among local labeled, local unlabeled, and imported avocados were analyzed using multinomial logistic regression. Statistical results showed that labels influence consumers’ decisions to buy local or imported avocados, and their preferences were also influenced by sociodemographic variables, avocado characteristics, and purchasing behavior. Key market segments to target and policy implications are also discussed.     

Why do companies transfer the trading compartment of their common stocks

We analyze the motives and determinants of voluntarily stock exchange section switching on the NYSE Euronext. By strategically deciding trading-section transfer when it is beneficial, managers expect to reduce their liquidity and invisibility costs, cost of capital, or their listing costs. We show that managers decide to change the trading compartment of their common stocks based on various factors including firm's size, liquidity level, debt ratio, and expected growth opportunities. Firms that move voluntarily from a less or non regulated compartments to a more regulated one are likely to have transferred to increase their credibility, improve their stocks’ liquidity, re-balance their leverage, and to finance their growth opportunities. Whereas those that move their common stocks toward a less-regulated compartments do it mainly for costs saving reasons.     

Ratio-based design hour determination for airport passenger terminal facilities

To avoid both over-design and under-sizing of airport passenger terminal facilities such as security checkpoints, the infrastructure is designed for a specifically determined design load. As such, the design load is considered for a short period of time, usually an hour of operation, during which peak, though not necessarily maximum, demand occurs. For strategic planning applications, future design loads can be determined by either fictitious flight schedules or ratio-based models which forecast the relationship between design load and annual demand. This study presents two ratio-based methods which allow the direct determination of design hour loads (DHL) for passenger terminal facilities. The unsaturated DHL model considers the relationship between observed passenger flows in the terminal and aggregated annual demand data. The saturated DHL model includes several operational constraints which limit the actual DHL, such as limitations in the runway system or the fleet mix operating at an airport. Both models are applied to two real-world airports, for which the DHL of the security checkpoint facilities is estimated from large datasets covering multiple years. Results are significant at the 5 % level and suggest that the proposed ratio-based methods are appropriate for airport strategic planning applications.     

Determinants of Consumer Preferences for Honey in the Democratic Republic of Congo

Honey is one of the important non-timber forest products consumed in the Democratic Republic of Congo (DRC) and, as such, an important source of income for many households. However, poor knowledge of buyers’ preferences in the marketing of honey presents a major challenge to the profitability of the honey enterprises. The research presented in this article identifies key consumer characteristics that influence the preferences of honey consumers in the DRC. The primary data source used in this analysis is drawn from a survey of 214 consumers. Results show that consumers who are married and have reached at least the level of secondary education have a strong preference for local forest and savannah honey. Those between the ages of 30 and 50 do not prefer honey from beekeeping, but would rather have it imported. We conclude that segmentation of the honey market and increasing consumer awareness must go in tandem in order to develop a good marketing strategy.     

Searching for empirical linkages between demographic structure and economic growth

Demographic structure could affect economic growth through many channels. However, little is known about how demographic structure affects economic growth since no study has examined an extensive collection of channels through which demographic structure could affect economic growth in a single context. This paper overcomes this limitation by examining 45 potential mediating variables between demographic structure and economic growth. A causal search algorithm is used to identify channels through which demographic structure affects economic growth. Our results suggest that demographic structure affects economic growth differently between developed and developing countries. For developed countries, we find that an increase in the share of middle-aged workers has a positive effect on economic growth through institutions, investment and education channels. On the other hand, an increase in the share of the senior population has a negative effect on economic growth through institutions and investment channels. For developing countries, we find (but with weak evidence) that an increase in the share of young workers has a negative effect on economic growth through investment, financial market development and trade channels.