Informed trading and liquidity in the Shanghai Stock Exchange

Dufour and Engle (J. Finance (2000) 2467) find evidence of increased presence of informed traders when the NYSE markets are most active. No such evidence, however, can be found by Manganelli (J. Financial Markets (2005) 377) for the infrequently traded stocks. This article investigates the issue of informed trading and its relation to liquidity in Shanghai Stock Exchange. Consistent with the hypothesis that information-based trade exists for all stocks, our findings suggest an increased presence of informed trading in both liquid and illiquid stocks when markets are active. Moreover, for the actively traded stocks, our results support the price formation model of Foster and Viswanathan (Rev. Financial Studies (1990) 593) that activities of informed traders deter uninformed investors from trading, thereby reducing market liquidity.     

Investment portfolio balancing: application of a generic self-organizing fuzzy neural network (GenSoFNN)

In contrast to short-term stock trading, portfolio managers are interested in the medium- to long-term peaks and troughs of the stock price cycles as signals to balance their stock portfolios – the predicted trough is the signal to buy the stock and the predicted peak is the signal to sell the stock. As statistical models are generally inadequate or incapable of providing such portfolio balancing signals, we propose using the generic self-organizing fuzzy neural network (GenSoFNN)—a fuzzy neural system – as a tool for portfolio balancing. The network adopts the supervised learning approach to detect inflection points in the stock price cycles, and a modified locally weighted regression algorithm is employed to smooth the stock cycles. The GenSoFNN-based portfolio balancing system was evaluated with experiments conducted using 23 stocks from the New York Stock Exchange and NASDAQ, and the results showed an average profit return of 65.66%. The contributions of the proposed GenSoFNN intelligent portfolio balancing system are twofold: it can be used as an efficient trading solution and it can provide decision support in trading via its generated rules. Copyright © 2009 John Wiley & Sons, Ltd.     

Policy Implications of the Effects of Concentration and Multimarket Contact in China’s Airline Market

Unlike many other mergers in developed countries, which might have been assessed and their effects estimated by antitrust authorities before being granted antitrust immunity, the airline mergers that swept China’s airline industry in 2002 occurred with no antitrust challenge. These mergers provide the opportunity to study important market power issues in China’s airline markets. Given that increased concentration and multimarket contact are the main legacies of an airline merger, the effects of mergers on these variables can raise the potential for the exercise of market power. However, an examination of the period 2002–2004 during which the Chinese airline mergers occurred shows that the resulting increased concentration and enhanced multimarket contact did not have important consequences for airfares in Chinese city-pair markets. The presence of Hainan Airlines appears to have played an important role in suppressing the airfares charged by China Eastern and China Southern.     

Centralized and decentralized control polices for a two-stage stochastic supply chain with subcontracting

An analysis of control policies for a two-stage supply chain with subcontractors at each stage is presented when decisions at each stage concerning safety stocks, backorders, and subcontracting are made jointly or in a decentralized manner. The inventory/admission control policies considered are base stock, echelon base stock and partial backordering, and the objective is to maximize the mean profit rate of the system. The optimal control parameters are found by exhaustive search using Markov chains. From numerical examples it appears that the policies which manage jointly sales and production levels in each stage provide much higher overall (system) profits than decentralized policies, although the latter are individually more profitable for the second stage. In addition, partial backordering provides the system with an extra profit above those that result from the lost sales (no backordering) and complete backordering policies. Finally, a number of numerical results show the impact of variations in certain system parameters on the optimal control parameters and the corresponding profit.     

Some observations on the state of the art in marketing research

This paper provides some observations on the state of the art in marketing research. The primary and secondary thrust of the articles published in the Journal of Marketing Research during 1980–1986 is briefly reviewed to identify important areas of inquiry. In each of these areas, we summarize recent developments, highlight the state of the art, offer some critical observations, and identify directions for future research. A cross-classification of various techniques and problem areas is also presented and some observations are made on the application of these techniques to address specific substantive and methodological issues in marketing research.     

The Impacts of Promotions/Marketing, Scheduling, and Economic Factors on Total Gross Revenues for Minor League Baseball Teams

The purpose of this empirical study is to identify the key marketing and scheduling determinants of game attendance at minor league baseball games. Identification of such marketing and scheduling factors can provide the management of minor league teams in similar environments with information to more efficiently pursue the goal of game attendance maximization. To ensure greater comparability of data between teams and hence relevance of results, this study focuses upon a single grouping of teams, the Carolina League, and a single minor league baseball season, 2006. The Carolina League consists of eight teams serving eight metropolitan areas: Lynchburg City, VA; Winston-Salem, NC; Wilmington, DE; Salem City, VA; Myrtle Beach, SC; Prince William County, VA; Lenoir City, NC; and Frederick County, MD.