TRADE IMPACTS ON SKILL ACQUISITION VIA VARIETY EXPANSION*

We show that trade enhances skill formation through gains from trade via variety expansion à la Krugman. Although workers are identical as unskilled labour, they differ in productivity as skilled labour. Workers become skilled by incurring training costs. By introducing these settings into a trade model with monopolistic competition, we show that, although trade makes all agents better off, its effect is stronger for skilled than unskilled workers, which stimulates skill acquisition. As a result of less productive workers becoming skilled, the wage dispersion among skilled workers increases.     

THE MIDDLE CLASSES IN THAILAND: THE RISE OF THE URBAN INTELLECTUAL ELITE AND THEIR SOCIAL CONSCIOUSNESS

This paper, based on an analysis of quantitative survey data, reexamines the stereotype of the Thai “middle class” as a homogeneous elite class at loggerheads with the lower strata. Our results support the contention that the Thai middle classes are, in fact, of mixed social origins, and suggest that their process of emergence is characterized by the fact that a large number have risen from the lower urban strata We argue that because of these characteristics, the social consciousness of the Thai middle classes is more complex than the stereotyped explanation, and contains elements that cannot be fully explained by a perspective based on class theory     

Stock Price Prediction Using Prior Knowledge and Neural Networks

In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. We make use of prior knowledge of stock price predictions and newspaper information on domestic and foreign events. Event-knowledge is extracted from newspaper headlines according to prior knowledge. We choose several economic indicators, also according to prior knowledge, and input them together with event-knowledge into neural networks. The use of event-knowledge and neural networks is shown to be effective experimentally: the prediction error of our approach is smaller than that of multiple regression analysis on the 5% level of significance. © 1997 by John Wiley & Sons, Ltd.     

A Dynamic Volume–Return Relation and Investors' Positive Feedback Trading

With individual stocks, a larger increase in trading volume indicates a stronger short‐term return persistence. A reason for this short‐horizon ‘volume–return relation’ is that it can signal the existence of fundamental news, which can be gradually incorporated into stock price. In this study, we present another plausible explanation by considering investors' short‐term positive feedback trading. First, through empirical analysis, we show that the volume–return relation remains strong among stocks for which there is little fundamental news. Through a model‐based analysis, we demonstrate that positive feedback trading can cause this relation even when there is no news. Our findings raise the possibility that the short‐horizon volume–return relation is also caused by short‐term positive feedback trading.