Businesses can choose who they want to be online. Product and company attributes that are directly perceivable in the real world can be manipulated to make a favorable impression on online buyers. This study examines whether creating a more professional online e-image can signal consumers about unobservable product or company quality, and whether this signal influences their willingness to transact with the company, and ultimately the prices they are willing to pay for the company's goods and services. An empirical study is presented that examines two online auction businesses utilizing different company names and auction listing styles to sell items in parallel over the course of one year. The findings suggest that increasing the quality of an auction business's e-image does increase consumers' willingness to transact with the business, and increases prices received at auction. The study also demonstrates the ability to use eBay as an experimental laboratory for testing a variety of hypotheses about purchasing behavior online.
Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neural network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selection and system architecture design have been widely researched, but the corresponding question of how much information to use in producing high-quality neural network models has not been adequately addressed. In this paper, the effects of different sizes of training sample sets on forecasting currency exchange rates are examined. It is shown that those neural networks--given an appropriate amount of historical knowledge--can forecast future currency exchange rates with 60 percent accuracy, while those neural networks trained on a larger training set have a worse forecasting performance. In addition to higher-quality forecasts, the reduced training set sizes reduce development cost and time.
Emerging capital markets may not be as efficient as the more established equity markets. Because of the possible inefficiency in these markets, various indicators that are external to the emerging capital market may provide a significant trading advantage. A preliminary analysis suggests that the Singapore market appears to be efficient. Neural network models are used to evaluate the claim that emerging equity markets, specifically the Singapore exchange, are affected by external signals and attempt to exploit any trading advantage imparted by these signals. The neural network technique as it is applied to trading on market indices in the "emerging" Singapore market is compared with the more established Dow Jones market index. Results indicate that external market signals can significantly improve forecasting on the Singapore DBSS0 index but have little or no effect on forecasts for the more established Dow Jones Industrial Average index. The research demonstrates the efficacy of using neural network methods to capitalize on discovered market inefficiencies. Utilizing external market signals, a neural network forecasting model achieved a 63 percent trading prediction accuracy.