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AbstractsStock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series.One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction.Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be 2 significantly enhanced by using the two-stage architecture in comparison with a single SVR model.

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The mission of higher education is both to transfer knowledge to students and to emphasise the students' total development. Higher education institutions can accomplish this mission by collecting information on student satisfaction, because understanding the students' perspectives can provide valuable information to management for allocating resources to better design education programmes. Although some attempts to quantify student satisfaction have been made through student questionnaires, these tend to be quite crude and ad hoc. As there is competitive pressure to provide quality education, this study aims to explore the determinants of alumni satisfaction in higher education. Although there is no alumni satisfaction model, the use of the customer satisfaction index (CSI) is unanimously acknowledged. The CSI provides a common set of measures to interpret customer satisfaction. Although CSI models can measure customer satisfaction, they lack the ability to diagnose areas for improvement. To address this issue, in this study we suggested a decomposed alumni satisfaction model. Decomposed models provide greater explanatory strength than pure models. Based on the results, we built several strategic management maps to help the department of a higher education institution identify areas for continuous improvement. The results showed that course design needs the most attention. Based on this result, the department took several measures to improve the quality of course design. The second survey showed that the improvement was significant, with the score for course design having the greatest improvement.

People are the most important asset of any organisation' is a highly over-used and under-believed statement. We spend an inordinate amount of time and energy focusing on customer experience, without giving thought to employee satisfaction. Yet employee satisfaction is paramount, because it will determine the success or failure of what the customer experiences. This study proposes an Employee Satisfaction Index (ESI) model with an aim of simplicity, diagnostics and comparability that can explore the causal relationship of employee satisfaction. We surveyed 132 employees working for a high-tech firm. High-tech firms often find themselves operating in business environments fraught with unprecedented, unparalleled, unrelenting, and largely unpredictable change. Therefore, a company's success depends on its ability to attract and continually motivate their employees to contribute their knowledge. Partial Least Squares (PLS) was used to test the theoretical model and to derive an ESI score.