Applying the Support Vector Machine for Testing Pricing Inefficiency on the Stock Exchange of Mauritius

A popular Machine Learning Technique called the Support Vector Machine (SVM) is adopted on the Stock Exchange of Mauritius (SEM) to determine if stock market returns are predictable based on information from past prices, allowing arbitrage opportunities for abnormal profit generation. The serial correlation test, used as benchmark, and the SVM technique show evidence that previous information on share prices as well as the indicators constructed are useful in predicting share price movements. The implications of the study are that investors have the prospect of adopting speculative strategies and profits from trading based on information and advanced techniques and models are possible

Analysis of diabetic patients through their examination history

The analysis of medical data is a challenging task for health care systems since a huge amount of interesting knowledge can be automatically mined to effectively support both physicians and health care organizations. This paper proposes a data analysis framework based on a multiple-level clustering technique to identify the examination pathways commonly followed by patients with a given disease. This knowledge can support health care organizations in evaluating the medical treatments usually adopted, and thus the incurred costs. The proposed multiple-level strategy allows clustering patient examination datasets with a variable distribution. To measure the relevance of specific examinations for a given disease complication, patient examination data has been represented in the Vector Space Model using the TF-IDF method. As a case study, the proposed approach has been applied to the diabetic care scenario. The experimental validation, performed on a real collection of diabetic patients, demonstrates the effectiveness of the approach in identifying groups of patients with a similar examination history and increasing severity in diabetes complication

A Survey of Experimental Research on Contests, All-Pay Auctions and Tournaments

Many economic, political and social environments can be described as contests in which agents exert costly efforts while competing over the distribution of a scarce resource. These environments have been studied using Tullock contests, all-pay auctions and rankorder tournaments. This survey provides a review of experimental research on these three canonical contests. First, we review studies investigating the basic structure of contests, including the contest success function, number of players and prizes, spillovers and externalities, heterogeneity, and incomplete information. Second, we discuss dynamic contests and multi-battle contests. Then we review research on sabotage, feedback, bias, collusion, alliances, and contests between groups, as well as real-effort and field experiments. Finally, we discuss applications of contests to the study of legal systems, political competition, war, conflict avoidance, sales, and charities, and suggest directions for future research. (author's abstract