| dc.description.abstract | this paper we consider prospective decision rules that aim at extracting early signals about what decision to make, e.g. sell an asset. The decision rules are based on prospective monitoring of a statistic in order to detect a regime shift, e.g. a turn in the trend of asset prices. In the finance literature there are several suggested prospective decision rules that aim at detecting a turn in the price, for example the Filter rule and the rules that use moving averages. Another approach that has been proposed is the hidden Markov model (HMM) approach, where the price level is assumed to change between an upward and a downward trend according to a Markov chain. An approach not often used in a financial setting is statistical surveillance, which deals with the theory and methodology of online detection of an important change in the underlying process of a time series as soon as possible after it has occurred. In this paper inferential differences and similarities between statistical surveillance, two variants of the Filter rule, a rule based on moving averages and an HMM approach are investigated. A new non-parametric and robust approach never used in financial settings is proposed. Further, the purpose is to enhance the use of proper evaluation, where the timeliness of the alarm is considered. Evaluation measures and optimality criteria commonly used in statistical surveillance are reviewed and compared with those generally used in a financial setting. The methods are evaluated on Hang Seng Index. | sv |
