In this study we combine clustering techniques with a moving window algorithm in order to filter financial market data … outliers. We apply the algorithm to a set of financial market data which consists of 25 series selected from a larger dataset … of a cluster that represents a different segment of the market. We set up a framework of possible algorithm parameter …

. It is a bedrock assumption in theory that securities prices reveal how effectively public companies utilize capital. This …

economy.Theory and regulation have failed to keep pace with markets where traders rely on pre-programmed algorithms to execute … fully control the operation of the algorithm. Algorithms can execute many thousands of trades in milliseconds, crunching … for a trader to fully predict how an algorithm might behave ex ante and near-impossible for her to track and control its …

their prices after observing trades, as predicted by economic theory. However, in contrast to theory, AMMs charge a mark …’ exposure to adverse selection. Accordingly, the sensitivity of quotes to trades is stronger than that predicted by theory and …

the task, and the performance level of the algorithm. We found that when participants could freely decide on their final … forecast after observing the one produced by the algorithm (a condition found to mitigate algorithm aversion), the average … in the task. Experienced participants relied less on the algorithm than inexperienced participants, regardless of its …

We apply the well-known CUSUM and the Girshick-Rubin algorithm as trading strategies involving only mutually exclusive … but also the alternative Girshick-Rubin algorithm. …