Anomaly

Prediction of stock returns is always one of the most important discussions of financial markets, which has led to introducing of various models to pricing financial assets, one of the most important of these models is to measure the surplus returns by Fama &  French model was introduced in the form of a 5-factor model which, in spite of its satisfaction with the model, is still in conflict with many anomalies in the market, which the model can not explain, in the same way The purpose of this paper is to examine the strength of Five Factor Model of Fama & French (2015) for explaining volatility as a market anomaly.The sample consists of 168 companies listed in Tehran Stock Exchange. Portfolio Analysis is the approach of this paper for testing explanatory power of the Five Factor Model. Results show that profitability and investment factors couldn’t explain excess returns. This conclusion contradicts the model of Fama and French (2016).

The majority of wireless sensor network (WSN) security protocols state that a direct connection from an attacker can give them total control of a sensor node. A high level of security is necessary for the acceptance and adoption of sensor networks in a variety of applications. In order to clarify this issue, the current study focuses on identifying abnormalities in nodes and cluster heads as well as developing a method to identify new cluster heads and find anomalies in cluster heads and nodes. We simulated our suggested method using MATLAB tools and the Database of the Intel Research Laboratory. The purpose of the performed simulation is to identify the faulty sensor. Using the IBRL database, sensors that fail over time and their failure model is the form that shows the beats in the form of pulses, we find out that the sensor is broken and is of no value. Of course, this does not mean that the sensor is invasive or intrusive. We have tried by clustering through Euclidean distance that identify disturbing sensors. But in this part of the simulation, we didn't have any data that shows disturbing sensors, it only shows broken sensors. We have placed the sensors randomly in a 50 x 50 space and we want to identify the abnormal node.