Summary Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.

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Certificate authorities (CAs) are the main components of PKI that enable us for providing basic security services in wired networks and Internet. But, we cannot use centralized CAs, in mobile ad hoc networks (MANETs). So, many efforts have been made to adapt CA to the special characteristics of MANETs and new concepts such as distributed CAs (DCAs) have been proposed that distribute the functionality of CA between MANET nodes. In this article, we study various proposed DCA schemes for MANET and then classify these schemes according to their internal structures and techniques. Finally, we propose the characteristics of an ideal DCA system that can be used to verify the completeness of any DCA scheme. This classification and taxonomy identify the weakness and constraints of each scheme, and are very important for designing more secure, scalable, and high performance DCA systems for MANETs and other networks.

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