In the data mining and machine learning fields, forecasting the direction of price change can be generally formulated as a supervised classfii cation. This paper attempts to predict the direction of daily changes of the Nasdaq Composite Index (NCI) and of the Standard & Poor's 500 Composite...

This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes' consequences reflecting on the Internet. The study database was formed based on online users' search engine requests regarding the terms "Cyberattacks on the computer systems...

We show that classical insurance models based on some compound distributions can well predict information leakage by cyber incidents with reducing the computational cost thanks to the model’s simplicity. We use the negative binomial distribution, a renewal process and a Hawkes process as the...

We document that a theoretically founded, real-time, and easy-to-implement option-based measure, termed synthetic-stock difference (SSD), accurately estimates the part of stock's expected return arising from stock's transaction costs. We calculate SSD for U.S. optionable stocks. SSD can be more...