This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning...

Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of...

We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and policy analysis. To do this, we exploit archives of the model code, coefficients, baseline databases and stochastic shock sets stored after each FOMC meeting from the model's...

This paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyse text data to determine the sentiment and the emotion embedded in the Tweets and use them as explanatory variables to predict stock market movements....

I study 46 vintages of FRB/US, the principal macro model used by Federal Reserve Board staff for forecasting and policy analysis, as measures of real-time model uncertainty. I also study the implications of model uncertainty for the robustness of commonly applied, simple monetary policy rules. I...

This paper aims to solve the problem of information asymmetry in the supply chain through block-chain technology. We consider a drop shopping supply chain, which includes one supplier and one retailer who has more accurate demand forecast. The demand forecast of the retailer is mainly based on...

In econometrics, long memory models for variance modeling like FIGARCH or FIAPARCH are characterized by a Fractional Differencing term. In order to estimate and apply these models, the infinite MacLaurin expansion of the differencing term has to be truncated at a certain level. We transfer the...

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment....

Conditional quantile prediction involves estimating/predicting the quantile of a response random variable conditioned on observed covariates. The existing literature assumes the availability of independent and identically distributed (i.i.d.) samples of both the covariates and the response...