Business Integration as a Service

This paper presents Business Integration as a Service (BIaS) which enables connections between services operating in the Cloud. BIaS integrates different services and business activities to achieve a streamline process. We illustrate this integration using two services; Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS) in two case studies at the University of Southampton and Vodafone/Apple. The University of Southampton case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. The Vodafone/Apple case study illustrates statistical analysis and 3D Visualisation of expected revenue and associated risk. These two cases confirm the benefits of BIaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management of University of Southampton and potential and current investors for Vodafone/Apple

Monte Carlo simulation as a service in the Cloud

Copyright © 2015 Inderscience Enterprises Ltd. We propose a Monte Carlo simulation as a service (MCSaaS) which takes the benefits from two sides: the accuracy and reliability of typical Monte Carlo simulations and the fast performance of offering services in the Cloud. In the use of MCSaaS, we propose to remove outliers to enhance the improvement in accuracy. We propose three hypotheses and describe our rationale, architecture and steps involved for validation. We set up three major experiments. We confirm that firstly, MCSaaS with outlier removal reduces percentage of errors to 0.1%. Secondly, MCSaaS with outlier removal is expected to have slower performance than the one without removal but is kept within one second difference. Thirdly, MCSaaS in the Cloud has a significant performance improvement over a popular model on desktop. We demonstrate our approach can meet the demands for accuracy and performance

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

Active Portfolio-Management based on Error Correction Neural Networks

This paper deals with a neural network architecture which establishes a portfolio management system similar to the Black / Litterman approach

Keywords: portfolio management, financial forecasting, recurrent neural networks. Active Portfolio-Management based on Error Correction Neural Networks

This paper deals with a neural network architecture which establishes a portfolio management system similar to the Black / Litterman approach. This allocation scheme distributes funds across various securities or financial markets while simultaneously complying with specific allocation constraints which meet the requirements of an investor. The portfolio optimization algorithm is modeled by a feedforward neural network. The underlying expected return forecasts are based on error correction neural networks (ECNN), which utilize the last model error as an auxiliary input to evaluate their own misspecification. The portfolio optimization is implemented such that (i.) the allocations comply with investor’s constraints and that (ii.) the risk of the portfolio can be controlled. We demonstrate the profitability of our approach by constructing internationally diversified portfolios across ¡£¢ different financial markets of the G7 contries. It turns out, that our approach is superior to a preset benchmark portfolio.