Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data

Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. realized variance (RV) can be computed. Commonly used models for RV forecasting suffer from strong persistence with a high sensitivity to the returns distribution assumption and they use only daily returns. Objectives: The main objective is measurement and forecasting of RV. Two approaches are compared: Heterogeneous AutoRegressive model (HAR-RV) and Feedforward Neural Networks (FNNs). Even though HAR-RV-type models describe RV stylized facts very well, they ignore its nonlinear behaviour. Therefore, FNN-HAR-type models are developed. Methods/Approach: Firstly, an optimal sampling frequency with application to the DAX index is chosen. Secondly, in and out of sample predictions within HAR models and FNNs are compared using RMSE, AIC, the Wald test and the DM test. Weights of FNN-HAR-type models are estimated using the BP algorithm. Results: The optimal sampling frequency of RV is 10 minutes. Within HAR-type models, HAR-RV-J has better, but not significant, forecasting performances, while FNN-HAR-J and FNN-LHAR-J have significantly better predictive accuracy in comparison to the FNN-HAR model. Conclusions: Compared to the traditional ones, FNN-HAR-type models are better in capturing nonlinear behaviour of RV. FNN-HAR-type models have better accuracy compared to traditional HAR-type models, but only on the sample data, whereas their out-of-sample predictive accuracy is approximately equal

Machine Learning and Portfolio Optimization: an application to Italian FTSE-MIB Stocks

A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation.A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation

Analysis and Management of the Price Volatility in the Construction Industry

The problem of price volatility as it pertains to material and labor is a major source of risk and financial distress for all the participants in the construction industry. The overarching goal of this dissertation is to address this problem from both viewpoints of risk analysis and risk management. This dissertation offers three independent papers addressing this goal. In the first paper using the Engineering News Record Construction Cost Index (ENR CCI), a predictive model is developed. The model uses General Autoregressive Conditional Heteroscedastic (GARCH) approach which facilitates both forecasting of the future values of the CCI, and capturing and quantifying its volatilities as a separate measure of risk through the passage of time. GARCH (1,1) was recognized as the best model. The maximum volatility was observed in October 2008 and results showed persistent volatility of the CCI in the case of external economic shocks. In the second paper using the same cost index (ENR CCI), the methodology of the first paper is integrated with Value at Risk concept to cautiously estimate the escalation factor in both short and long-term construction projects for avoiding cost overrun due to price volatilities and inflation. Proposed methodology was also applied to two construction projects in which the estimated escalation factors revealed satisfactory performances in terms of accuracy and reliability. Finally, the third paper addresses the price volatility from the view of risk management. It entails two objectives of identifying and ranking of potential management strategies. The former is achieved via in-depth literature review and questionnaire interviews with industry experts. The latter is done using Analytic Hierarchy Process (AHP). Quantitative risk management methods, alike those offered in foregoing papers are considered as one of the candidates in dealing with the price volatility risk. Cost, risk allocation and duration were perceived as the most significant criteria (project indicators) in construction projects. Also, Integrated Project Delivery (IPD) with respect to project duration; quantitative risk management methods with respect to the cost; and Price Adjustment Clauses (PAC) with respect to the risk allocation, were recognized as the top strategies to manage the risk of price volatilities