A framework for regime identification and asset allocation

The purpose of this thesis is to examine a regime-based asset allocation strategy and evaluate whether accounting for regime-dependent risk and return of asset classes provides any significant improvement on portfolio performance. The South African market and economy are considered as a proxy for the analysis. Motivation of this thesis stems from the growing body of research by practitioners devoted to models that are reflective of the interdependency between financial assets and the real economy. The asset classes under consideration for the analysis are domestic and foreign cash, domestic and foreign bonds, domestic and foreign equity, inflation linked bonds, property, gold and commodities. In order to evaluate the performance of the regime-based strategy, this thesis proposes a framework based on Principal Component Analysis and Fuzzy Cluster Analysis for regime identification and asset allocation. The performance of the strategy is tested against two strategies that are not cognizant of regime changes. These are an equally weighted portfolio and a buy-and-hold strategy. Furthermore, relative performance analysis was performed by comparing the regime-based strategy proposed in this thesis against the Alexander Forbes Large Manager Watch Index. Due to data limitations, the analysis is done on an in-sample basis without an out-of-sample testing. The results from the analysis showed the extent of outperformance of the proposed regime-based strategy relative to an equally weighted strategy and a buy-and-hold strategy. These results were consistent with existing literature on regime-based strategies. Furthermore, the results provided strong motivation for the use of the regime identification framework together with tactical asset allocation proposed in this thesis

Rapid validity testing at the front end of innovation

To efficiently and effectively reduce the uncertainty inherent in the front‐end of innovation processes, recent literature emphasizes new approaches that facilitate rapid knowledge generation and learning such as design thinking, lean innovation, and pretotyping. However, these approaches differ in their conceptualizations and, despite their popularity, the empirical evidence on the performance relevance of such approaches for established organizations is limited. In this research, we propose rapid validity testing (RVT), in which we conceptualize and harmonize existing approaches toward a unique and comprehensive set of front‐end activities necessary to reduce uncertainty and equivocality inherent to this phase and enable planned flexibility. Drawing on information processing theory, we argue that organizations implementing RVT also increase the probability of achieving innovation outcomes of superior quality on time and within budget. We further argue that the effectiveness of RVT depends upon internal and external environmental factors. Drawing on multirespondent data collected from 1022 informants in 129 firms, we find empirical evidence that organizations implementing the RVT approach in their innovation activities achieve higher performance of their innovation programs, and that the performance relevance of RVT depends upon technological turbulence and the organization's long‐term orientation and risk propensity. We contribute to the literature by conceptualizing RVT as a set of activities that enable planned flexibility. Furthermore, we overcome empirical shortcomings of studies on popular approaches that relied primarily on anecdotal or case study evidence and imply the generalizability of their effectiveness. Our findings highlight that organizations indeed not only benefit from RVT but also challenge the notion of a one‐size‐fits‐all approach to the front end of innovation

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Resilience and coping behaviour among micro and small sized enterprises in times of economic crisis: A mixed-methods exploration of Greek and Cypriot firms

The focus of this study was to explore organisational resilience in the context of micro, small, and medium sized enterprises (SMEs). Following the 2008 Global Financial Crisis (GFC), the deteriorating business conditions worldwide posed serious challenges to many firms. SMEs were particularly affected by the volatile environment due to acknowledged operational limitations (e.g., scarce resources) restricting their capacity to effectively respond to the new business reality. The impact was extremely high for businesses in the Southern European periphery, including Greece and Cyprus. Despite the growing academic interest in the concept of resilience over the past years, it remains unclear how SMEs can develop resilience and cope with different type of shocks (e.g., 2008 GFC, COVID-19). Due to the significant role SMEs have for the Greek and Cypriot economies (e.g., employment), among other countries, and various ongoing challenges (e.g., access to finance) that leave them exposed to impending turbulences, it is urgent to further explore the antecedents of resilience and determine how SMEs can promote resilience capabilities to adapt to volatile operational conditions. Based on a mixed-methods approach (parallel mixed design), empirical data were collected via semi-structured interviews (n=135) and questionnaires (n=406) from micro and small businesses (MSEs) in Greece and Cyprus; the lack of responses from medium sized enterprises represents one of the limitations of the study. In line with a parallel mixed data analysis, a distinct thematic analysis of qualitative data resulted in several themes that reflect the post-2008 business environment in Greece and Cyprus, and the factors that influence the resilience capacity of MSEs. Additional descriptive and inferential statistical tests provided evidence regarding the performance of Greek and Cypriot MSEs after the 2008 GFC and identified the critical success factors associated with a firm’s coping capacity, among other results. Following the integration of qualitative and quantitative findings (narrative, joint display approaches), the resulting meta-inferences highlight several characteristics that influence the different resilience phases, namely anticipation (e.g., environmental scanning), coping (e.g., financial resourcefulness), and adaptation (e.g., innovative activities). The findings provide additional empirical support about the antecedents of resilience, specifically in the context of micro and small firms from Greece and Cyprus, and respond to acknowledged knowledge gaps, thereby contribute to the existing body of literature focusing on organisational resilience

Sustainability in design: now! Challenges and opportunities for design research, education and practice in the XXI century

Copyright @ 2010 Greenleaf PublicationsLeNS project funded by the Asia Link Programme, EuropeAid, European Commission

On Prospective Technology Studies

This volume includes papers to technological foresight, roadmapping and TA from two sources. On the one side it is based on a workshop in Budapest at the end of 2007, that was organized in the framework of the International Forum on Sustainable Technological Development. On the other side selected presentations from the symposium on History of Prospective Technology Studies, in the framework of the XXIII International Congress of History of Science and Technology, Budapest, July 2009