Generating a Target Payoff Distribution with the Cheapest Dynamic Portfolio: an Application to Hedge Fund Replication

This paper provides a new method to construct a dynamic optimal portfolio for asset management in a complete market. The method generates a target payoff distribution by the cheapest dynamic trading strategy. It is regarded as an extension of Dybvig (1988a) to continuous-time framework and dynamic portfolio optimization where the dynamic trading strategy is derived analytically by applying Malliavin calculus. As a practical example, the method is applied to hedge fund replication, which extends Kat and Palaro (2005) and Papageorgiou, Remillard and Hocquard (2008) to multiple trading assets with both long and short positions.

Is agile project management applicable to construction?

This paper briefly summarises the evolution of Agile Project Management (APM) and differentiates it from lean and agile production and ‘leagile’ construction. The significant benefits being realized through employment of APM within the information systems industry are stated. The characteristics of APM are explored, including: philosophy, organizational attitudes and practices, planning, execution and control and learning. Finally, APM is subjectively assessed as to its potential contribution to the pre-design, design and construction phases. In conclusion, it is assessed that APM offers considerable potential for application in predesign and design but that there are significant hurdles to its adoption in the actual construction phase. Should these be overcome, APM offers benefits well beyond any individual project

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Industrial districts as organizational environments: resources, networks and structures

The paper combines economic and sociological perspectives on organizations in order to gain a better understanding of the forces shaping the structures of industrial districts (IDs) and the organizations of which they are constituted. To effect the combination , the resource based view (RBV) and resource dependency theory are combined to explain the evolution of different industry structures. The paper thus extends work by Toms and Filatotchev by spatializing consideration of resource distribution and resource dependence. The paper has important implications for conventional interpretations in the fields of business and organizational history and for the main areas of theory hitherto considered separately, particularly the Chandlerian model of corporate hierarchy as contrasted with the alternative of clusters of small firms coordinated by networks

Industrial districts as organizational environments: resources, networks and structures

The paper combines economic and sociological perspectives on organizations in order to gain a better understanding of the forces shaping the structures of industrial districts (IDs) and the organizations of which they are constituted. To effect the combination , the resource based view (RBV) and resource dependency theory are combined to explain the evolution of different industry structures. The paper thus extends work by Toms and Filatotchev by spatializing consideration of resource distribution and resource dependence. The paper has important implications for conventional interpretations in the fields of business and organizational history and for the main areas of theory hitherto considered separately, particularly the Chandlerian model of corporate hierarchy as contrasted with the alternative of clusters of small firms coordinated by networks.clustering; dynamics; resource-based views; resource dependency

The Self-Organisation of Strategic Alliances

Strategic alliances form a vital part of today's business environment. The sheer variety of collaborative forms is notable - which include R&D coalitions, marketing and distribution agreements, franchising, co-production agreements, licensing, consortiums and joint ventures. Here we define a strategic alliance as a cooperative agreement between two or more autonomous firms pursuing common objectives or working towards solving common problems through a period of sustained interaction. A distinction is commonly made between 'formal' and 'informal' inter-firm alliances. Informal alliances involve voluntary contact and interaction while in formal alliances cooperation is governed by a contractual agreement. The advantage of formal alliances is the ability to put in place IPR clauses, confidentially agreements and other contractual measures designed to safeguard the firm against knowledge spill-over. However, these measures are costly to instigate and police. By contrast, a key attraction of informal relationships is their low co-ordination costs. Informal know-how trading is relatively simple, uncomplicated and more flexible, and has been observed in a number of industries. A number of factors affecting firms' decisions to cooperate or not cooperate within strategic alliances have been raised in the literature. In this paper we consider three factors in particular: the relative costs of coordinating activity through strategic alliances vis-a-vis the costs of coordinating activity in-house, the degree of uncertainty present in the competitive environment, and the feedback between individual decision-making and industry structure. Whereas discussion of the first two factors is well developed in the strategic alliance literature, the third factor has hitherto only been addressed indirectly. The contribution to this under-researched area represents an important contribution of this paper to the current discourse. In order to focus the discussion, the paper considers the formation of horizontal inter-firm strategic alliances in dynamic product markets. These markets are characterised by rapid rates of technological change, a high degree of market uncertainty, and high rewards (supernormal profits) for successful firms offset by shortening life cycles.Strategic Alliances, Innovation Networks, Self-Organisation