Learning by doing in market reform: Lessons from a regional bond fund

Local currency bond markets in East Asia and the Pacific have grown impressively since the 1997 Asian crisis, but policy authorities in the region realize they still have some work to do to allow the markets to realise their true potential. Hence, there have been a variety of regional initiatives to develop these markets. One of these initiatives is the Asian Bond Fund II, which was established by 11 central banks in East Asia and the Pacific in 2005. In creating a regional index bond fund and eight single-market funds, the central banks worked together to identify and come up with ways to reduce market impediments in eight local currency bond markets. Moreover, they built into the regional fund's structure an incentive mechanism for reducing impediments further. --

Market returns and mutual fund flows

With the increased popularity of mutual funds come increased concerns. Namely, could a sharp drop in stock and bond prices set off a cascade of redemptions by mutual fund investors and could the redemptions exert further downward pressure on asset markets? The authors analyze this relationship by using instrumental variables--a measuring technique previously unapplied to market returns and mutual fund flows--to determine the effect of returns on flows. Despite market observers' fears of a downward spiral in asset prices, the authors conclude that the short-term effect of market returns on mutual fund flows typically has been too weak to sustain such a spiral.Mutual funds ; Stock - Prices

HOLMES: A Hybrid Ontology-Learning Materials Engineering System

Designing and discovering novel materials is challenging problem in many domains such as fuel additives, composites, pharmaceuticals, and so on. At the core of all this are models that capture how the different domain-specific data, information, and knowledge regarding the structures and properties of the materials are related to one another. This dissertation explores the difficult task of developing an artificial intelligence-based knowledge modeling environment, called Hybrid Ontology-Learning Materials Engineering System (HOLMES) that can assist humans in populating a materials science and engineering ontology through automatic information extraction from journal article abstracts. While what we propose may be adapted for a generic materials engineering application, our focus in this thesis is on the needs of the pharmaceutical industry. We develop the Columbia Ontology for Pharmaceutical Engineering (COPE), which is a modification of the Purdue Ontology for Pharmaceutical Engineering. COPE serves as the basis for HOLMES. The HOLMES framework starts with journal articles that are in the Portable Document Format (PDF) and ends with the assignment of the entries in the journal articles into ontologies. While this might seem to be a simple task of information extraction, to fully extract the information such that the ontology is filled as completely and correctly as possible is not easy when considering a fully developed ontology. In the development of the information extraction tasks, we note that there are new problems that have not arisen in previous information extraction work in the literature. The first is the necessity to extract auxiliary information in the form of concepts such as actions, ideas, problem specifications, properties, etc. The second problem is in the existence of multiple labels for a single token due to the existence of the aforementioned concepts. These two problems are the focus of this dissertation. In this work, the HOLMES framework is presented as a whole, describing our successful progress as well as unsolved problems, which might help future research on this topic. The ontology is then presented to help in the identification of the relevant information that needs to be retrieved. The annotations are next developed to create the data sets necessary for the machine learning algorithms to perform. Then, the current level of information extraction for these concepts is explored and expanded. This is done through the introduction of entity feature sets that are based on previously extracted entities from the entity recognition task. And finally, the new task of handling multiple labels for tagging a single entity is also explored by the use of multiple-label algorithms used primarily in image processing

Risk management by structured derivative product companies

In the early 1990s, some U.S. securities firms and foreign banks began creating subsidiary vehicles--known as structured derivative product companies (DPCs)--whose special risk management approaches enabled them to obtain triple-A credit ratings with the least amount of capital. At first, market observers expected credit-sensitive customers to turn increasingly to these DPCs. However, the authors find that structured DPCs--despite their superior ratings--have failed to live up to their initial promise and have yet to gain a competitive edge as intermediaries in the derivatives markets.Derivative securities ; Risk

Financing the Budget Deficit in the Philippines

For the last 25 years, the government has been incurring heavy budgetary deficits. Yet, data on recent Philippine experience reveal that no well-established guidelines on how to finance a budget deficit have been formulated. This paper investigates the means and measures to finance a deficit, considers a theory that chooses an optimal mix of policy instruments and studies the experience of less developed country like the Philippines as a case in point.fiscal deficit, fiscal policy, fiscal sector