Deterring nuclear weapons proliferation

In the wake of the dissolution of the Soviet Union and the end of the Cold War, the problem of nuclear proliferation has become one of the primary issues of strategic concern The proliferation problem is an old one, but the problem today is very different from that of even a few years ago. calling for new approaches. By considering demand and supply factors, the old approaches that rely on limits on the technology or supply appear inadequate in the current situation Rather, the best approaches today are those that influence demand, making it less attractive for nations to develop nuclear weapons. The constraints on the United States that inhibit it from using its nuclear weapons to threaten new nuclear states make both nuclear deterrence and guarantees to countries not acquiring such weapons ineffectual in preventing further proliferation. The use of conventional forces to prevent proliferation or to protect threatened nations may also be politically difficult unless there are clear national interests at stake.
A possible way out of this dilemma is to develop a system of conventional deterrence using precision guided munitions and other recent technological developments that have increased the accuracy of weapons delivery systems. Such a system of conventional deterrence could play an important role in making nuclear weapons of less perceived value to potentially proliferating nations and in protecting regions that these nations threaten.     

INTERNATIONAL LISTINGS, THE SECURITY MARKET LINE AND CAPITAL MARKET INTEGRATION: THE CASE OF US LISTINGS ON THE LONDON STOCK EXCHANGE

This paper examines for international capital market segmentation by testing for changes (both inter-temporally and inter-beta) in the parameters of the riskreturn pricing relationship caused by the listing of US stocks on the London Stock Exchange (LSE) between 1965 and 1987. It is hypothesized that international listings reduce the negative effects associated with barriers to international investments, help integrate world markets and therefore decrease internationally listed stock's required returns. Significant negative deviations from the Sharpe-Lintner (SL) pre-listing pricing relationship during the postlisting period are therefore expected, primarily caused by decreases in the intercept parameter. We find, in support of the hypothesis, significant negative deviations from the predictions of SL for our sample, although they do not appear to have an intertemporal dimension. These deviations are largely associated both with decreases in the value of the SL model's intercept parameter and with low beta firms, and point toward some integration benefits from US listings on the LSE.     

Privatisation in Russia Has Led to Criminalisation

Abstract
Russia has recently embarked on a dramatic program of privatisation as part of the process of shock therapy which aims to convert the command economy to a market economy. This program has been undertaken in the absence of a well-developed set of institutions needed for the proper functioning of a market economy. These institutions include a legal system, a related system of property rights, a credit system, a system of commercial banks, classified advertising, an accounting system and others including a sound currency and a social safety net. In the absence of these institutions the incentives facing agents in economic transactions have been perverted. This has led to criminalisation of the economy, dramatic falls in production, loss of confidence in all aspects of commerce, and political instability. The recent Russian experience of privatisation has important lessons for other economies in transition, for developing economies and for economic theory.     

VOLUNTARY DISCLOSURE OF SEGMENT INFORMATION: FURTHER AUSTRALIAN EVIDENCE

This study provides further empirical evidence on incentives for Australian firms to voluntarily report segment information. Various economic reasons why firms may elect to present segment information have been hypothesised in previous research. Bradbury [1992] and McKinnon and Dalimunthe [1993] found firm size, minority interest and industry membership as significant identifiable characteristics motivating voluntary segmental disclosure. Variables found to be insignificant in Bradbury [1992] which were not examined by McKinnon and Dalimunthe [1993] are tested in this paper. Hypotheses relating to size, financial leverage, assets-in-place, earnings volatility, ownership diffusion, outside equity (minority) interest, overseas association as well as diverse and mining and oil classification hypotheses are empirically examined. Univariate tests and multivariate logit analysis suggest that for a extensive sample of diversified firms, voluntary segment disclosure is significantly related to size, leverage and involvement in mining or oil activities.     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.     

Do Expected Shifts in Inflation Affect Estimates of the Long-Run Fisher Relation?

Recent empirical studies suggest that nominal interest rates and expected inflation do not move together one-for-one in the long run, a finding at odds with many theoretical models. This article shows that these results can be deceptive when the process followed by inflation shifts infrequently. We characterize the shifts in inflation by a Markov switching model. Based upon this model's forecasts, we reexamine the long-run relationship between nominal interest rates and inflation. Interestingly, we are unable to reject the hypothesis that in the long run nominal interest rates reflect expected inflation one-for-one.