Rational Expectations, Market Fundamentals and Housing Price Volatility

This paper derives a forward-looking rational expectations house price model and empirically tests its ability to explain short-run fluctuations in real house prices. A novel approach to proxying the imputed rents of owner-occupied housing, as a function of observable housing market fundamentals, is combined with a housing market arbitrage relation to derive a present value model for real house prices. Tests of the rational expectations, nonlinear cross-equation restrictions reject the joint null hypothesis of rational expectations and the asset-based housing price model for quarterly, single-detached house prices in the city of Vancouver, British Columbia from 1979–1991. The model fails to fully capture observed house price dynamics in two real estate booms but tracks real house prices well in less volatile times, suggesting that prices may temporarily deviate from fundamental values in real estate price cycles.     

A Dynamic-Programming Approach to Multiperiod Asset Allocation

Academicians and practitioners recently have focused a great deal of attention on the issue of retirement asset allocation. However, research on the academic side typically has assumed a static allocation of a fixed amount over the investor's lifetime, while the advice on the practitioner side has been largely ad hoc in nature. Moreover, both academics and practitioners often fail to link allocations to the individual's attitude toward risk. This paper uses several performance measures that incorporate the individual's aversion to risk and finds the allocations in the year before retirement that maximize the expected value of those performance measures. It then uses a dynamic programming procedure to roll back one year at a time to determine optimal allocations for previous years as well. We find that the traditional advice that young investors should invest more heavily in equity (with a gradual shift to more debt as they near retirement) indeed is correct, and in fact the optimal equity allocation is even higher than commonly suggested. Deviations of the growth in an individual's income from a long-term national average did not seem to significantly affect the optimal allocations. The optimal allocations, however, vary widely as a function of (1) investor attitudes toward risk and (2) accumulated savings to date. These results suggest greater care should be taken to assess and incorporate these factors into the asset-allocation decision.     

Take command of your growth

Ask senior managers to pare costs by 10%, and they know just what to do. Ask them to boost growth by 10%, and they're stymied, assuming that growth is not really something they can influence. But managers can control their company's growth if they have better information about where their revenues are coming from. Rather than sort sales by geographic market, business unit, or product line, they should break them out in a way that reveals which part of their strategy is responsible for what part of their revenue. This article presents a tool--the sources of revenue statement (SRS)--that does just that. Through straightforward calculations using data taken from a company's balance sheet, along with estimations of customer-churn and industry growth rates, the SRS enables managers to classify their revenue according to five sources of growth: continuing sales to established customers (base retention); sales won from the competition (share gain); sales that fell into their laps because the market was expanding (market position); sales from moves into adjacent markets; and sales from entirely new lines of business. Once sorted in this way, revenue can be viewed as the outgrowth of manageable circumstances. At one company, seemingly healthy 10% total revenue growth masked substantial customer defections counter-balanced only by sales in a fast-expanding market--a market that actually grew faster than the company did. Rather than doing well, the company was ceding customers and market share to competitors. Comparing the sources of revenue across divisions can uncover similarly profound insights, which can suggest smart ways to change strategy or set stretch goals. Hundreds of companies are perched atop enormous potential that they can't see and so don't exploit. The SRS can endow them with sight and, more important, with understanding.     

A taxonomy of ecopreneurship in small manufacturing firms: A multidimensional cluster analysis

This study presents a multidimensional taxonomy of “ecopreneurship” for small manufacturing firms. Based on a cluster analysis of 312 Swedish firms, four distinct clusters are identified: pioneers, green dumpers, overlookers, and recyclers. These clusters are compared regarding their level of entrepreneurial orientation and firm performance. Based on the results, and because of the resource constraints associated with small firms, managers of such companies are advised to examine the economic consequences of specific environmental business practices and to adopt a less aggregated approach to ecopreneurship. This study illustrates the usefulness of a multidimensional scale when researching environmental behaviors and is a response to the lack of an empirically based classification of ecopreneurship configurations.     

Bayesian stochastic frontier analysis using WinBUGS

Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of stochastic frontier models using the WinBUGS package, a freely available software. General code for cross-sectional and panel data are presented and various ways of summarizing posterior inference are discussed. Several examples illustrate that analyses with models of genuine practical interest can be performed straightforwardly and model changes are easily implemented. Although WinBUGS may not be that efficient for more complicated models, it does make Bayesian inference with stochastic frontier models easily accessible for applied researchers and its generic structure allows for a lot of flexibility in model specification.      

The Relative Importance of Stock, Bond and Real Estate Factors in Explaining REIT Returns

This paper examines the link between REIT, financial asset and real estate returns, and tests whether it changed subsequent to the “REIT boom” of the early 1990s. The main focus is on answering the question do REIT returns now better reflect the performance of underlying direct (unsecuritized) real estate? We develop and implement a variance decomposition for REIT returns that separates REIT return variability into components directly related to major stock, bond, and real estate-related return indices, as well as idiosyncratic or sector-specific effects. This is applied to aggregate REIT sector (NAREIT) returns as well as returns to size and property-type based REIT portfolios. Our results show that the REIT market went from being driven largely by the same economic factors that drive large cap stocks through the 1970s and 1980s to being more strongly related to both small cap stock and real estate-related factors in the 1990s. There is also a steady increase over time in the proportion of volatility not accounted for by stock, bond or real estate related factors. We also find that small cap REITs are “more like real estate” compared to larger cap REITs, at least over the 1993–1998 period. We argue that this could be a result of the institutionalization of the ownership of larger cap REITs that took place in the 1990s.     

BRICS: How important is the exchange rate pass-through?

This paper analyses the exchange rate pass-through (ERPT) into domestic consumer prices in BRICS (Brazil, Russia, India, China and South Africa) countries from mid-1990s onwards by using three different econometric approaches (i.e., the single equation approach, the VAR approach and the time-varying parameter approach). It is also studied the role of macroeconomic determinants in ERPT. Our results suggest that (a) ERPT is higher for the emerging markets with mostly floating exchange rates (Brazil, Russia and South Africa) than for the other BRICS countries; (b) exchange rate explains, on average, around the 40% of the price variance for Brazil, Russia and South Africa; and (c) inflation volatility, exchange rate volatility and openness seem to be the key macroeconomic determinants in BRICS countries.     

Do principles‐based accounting standards lead to biased financial reporting? An Australian experiment

International accounting standards are deliberately designed to be principles‐based (i.e. ‘substance over form’). With Australia's recent adoption of international accounting standards, a relevant question is, do principles‐based accounting standards lead to biased financial reporting? The present paper describes a study that analysed the consolidation judgements of senior accounting officials from Australian listed companies. Participants made consolidation judgements based on AASB 1024 Consolidated Accounts. Although AASB 1024 is not identical to IAS 27 Consolidated and Separate Financial Statements, there are many similarities and both follow a principles‐based approach. In aggregate, the present study finds that principles‐based accounting standards do not necessarily lead to biased financial reporting.     

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DMs or group of DMs preferences separately. This involves assessing the DMs or group of DMs component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.