Regime Shifts in Asian Equity and Real Estate Markets

This paper applies a new statistical technology for identifying regime shifts to analyze recent data on real estate and equity markets in eight developing Far Eastern countries in the 1992–1998 time period. We find that regime shifts in volatility occur in the summer of 1997; however, most of the regime shifts in returns occur in the spring of 1998. While the clustering of regime breaks does not seem to follow any obvious pattern, the country's exposure to trade and firm leverage are important. An analysis of Granger causality suggests that, in most cases, equity returns cause real estate returns but the converse is not true. We also find two-way causality in volatility, suggesting that a common factor drives volatility in these markets. Finally, we provide evidence that the regime shifts generally imply higher relative risk for real estate securities after the estimated breaks.     

Model Uncertainty, Optimal Monetary Policy and the Preferences of the Fed

Monetary policy in the US is characterized by a substantial degree of inertia. While in principle this may well be the outcome of an optimizing central bank behaviour, the ability of any derived policy rule to match the data relies on so large weights for interest rate smoothing into policy makers' preferences as to be theoretically flawed. In this paper we investigate whether such a puzzle can be interpreted as resulting from the concern of monetary authorities for potential misspecifications of the macroeconomic dynamics. Accordingly, we propose a novel thick modelling approach that incorporates model uncertainty into the identification of central bank's preferences. The thick robust policy rule shows the kind of smoothness observed in the data without resorting to implausible values for the preference parameters.     

Do homeowners achieve more household wealth in the long run?

Despite the public’s faith in homeownership as a vehicle for wealth creation, there are surprisingly few empirical studies of the independent impact of homeownership and its duration on household wealth accumulation. This paper provides the first empirical evidence that homeownership, after controlling for other drivers of wealth accumulation, is positively and significantly associated with wealth accumulation over time. Using the Panel Survey of Income Dynamics, it examines the influence of housing tenure choices between 1989 and 2001 on household net wealth levels in 2001 after controlling for initial wealth in 1989, location, income, education, and other family and personal characteristics that might influence the rate of wealth accumulation. Importantly, the models used also control for the tendency of households to accumulate wealth between 1984 and 1989 (five years prior to the studied period). This approach is used to address the possibility that an unobserved variable—the propensity to save or accumulate wealth—may be associated with both the probability and duration of homeownership and the rate of wealth accumulation. All else equal, those who owned homes and owned for longer periods of time had significantly higher household net wealth by 2001. These results are compelling because house price appreciation over the period was near its long-run average while stock gains were above and real rent increases below their long-run averages. Hence, the findings are suggestive of a positive influence of ownership over long periods on net wealth, even during a period when alternative investments produced higher than normal returns and rents grew slowly. This is especially important because the overwhelmingly majority of households do not sell their homes shortly after buying them. In our sample, those who became owners typically owned for 7 years. Furthermore, most households that bought during a period of declining real home values in the early 1990s continued to own their homes for at least eight years and came out well ahead of those who did not own.     

A comparison of different trading protocols in an agent-based market

We compare price dynamics of different market protocols (batch auction, continuous double auction and dealership) in an agent-based artificial exchange. In order to distinguish the effects of market architectures alone, we use a controlled environment where allocative and informational issues are neglected and agents do not optimize or learn. Hence, we rule out the possibility that the behavior of traders drives the price dynamics. Aiming to compare price stability and execution quality in broad sense, we analyze standard deviation, excess kurtosis, tail exponent of returns, volume, perceived gain by traders and bid-ask spread. Overall, a dealership market appears to be the best candidate, generating low volume and volatility, virtually no excess kurtosis and high perceived gain.     

Drivers and implications of entrepreneurial orientation for academic spin-offs

International Entrepreneurship and Management Journal - This paper has been designed with a dual goal. On the one hand, the paper aims to examine the role of management teams, industry agents, and...     

物业服务企业员工培训工作的改进方法

物业企业具有劳动人员密集、员工学历普遍不高、重复性工作较多等特点,物业企业的日常培训需发挥培训对业务发展和经营管理的支撑作用,坚持问题导向,建立日趋完善的培训内容体系,制定有针对性的培训方案,不断丰富方法手段,贴近业务与一线。培训要紧扣企业发展目标,要以服务企业发展、提升员工素质为目标,满足企业与员工的培训需求,为企业可持续发展提供人才支撑。     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.