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Purpose – This study examines whether or not holding a greater percentage of real assets significantly impacts the risk and risk‐adjusted return of U.S. based multinational companies. Design/methodology/approach – A series of rolling Two Stage Least Squares (2SLS) regression models are used to analyse the relationships among corporate real assets, systematic risk (beta), and risk‐adjusted return. Findings – The results of this study show that U.S. based multinational companies do have lower betas. However, U.S. based multinational companies’ cross border real asset holdings do not affect diversification and do not provide significantly higher risk‐adjusted returns to stockholders. Originality/value – This study builds upon the prior work of Seiler, Chatrath and Webb to consider multinational firms. This had never been done previously.

Using an instant response device within the context of a controlled experiment, we find that people’s self‐assessment of susceptibility to normative influence (SNI) differs substantially from the actual, or true, degree to which they are influenced by the actions of others. Actual SNI, a subconscious reaction to the behavior of those around us, can be altered when participants (falsely) believe their peers differ in their willingness to sign a new lease under various rental reduction incentives when their landlord has defaulted on his mortgage. The results are insensitive to eight alternative measures of actual SNI. This study supports the behavioral finance literature relating to herding in that we show people are very much willing to follow the lead of their peers, even in situations where information gain is not the likely derived benefit. Instead, people appear to herd in our study for social reasons.

Arditti (1973) was the first article to discuss the weighted average cost of capital (WACC). Since then, numerous papers have fine tuned the exact definition and interpretation of the WACC and how it can be used in capital budgeting as a cutoff rate [Ang (1973), Babcock (1985), Ben‐Horin (1979), and Miles and Ezzell (1980)]. To date, however, no article has quantified the magnitude and frequency of capital budgeting errors. The purpose of the article is to show the significance and frequency of errors that will occur when the WACC is even slightly miscalculated.

Given the paucity of good international unsecuritized real estate data and the growing interest in diversifying real estate only and mixed‐asset portfolios beyond national borders, this study introduces an unsecuritized Japanese total return index for office real estate dating back to 1970. Moreover, this study analyses risk and return characteristics and compares them with the risk and return characteristics of US domestic unsecuritized office real estate. The results indicate that first, National Council of Real Estate Investment Fiduciaries (NCREIF) and the Mitsubishi Trust and Banking Corporation and IKOMA Data Service System Co. Ltd (MTB‐IKOMA) geographic region returns are generally from a normal distribution, although neither is without exception; second, all ten sub‐indices suffer from autocorrelation at two or three lags even after lower‐order effects have been removed; third, returns in Japan are historically higher, although so too are the risks; and most importantly fourth, while returns within Japan and the USA, separately, are highly correlated, correlation between Japan and the USA is quite low. In sum, given the results of this study, it is likely that Japanese unsecuritized real estate could be used to diversify international real estate only, and mixed‐asset portfolios.

Reviews previous research on the announcement effects of changes in US Federal Reserve policies and presents a study of the impact of federal funds rate and discount rate changes since the current chairman took office (1988) on the treasury bills/bonds and stock markets. Uses event study methodology to show that there is no significant effect on cumulative excess returns for any of the three markets, although the treasury bill market displayed the greatest reaction; and that market reactions were similar for either type of rate change.

This study examines the historical return behavior surrounding foreseeable or expected special closings of the New York Stock Exchange. Pre and Post special closing return behavior supports no existing hypothesis, but instead clearly demonstrates a new pre and post special closing effect similar to the pre and post holiday effect found by Ariel (1990, 1987).

Outlines previous research on reasons for the many failures of US thrifts and loans during the 1980s, and suggests that crime played a larger part than is generally supposed. Explains the regulatory system set up to deal with failed thrift institutions and analyses all disposals made by the Resolution Trust Corporation between 1989 and 1995, including its referrals for criminal investigation. Shows for each year and in total the proportions disposed of by insured deposit transfers, pay‐outs and purchase and assumption contracts; the value of assets/liabilities involved; and the cost of resolution to the taxpayer. Goes on to show that the failed thrifts affected by criminal activity (almost half!) accounted for almost 80 per cent of total resolution costs and were more likely to be larger institutions. Contrasts these results with previous research, recognizes the limitations of the study and calls for further investigation.

Despite its shortcomings, the IRR method continues to be a widely employed evaluation technique in capital budgeting. This paper demonstrates the reasons for its continued popularity. Specifically, the non‐requirement of a discount rate is suggested to be an important factor in the choice of IRR over the NPV criterion. A major implication is that managers face a very elusive, or stochastic, discount rate for NPV analysis. Thus, the aversion to NPV may go beyond simple aesthetics.

Refers to previous research on the relationship between returns for different asset classes and on cointegration; and applies Johansen’s (1988) methodology to develop a prediction model. Uses 1978‐1995 data on five US asset classes (treasury bills, long‐term bonds, large capitalization common stocks, unsecuritized real estate and securitized real estate equity) to investigate cointegration between them. Shows that the index of unsecuritized real estate is positively related to treasury bills and negatively related to long‐term bonds and securitized real estate; and that returns for it can be forecast more accurately by using VECM models rather than unrestricted VAR models. Considers the implications for portfolio allocation, compares the results with other research fundings and calls for further research.

Agent-based modelling and simulation (ABMS) has seen wide-spread success through its applications in the sciences and social sciences over the last 15 years. As ABMS is used to model more and more complex systems, there is going to be an increase in the number of input variables used within the simulation. Any uncertainty associated with these input variables can be investigated using sensitivity analysis, but when there is uncertainty surrounding several of these input variables, a single parameter sensitivity analysis is not adequate. Latin hypercube sampling (LHS) offers a way to sample variations in multiple parameters without having to consider all of the possible permutations. This paper introduces the application of LHS to ABMS via a case study that investigates the mortgage foreclosure contagion effect. This paper aims to discuss these issues.

Traditionally, uncertainty surrounding a single input variable is investigated using sensitivity analysis. That is, the variable is allowed to change to determine the impact of this variation on the simulation's output. When there is uncertainty about multiple input variables, then the number of simulation runs required to undertake this investigation greatly increases due to the permutations that need to be considered. LHS, which was first derived by McKay et al., offers a proven mechanism to reduce the number of simulation runs needed to complete a sensitivity analysis. This paper describes the LHS technique and its applications to an agent-based simulation (ABS) for investigating the foreclosure contagion effect.

The results from the foreclosure ABS runs have been characterized as “good”, “bad” or “ugly”, corresponding to whether or not a property market crash has occurred. As the only thing that can induce a property market crash within our model is the spread of foreclosing properties, these results indicate that the foreclosure contagion effect is dependent on how much impact a foreclosed property has on the price of the surrounding properties.

This paper describes the application of LHS to an agent-based foreclosure simulation. The foreclosure model and its results have been described in Gangel et al. Given a certain output “boundary” found within these results, it was highly appropriate to conduct an extensive sensitivity analysis on the simulation's input variables. The outcome of the LHS sensitivity analysis has given further insight into the foreclosure contagion effect thus demonstrating it was a beneficial exercise.