Corporate Diversification: What Gets Discounted?

Prior literature finds that diversified firms sell at a discount relative to the sum of the imputed values of their business segments. We explore this documented discount and argue that it stems from risk–reducing effects of corporate diversification. Consistent with this risk–reduction hypothesis, we find that (a) shareholder losses in diversification are a function of firm leverage, (b) all equity firms do not exhibit a diversification discount, and (c) using book values of debt to compute excess value creates a downward bias for diversified firms. Overall, the results indicate that diversification is insignificantly related to excess firm value.     

Artificial life

E-business processes between insurance companies and their private customers allow — in comparison to conventional business processing — savings in administrative expenses up to 30 per cent. However, today’s e-business processes are often described as little transparent and stereotyped. Users, therefore, complain them being far to complex and, as a consequence, do not accept them. Relating to research in the field of customer behaviour, it has been proved that even in e-business processes customers like to be contacted and treated individually. This can be reached by corresponding to the cognitive ability and the personal involvement of each customer. By using those mechanisms information processing becomes ?hospitable‘ or even ?human-alike‘. Chatter-bots simulate human behaviour in e-business processes and will possibly replace personnel in certain areas. Therefore, chatter-bots seem a suitable instrument to introduce successfully e-business processes between insurance companies and their private customers. Two positive consequences could be reached: first, process efficiency could be raised and second, cost-savings in processing could be gained.     

Pricing U.S. Dollar Index Futures Options: An Empirical Investigation

This paper develops a pricing model and empirically tests the pricing efficiency of options on the U.S. Dollar Index (USDX) futures contract. Empirical tests of the model indicate that the market consistently overprices these options relative to the derived model. This overpricing is more pronounced for out‐of‐the‐money options than for in‐the‐money options and more pronounced for put options than for call options. To validate the above results, delta neutral portfolios are created for one‐ and two‐day holding periods and consistently generate positive arbitrage profits, indicating that on average the market overprices the options on the USDX futures contracts.     

The Cost Efficiency of Real Estate Investment Trusts: An Analysis with a Bayesian Stochastic Frontier Model

Using a stochastic frontier methodology that incorporates Bayesian statistics, this paper analyzes the cost efficiency of real estate investment trusts (REITs) by observing the deviations of the measured costs of individual REITs from a defined efficient cost frontier. Using 1995–1997 data, we extend the previous research in this area and measure REIT efficiency more precisely by isolating random measurement error from the overall deviations from the efficient cost frontier. We calculate the magnitude of each REIT's managerial inefficiency, the industry inefficiency, and returns to scale. In addition, we assess specific characteristics of REITs for their contribution to inefficiency by calculating the odds ratio that a REIT with a specific characteristic is more efficient than a REIT with an alternative characteristic. The results show that, for the years studied, REITs are relatively cost efficient with most REITs facing increasing returns to scale. Additionally, the REIT's use of debt and the REIT's management style significantly affect the cost performance of REITs during the aforementioned time period. Finally, diversification across property types, as measured, does not seem to influence REIT cost efficiency.     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.