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Investment Models (investment + models)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	80%

Selected Abstracts

The Role of Uncertainty in Investment: An Examination of Competing Investment Models Using Commercial Real Estate Data

REAL ESTATE ECONOMICS, Issue 1 2000
A. Steven Holland
Neoclassical investment decision criteria suggest that only the systematic component of total risk affects the rate of investment, as channeled through the built-asset price. Alternatively, option-based investment models suggest a direct role for total uncertainty in investment decisionmaking. To sort out uncertainty's role in investment, we specify and empirically estimate a structural model of asset-market equilibrium. Commercial real estate time-series data with two distinct measures of asset price and uncertainty are used to assess the competing investment models. Empirical results generally favor predictions of the option-based model and hence suggest that irreversibility and delay are important considerations to investors. Our findings also have implications for macroeconomic policy and for forecasts of cyclical investment activity. [source]

Games Hospitals Play: Entry Deterrence in Hospital Procedure Markets

JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY, Issue 3 2005
Leemore S. Dafny
Strategic investment models, though popular in the theoretical literature, have rarely been tested empirically. This paper develops a model of strategic investment in inpatient procedure markets, which are well-suited to empirical tests of this behavior. Potential entrants are easy to identify in such markets, enabling the researcher to accurately estimate the entry threat faced by different incumbents. I derive straightforward empirical tests of entry deterrence from a model of patient demand, procedure quality, and differentiated product competition. Using hospital data on electrophysiological studies, an invasive cardiac procedure, I find evidence of entry-deterring investment. These findings suggest that competitive motivations play a role in treatment decisions. [source]

Allocation of quality improvement targets based on investments in learning

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 8 2001
Herbert Moskowitz
Abstract Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across-the-board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an "across-the-board" allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684,709, 2001 [source]