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Generalized DEA model of fundamental analysis and its application to portfolio optimization
Fundamental analysis is used in asset selection for equity portfolio management. In this paper, a generalized data envelopment analysis (DEA) model is developed to analyze a firm’s financial statements over time in order to determine a relative financial strength indicator (RFSI) that is predictive of firm’s stock price returns. RFSI is based on maximizing the correlation between the DEA-based score of financial strength and the stock market performance. This maximization involves a difficult binary nonlinear program that requires iterative re-configuration of parameters of financial statements as inputs and outputs. We utilize a two-step heuristic algorithm that combines random sampling and local search optimization. The proposed approach is tested with 230 firms from various US technology-industries to determine optimized RFSI indicators for stock selection. Then, those selected stocks are used within portfolio optimization models to demonstrate the usefulness of the scheme for portfolio risk management.
The Determination of Optimum Buffer Stock Intervention Rules
It has been established for some time in the case of linear supply and demand curves that are subject to parallel stochastic shift factors that the joint welfare of trading partners measured as the sum of consumers' and producers' surpluses is increased by a reduction of price instability. This paper seeks to combine this result with information on the costs of buffer stock schemes to establish the optimum degree of price stabilization. The maximization of the partners' joint expected net benefit yields an optimum intervention price range that will vary with demand and supply elasticities, and other market conditions.
A note on the determinants of unexpected exchange rate movements
The Dynamics of Short-Term Interest Rate Volatility Reconsidered
Abstract In this paper we present and estimate a model of short-term interest rate volatility that encompasses both the level effect of Chan, Karolyi, Longstaff and Sanders (1992) and the conditional heteroskedasticity effect of the GARCH class of models. This flexible specification allows different effects to dominate as the level of the interest rate varies. We also investigate implications for the pricing of bond options. Our findings indicate that the inclusion of a volatility effect reduces the estimate of the level effect, and has option implications that differ significantly from the Chan, Karolyi, Longstaff and Sanders (1992) model.
Contingent Capital: The Case of COERCs
Abstract This paper introduces and analyzes a new form of contingent convertible: a call option enhanced reverse convertible (COERC). If an issuing bank’s market value of capital breaches a trigger, COERCs convert to many new equity shares that would heavily dilute existing shareholders, except that shareholders have the option to purchase these shares at the bond’s par value. COERCs have low risk: They are almost always fully repaid in cash. Yet, they reduce government bailouts by replenishing a bank’s capital. COERCs’ design also avoids problems with market-value triggers, such as manipulation or panic, while reducing moral hazard and debt overhang.
A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia
This paper develops a model for interdependent demand for health insurance and health care under uncertainty to throw light on the issue of insurance-induced distortions in the demand for health care services. The model is used to empirically analyse the determinants of the choice of health insurance type and seven types of health care services using micro-level data from the 1977–78 Australian Health Survey. Econometric implementation of the model involves, simultaneously, issues of discreteness of choice, selectivity and stochastic dependence between health insurance and utilization. Health status appears to be more important in determining health care service use than health insurance choice, while income appears to be more important in determining health insurance choice than in determining health care service use. For a broad range of health care services both moral hazard and self selection are found to be important determinants of utilization of health care services.
New Books on the Principle of Population
Journal Article New Books on the Principle of Population Get access C. P. Wright C. P. Wright Food Research Institute, Stanford University, Calif. Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 38, Issue 4, August 1924, Pages 666–682, https://doi.org/10.2307/1884596 Published: 01 August 1924
Status in Markets
This project tests for the effect of social status in a laboratory experimental market. We consider a special “box design” market in which a vertical overlap in supply and demand ensure that there are multiple equilibrium prices. We manipulate the relative social status of our subjects by awarding high status to a subset of the group based on one of two procedures. In the first, a subject's score on a trivia quiz determines his or her status; in another, subjects are assigned randomly to a higher-status or lower-status group. In both treatments we find that average prices are higher in markets where higher-status sellers face lowerstatus buyers, and lower when buyers have higher status than sellers. Across all sessions, the higher-status side of the market captures a greater share of the surplus, earning significantly more than their lower-status counterparts.
Nonstandard Errors
ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.