Shahriar Khaksari

A new approach to determining optimum portfolio mix

investors differ in their objectives complex because and constraints. In addition, qualitative and quantitative attributes often conflict with one another. There is no widely accepted decision model that integrates all relevant factors in a cohesive and logical manner to produce guidelines that satisfy investors’ objectives and constraints. The purpose of this paper is to develop such a decision model by applying Saaty’s Analytic Hierarchy Process (1980) to asset allocation. The model assists decision-makers in assessing interacting factors to derive priorities that guide them to the best alternative. Using the Analytic Hierarchy Process (AHP), we have designed a model that blends state preference and distribution approaches to investments. The model allows preferences among distribution-based risk measures, in conjunction with expectations, to determine asset allocation.

Capital budgeting in hospital management using the analytic hierarchy process

In recent years, the health care industry has been experiencing change to a degree unprecedented since the inception of the Medicare program. With traditional in-hospital care on the decline, hospitals are being forced to compete for business. They must identify within their own systems feasible alternatives for dealing with these changes and then determine which ones will best accomplish the goals of the organization. This paper offers a procedure that utilizes the analytic hierarchy process--a multicriteria decision-making tool that helps arrange the possible alternatives in hierarchical order given the priorities of relevant decision makers. An application of the method to a mid-sized hospital is presented. Although the procedure is structured, it is flexible enough to be updated for the realities of any health care institution.

Bank Executive Compensation Structure, Risk Taking and the Financial Crisis

This paper investigates (1) how the composition of executive compensation is related to a bank’s incentive to take excessive risk, (2) whether executive compensation in larger banks, especially the too-big-to-fail (TBTF) banks, induces more severe moral hazard behavior, and (3) how the relation between bank executive compensation and risk taking changes before and during the recent financial crisis. We find that bank risk measured by the Z-score and the volatility of stock returns increases with both the percentages of short-term and long-term incentive compensation. However, greater proportion of incentive pay decreases the likelihood for a bank to become a problem or failed institution. This result holds for the periods before and during the recent financial crisis. The distress-mitigating effects of incentive compensation are further confirmed by our finding that both the proportions of bonus and long-term incentives are positively related to bank valuation and performance. Interestingly, we find that TBTF banks experience greater risk taking (lower Z-score) and are more likely to be in financial distress than smaller banks. However, greater incentive compensation in TBTF banks helps reduce their insolvency risk relative to smaller institutions.

Intermarket Efficiency: An Application of Interbattery APT to Mortgage-Backed Securities

Increasing popularity of investments in mortgage-backed securities has led to closer integration of the mortgage market into traditional capital markets. Using monthly returns during 1982–1988 for common stocks, Treasury bonds and GNMA and FHLMC mortgage-backed securities, the interbattery factor analytic Arbitrage Pricing Theory of (Cho, 1984) is used to test five hypotheses for intramarket and intermarket integration. Results indicate that three to five common factors are found within the same security market, while only one to three factors are found common between different markets.The APT could not be rejected within the same security market, but was rejected in most intermarket comparisons. While risk-free rates are found to differ between markets, the risk premium tests are conclusive indicators of integration. Our results support claims that the stock, bond, and the mortgage-backed securities markets are integrated.

Implicit Government Guarantee and the CDS Spreads

It is commonly agreed that the government is more likely to step in and rescue some troubled companies labeled as “too big to fail” or “too interconnected to fail.” Since there is no formal contract between these companies and the government, this potential intervention is referred to as an implicit government guarantee. We propose a new approach of assessing and estimating the implicit government guarantee and analyze whether it is reflected in the CDS spreads. We define the implicit government guarantee for a given company as the probability that the government will bail it out in case of a default. Although the company’s size affects the likelihood of government intervention, we find that financial industry membership is a more important factor. Furthermore, we find that the implicit government guarantee is priced into the CDS spreads. The government guarantee for large companies reduces the CDS spread by 16.11 bps and for small companies only by 3.73 bps. Similarly, for the financial industry, we find that the government guarantee reduces the CDS spread by 76.29 bps and for the nonfinancial industry only by 7.50 bps.

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The impact of state ownership on performance differences in privately-owned versus state-owned banks: An international comparison

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Management of Excess Cash: Practices and Developments

tion of the AHP Model for pension fund optimum asset mix decisions was to demonstrate use of the model. We did not intend to provide exhaustive discussion of the underlying theory or derive specific conclusions with respect to our hypothetical example hierarchy. Figure 1B shows an alternate type of hierarchy, which, at first glance, looks like Figure lA, but actually not every element in Level 2 affects each element in Level 3. The hierarchy in Figure 1B is not a complete results. Gordon McMeekin states that the primary purpose of his comment on our paper is to correct some of the errors he perceives to exist in our computational procedures. He also suggests an extension to the model and advocates the use of a software package he developed for this application. We appreciate Dr. McMeekin’s interest in our work, but we believe that most of his comments stem from a misunderstanding of our methodology. Dr. McMeekin refers to ”Saaty’s Analytical Network Process.” This is the first time we have seen this term, and without a full reference we are unable to verify whether this term was actually used by Saaty or if it is Dr. McMeekin’s capsulation of the literature. AHP in its general form is modeled so that each level in the hierarchy dominates the level below it. The asset allocation problem, for example, was modeled hierarchically from higher to lower levels to develop a framework for measuring the priority of elements in different levels of the hierarchy, with respect to elements in higher levels, and with respect to the overall purpose of the hierarchy. fgures 1A and 1B show two different types of hierarchies. Figure 1A shows a model where each element in the higher level influences all the elements in the level immediately below it. Because of this complete set of impact of higher elements on the lower one.