Diversification of infrastructure projects for emergent and unknown non‐systematic risks

Allocating resources to competing large‐scale infrastructure projects involves multiple objectives. Traditional decision‐aiding methodologies focus on the trade‐offs among performance and resource objectives. Existing methodologies may fail to account for unknown and emergent risks that are typical of large‐scale infrastructure investment allocation problems. In modern portfolio theory, it is well known that a diversified portfolio can be very effective to reduce non‐systematic risks. The approach of diversification is equally important in choosing robust portfolios of infrastructure projects that may be subject to emergent and unknown risks. In this paper, we demonstrate a methodology to analyze and compare the diversification of portfolios of large‐scale infrastructure projects. We classify and explore several metrics of diversification and integrate them with risk and other performance objectives in a multiobjective approach. We test the new metrics and the methodology in a case study of hundreds of millions of dollars of infrastructure investments. The results suggest that the solutions that consider diversification are more robust to emergent risks, thus, identifying an opportunity to incorporate diversification‐based optimization methodologies to support a variety of problems involving large‐scale infrastructure investments.     

Predictive safety analytics: inferring aviation accident shaping factors and causation

This paper illustrates the development of an object-oriented Bayesian network (OOBN) to integrate the safety risks contributing to an in-flight loss-of-control aviation accident. With the creation of a probabilistic model, inferences about changes to the states of the accident shaping or causal factors can be drawn quantitatively. These predictive safety inferences derive from qualitative reasoning to conclusions based on data, assumptions, and/or premises, and enable an analyst to identify the most prominent causal factors leading to a risk factor prioritization. Such an approach facilitates a mitigation portfolio study and assessment. The model also facilitates the computation of sensitivity values based on perturbations to the estimates in the conditional probability tables. Such computations lead to identifying the most sensitive causal factors with respect to an accident probability. This approach may lead to vulnerability discovery of emerging causal factors for which mitigations do not yet exist that then informs possible future R&D efforts. To illustrate the benefits of an OOBN in a large and complex aviation accident model, the in-flight loss-of-control accident framework model is presented.     

Dynamic hedging of single and multi-dimensional options with transaction costs: a generalized utility maximization approach

We propose a new methodology for discrete time dynamic hedging with transaction costs that has three key performance features. First, the methodology can accommodate the use of a wide range of objective functions, from the use of many types of utility functions to the more traditional objectives of hedging error minimization. Second, our methodology can significantly outperform traditional dynamic hedging methodologies across a range of objective functions. Third, our methodology can be applied to both single and multi-dimensional options while analytical methods typically can only be applied to single dimensional options.     

A Test of Technical Analysis: Matching Time Displaced Generalized Patterns

We use a least squares metric to match the return pattern of a target stock with that of an out‐of‐sample‐twin. The twin with the smallest metric is found by a comprehensive period‐by‐period search of stocks in the Center for Research in Security Prices data set extending back to 1926. If technical analysis has value, targets of twins producing the highest returns in the twin postperiod should also have the highest performance in the target postperiod. Using a randomly selected sample of 66,000 return patterns, we find higher means for targets corresponding to the highest returning twin quintile. We also use regressions to risk adjust target returns and find that twin returns in the postmatch period significantly predict risk‐adjusted target returns.     

Labor unions and tax aggressiveness

We examine the impact of unionization on firms' tax aggressiveness. We find a negative association between firms' tax aggressiveness and union power and a decrease in tax aggressiveness after labor union election wins. This relation is consistent with labor unions influencing managers' in one, or both, of two ways: (1) constraining managers' ability to invest in tax aggressiveness through increased monitoring; or (2) decreasing returns to tax aggressiveness that arise from unions' rent seeking behavior. We also find preliminary evidence that the market expects these reductions around union elections and discounts firms that likely add shareholder value via aggressive tax strategies.     

Boards,CEO entrenchment,and the cost of capital

Existing research on chief executive officer (CEO) turnover focuses on CEO ability. This paper argues that board ability is also important. Corporate boards are reluctant to replace CEOs, as this makes financing expensive by sending a negative signal about board ability. Entrenchment in this model does not result from CEO power, or from agency problems. Entrenchment is mitigated when there are more assets-in-place relative to investment opportunities. The paper also compares public and private equity. Private ownership eliminates CEO entrenchment, but market signals improve investment decisions. Finally, the model implies that board choice in publicly listed firms will be conservative.     

Cointegration and detectable linear and nonlinear causality: analysis using the London Metal Exchange lead contract

This study applies linear and nonlinear Granger causality tests to examine the dynamic relation between London Metal Exchange (LME) cash prices and three possible predictors. The analysis uses matched quarterly inventory, UK Treasury bill interest rates, futures prices and cash prices for the commodity lead traded on the LME. The effects of cointegration on both linear and nonlinear Granger causality tests is also examined. When cointegration is not modelled, evidence is found of both linear and nonlinear causality between cash prices and analysed predictor variables. However, after controlling for cointegration, evidence of significant nonlinear causality is no longer found. These results contribute to the empirical literature on commodity price forecasting by highlighting the relationship between cointegration and detectable linear and nonlinear causality. The importance of interest rate and inventory as well as futures price in forecasting cash prices is also illustrated. Failure to detect significant nonlinearity after controlling for cointegration may also go some way to explaining the reason for the disappointing forecasting performances of many nonlinear models in the general finance literature. It may be that the variables are correct, but the functional form is overly complex and a standard VAR or VECM may often apply.