Credit Enhancement through Financial Engineering: Freeport-McMoRan's Gold-Denominated Depository Shares

In 1993 and early 1994, Freeport McMoRan Copper and Gold (FCX), a mining company, issued two series of gold-denominated depositary shares to raise 430 million dollars expanding their mining capacity in Indonesia. We price the depositary shares using a term structure model for the forward rates implied by gold futures and we show that FCX successfully enhanced the credit quality of the issue. This credit enhancement is achieved because the effect of linking the payoff of the depositary shares to gold reduces default risk and is similar to conventional risk management. However, the bundling of financing and risk management allows the firm to target hedging benefits only to the newly issued securities. The design of the security also overcomes the asset substitution problem. The depositary shares issued by FCX illustrate how firms can enhance credit quality through financial engineering without changing the existing priority ordering of their capital structure.Risk management, Gold-linked, Hybrid Securities

Credit Enhancement Through Targeted Risk Managment: Freeport-McMoRan's Gold-Dominated Depository Shares

In 1993 and early 1994, Freeport McMoRan Copper and Gold (FCX), a mining company, issued two series of gold-denominated depositary shares to raise 430 million dollars for expansion of their mining capacity in Indonesia. We price the depositary shares using a term structure model for the forward rates implied by gold futures and we show that FCX successfully enhanced the credit quality of the issue. This credit enhancement is achieved because the effect of linking the payoff of the depositary shares to gold reduces default risk and is similar to conventional risk management. The building of financing and risk management, however, allows the firm to target hedging benefits only to the newly issued securities. The design of the security overcomes the asset substitution problem and credibly commits the firm to hedging. The depositary shares issued by FCX illustrate how firms can enhance credit quality through financial engineering without changing the existing priority ordering of their capital structure

Relationship Investing: Large Shareholder Monitoring with Managerial Cooperation

We characterize conditions under which a large institutional shareholder and the manager of a firm will establish relationship investing, wherein the manager actively cooperates with the institution in the monitoring process, to resolve agency problems. The setting of our model is that of a privately informed manager choosing between a project that has a faster resolution of uncertainty and a project that has a delayed resolution of uncertainty. The agency problem arises because the manager has incentives to focus on the firm's perceived market value, rather than its true long-term value, through his compensation contract and leads to investment distortions. We show that relationship investing solves the agency problem and reduces the free-riding problem associated with large shareholder monitoring. We also show that under some conditions it is optimal for shareholders to make the manager's compensation more distortionary by increasing the manger's incentives to focus on the firm's perceived market value, in order to induce him to cooperate in the monitoring process

High frequency trading from an evolutionary perspective: financial markets as adaptive systems

The recent rapid growth of algorithmic high‐frequency trading strategies makes it a very interesting time to revisit the long‐standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond timeframe and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) trading algorithm. We simulate real‐life trading by applying STGP to millisecond data of the three highest capitalized stocks: Apple, Exxon Mobil, and Google and observe that profit opportunities at the millisecond time frame are better modelled through an evolutionary process involving natural selection, adaptation, learning, and dynamic evolution than by using conventional analytical techniques. We use combinations of forecasting techniques as benchmarks to demonstrate that different heuristics enable artificial traders to be ecologically rational, making adaptive decisions that combine forecasting accuracy with speed

Estimating the Cost of Executive Stock Options: Evidence from Switzerland

It is often argued that Black-Scholes (1973) values overstate the subjective NEWLINE value of stock options granted to risk-averse and under-diversified executives. NEWLINE We construct a “representative” Swiss executive and extend the certainty- NEWLINE equivalence approach presented by Hall and Murphy (2002) to assess NEWLINE the value-cost wedge of executive stock options. Even with low coefficients NEWLINE of relative risk aversion, the discount can be above 50% compared to the NEWLINE Black-Scholes values. Regression analysis reveals that the equilibrium level NEWLINE of executive compensation is explained by economic determinant variables NEWLINE such as firm size and growth opportunities, whereas the managers’ pay-forperformance NEWLINE sensitivity remains largely unexplained. Firms with larger NEWLINE boards of directors pay higher wages, indicating potentially unresolved NEWLINE agency conflicts. We reject the hypothesis that cross-sectional differences in NEWLINE the amount of executive pay vanish when risk-adjusted values are used as NEWLINE the dependent variable

Applications of Genetic Programming to Finance and Economics: Past, Present, Future

While the origins of Genetic Programming (GP) stretch back over fifty years, the field of GP was invigorated by John Koza’s popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the Genetic Programming Bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics

Density estimation through quasi-analytic Monte-Carlo simulation: Options arbitrage with transactions costs

Discretely rebalanced options arbitrage strategies in the presence of transaction costs have path dependent returns that are difficult to model analytically. I instead use a quasi-analytic procedure that combines the computational efficiency of analytical solutions with the flexibility of simulations. The central feature is the estimation of the distribution of returns of the arbitrage strategy by mapping simulated returns percentiles and the input parameter set. Using the estimated density, I evaluate the tradeoff between transaction costs and risk exposure under generalized transaction costs structures that includes bid-ask spread and brokerage commission. I show that the optimal strategy depends on transaction costs, volatility, and option moneyness. Strategies such as rebalancing when the hedge ratio changes by 0.25, balances transaction costs and risk exposure, and can be optimal. Copyright Springer Science+Business Media, LLC 2007Rebalancing, Discrete, Frequency, Options arbitrage, Density estimation, Transactions cost,

Relationship Investing: Large Shareholder Monitoring with Managerial Cooperation

We characterize conditions under which a large institutional shareholder and the manager of a firm will establish relationship investing, wherein the manager actively cooperates with the institution in the monitoring process, to resolve agency problems. The setting of our model is that of a privately informed manager choosing between a project that has a faster resolution of uncertainty and a project that has a delayed resolution of uncertainty. The agency problem arises because the manager has incentives to focus on the firm's perceived market value, rather than its true long-term value, through his compensation contract and leads to investment distortions. We show that relationship investing solves the agency problem and reduces the free-riding problem associated with large shareholder monitoring. We also show that under some conditions it is optimal for shareholders to make the manager's compensation more distortionary by increasing the manger's incentives to focus on the firm's perceived market value, in order to induce him to cooperate in the monitoring process.