Knowledge that Transforms

This article presents a general framework for identifying and modeling the joint-tail distribution based on multivariate extreme value theories. We argue that the multivariate approach is the most efficient and effective way to study extreme events such as systemic risk and crisis. We show, using returns on five major stock indices, that the use of traditional dependence measures could lead to inaccurate portfolio risk assessment. We explain how the framework proposed here could be exploited in a number of finance applications such as portfolio selection, risk management, Sharpe ratio targeting, hedging, option valuation, and credit risk analysis.

We study benefits received by target chief executive officers (CEOs) in completed mergers and acquisitions. Certain target CEOs negotiate large cash payments in the form of special bonuses or increased golden parachutes. These negotiated cash payments are positively associated with the CEO's prior excess compensation and negatively associated with the likelihood that the CEO becomes an executive of the acquiring company. Regression estimates suggest that target shareholders receive lower acquisition premia in transactions involving extraordinary personal treatment of the CEO. Target CEOs experience very high turnover rates both at the time of acquisition and, for those who remain employed, for several years thereafter.

The experimental results of prospect theory (PT) reveal suggest that investors make decisions based on change of wealth rather than total wealth, that preferences are S-shaped with a risk-seeking segment, and that probabilities are subjectively distorted. This article shows that while PT's findings are in sharp contradiction to the foundations of mean-variance (MV) analysis, counterintuitively, when diversification between assets is allowed, the MV and PT-efficient sets almost coincide. Thus one can employ the MV optimization algorithm to construct PT-efficient portfolios.

We derive the optimal investment policy of a risk-averse investor in a market where there is a textbook arbitrage opportunity, but where liabilities must be secured by collateral. We find that it is often optimal to underinvest in the arbitrage by taking a smaller position than collateral constraints allow. Even when the optimal policy is followed, the arbitrage portfolio typically experiences losses before the final convergence date. In fact, its initial performance may be indistinguishable from that of a conventional portfolio with a poor track record. These results have important implications for the role of arbitrageurs in financial markets.

Little is known about the timing and execution of open market repurchases. U.S. firms are under no obligation to disclose when they are trading, and generally report only quarterly changes in shares outstanding. We use 64 firms' supplementally disclosed repurchase trading data to provide the first examination of repurchase timing and execution. Across the days reported in our sample, firms adopted a variety of execution styles ranging from immediate intense repurchasing to delayed and smoothed repurchasing. We find no clear evidence that repurchases are timed to coincide with, precede, or follow, days on which information is released. We benchmark the costs and value of a given repurchase program against naive accumulation strategies achieving the same terminal portfolio. While there is considerable variation across the firms, NYSE firms on average beat their benchmarks, whereas NASDAQ firms do not. Finally, we document the liquidity impact of open market repurchases. We find that repurchasing contributes to market liquidity by narrowing bid-ask spreads and attenuating the price impact of order imbalances on days when repurchase trades are completed.

We investigate resource allocation decisions in conglomerates when managers are motivated by career concerns. When divisional cash flows are differentially informative about managerial ability, we show that it is in the managers' interest to overallocate unobservable intangible resources to the more informative divisions. Anticipating this bias, it is optimal for the firm's owners to also overallocate observable capital to the more informative divisions. The model provides rationale for corporate socialism and corporate hedging. It also highlights a cost of segment reporting and tracking stocks, namely, that they allow managers to distort their perceived ability at the expense of investors.

This article presents a theory of capital allocation that shows how the use of net present value (NPV) as an investment criterion leads to inefficient capital budgeting outcomes and how this criterion may be dominated by other capital budgeting criteria, like the internal rate of return and the profitability index. The essence of our theory is rooted in the mainstream paradigm of corporate finance: while firms use NPV to measure the addition to firm value from prospective projects, "classical" informational and agency considerations prevent it from implementing the optimal capital budgeting outcome. Our theory also identifies conditions when alternative criteria should be used. Finally, we characterize when direct monitoring through capital budgeting dominates compensation contracts in alleviating the agency problem.

There are relatively few direct tests of the economic effects of asymmetric information because of the difficulty in identifying exogenous information measures. We propose a novel exogenous measure of information based on the quality of property tax assessments in different regions and apply this to the U.S. commercial real estate market. We find strong evidence that information considerations are significant. Market participants resolve information asymmetries by purchasing nearby properties, trading properties with long income histories, and avoiding transactions with informed professional brokers. The evidence that the choice of financing is used to address information concerns is mixed and weak.

This article develops an asset allocation framework that incorporates prior beliefs about the extent of stock return predictability explained by asset pricing models. We find that when prior beliefs allow even minor deviations from pricing model implications, the resulting asset allocations depart considerably from and substantially outperform allocations dictated by either the underlying models or the sample evidence on return predictability. Under a wide range of beliefs about model pricing abilities, asset allocations based on conditional models outperform their unconditional counterparts that exclude return predictability.

In uniform auctions, buyers choose demand schedules as strategies and pay the same "market clearing" price for units awarded. Despite the widespread use of these auctions, the extant theory shows that they are susceptible to arbitrarily large underpricing. We make a realistic modification to the theory by letting prices, quantities, and bids be discrete. We show that underpricing can be made arbitrarily small by choosing a sufficiently small price tick size and a sufficiently large quantity multiple. We also show how one might improve revenues by modifying the allocation rule. A trivial change in the design can have a dramatic impact on prices. Our conclusions are robust to bidders being capacity constrained. Finally, we examine supply uncertainty robust equilibria.