We present an equilibrium model of hedging for commodity processing firms. We show the optimal hedge ratio depends on the convexity of the firm’s cost function and the elasticity of the supply of the input and the demand for the output. Our calibrated model suggests that hedging tends to be ineffective. When uncertainty comes exclusively from either the supply or from the demand side, updating the hedge dynamically, and using nonlinear contracts improves hedging effectiveness. However, with both supply and demand uncertainty, hedging effectiveness can be low even with option-based and dynamic hedging strategies.

Previous literature finds anomalies are at least as prevalent in developed markets as in emerging markets; namely, the global anomaly puzzle. We show that while market development and information diffusion are linearly related, information diffusion has a nonlinear impact on anomalies. This is consistent with theoretical developments concerning the process of information diffusion. In extremely low-efficiency regimes, without newswatchers sowing the seeds of price discovery and ensuring the long-run convergence of price to fundamentals, initial mispricing and subsequent correction will not occur. The concentration of emerging countries in low-efficiency regimes provides an explanation to the puzzle.

Two prominent views pertaining to measures of risk aversion can be found in the literature. First, Arrow [2] and Pratt [3]developed risk aversion measures based on the curvature characteristics of the individual investor's utility for wealth function. If the investor's utility for wealth function is given by V(W), then

are the Arrow-Pratt measures of absolute and relative risk aversion, respectively. The investor is risk averse or a risk lover as r(W) and r* (W) are positive or negative. The investor exhibits increasing, constant, or decreasing absolute risk aversion as while he exhibits increasing, constant, or decreasing relative risk aversion as .

I propose a novel measure to identify family firms based on the number of family links between high-ranking coworkers. Leveraging this measure, I reexamine previous findings in the literature and derive four novel facts: i) Measures of stock ownership misclassify firms with a large family presence. ii) Family-run firms exhibit value stock characteristics, whereas founder-CEO firms display growth stock characteristics. iii) Family-run firms pay lower costs. iv) Family managers behave myopically. I conclude that failing to consider family links can lead to highly misleading results in the study of family firms.

A relationship between money supply and stock prices is fairly well recognized in the literature. More recently the studies of Hamburger and Kochin [7], Modigliani [12], Keran [9], and Homa and Jaffee [8] have attempted to specify the short- and long-run nature and the direct and indirect nature of these relationships. Also, these studies have focused on determining the transition variables through which the money-supply effect is transmitted to stock prices. A more pragmatic approach is that of Sprinkel [17 and 18] and Palmer [14] who have attempted to analyze the money-supply and stock-market relationships to see if the former can be a predictor of the latter. More reliable forecasts of future market movements, if available, could be extremely useful for individual and institutional investors. At one extreme, information could be used to time the investment in and out of the market portfolio. Alternatively, the investor could more profitably use the B information on market volatility of stocks available from the capital-asset pricing model, relating expected rate of return on a security, E(Ri), with that on the market portfolio, E(Rm). Accordingly, the prediction of the market would indicate when to shift the composition of the portfolio from relatively low to high or from relatively high to low β stocks and cash.

The basic model of this paper is that of a transactor who is to receive at a specified time t in the future a fixed quantity of domestic funds Ad and a fixed quantity of foreign funds Af. It is further assumed that there exists a forward market in foreign exchange in which one unit of foreign currency can be bought and sold at a given and known forward rate rf, the domestic currency price of one unit of foreign currency. Let X be the net forward purchases of foreign exchange that the transactor undertakes at the market rate rf; a negative value of X indicates net sales of forward exchange. It is assumed that at time t the foreign currency will be convertible for the transactor at a fixed but unknown spot exchange rate and that the transactor can assess or derive a probability distribution on this spot exchange rate f(rt) for rt > 0. Finally, it is assumed that the transactor can express a utility function u on the domestic currency equivalent of his ending currency holdings. This paper considers the problem of determining the optimal level of forward exchange purchases X0.

The focus of this study is the role of default risk in capital market theory. The impact of default risk on the value of securities has been a major concern of investors and academics alike. Several authors have examined the relationship between bond ratings, the probability of default, and security value [5, 12]. In this context, the ability to avoid or reduce expected bankruptcy costs and thereby increase value has been suggested as a reason for mergers and consolidations [16, 18]. In other studies, models have been developed for predicting ratings [17, 20, 21, 28], for predicting bankruptcy using accounting and other financial variables [1, 6, 7], and for approximating default premiums in the credit markets [22]. Finally a question which has received considerable attention is the effect of bankruptcy on a company's cost of capital. When bankruptcy is possible and there exists a positive bankruptcy transaction cost, it has been argued that there is an optimal capital structure [24, 26].

We study the effect of trust on debt contracting. We find that, after the revelation of option backdating, borrowers that likely backdated their previous option grants pay higher interest rates on loans. This adverse effect is mitigated by CEO replacements. Results are similar for public debt, but only if a third party identified the backdaters. After the backdating revelation, firms that engaged in backdating increase their reliance on public debt, and those without access to the public debt market experience capital constraints.

I examine whether the time-varying cost of capital is considered in firms’ capital budgeting decisions. For this test, I measure the conditional cost of equity using individual equity option prices. I find that corporate investment responds negatively to fluctuations in the option-implied cost of equity and the weighted average costs of capital. Furthermore, through decomposing marginal $ q $, I reveal that the cost-of-capital elasticity of empirical investment is almost identical to its productivity elasticity, as theory predicts. These findings suggest that firms’ discount rates are updated accurately in practice despite the failure of conventional frameworks, such as factor-based models, in this regard.

This article proposes a new method for examining the impact on a firm’s investment of uncertainty reflected in its stock-return volatility. We simultaneously address the endogeneity of uncertainty and mismeasurement in Tobin’s Q, but earlier empirical work often neglects one of the two issues. Our nonparametric estimates further suggest that the relation between investment and uncertainty is significantly decreasing and strongly concave. This result contrasts with the existing literature that widely adopts linear regressions. Ignoring nonlinearity or measurement error in Q can lead to a substantial estimation bias. However, the bias due to the endogeneity of uncertainty is small.

This study examines corporate leverage during systemic banking crises in an international setting, including 85 countries from 1987 to 2017. Using the historically determined component of institutions and exogenous variations in institution building, the analyses show that leverage cyclicality varies substantially across institutional settings. Leverage is strongly countercyclical under more binding constraints on the capital supply, suggesting important supply effects of such crises on leverage. Weak institutions are more conducive to crises and uncertainty. Leverage countercyclicality is more pronounced during crises that coincide with higher uncertainty, whereas leverage is procyclical with stronger legal systems and information sharing in capital markets.

The relationship between an investor's attitude toward risk and the shape of his preference functions has long been recognized in both the general portfolio problem and the mean-variance model. By contrast, the literature has largely ignored the connection between general measures of an investor's attitude toward risk and the shape of his mean-variance or mean-standard deviation indifference curves. Yet this relationship is significant. Through general measures of risk aversion, assumptions about an investor's behavior under uncertainty imply restrictions on indifference curves. Conversely, assumptions about indifference curves impose restrictions on an investor's behavior under uncertainty. The development of this relationship and its implications is the objective of this note.

The theory on the relationship between real and nominal interest rates is based on the well-known Fisher equation:

where: i = nominal interest rate;

r = real interest rate;

λ = percentage change in price level: P /P0 - 1 where P and P0 denote end-of-period and current levels of some aggregate price index, respectively.

In my paper utilized time-variance relationships to detect the impact of NYSE specialists on stock price changes. Schwartz and Whitcomb (SW) seem to agree that return variability and not average bid ask spreads is the appropriate measure on which performance of NYSE specialists should be evaluated. They raise, however, several objections regarding the use of time-variance relationships to evaluate specialists' performance. In particular they show that the proposed performance measure which is the average (per specialist unit) ratio of short term relative to long term return variance depends on the degree of autocorrelation in the return series. For first order serial correlation of returns they obtain,

where r is the performance measure (in terms of a single security),

T is the length of the differencing interval,

are returns variances for one day and T day intervals respectively,

ρ1,2 is first order serial correlation.

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.

We provide new evidence on how performance-based compensation plans affect CEO decision-making, especially risk-taking. Our main finding is that relative performance evaluation (RPE) plans provide incentives for CEOs to make decisions that generate more idiosyncratic performance outcomes; absolute performance evaluation (APE) plans do not. After switches from APE to RPE, the correlation between firm stock return and industry index return falls and firm idiosyncratic risk increases. Further, switches to RPE are followed by larger deviations in financial, investment, and operating policies from industry norms (i.e., more idiosyncratic strategies). All results are opposite for switches to APE.

Using real-time data, we show that currency excess return predictability is in part due to mispricing. First, the risk-adjusted profitability of systematic trading strategies decreases after dissemination of the underlying academic research, suggesting that market participants learn about mispricing from publications. Moreover, the decline is greater for strategies with larger in-sample profits and lower arbitrage costs. Second, the effect of comprehensive risk adjustments on trading profits is limited, and signal ranks and alphas decay quickly. The finding that analysts’ forecasts are inconsistent with currency predictors implies that investors’ trading contributes to mispricing and suggests biased expectations as a possible explanation.

We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to 0. We explore our text-based empirical model to show that the calls’ news content is about details behind the earnings number and the fundamentals of the firm.

We explore the effects of online customer ratings on financial policy. Using a large sample of Parisian restaurants, we find a positive and economically significant relationship between customer ratings and restaurant debt. We use the locally exogenous variations in customer ratings resulting from the rounding of scores in regression discontinuity tests to establish causality. Favorable online ratings reduce cash flow risk and increase resilience to demand shocks. Consistent with the view that good online ratings increase the debt capacity of restaurants and their growth opportunities, restaurants with good ratings use their extra debt to invest in tangible assets.