Credit default swaps (CDSs) are a major financial innovation related to debt contracting. Because CDS markets facilitate bad news being incorporated into equity prices via cross-market information spillover, CDS availability may curb firms’ information hoarding. We find that CDS trading on a firm’s debt reduces the future stock price crash risk. This effect is stronger in active CDS markets, when the main lenders are CDS market dealers with securities trading subsidiaries, or when managers have more motivation to hoard information. Our findings suggest that debt market financial innovations curtail the negative equity market effects of firms withholding bad news.

I use an accounting reform to assess the agency cost of debt in diversified firms. Those firms that switch from single to multiple segments following the reform suffer a 12% increase in their bond spread when compared with their stand-alone peers. Consistent with lenders anticipating underinvestment and asset-substitution incentives, diversified firms with high cash-flow volatility across divisions suffer the highest increase in borrowing costs. I employ a novel approach that allows abstracting from unobservable characteristics that would otherwise influence the pricing of diversified firms’ debt.

According to a now classic study of stock market price behavior by Fama [6], the empirical distributions of daily log price relatives are usually stable Paretian, non-Gaussian. However, there appears to have been substantial reluctance to accept Fama's [6] research results as indicative of a fundamental return generating process which is stable Paretian, non-Gaussian. Blattberg and Gonedes [1], Clarke [4], Officer [18], Praetz [19], and Press [20] have each in their own way questioned the Fama [6] results. Most recently Hsu, Miller, and Wichern (HMW) [13] have suggested that in periods of homogeneous activity for a firm the empirical distribution of rates of return on a common share may be Gaussian, in other words, that the fundamental return generating process may be normal.

In the standard setting in which an individual or firm has preferences for probabilistic monetary outcomes that satisfy the Neumann-Morgenstern [10] assumptions of “rational behavior” and has an exponential utility function for money, a popular index for evaluating any proposed single-period probabilistic project is its “risk-adjusted value” (RAV), i.e., its certainty equivalent, the certain amount of money that has the same utility as the expected utility for the project. Since the exponential function is completely characterized by just a single parameter, its risk aversion level, in comparing mutually exclusive projects one can simply plot their RAVs or their expected utilities as a function of this parameter and then, for any given value or range of values of the parameter, read off which project is best, i.e., has the highest RAV or, equivalently, the highest expected utility. (See, e.g., [7], p. 203 or [2].)

In the past most of the studies whose aim was to explain earnings-to-price ratios (or alternatively, price-earnings ratios) of common stocks attempted to answer the much-debated question raised in both investment and academic circles: Why does a given common stock consistently command a higher price relative to its earnings vis-a-vis other stocks? Success in answering this question has been limited, largely due to (1) researchers' inability to incorporate expected earnings (E) correctly into the empirical measurement of earnings-to-price ratio and/or (2) inadequate treatment of risk variables to explain variability of earnings-to-price ratio.

We propose a new approach, based on investment data, to determine firms’ return exposure to investment-specific technology (IST) shocks. When applied to U.S. data, we find that, in contrast to the pattern estimated from empirical IST proxies, value firms have higher exposure to IST shocks than growth firms. When applied to simulated data from existing theoretical models, our approach reveals that existing empirical findings may result from measurement errors in the IST proxies. Importantly, our simulation analysis uncovers the key role played by investment data in determining the economic mechanism through which IST shocks affect cross-sectional asset prices.

During the past 20 years several theoretical valuation models with empirically testable implications regarding the impact of financial decisions, particularly debt policy and dividend policy, have appeared in the financial literature. Some of these models have been subjected to extensive testing. However, results of these investigations are inconclusive and/or conflicting as to the impact of financial policies on share value. For instance, on the basis of a study of electric utility firms, Miller and Modigliani (M&M) find that neither dividend nor debt policies of the firm affect the value of the firm. On the other hand, Weston, as well as Brigham and Gordon, disagree with M&M's findings based upon their own investigations of electric utility firms.

This paper reexamines the empirical relation between inflation and interest rates concentrating on the tax effect proposed by Darby and Feldstein. Using the random walk intercept model, relative responses of taxable yields and tax-exempt yields to expected inflation are estimated. The results show that for the sample period 1953–1982, the nominal yields on Treasury bills rise at a rate greater than one-for-one with expected inflation, while the nominal yields on default-free municipal bonds rise approximately one-for-one with expected inflation. Thus, the tax-adjusted Fisher hypothesis by Darby and Feldstein is empirically supported. The components of Treasury bills are also extracted and their variances are compared. It is shown that variation of expected inflation is not overwhelmingly larger than that of the after-tax real rate. Therefore, on an after-tax basis, changes in the real rate explain a substantial portion of changes in the nominal rate of interest.

Errors in recorded security prices are a source of misspecification in the market model. If recorded price errors are sufficiently nonrandom, they result in biased returns and in biased and inconsistent estimates of market model regression coefficients. This paper argues that tax-induced flow-supply pressures cause end-of-the-year recorded price errors to be nonrandom enough to create the appearance of anomalous turn-of-the-year stock return behavior. Empirical tests of returns and market model regression coefficients during the turn-of-the-year period cannot reject this errors-in-variables explanation of the turn-ofthe-year effect.

The purpose of this article is to explore the relevance of the theory of Markov processes to the analysis of stock price movements.

The present study was prompted by the work of Dryden [6], in which aggregate data on United Kingdom share prices were analyzed within a Markovian framework, and which indicated that it might be fruitful to apply the Markov model to more disaggregated data, specifically to individual stock price data.

This paper analyzes the optimal quality decision of a producer in a multi-period setting with reputation effects. Using a unique database of returns on real estate limited partnerships (RELPs), we empirically examine alternative theoretical predictions of optimal producer strategy. In particular, we test whether the producers in our market invest in reputation building by initially selling high quality goods and then lowering quality. Using a variety of statistical tests, we find evidence consistent with reputation building, both in the aggregate and for individual developers.

In recent years we have seen significant progress in the development of models of capital asset pricing and the related models of portfolio selection and performance appraisal. The central focus, in all of these models, has been one of dealing directly with the difficult and perplexing issues of the measurement of risk and the underlying theories of investor choice-making under uncertainty.

We document the unique structure of the peer-to-peer lending market. Originally designed as decentralized, the market has become highly, but not fully, reintermediated. The platforms’ software now performs essentially all tasks related to loan evaluation, whereas most lenders are passive and automatically fund most applications on offer. Yet unlike banks, and in contrast to theories predicting full reintermediation, the platforms provide detailed loan information, and some active loan pickers coexist with passive investors. We argue that while intermediation attracts unsophisticated passive investors, transparency in the presence of active investors resolves the lending platform’s moral hazard problem inherent in intermediated markets.

Existing studies relate the puzzling low average returns on out-of-the-money (OTM) index call and put options to nonstandard preferences. We argue the low option returns are primarily due to the pricing of market volatility risk. When volatility risk is priced, expected option returns match the realized average option returns. Moreover, consistent with its theoretical effect on expected option returns, the volatility risk premium is positively related to future index option returns and this relationship is stronger for OTM options and at-the-money straddles. Finally, we find the jump risk premium contributes to some portion of OTM put option returns.

We study how U.S. manufacturing firms’ investment responds to tariff reductions in supplier industries. Our estimates, based on tariff reductions following multinational trade agreements, suggest that a hypothetical 10% reduction of all upstream tariffs would increase downstream investment by 4% to 6%. This estimate is not explained by decreasing uncertainty and stems from tariff reductions for homogeneous and low-R&D inputs, consistent with the investment response resulting from cost reductions rather than superior foreign technology embodied in imported inputs. Evidence from an instrumental variable estimation using the sudden increase in Chinese import penetration suggests that import competition also increases downstream investment.

In recent years the cost of fuel to operate power plants for electric utilities has increased much faster than many utility executives and regulators have anticipated. In the presence of substantial regulatory lag in adjusting rates, stockholders have had to absorb the difference between revenues and unanticipated increases in fuel expenses. In an effort to shorten the time required for increased costs to be reflected in increased prices to consumers, many firms have been allowed to use an automatic fuel adjustment clause (FAC) to pass on increased costs to consumers as they occur. The FAC usually allows the firm to adjust the price of electricity when the price of fuel deviates from some fixed base price. Presently more than 40 of the 50 state regulatory commissions allow some type of FAC.

Under certainty the firm's average cost of capital is a directly observable magnitude — the rate of interest. Under uncertainty the firm–s average cost of capital is an ex ante expectational concept which is not directly observable. In a seminal application of their prior theoretical contributions to corporation finance, Miller and Modigliani (M-M) [11] obtained indirect econometric estimates of the cost of capital for electric utility firms. A sample of electric utilities was ideal for an empirical application of the A M-M valuation model which is rooted in the partial equilibrium framework of an equivalent risk class. For other industries where interfirm differences in operating risk are greater, the equivalent risk class assumption would be less appropriate. That is, each firm in a heterogeneous industry may be considered in a unique risk class and the concept of an industry cost of capital would be suspect. Furthermore, the M-M theoretical construct does not provide insights into the relationship among costs of capital of firms in divergent risk classes.

In a recent article in this Journal, Amir Barnea [1] proposes a criterion for assessing the market-making efficiency of New York Stock Exchange specialists. The appealing aspects of Barnea's method are that it uses publicly available data (common stock prices) and operates on a variable of primary concern to investors, the variance of returns on common stock. The difficulty we see in applying his approach, however, is that Barnea's performance criterion can be sensitive to a number of factors in addition to any impact the specialist might have, and that effective specialist intervention might have either a positive or negative impact on the performance measure. Thus, his specialist ranking seems to be quite misleading, and his empirical findings appear to be amenable to a substantially different interpretation than that which he provides.

This paper examines the effects of different types of private debt on firm cash balances, equity risk, and investment. Firms with more bank loans have more cash and investment, but lower equity risk. Firms with more nonbank private debt have more cash, lower equity risk, and less investment. Firms with more unused credit lines have less cash and lower equity risk, but greater investment. Results suggest that financial intermediaries' monitoring intensity increases with loan size. Depending on type, private debt mitigates information asymmetry or asset substitution, or both. Deposit relations associated with bank borrowing also contribute to banks' information advantage.