A normative theory of capital budgeting requires determination of the correct cost of capital for the evaluation and selection of risky investment projects. Since different uses of funds within the firm may involve different degrees of uncertainty, the normative theory should take into account the effects of changes in the composition of the firm's portfolio of productive assets on its market valuation. The normative theory must therefore be based on a positive theory of market valuation. The objective of this paper is to develop and test an empirical specification of the positive theory.

This paper tests conditional capital asset pricing models in a multivariate GARCH framework employing both constant and time-varying prices of market and bond risk. Depending on the interpretation of the market portfolio, the ICAPM with one hedge portfolio or the two-factor myopic CAPM are supported using weekly data from July 1983 to December 1989. In contrast, we reject the myopic single-factor CAPM under a constant price of market risk. We find that interest rate risk is highly significant, which suggests that previous rejections of the conditional CAPM using only stock market data may be due to omitted hedge terms or an incomplete market factor.

We examine the yield curve behavior and the relative performance of affine term structure models (ATSMs) using government bond yield data from Canada, Germany, Japan, the U.K., and the U.S. We find strong predictability of forward rates for excess bond returns and reject the expectations hypothesis in all five countries. A three-factor model is sufficient to capture movements in the yield curve of Canada, Japan, the U.K., and the U.S., but may not be enough for Germany. An exhaustive comparison among ATSMs with no more than three factors reveals that the three-factor essential affine model (A1(3)E), with only one factor affecting the volatility of the short rate but with all three factors affecting the price of risk, performs best in all five countries. Simulations provide inconclusive evidence on whether this best affine model can successfully generate the rich yield curve behavior observed in the data.

Ambiguity and learning about the equity premium can simultaneously explain the low fraction of financial wealth allocated to stocks over the life cycle and the stock market participation puzzle. Individuals are ambiguous about the size of the equity premium and are averse to this ambiguity, resulting in lower stock allocations over the life cycle, consistent with the data. As agents get older, they learn about the equity premium and increase their allocation to stocks. Furthermore, I find that ambiguity leads to underdiversification, home bias, lower Sharpe ratios, and higher savings. Similar results cannot be obtained by assuming higher risk aversion.

We study the role played by the informal links, or “connections,” between the chief executive officer (CEO) and the divisional managers of conglomerate organizations. Using data on a large sample of multisegment U.S. corporations from 1996 to 2004, we show that segments run by connected managers receive more investment and exhibit lower sensitivity to cash flow shortfalls (and exhibit higher sensitivity to other segments’ cash flow). At the firm level, having more connected managers presiding over segments with high Tobin’s Q improves resource allocation and increases firm value. These findings are consistent with the hypothesis that the mutual trust associated with connections reduces the need for wasteful reallocation of resources across divisions of conglomerate firms.

We test whether asymmetric preferences for losses versus gains affect the prices of cash flow versus discount rate risk. We construct a return decomposition distinguishing cash flow and discount rate betas in up and down markets. Using U.S. data, we find that downside cash flow and discount rate betas carry the largest premia. Downside cash flow risk is priced consistently across different samples, periods, and return decomposition methods. It is the only component of beta with significant out-of-sample predictive ability. Downside cash flow premia mainly occur for small stocks, while large stocks are compensated for symmetric cash-flow-related risk.

Based on the Beveridge-Nelson (1981) decomposition of an ARIMA process, I present a measure of true stock index value that is not directly observable due to infrequent trading of stocks. The technique is illustrated with daily observations of the Russell 2000 index. This new measure might well prove useful in studies of lead-lag relationships between index derivatives and spot market and futures basis measurements.

The investment performance of professionally managed portfolios, in general, and mutual funds, in particular, has been the subject of considerable attention in finance. Fama [9] has suggested that overall portfolio performance be broken down in such a manner that the individual sources of performance can be identified. Two basic sources are: (1) the ability of the portfolio manager to forecast price movements of individual common stocks relative to stocks in general (selectivity or microforecasting); and (2) the ability to forecast the direction of the stock market relative to fixed income securities (timing or macroforecasting).

What I have attempted to do in this paper and a companion paper [1] on dividend-signaling is to delineate two “polar cases” of signaling in which firms either can or cannot (at all) directly communicate the ex-post profitability of their business without moral hazard. In this paper, we assume that they can, and signaling through dividends or earnings forecasts “merely” serves to bring forward the timing of communication of insiders' expectation of profitability to the outside market. The model is “nondissipative” because the incentivestructure that leads to self-selection using the signal is based on market value revisions which are themselves based on the discrepancy between the signal and the ex-post indicator, not on any exogenous or “third party” costs, unlike the model in the companion paper [1].

Macro announcements change the equilibrium risk-free rate. We find that Treasury prices reflect part of the impact instantaneously, but intermediaries rely on their customer order flow after the announcement to discover the full impact. This customer flow informativeness is strongest when analyst macro forecasts are most dispersed. The result holds for 30-year Treasury futures trading in both electronic and open-outcry markets. We further show that intermediaries benefit from privately recognizing informed customer flow, as their own-account trading profitability correlates with customer order access.

We study consumption and investment decisions given realistic time-varying constraints on borrowing. We first consider the case where borrowing is constrained by a maximum debt-to-income ratio. We then consider collateral borrowing with a maximum loan-to-value ratio. The resulting implications for optimal policies differ considerably from those obtained in the existing literature based on fixed borrowing limits but are consistent with those documented in the empirical literature.

Since call option trading started on the Chicago Board Options Exchange (CBOE) in April 1973, the interest shown by both the investment and academic communities has grown as rapidly as the volume of option trading. In May 1973, the first full month of trading on the CBOE, a total of 34,599 contracts were traded; during 1976, the monthly volume reached 1.5 million contracts on the CBOE and 800,000 contracts on the American Stock Exchange. At present the New York Stock Exchange and certain regional exchanges are evaluating the feasibility of adapting option trading for their respective exchanges.

A market is commonly called thin if a large change in price is associated with a small change in supply or demand. The concept of thinness can refer to the markets for stocks, bonds, any category of financial instrument, or even any type of good. Most frequently, thinness has been casually discussed with regard to bond markets and stock markets.

This paper reexamines a familiar but unsettling result in the foreign exchange literature: that the forward rate is not an unbiased predictor of the future spot rate. The paper outlines why some frequently used tests of unbiasedness are non-informative in the sense that they are incapable of correctly testing the hypothesis. Specifically, many of these tests are based on regressions that suffer from simultaneity bias, resulting in biased and inconsistent estimators. This is true whether the tests are conducted using stationary or non-stationary data. We demonstrate this point both analytically and with simulations. Tests of co-integration, which are not subject to the critique presented in the paper, generally fail to reject unbiasedness.

In an efficient capital market, prices fully reflect available information and adjust to new information in a rapid and unbiased fashion. As a result, prices provide unbiased estimates of the underlying values. No known trading rule or security selection strategy which uses only publicly available information would provide an investor with the ability to earn, on average, positive “abnormal” returns in a market that is efficient in the semi-strong sense. Thus, a finding that common stocks selected, using a readily available, widely disseminated set of rules which requires only publicly available information for decision-making purposes, earn, on average, positive abnormal returns represents strong contradictory evidence regarding the semi-strong form of the efficient markets hypothesis.

Someone has said that maternity is a matter of fact, while paternity is, at best, a matter of opinion. Concerning the field to which this issue of the Journal is devoted, however, there is little difference of opinion — Harry Markowitz is almost universally considered to be the father of portfolio analysis. The essence of the theory appeared in his 1952 article, although the full exposition was not available until several years later.

There is a considerable body of empirical research in accounting devoted to the analysis of relationships between accounting numbers and security prices. Very roughly, this body of research may be classified into three different categories: (i) share price valuation models and the determination of market equity values; (ii) the measurement of “unexpected earnings” and their contemporaneous association with security returns; (iii) the forecasting of future security returns. The selection and definition of accounting numbers in most of these types of studies have, by and large, been quite heuristic. The accounting variables are usually selected with little consideration given to their empirical time-series behavior; more important appears to be their intrinsic economic connotations. The approaches can thus be thought of as stipulating the existence of “real” economic variables, e.g., real income for a period, and then using numbers of published accounting statements as estimates of the real variables. The errors in estimates of the true variables are then often minimized by the use of aggregation procedures and the diversification effects of such procedures. The postulating of real economic variables has another methodological advantage: it permits the use of comparative statics analysis of corporate behavior and its effect on equity risk and return. For example, Hamada [7], among others, has shown that leverage affects risk in the usually hypothesized manner but this analytical result depends on the assumption that leverage and earnings are real and unambiguous economic variables without “measurement” errors.

This paper extends the benchmark macro-finance (MF) model by introducing, next to the standard macroeconomic factors, additional liquidity-related and return-forecasting factors. Liquidity factors are obtained from a decomposition of the money market spread, while the return-forecasting (risk premium) factor is extracted by imposing a single-factor structure on the 1-period expected excess holding return. The model is estimated on U.S. data using Markov chain Monte Carlo techniques. Two findings stand out. First, the model significantly outperforms most structural and nonstructural MF yield curve models in terms of the cross-sectional fit of the yield curve. Second, financial shocks have a statistically and economically significant impact on the yield curve.

This paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.

Using unique transactions data for individual high-frequency trading (HFT) firms in the U.K. equity market, we examine the extent to which the trading activity of individual HFT firms is correlated with each other and the impact on price efficiency. We find that HFT order flow, net positions, and total volume exhibit significantly higher commonality than those of a comparison group of investment banks. However, intraday HFT order flow commonality is associated with a permanent price impact, suggesting that commonality in HFT activity is information based and so does not generally contribute to undue price pressure and price dislocations.