Commonly defined, a market is efficient if prices always fully reflect available information. That market might be viewed as consisting of two major segments: an information market and pricing mechanism. The efficiency has been amply documented elsewhere. The information market, however, should be afforded increased attention. In particular, the efficiency of the information market may vary across securities and with respect to particular securities, across time. Stated another way, the degree of imperfection in the information market may vary across securities and across time, resulting in a relative efficiency phenomenon. The presence of such a phenomenon would offer research opportunities yielding a greater understanding of the functioning of the information market and the pricing of securities.

A capital asset-pricing model which relates risk and return under conditions of changing price levels has been developed in this paper. The resulting model implies that price-level changes do not affect the expected real returns on individual assets except through their impact on the return of the market portfolio. If real market returns are independent of price-level movements, the model is very much like the standard capital asset-pricing model expressed in real returns. This version of the capital asset-pricing model does not, however, resolve all the difficulties associated with changing price levels, since we have assumed that the nominal default-free rate is determined outside the model and that relative prices do not change. These limitations, however, also apply to all other single-period capital asset-pricing models.

In addition, the model was converted into nominal returns by assuming that price-level changes and the real market returns are uncorrelated. The resulting equation illustrates the difficulty involved in using nominal returns to test a model expressed in real returns. The same equation also provides a possible explanation for the noted discrepancies between the empirical' evidence found by Black, Jensen, and Scholes [3] and the prediction of the traditional capital asset-pricing model.

Rogalski-Tinic have reported a monthly pattern in ex post stock return variances that differs between small and large market capitalization firms. Maloney-Rogalski find that option prices reflect these monthly patterns ex ante. This study extends Maloney-Rogalski's work by devising an expiration-specific weighted implied standard deviation (WISD). It is found that: i) the monthly patterns in one-month WISDs are basically similar to the monthly patterns in ex post variances detected by Rogalski-Tinic for both large and small size firms, and ii) use of expiration-specific WISDs, as opposed to standard composite WISDs, results in improved performance of option pricing models.

Where rates of return are perfectly correlated, risk reduction through diversification cannot be achieved. Where rates of return are less than perfectly correlated, however, then, to the extent that these interrelationships can be known, modern portfolio theory provides a framework in which risk reduction through diversification can be achieved. Markowitz was the first to give rigorous content to the concept of portfolio diversification [14], and to introduce a formulation for treating portfolio selection as a mathematical optimization problem. In order to facilitate application of his own covariance approach, Markowitz first suggested [15, pp. 96–101], and Sharpe later developed a market model formulation according to which it is assumed that the rates of return on various securities “are related only through common relationships with some basic underlying factor” [18, p. 281]. More than 25 years have passed since Markowitz introduced his original formulation, and the literature dealing with the portfolio selection problem that he identified has grown considerably since then. Unfortunately, many problems remain which prevent full and effective implementation of this framework for investment analysis.

Prior research documents that asset growth is negatively associated with future firm performance. In contrast, we show that growth financed by product market stakeholders (i.e., “operating growth”) is positively associated with future firm performance. Investors and security analysts underestimate the positive effects of operating growth on future performance, resulting in return predictability and overly pessimistic earnings forecasts for firms with high operating growth. Future stock returns largely concentrate around subsequent earnings announcements with declining magnitudes, consistent with the error-in-expectation explanation. Results from cross-sectional tests further support the hypothesis that operating growth signals high future performance but investors underreact to it.

Jensen [6] employed the instantaneous systematic risk concept to eliminate the problem associated with time horizon. Based upon the effective rate of return argument, Cheng and Deets [3] claimed that Jensen instantaneous risk is not independent of the time horizon used in the investment analysis. They have also proposed a so-called Cheng-Deets instantaneous risk to substitute for the Jensen instantaneous risk.

Following the log normal distribution assumption, this paper has shown that Cheng-Deets instantaneous risk is identical to Jensen instantaneous risk. The relationship between finite systematic risk and instantaneous risk is also identified. The roles played by the effective and the nominal rate-of-return concepts in the capital asset pricing process are also clarified. It is shown that both Jensen and CD instantaneous risks are biased unless the investment horizon is instantaneous. A testable generalized CAPM is derived to test the instantaneous investment horizon assumption. Finally, 30 securities of the Dow- Jones industrial average were used to test the generalized CAPM derived in this paper.

This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

Investor behavior was measured on a firm-by-firm basis by the volume of transactions in the stock of a firm. While data on an individual investor by individual investor basis would be desirable, it is not as readily available as stock volume data. Volume represents a simple summation of individual actions and can be considered at least a partial disaggregation of stock-market activity.

The reasons for individual investor action were considered to be (1) a change in trade-off between risk and return, (2) the unfolding of time-dependen consumption plans, and (3) perception of information that changes expectations. It was argued that, in general, the first reason can be ignored, occurring at discrete and probably lonq intervals, and that the second reason is unlikely to have an effect over a few years on the total volume of transactions in the stock of a given firm. Thus, fluctuations in such volume were considered to reflect perception of information about the given firm by investors in that firm.

Market behavior was analyzed in terms of general market movements and market movements specific to the firm. Market-wide effects on both price changes and volume of transactions.in the stock of a given firm were filtered out. This left price changes indicating the flow of, and volume indicating a reaction to, information unique to the given firm.

Perception of information about a given firm by investors, measured as indicated above, was then examined for association with the flow of information coming onto the market as indicated by fluctuations in the price of the stock of the firm net of market-wide effects. The percentage of volume of transactions in the stock of a given firm that was explained by fluctuations in the stock price was taken as a measure of the efficiency of investor behavior with respect to the given firm. The validity of accepting this interpretation is based on the following assumptions: (1) the probability that investors' demands for a given stock continually exactly offset each other in such a manner that volume occurs without price change is negligible, (2) specialists in securities are unable to perfectly anticipate changes in market demand in such a manner that price changes occur without volume.

For almost a decade, Myron Gordon has argued repeatedly that an enterprise's dividend policy can affect its share price [2, 3, and 4]. The essence of his argument is that risk-averse investors are likely to perceive current dividends as less risky than future ones. Consequently, a corporate decision to reduce current, in favor of increased future, dividends will reduce share prices, even when the funds are invested to yield the firm's cost of capital.

Empirical research has cast so much doubt on chart readers that most capital theorists have about as much faith in charts as astronomers have in astrology. Certainly there is overwhelming evidence that attempting to predict future price changes on the basis of past price behavior is unproductive. There is, however, another aspect of technical analysis which has received much less attention from academicians. In its narrow form technical analysis seeks to forecast the direction of price movements of individual securities from past price and volume data. A second and somewhat broader type of technical analysis concentrates on the prediction of general market movements and trends relying on a broader set of information. Various market indicators are said to offer signals useful in forecasting future prices. One type seeks to measure investor sentiment through what might be called mood variables. A second type of indicator is more closely related to fundamental factors affecting future supply and demand for securities. Both types of indicators, however, are designed to be used in predicting future market movements rather than the movements of individual stock prices. This is to be contrasted with fundamental analysis which is concerned with predicting future prices of individual securities by analyzing the underlying factors related to the firm's future profitability. Most of the prior work with market indicators takes one or another proposed market indicator and examines the historical relation, between the indicator and some market index such as the Dow Jones Industrial Average.

In closed-end funds, a managed distribution policy (MDP) is a dividend commitment potentially requiring the liquidation of assets. We argue that MDPs lower managerial claims on fund assets and, when the fund is at a discount, increase shareholder value. This transfer of wealth can be rationalized by managers wishing to deter a challenge from activist shareholders through a costly proxy vote. We find strong empirical evidence that managers respond to the presence of activists using MDPs, that MDPs constitute an effective wealth transfer to shareholders, and that activists are less likely to challenge management when an MDP is in place.

Exploiting exogenous variations in corporate ratings due to sovereign credit downgrades and sovereign ceiling policies, we assess how firms respond to a reduction in credit ratings. We find that firms bounded by the sovereign ceiling significantly increase information production in response to a sovereign downgrade. The effects are stronger for firms relying more heavily on external finance and operating in a more opaque environment. Enhanced information production, in turn, affects firms’ subsequent access to bond markets. These findings suggest that firms actively manage information environments to maintain access to public debt markets.

The Capital Asset Pricing Model (CAPM), an equilibrium model for the price determination of risky assets, was developed by Sharpe [16], Lintner [9, 10] and Treynor [21], following the pioneering work of Markowitz [12, 13] and Tobin [20]. In spite of the tremendous impact of this model on the profession, the CAPM still raises many questions, and is inconsistent with a considerable body of empirical evidence.

This paper values American options on foreign assets in a stochastic interest rate economy using a two-point Geske and Johnson (1984) technique. The method requires the valuation of just two options: a European option and a twice-exercisable option. I first derive the risk-neutral distributions of asset prices under two forward risk-adjusted measures. Closed form solutions for European options on foreign assets are then obtained by applying these risk-neutral distributions. This article also provides analytic solutions for pricing twice exercisable options that are at most two-dimensional even though the valuation problem involves four risk factors at two exercise dates. I report the results of numerical evaluations of American option values using my method and show how they vary with the interest rate parameters. I also verify the accuracy of the proposed method by comparing with the benchmark values obtained from the least-square method of Longstaff and Schwartz (2001).

We use the 2015 Chinese stock market crash to study the effects of government stock purchases. The Chinese government purchased stocks to stabilize the markets through state-owned financial institutions known as the “National Team.” We find that the intervention led to reduced volatility and price informativeness. These impacts are driven by the disclosure of government portfolios. Consistent with investors having a stronger incentive to acquire government intervention information instead of fundamental news, we find reduced information production and information asymmetry following intervention disclosure. The article suggests that government stock purchases involve a trade-off between stability and informational efficiency.

In this study, we examine the effect of worldwide board reforms on the cost of debt financing. We document an increase of loan spread after a country initiates the reform. The increase is larger among firms that are more exposed to shareholder–debtholder conflicts. The results suggest that board reforms empower shareholders at the cost of debtholders. However, we also find that, while the reform component related to board independence leads to the increase in the cost of debt, the component related to audit committee independence helps decrease the cost.

An algorithm to estimate the composition of efficient sets by the stochastic dominance (SD) rules was developed and made available to the public by Porter, Wart, and Ferguson (PWF) [1]. Up to that time the computer central processing unit (CPU) time required for the determination of the SD efficient set was prohibitive for a large number of portfolios. The Porter, Wart, and Ferguson algorithm made possible studies that examined the performance of mutual funds, common stocks, and portfolios using the SD rules. Unfortunately, the FORTRAN program developed by PWF and titled D0MIN2 still requires a large amount of CPU time when the number of portfolios exceeds 100.

Bawa [3] has argued that mean-lower partial moment portfolio selection rules are more general than mean-variance rules in that they rely on fewer restrictive assumptions regarding investor utility functions and/or distributions of security returns. As with the mean-variance model, it is possible to formulate equilibrium security prices under the assumption that expected utility-maximizing investors utilize mean-lower partial moment portfolio selection rules. This paper has investigated the empirical relationship between the resultant mean-lower partial moment pricing model and the long established mean-variance pricing model.

Capital budgeting can be described as the problem of allocating scarce capital among a number of investment opportunities in such a manner that the outcome most preferred by a decision maker will result. When a single, mathematically explicit criterion is assumed, mathematical programming techniques can be applied. However, a single criterion, such as maximizing the return on investment or minimizing the risk of losing a sizable fraction of the original investment, is not appropriate for a significant number of real-world decision makers for whom two or more criteria, e.g., a judicious combination of return on investment and risk, are important. It has been argued [1] that it is usually not possible to obtain an explicit utility function for the decision maker and, consequently, that it is usually not possible to apply conventional (optimizing) mathematical programming techniques to find the most preferred outcome.

We use a unique set of equities in the when-issued market to provide new tests of the law of one price in financial markets. We compare the prices of when-issued and regular way shares of publicly traded subsidiaries and their parents around the time the subsidiaries are fully divested and we find that the when-issued shares of the subsidiary trade at a discount. Pricing differences stem from measurement factors such as exchange location and bid-ask clustering that bias the observed when-issued pricing differential away from zero. The remaining difference is due to asymmetric movements in bid and ask quotes in the two markets. We also find evidence of temporary price pressures on the date of execution of the spinoff of the subsidiary firms that bear resemblance to the pricing in the when-issued market. We interpret the evidence as consistent with the law of one price in the presence of transaction costs and microstructure phenomena.