We present evidence that short sellers alternate between stock picking during expansions and market timing during recessions. First, firm-level short interest is a much stronger negative predictor of the cross-section of stock returns during expansions than it is during recessions. High short interest also only predicts negative future earnings announcement returns during expansions. We attribute these findings to short sellers’ emphasis on collecting firm-specific signals. Second, short sellers appear to make factor bets more so during recessions than during expansions. These bets tend to pay off as we observe a strong negative relation between the betas of highly shorted stocks and future stock market returns, a result that disappears during expansions. Together, these findings are consistent with theories of information acquisition under attention constraints, endogenous information production, as well as theories of time variation in aggregate overconfidence amongst traders.

Disequilibrating macro shocks affect different firms’ prospects differently, increasing idiosyncratic variation in forward-looking stock returns before affecting economic growth. Consistent with most such shocks from 1947 to 2020 enhancing productivity, increased idiosyncratic stock return variation forecasts next-quarter real GDP growth, industrial production growth, and consumption growth both in-sample and out-of-sample. These effects persist after controlling for other leading economic indicators.

This study employs variance ratios (VRs) to assess the roles of regulation and liquidity on cryptocurrency market efficiency, focusing on crypto-assets subject to varying degrees of regulation. Our findings reveal that cryptocurrencies supervised by FinCEN-licensed exchanges (IEO-L) exhibit market efficiency similar to SEC-regulated traditional stock offerings (IPOs). Conversely, noncompliant crypto-assets display higher market inefficiency. We also establish a connection between regulatory compliance and issuing entity reputation mechanisms. Our results indicate that compliance with existing regulatory norms enhances efficiency and reduces investor risks in crypto-assets. Furthermore, assets voluntarily adhering to regulatory norms can attain efficiency akin to government-regulated assets.

In a general equilibrium framework, we show that banks may “buy” political influence at a discount: They offer disproportionately small campaign contributions compared to the influence they exert, thus generating abnormal returns. We distinguish between the direct effect of contributions which, as a cost, reduce bank returns, and the indirect effect of contributions which boost returns via inducing bank-favoring policies. Therefore, abnormal returns may or may not increase with the amount of contributions, depending on which effect dominates: Stricter capital requirements decrease contributions and abnormal returns. When politicians attach more weight to households’ welfare, contributions increase and abnormal returns decrease.

Existing theory suggests that, in an unregulated market for corporate control, the level of takeovers is suboptimal because shareholders do not receive the full benefit from them. However, existing theory neglects that the threat of takeover may divert managerial effort from productive to defensive activities. This paper shows that, when this is considered, takeovers may, in fact, be excessive.

As an extension of previous works, we develop a theoretical framework for the relationship between monetary changes and market risk premia. The wealth effect and the return variability effect of money are shown to be the two important channels of the monetary impact on the market risk premium for three representative classes of utility functions. The theory also states that the market risk premium will be an increasing function of monetary changes given that the two component effects of money are positive.

Speculative bubbles have been offered to explain the excess volatility results by Shiller (1981) and LeRoy and Porter (1981). Recent work by Flood, Hodrick, and Kaplan has shown that rational speculative bubbles cannot be the explanation for the excess volatility results. This paper provides an analytical framework for examining the hypothesis that the simple present value model used for the excess volatility studies is a misspecification of the true model. The method is applied to data from a market index and four large corporations. The results are consistent with the hypothesis that the simple present value model is not the correct specification for stock market pricing.

The question of stock market efficiency has received considerable play in the financial press in recent years and understandably so. Not only is this a topic of interest to national policymakers charged with monitoring and promoting market efficiency, but answers to this question have rather important implications for the management of market participants' portfolios. Our interest in this subject focuses on a subsegment of the larger question of market efficiency, in particular on so-called technical theories of stock market behavior.

The investment managers of financial institutions face a multiplicity of factors that influence the performance of fixed coupon securities in their investment portfolios. These factors can be classified under three headings:

1) Condition of the economy–changes in interest rate levels and spreads;

2) Nature of the financial institution–institution's tax rate, economic factors such as deposit variability and loan demand for banks, and premium inflow and benefit payments for insurance companies;

3) Characteristics of individual securities–(a) their attractiveness in terms of risk level and marketability; and (b) their contractual factors (coupon level and maturity) and tax status.

This study deals with the problem of debt — composition of the firm — specifically, the optimal mix of short- and long-term debt, or the optimal maturity composition of the total debt. It can be viewed as a subproblem of the more familiar determination of the optimal mix between total debt and owner's equity (Modigliani and Miller [15 and 16], Durand [3], Schwartz [18], Solomon [20], etc.).

The purpose of this paper is to describe a computerized classroom teaching tool for use in money and banking courses. A basic macroeconomic model and simulation program are introduced, along with classroom presentations and sample output. In addition, the technical details of the simulation, including operating costs, are explained.

One of the most vexing areas of business finance is empirical validation of theory. Many problems arise that the investigator cannot avoid. The purpose of this paper is to define a class of problems that can be avoided but that have caused many previous investigators difficulty, even though they may not have realized it. In what follows, the concept of dimensional analysis, borrowed from the physical sciences, is introduced. Then dimensional analysis is extended to the interpretation of estimated regression coefficients.

In our earlier note, we drew attention to the problem of interdependency between the opportunity cost used as a discount rate in determining the net present values of the objective function and the optimal solution of a linear program. In expanding on our article, Lockett and Tompkins (L and T) rightly point to the need for an appropriate definition of opportunity costs.

When a company enters into a bond indenture or loan agreement, certain restrictions usually are placed on it. These restrictions, known as protective covenants, may have a significant influence on profitability, making the bargaining strategy of the company very important. But to bargain effectively over the restrictiveness of the protective covenants, management must know the impact that the covenants have on profitability.

Harry C. Friedman in his paper, “Real Estate Investment and Portfolio Theory,” has put his mind to an increasingly important area of financial concern, the relationship of real estate as a security in developing portfolio theory. Between now and the year 2000, it has been estimated that $1,500 billion will be spent on building and remodeling nonfarm housing. There will be an estimated $1,000 billion spent on commercial, industrial, and utility construction. In addition, $1,000 billion will probably be spent on public utilities and service institutions, plus $30 billion annually on community facilities. It is anticipated that each year in coming generations we will add to our existing inventory the equivalent of fifteen cities of 200,000 persons each. Predictions are that there will be a need for an additional 2 million dwelling units per year during the 1970's and that this need will climb steadily thereafter.

In this paper a recursive programming model is constructed and optimized so that a mix of bank assets may be selected. The purpose is to link optimization over time with a commercial bank asset management model.

It is often necessary to estimate a frequency function or certain points on a frequency function from very limited data. A usual procedure for this estimation involves two steps. From the set of “well-known” frequency functions, e.g., the normal, poisson, binomial, etc., one chooses that function which seems likely to best “fit” and then uses the available data to estimate the parameters of the chosen distribution. If no one “well-known” function can be chosen a priori, then perhaps several likely candidates are tried and the one which fits best according to some criterion is chosen. For many purposes, this procedure is quite unobjectionable.

This paper investigates optimal consumption and portfolio mixture for a new discrete-time, discrete-state preference model. In this model, the investor's preferences for future consumption depend on current wealth and on past consumption experience through a summary descriptor of past consumption. Relations between the optimal consumption/investment decisions and the wealth and summary descriptor states are found.

There is strong theoretical support for the notion that prices in a perfect capital market will vary randomly ([22], [16]). However, the existence of some nonrandomness in stock prices is well documented, see ([10], [11], [13], [14], [20]). Of special importance for this study is the research by Young [24] who finds predominantly negative autocorrelation for a sample of securities using a monthly differencing interval. Autocorrelation coefficients are often used as a measure of nonrandom price behavior; negative autocorrelation is an indication of price reversals.

The Capital Asset Pricing Model (CAPM) developed and popularized by Treynor [27], Sharpe [26], Lintner [16], Mossin [19], and Fama [6] is of the form

where

E is the expected return at time t for firm i (conditional on information available at time t); the subscript m denotes the analogous market variable; rf is the risk-free rate; and . Black [2] has developed a similar form with expected returns from a zero beta portfolio, , assuming the role of the risk-free rate.