Alan J. Marcus

Deregulation and bank financial policy

The standard view that banks can value maximize by exploiting non-risk-rated deposit insurance ignores the potential loss of a valuable bank charter due to insolvency. Recognition of this effect changes the banks optimal financial policy and can induce extreme risk-averting as well as risk-taking behavior. However, as the value of the bank charter falls, the risk-taking strategy is more apt to dominate. Therefore, current deregulation of the banking industry, by easing entry and devaluing charters, holds the potential for increases in the incidence of insolvency unless offsetting policies are instituted.

What's special about the specialist?

Abstract Exchange members claim that the professional relationships that evolve on exchange floors yield benefits not easily duplicated by an anonymous exchange mechanism. We show that longstanding relationships between brokers and specialists can mitigate the effects of asymmetric information. Moreover, a specialist who actively attempts to differentiate between informed and uninformed traders can achieve equilibria that Pareto-dominate an equilibrium in which the two types of trades are pooled. Our model also elucidates the role of block trading houses in mitigating information problems in the block market.

Geometric or Arithmetic Mean: A Reconsideration

An unbiased forecast of the terminal value of a portfolio requires compounding of its initial value at its arithmetic mean return for the length of the investment period. Compounding at the arithmetic average historical return, however, results in an upwardly biased forecast. This bias does not necessarily disappear even if the sample average return is itself an unbiased estimator of the true mean, the average is computed from a long data series, and returns are generated according to a stable distribution. In contrast, forecasts obtained by compounding at the geometric average will generally be biased downward. The biases are empirically significant. For investment horizons of 40 years, the difference in forecasts of cumulative performance can easily exceed a factor of 2. And the percentage difference in forecasts grows with the investment horizon, as well as with the imprecision in the estimate of the mean return. For typical investment horizons, the proper compounding rate is in between the arithmetic and geometric values. An unbiased forecast of the terminal value of a portfolio requires the initial value to be compounded at the arithmetic mean rate of return for the length of the investment period. An upward bias in forecasted values results, however, if one estimates the mean return with the sample average and uses that average to compound forward. This bias arises because cumulative performance is a nonlinear function of average return and the sample average is necessarily a noisy estimate of the population mean. Surprisingly, the bias does not necessarily disappear asymptotically, even if the sample average is computed from long data series and returns come from a stable distribution with no serial correlation. Instead, the bias depends on the ratio of the length of the historical estimation period to that of the forecast period. Forecasts obtained by compounding at the geometric average will generally be downwardly biased. Therefore, for typical investment horizons, the proper compounding rate is in between the arithmetic and geometric rates. Specifically, unbiased estimates of future portfolio value require that the current value be compounded forward at a weighted average of the two rates. The proper weight on the geometric average equals the ratio of the investment horizon to the sample estimation period. Thus, for short investment horizons, the arithmetic average will be close to the “unbiased compounding rate.” As the horizon approaches the length of the estimation period, however, the weight on the geometric average approaches 1. For even longer horizons, both the geometric and arithmetic average forecasts will be upwardly biased. The implications of these findings are sobering. A consensus is already emerging that the 1926–2002 historical average return on broad market indexes, such as the S&P 500 Index, is probably higher than likely future performance. Our results imply that the best forecasts of compound growth rates for future investments are even lower than the estimates emerging from the research behind this consensus. The choice of compounding rate can have a dramatic impact on forecasts of future portfolio value. Compounding at the arithmetic average return calculated from sample periods of either the most recent 77 or 52 years results in forecasts of future value for a sample of countries that are roughly double the corresponding unbiased forecasts based on the same data periods. Indeed, for reasonable risk and return parameters, at investment horizons of 40 years, the differences in forecasts of total return generally exceed a factor of 2. The percentage differences between unbiased forecasts versus forecasts obtained by compounding arithmetic or geometric average returns increase with the ratio of the investment horizon to the sample estimation period as well as with the imprecision in the estimate of the mean return. For this reason, emerging markets present the greatest forecasting problem. These markets have particularly short historical estimation periods and return histories that are particularly noisy. For these markets, therefore, the biases we analyzed can be especially acute. Even for developed economies, however, with their longer histories, bias can be significant if one disregards data from very early periods.

Risk Sharing and the Theory of the Firm

When effort cannot be costlessly monitored, Pareto optimal employee compensation schemes require that owners and managers deviate from perfect risk sharing to improve the work incentives facing the manager. This article investigates the implications of this misallocation of risk for the behavior of firms in which managers make decisions for owners. The presented model predicts that, from the owners perspective, managers will exhibit excessive risk aversion and underinvest in risky projects.

Efficient Asset Portfolios and the Theory of Normal Backwardation: A Comment

In a recent paper, Carter, Rausser, and Schmitz (1983) (hereafter CRS) argue that previous studies of the risk premium on agricultural commodity futures contracts used incorrect proxies for the market index and consequently mismeasured the equilibrium risk premium on those contracts. CRS show that when an alternative market index is used to measure systematic risk, the required risk premium is positive and of economically significant magnitude. This result contradicts that of Dusak (1973) and supports the notion of a generalized Keynesian theory of normal backwardation. The purpose of this Comment is to argue that the market index constructed by CRS is inappropriate and that their empirical results stem directly from the use of this index.

The Valuation of a Random Number of Put Options: An Application to Agricultural Price Supports

We show that the U.S. agricultural price support system, and certain other government insurance programs, can be interpreted as the provision of a random number of put options to program beneficiaries. Because the number of puts being supplied is random, the value of the guarantees is no longer given by the standard Black-Scholes put option formula. This paper uses the contingent-claims methodology of modern finance theory to derive an appropriate valuation formula for such programs. We estimate the value to farmers of agricultural price supports for several commodities covered by the U.S. agricultural price support system. Our results indicate that the current system raises the ex ante value of some crops by as much as 9 percent. The method of valuation is applicable to other forms of government guarantees, as well, such as exchange rate insurance and export subsidy guarantees.

Opaque Financial Reports, R-Square, and Crash Risk

We investigate the relation between the transparency of financial statements and the distribution of stock returns. Using earnings management as a measure of opacity, we find that opacity is associated with higher R2s, indicating less revelation of firm-specific information. Moreover, opaque firms are more prone to stock price crashes, consistent with the prediction of the Jin and Myers (2006) model. However, these relations seem to have dissipated since the passage of the Sarbanes-Oxley Act, suggesting that earnings management has decreased or that firms can hide less information in the new regulatory environment.

Asset Allocation Models and Market Volatility

Asset allocation and risk management models assume at least short–term stability of the covariance structure of asset returns, but actual covariance and correlation relationships fluctuate dramatically. Moreover, correlations tend to increase in volatile periods, which reduces the power of diversification when it might most be desired. We propose a framework to both explain these phenomena and to predict changes in correlation structure. We model correlations between assets as resulting from the common dependence of returns on a marketwide factor. Through this link, an increase in market volatility increases the relative importance of systematic risk compared with the unsystematic component of returns. The increase in the importance of systematic risk results, in turn, in an increase in asset correlations. We report that a large portion of the variation in correlation structures can be attributed to variation in market volatility. Moreover, market volatility contains enough predictability to construct useful forecasts of covariance. Asset allocation and risk management models assume at least short–term stability of the covariance structure of asset returns, but actual covariance and correlation relationships fluctuate wildly, even over short horizons. Moreover, correlations increase in volatile periods, which reduces the power of diversification when it might most be desired. This phenomenon, often called “correlation breakdown,” has been widely recognized in the international context, but the pattern is even more characteristic of cross-industry correlations in a domestic context. We attempt to explain correlation breakdown and to present a framework for predicting short–horizon changes in correlation structure. We modeled correlations between assets as resulting from the common dependence of returns on a systematic, marketwide factor. Through this link, an increase in factor volatility increases the importance of systematic risk relative to the unsystematic component of returns. The result is an increase in asset correlations. We found that a simple index model with only one systematic factor can explain a surprisingly large portion of the short–horizon time variation in correlation structure. This finding suggests that univariate models of time variation in volatility, such as the ARCH (autoregressive conditional heteroscedasticity) model and its variants, which are already widely and successfully applied, can be integrated with the index model to form useful short–horizon forecasts of cross-sector correlations. We examined the source of correlation breakdown in the domestic context using returns on 12 industry groups and treating the value–weighted NYSE Index as the systematic factor; in the international context, we used returns on 10 major country indexes and treated the MSCI World Index as the systematic factor. We document that variation in cross-sector correlation is highly associated with market volatility (where “sector” means industry in the U.S. context and country in the international context). Using daily data within quarters to calculate both cross-sector correlations and the volatility of the market index, we measured the tendency for time variation in correlation (across quarters) to track time variation in market volatility. In both the domestic and international contexts, we found that correlation clearly fluctuates in line with market volatility. We found that short–term variation across time in the volatility of the market index can be used to forecast most of the time variation in correlation structure and thus guide managers in updating portfolio positions. The results are qualitatively the same in the international and domestic settings. We found considerably more country-specific volatility, however, than industry-specific volatility, which implies that, although the proposed methodology can be quite effective in the domestic setting, it will be less useful in the international setting. Having established that predictions of market volatility are useful in predicting correlation structure, we next examined the extent to which this methodology can be used in risk management applications. Can predictions of market volatility in conjunction with the index model be used to efficiently diversify portfolio risk? We compared the predictive accuracy of several forecasts of covariance and found that a constrained correlation using a simple autoregressive relationship to forecast next-quarter market variance from current-quarter market variance is highly accurate. In fact, the predictive accuracy of this model is equal to a model of “full-sample constant covariance” (i.e., a covariance estimate obtained by pooling all daily returns and calculating the single full-sample covariance matrix). This latter forecast obviously is not feasible for actual investors because it requires knowledge of returns over the full sample period. It turned out to be the best unconditional covariance estimator, but our constrained index model estimator conditioned on a forecast of market volatility was equally accurate. We conclude that portfolios constructed from covariance matrixes based on an index model and predicted market volatilities will perform substantially better than portfolios that do not account for the impact of time-varying volatility on correlation and covariance structure. The ability of market volatility to explain correlation structure suggests that univariate models of time variation in that volatility can be integrated with the index model to make useful short–horizon forecasts of cross-sector correlations.

Relative Sentiment and Stock Returns

The sentiment of retail investors relative to that of institutional investors was measured by comparing their respective portfolio allocations to equity versus cash and fixed-income securities. The results suggest that fluctuations in retail sentiment are a primary driver of equity valuations for reasons unrelated to fundamentals. In the study reported, we measured the sentiment of retail investors versus the sentiment of institutional investors by comparing their respective portfolio allocations to equity versus cash and fixed-income securities. And we considered whether fluctuation in relative sentiment is associated with variation in expected stock market returns. Whereas several other studies used indirect proxies for aggregate investor sentiment, we used actual asset allocation decisions of investors as direct evidence of their sentiment. We could thus focus on the essential meaning of sentiment: a time-varying propensity to invest in risky assets that is unrelated to fundamentals. The cost of our approach, however, is that asset allocations can reveal only the sentiment of one group of investors relative to the sentiment of another group. We found that relative sentiment is uncorrelated with indicators of absolute investor sentiment and appears to have considerable value as a (contrarian) market-timing tool at a quarterly frequency. High levels of relative retail sentiment are associated with significantly lower future excess equity returns, and the change in relative sentiment is strongly positively related to concurrent market returns. This pattern is consistent with the hypothesis that retail sentiment is more variable than institutional sentiment and retail investors move prices as they update their asset allocations to reflect their shifting sentiment rather than for reasons related to fundamentals. The relationships between relative sentiment and stock returns that we documented are economically as well as statistically significant. For example, sorting on values of our index of relative sentiment yielded an annualized average market return in the following quarter equal to 25.6 percent when relative sentiment was low (in the lower quartile of the distribution) and only 4.5 percent when relative sentiment was high (in the upper quartile). Although we follow convention in labeling shifts in retail demand for equities independent of fundamentals as “sentiment driven,” our results are fully consistent with a rational interpretation of retail-side behavior. Shifts in retail risk tolerance lead to precisely the same pattern as shifts in the optimism of cash flow forecasts (relative to fundamentals). Increases in risk tolerance will induce contemporaneous increases in both prices and retail equity allocations as the retail sector bids up shares from the institutional side and will be followed by lower future expected returns. Increases in risk aversion will work similarly. In our framework, sentiment should be interpreted broadly—and not necessarily pejoratively—as also encompassing variation in risk tolerance. Our results are consistent with a smart money/dumb money view of the world in which all investors use the same risk-adjusted discount rate but one group (institutions) is better at forecasting future prospects. The key distinction between this view and a rational interpretation (that the difference in behavior comes from time-varying risk tolerance) lies with investors’ expectations. In the smart money/dumb money interpretation, if retail investors knew the conditional expected returns that we have documented, they would alter their behavior. In the rational interpretation, they would not. Unfortunately, these two interpretations are not readily distinguished by empirical analysis.

Information, motivation, and control in decentralized planning: the case of discretionary managerial behavior

Abstract A simple piecewise-linear managerial incentive scheme is analyzed in a decision-making environment in which a manager is allowed some discretionary activity (effort). Initially, he must report to the planner a target that will be used subsequently to evaluate his performance. If managerial effort is chosen after the random production components are realized, this predicted target will be more realizable than one reported in the absence of such discretionary adjustment. The sensitivity of target and performance to the parameters of the incentive scheme and the managers utility function is examined to study the planners ability to both acquire information and motivate performance. J. Comp. Econ. , Sept. 1979, 3 (3), pp. 235–253. Wesleyan University, Middletown, Connecticut, and Massachusetts Institute of Technology, Cambridge, Massachusetts.