Recently, Lusztig and Schwab [5] drew attention to a problem which occurs when applying linear programming methods to portfolio selection where capital is rationed over several investment periods. The problem is to determine the relevant discount rate with which to calculate the net present values of the objective function when this same rate is dependent upon the optimal solution to the linear program. Lusztig and Schwab suggested a neat procedure by which it was claimed that one could overcome this difficulty and which also does not rely upon the measurement of subjective utility as did the Baumol and Quandt approach [2]. It was decided to test the Lusztig and Schwab model (afterwards called the L-S procedure for brevity) on a hypothetical problem, and doing so resulted in two conclusions:

(a) The L–S procedure, as described, is incomplete but with small modification may be useful; however,

(b) concentration upon the discount rate problem in isolation from other capital budgeting problems may well be a pointless exercise.

This paper is concerned with the validity of the conventional t tests on regression coefficients when there is serious multicollinearity between the explanatory variables. It is well known that increasing multicollinearity causes the true standard errors of regression coefficients to rise. The crucial question, however, is whether the conventional formulas will in practice reflect this rise. The purpose of this note is to show that the conventional t tests will in practice reflect this rise. But this note also points out the danger involved in mechanically dropping variables from multiple regression equations by t tests because t values of the regression coefficients may not be significantly different from zero when the true (population) values of these coefficients are in fact not zero, if the explanatory variables are highly intercorrelated.

Corporate bonds’ book-to-market ratios predict returns computed from transaction prices. Senior bonds (even investment grade) with the 20% highest ratios outperform the 20% lowest by 3%–4% annually after non-parametrically controlling for numerous liquidity, default, microstructure, and priced-risk attributes: yield-to-maturity, bid–ask spread, duration/maturity, credit spread/rating, past returns, coupon, size, age, industry, and structural model equity hedges. Spreads for all-bond samples are larger. An efficient bond market would not exhibit the observed decay in the ratio’s predictive efficacy with implementation delays, small yield-to-maturity spreads, or similar-sized spreads across bonds with differing risks. A methodological innovation avoids liquidity filters and censorship that bias returns.

We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

I propose a novel measure to identify family firms based on the number of family links between high-ranking coworkers. Leveraging this measure, I reexamine previous findings in the literature and derive four novel facts: i) Measures of stock ownership misclassify firms with a large family presence. ii) Family-run firms exhibit value stock characteristics, whereas founder-CEO firms display growth stock characteristics. iii) Family-run firms pay lower costs. iv) Family managers behave myopically. I conclude that failing to consider family links can lead to highly misleading results in the study of family firms.

We study the drift and cyclical components in U.S. Treasury bonds. We find that bond yields are drifting because they reflect the drift in monetary policy rates. Empirically, modeling the monetary policy drift using demographics and productivity trends, plus long-term inflation expectations, leads to cyclical deviations of bond prices from their drift that predict bond returns in- and out-of-sample. These bond cycles can be interpreted as term premia or/and temporary deviations from rational expectations in a behavioral framework. Through the lens of our model, we detect a significant role of the latter in determining the cyclical properties of yields with short maturities.

Two prominent views pertaining to measures of risk aversion can be found in the literature. First, Arrow [2] and Pratt [3]developed risk aversion measures based on the curvature characteristics of the individual investor's utility for wealth function. If the investor's utility for wealth function is given by V(W), then

are the Arrow-Pratt measures of absolute and relative risk aversion, respectively. The investor is risk averse or a risk lover as r(W) and r* (W) are positive or negative. The investor exhibits increasing, constant, or decreasing absolute risk aversion as while he exhibits increasing, constant, or decreasing relative risk aversion as .

This paper by Hughes, Logue, and Sweeney offers an excellent summary of recent theoretical work on the advantages of multinational firms in providing opportunities for international diversification. In addition, some interesting empirical tests of an international version of the capital asset pricing model (IAPM) are reported. Neither of these topics is original as several writers have explored the theoretical advantages of the multinational firm providing international diversification, including this discussant [1]. The IAPM has been tested by Solnik [2] and others who find that systematic risk is lower in international financial markets than in domestic ones.

The mean-variance capital asset pricing model (CAPM) of Sharpe and Lintner was extended by Brennan [3] to incorporate divergent borrowing and lending rates. He found that in equilibrium the security market line (SML) has the same structure as the SML under the single-rate CAPM of Sharpe and Lintner. That is, the expected return of a security or a portfolio remains linear in its systematic risk, with the intercept replaced by an equivalent risk-free return, which is an average of the divergent borrowing and lending rates weighted by the investors' taste parameters. The equivalent risk-free return is larger than the riskless lending rate and, hence, does not represent an inconsistency with the empirical findings by Friend and Blume [4] and by Black, Jensen and Scholes [1[ that the intercept of empirical SML estimated for the single-rate CAPM is larger than the riskless rate. Moreover, Brennan attempted to show that his construct can be extended to the extreme case where there are no riskless opportunities. The case of no riskless opportunities was of course investigated by Black [2], who generalized the CAPM and SML by inventing the concept of zero-beta port-folio to account for the same empirical problem encountered in the traditional SML tests of CAPM. Since the Sharpe-Lintner single-riskless-rate CAPM implies a perfect loan market, we may view the attempts by Black and Brennan as generalizing the CAPM by incorporating financial restrictions and loan market imperfections. Their primary motive, however, is empirical, i.e., to reconcile the results from the traditional SML tests with their generalized CAPM.

A piecewise linear weighting function for Black-Scholes implied volatilities is used in conjunction with the Black-Scholes call pricing model to approximate stochastic volatility European call prices. A sensitivity analysis is conducted to compare simulated stochastic volatility call prices to the Black-Scholes prices calculated with the weighted implied volatilities. The analysis indicates that a simple model can provide close approximations to the simulated prices with far less computational effort.

The large volume of government bonds issued since 1975 has been a strong driving force for structural change in the Japanese bond markets during the last decade. The increasing amount of outstanding government bonds has made the trading volume in the secondary market increase rapidly, accompanied with the development of interdealer trading.

Although the effects of pure diversification and synergistic mergers on market valuation have been widely discussed and there have been a number of studies estimating. merger performance using the two-parameter model of Sharpe-Lintner, the investigation of the corporate determinants of beta is of recent vintage. A variation on this theme, namely whether merger activity is impounded into beta and its speed of being impounded, is the focus of the Joehnk-Nielsen (J-N) study.

Most of the insurance oriented literature contains several levels of abstraction regarding the financial solvency of insurance companies. The first level represents those advances in the actuarial profession which are destined to eventually solve the double problem of capacity and capitalization (hereinafter referred to as the Ruin Problem). Most of these works, however, are concerned with developing the tools necessary to determine the theoretical loss distribution in insurance. The emphasis, then, is upon the mean and variance of the total loss distribution as they can be derived from the distribution of frequency and severity.

The opening of equity markets to foreign investment by developing countries appears to generate an enormously large positive growth effect (see Bekaert, Harvey, and Lundblad (2005), Journal of Financial Economics 77, 3–55) in spite of a relatively small role of such markets for financing investment in most economies. We propose a spillover channel from equity market opening to lower costs of bank loans, which helps to explain this puzzle. From analyzing bank loan data associated with China’s introduction of the Qualified Foreign Institutional Investors program, we find significant support for this channel. Furthermore, we show that a reduction in the risk premium in loans is an important mechanism.

Using detailed data on U.S. households’ locations, employment, and financial portfolios, we document that individuals employed in locally clustered industries are more likely to invest in risky assets. This pattern is strongest among individuals with high labor income, employed in skilled occupations, and with strong cognitive skills. Our overall evidence suggests the relation between industry clusters and investment decisions is best explained by clusters enhancing human capital among local industry workers, in turn amplifying their effective risk tolerance. Our findings highlight the important role of local labor market composition in generating household portfolio patterns within and across geographies.

The theory on the relationship between real and nominal interest rates is based on the well-known Fisher equation:

where: i = nominal interest rate;

r = real interest rate;

λ = percentage change in price level: P /P0 - 1 where P and P0 denote end-of-period and current levels of some aggregate price index, respectively.

In my paper utilized time-variance relationships to detect the impact of NYSE specialists on stock price changes. Schwartz and Whitcomb (SW) seem to agree that return variability and not average bid ask spreads is the appropriate measure on which performance of NYSE specialists should be evaluated. They raise, however, several objections regarding the use of time-variance relationships to evaluate specialists' performance. In particular they show that the proposed performance measure which is the average (per specialist unit) ratio of short term relative to long term return variance depends on the degree of autocorrelation in the return series. For first order serial correlation of returns they obtain,

where r is the performance measure (in terms of a single security),

T is the length of the differencing interval,

are returns variances for one day and T day intervals respectively,

ρ1,2 is first order serial correlation.

The relationship between an investor's attitude toward risk and the shape of his preference functions has long been recognized in both the general portfolio problem and the mean-variance model. By contrast, the literature has largely ignored the connection between general measures of an investor's attitude toward risk and the shape of his mean-variance or mean-standard deviation indifference curves. Yet this relationship is significant. Through general measures of risk aversion, assumptions about an investor's behavior under uncertainty imply restrictions on indifference curves. Conversely, assumptions about indifference curves impose restrictions on an investor's behavior under uncertainty. The development of this relationship and its implications is the objective of this note.

A relationship between money supply and stock prices is fairly well recognized in the literature. More recently the studies of Hamburger and Kochin [7], Modigliani [12], Keran [9], and Homa and Jaffee [8] have attempted to specify the short- and long-run nature and the direct and indirect nature of these relationships. Also, these studies have focused on determining the transition variables through which the money-supply effect is transmitted to stock prices. A more pragmatic approach is that of Sprinkel [17 and 18] and Palmer [14] who have attempted to analyze the money-supply and stock-market relationships to see if the former can be a predictor of the latter. More reliable forecasts of future market movements, if available, could be extremely useful for individual and institutional investors. At one extreme, information could be used to time the investment in and out of the market portfolio. Alternatively, the investor could more profitably use the B information on market volatility of stocks available from the capital-asset pricing model, relating expected rate of return on a security, E(Ri), with that on the market portfolio, E(Rm). Accordingly, the prediction of the market would indicate when to shift the composition of the portfolio from relatively low to high or from relatively high to low β stocks and cash.

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.