A Dynamic Model of Characteristic‐Based Return Predictability

We present a dynamic model that links characteristic‐based return predictability to systematic factors that determine the evolution of firm fundamentals. In the model, an economy‐wide disruption process reallocates profits from existing businesses to new projects and thus generates a source of systematic risk for portfolios of firms sorted on value, profitability, and asset growth. If investors are overconfident about their ability to evaluate the disruption climate, these characteristic‐sorted portfolios exhibit persistent mispricing. The model generates predictions about the conditional predictability of characteristic‐sorted portfolio returns and illustrates how return persistence increases the likelihood of observing characteristic‐based anomalies.     

Dynamic Portfolio Choice with Stochastic Wage and Life Insurance

We study optimal insurance, consumption, and portfolio choice in a framework where a family purchases life insurance to protect the loss of the wage earner's human capital. Explicit solutions are obtained by employing constant absolute risk aversion utility functions. We show that the optimal life insurance purchase is not a monotonic function of the correlation between the wage and the financial market. Meanwhile, the life insurance decision is explicitly affected by the family's risk preferences in general. The model also predicts that a family uses life insurance and investment portfolio choice to hedge stochastic wage risk.     

Principles of Financial Regulation: A Dynamic Portfolio Approach

Economists seeking explanations for the global financial crisisof 1997–99 are reaching consensus that a major factorwas weak financial institutions, which resulted in part frominadequate government regulations. At the same time many developingcountries are struggling with an overregulated financial system—onethat stifles innovation and the flow of credit to new entrepreneursand that can stunt the growth of well-established firms. Inparticular, too many countries are relying excessively on capitaladequacy standards, which are inefficient and sometimes counterproductive.The author argues that financial systems can be reformed successfullyusing a "dynamic portfolio approach" aimed at managing the incentivesand constraints that affect not only financial institutions'exposure to risk but also their ability to cope with it. Thearticle sets out general principles of financial regulationand shows how the dynamic portfolio approach can help countriesdeal with the special problems that arise during the transitionto a more liberalized economy as well as those that arise indealing with a financial crisis similar to the 1997 crisis inEast Asia.      

Estimating Portfolio and Consumption Choice: A Conditional Euler Equations Approach

This paper develops a nonparametric approach to examine how portfolio and consumption choice depends on variables that forecast time-varying investment opportunities. I estimate single-period and multiperiod portfolio and consumption rules of an investor with constant relative risk aversion and a one-month to 20-year horizon. The investor allocates wealth to the NYSE index and a 30-day Treasury bill. I find that the portfolio choice varies significantly with the dividend yield, default premium, term premium, and lagged excess return. Furthermore, the optimal decisions depend on the investor's horizon and rebalancing frequency.     

Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets

This paper examines the optimal consumption and portfolio-choiceproblem of long-horizon investors who have access to a risklessasset with constant return and a risky asset ("stocks") withconstant expected return and time-varying precision—thereciprocal of volatility. Markets are incomplete, and investorshave recursive preferences defined over intermediate consumption.The paper obtains a solution to this problem which is exactfor investors with unit elasticity of intertemporal substitutionof consumption and approximate otherwise. The optimal portfoliodemand for stocks includes an intertemporal hedging componentthat is negative when investors have coefficients of relativerisk aversion larger than one, and the instantaneous correlationbetween volatility and stock returns is negative, as typicallyestimated from stock return data. Our estimates of the jointprocess for stock returns and precision (or volatility) usingU.S. data confirm this finding. But we also find that stockreturn volatility does not appear to be variable and persistentenough to generate large intertemporal hedging demands.     

An Exact Solution to a Dynamic Portfolio Choice Problem under Transactions Costs

The presence of any friction in financial markets qualitatively changes the nature of the optimization problem faced by an investor. It requires one to either act or do nothing, an issue which, of course, does not arise in frictionless situations. The investor considered here accumulates wealth without consuming until some terminal point in time when he consumes all. His objective is to maximize the expected utility derived from that terminal consumption. We postpone the terminal point far into the future to obtain a stationary portfolio rule. The portfolio policy is in the form of two control barriers between which portfolio proportions are allowed to fluctuate. We show how to calculate them.     

A Factor Allocation Approach to Optimal Bond Portfolio

This paper proposes a new method to a bond portfolio problem in a multi-period setting. In particular, we apply a factor allocation approach to constructing the optimal bond portfolio in a class of multi-factor Gaussian yield curve models. In other words, we consider a bond portfolio problem in terms of a factors’ allocation problem. Thus, we can obtain clear interpretation about the relation between the change in the shape of a yield curve and dynamic optimal strategy, which is usually hard to be obtained due to high correlations among individual bonds. We first present a closed form solution of the optimal bond portfolio in a class of the multi-factor Gaussian term structure model. Then, we investigate the effects of various changes in the term structure on the optimal portfolio strategy through series of comparative statics.     

Dynamic Portfolio Choice with Parameter Uncertainty and the Economic Value of Analysts' Recommendations

We derive a closed-form solution for the optimal portfolio ofa nonmyopic utility maximizer who has incomplete informationabout the alphas or abnormal returns of risky securities. Weshow that the hedging component induced by learning about theexpected return can be a substantial part of the demand. Usingour methodology, we perform an "ex ante" empirical exercise,which shows that the utility gains resulting from optimal allocationare substantial in general, especially for long horizons, andan "ex post" empirical exercise, which shows that analysts’recommendations are not very useful. (JEL C61, G11, G24)     

A Benchmark Approach to Portfolio Optimization under Partial Information

This paper proposes a filtering methodology for portfolio optimization when some factors of the underlying model are only partially observed. The level of information is given by the observed quantities that are here supposed to be the primary securities and empirical log-price covariations. For a given level of information we determine the growth optimal portfolio, identify locally optimal portfolios that are located on a corresponding Markowitz efficient frontier and present an approach for expected utility maximization. We also present an expected utility indifference pricing approach under partial information for the pricing of nonreplicable contracts. This results in a real world pricing formula under partial information that turns out to be independent of the subjective utility of the investor and for which an equivalent risk neutral probability measure need not exist.      

Losing Money on Arbitrage: Optimal Dynamic Portfolio Choice in Markets with Arbitrage Opportunities

We derive the optimal investment policy of a risk-averse investorin a market where there is a textbook arbitrage opportunity,but where liabilities must be secured by collateral. We findthat it is often optimal to underinvest in the arbitrage bytaking a smaller position than collateral constraints allow.Even when the optimal policy is followed, the arbitrage portfoliotypically experiences losses before the final convergence date.In fact, its initial performance may be indistinguishable fromthat of a conventional portfolio with a poor track record. Theseresults have important implications for the role of arbitrageursin financial markets.     

A Model of Return Volatility with Application to Estimating Relative Risk Aversion

We estimate a monthly return volatility model that allows for the abrupt changes in volatility often observed in returns data. Using this model we are able to identify key months likely to correspond to draws from a high volatility regime. Using our model in conjunction with Merton's (1980) model relating expected risk premia to risk we obtain reasonable estimates of the coefficient of relative risk aversion.     

A Portfolio Approach to Estimating the Average Correlation Coefficient for the Constant Correlation Model

This paper presents a portfolio approach to estimating the average correlation coefficient of a group of stocks which are considered for portfolio analysis. The average correlation coefficient has been shown to produce a better estimate of the future correlation matrix than individual pairwise correlations. The advantage of the approach described here is that it does not require the estimation of pairwise correlations for estimating their average.     

基于信息系统的纳税申报过程模型及仿真分析

目前,全国各地不同程度地存在着纳税申报期内办税服务厅排队拥挤的问题,既给纳税人带来了不便,也影响了税务机关的工作效率。笔者认为,解决排队拥挤问题的主要措施之一就是合理设置纳税申报窗口。本文拟以笔者所在省国税局集中处理数据的信息系统支撑下纳税人直接到税务机关办理纳税申报为对象,首先给出纳税申报业务过程的扩展广义随机Petri网模型;然后对办税服务厅纳税申报业务过程进行仿真模拟;最后对办税服务厅纳税申报窗口数量的配置进行分析,得到动态优化方案。     

Customized Assessment Group Initiative: A Complementary Approach to Students' Learning

Abstract

This study, conducted in a US setting, examines the importance of group dynamics that emphasize cooperative team building through the proposed grouping strategy called Customized Assessment Group Initiative (CAGI). CAGI is a student grouping strategy designed to operationalize the mutual accountability concept central to the definition of teams by Katzenbach and Smith [(1993). The wisdom of teams: Creating the high performance. Boston, MA: Harvard Business Press]. Spanning two semesters and using a sample of sophomore students in the Introductory/Principles of Accounting class, I implement CAGI in a five-stage process to show students’ performance differences between the conventional grouping technique and the CAGI grouping strategy, thereby highlighting the potential pedagogical value of CAGI in the classroom. The findings demonstrate that CAGI has the heightened capacity to enhance substantially and improve concretely students’ performances. I strongly believe that CAGI could be promising to accounting and other business school students at a university where emphasis is placed on ‘teamship’ and not just group membership.     

A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network-pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with the no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of S&P 500 futures options from 1987 to 1991.     

Money Laundering as a Crime in the Financial Sector: A New Approach to Quantitative Assessment,with an Application to Italy

This study provides an answer to the question of how much cash deposited via a financial institution can be traced back to criminal activities, by developing a new approach to measure money laundering and proposing an application to Italy. We define a model of cash in‐flows on current accounts considering, besides “dirty money” to be laundered, also the legal motivations to deposit cash and the role of the shadow economy. We find that the average amount of cash laundered in Italy is around 6% of GDP. These findings are coherent with estimates of the nonobserved economy obtained in previous studies.     

A Dynamic Approach to Accounts Receivable: a Study of Spanish SMEs

The main objective of this paper is to extend the literature on the granting of trade credit. The focus is to test whether the accounts receivable decisions follow a model of partial adjustment. To do that, we use a sample of 2,922 Spanish SMEs. Using a dynamic panel data model and employing the GMM method of estimation we control for unobservable heterogeneity and for potential endogeneity problems. The results reveal that firms have a target level of accounts receivable and take decisions in order to achieve that level. In addition, we find that sales growth (if positive), the size of the firms, their capacity to generate internal funds and get short term financing, and economic growth are important in determining trade credit granted by firms.     

Inference in Long-Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings

Statistical inference in long-horizon event studies has been hampered by the fact that abnormal returns are neither normally distributed nor independent. This study presents a new approach to inference that overcomes these difficulties and dominates other popular testing methods. I illustrate the use of the methodology by examining the long-horizon returns of initial public offerings (IPOs). I find that the Fama and French (1993) three-factor model is inconsistent with the observed long-horizon price performance of these IPOs, whereas a characteristic-based model cannot be rejected.