金融素养、金融资产配置与投资组合有效性

基于动态最优控制理论模型，运用中国家庭微观调查数据，系统研究了金融素养在家庭金融资产配置中的作用及对投资组合有效性的影响。理论分析表明，在一定条件下，金融素养能够显著提升家庭资产中风险性资产的配置比重，有助于实现消费效用最大化。考虑了内生性的实证分析结果表明：金融素养对于风险性资产与金融资产具有显著的正向影响，但无法作用于国债这类无风险资产；金融素养的提升有助于增加股票与基金的配置概率，有助于实施积极的投资策略，但对消极投资策略不显著；金融素养的提升能够显著增加家庭投资组合有效性，促使家庭获得更多的超额回报。     

IPO and Aftermarket Performance of Single‐Listed Chinese ADRs

This paper analyzes the post‐IPO and long‐run aftermarket performances of single‐listed Chinese ADRs during the 2004–2010 period. Single‐listed ADRs are traded daily in major exchanges in the United States, but their underlying shares are not traded in the issuer's home market. Our results show that over the short‐run, buy‐and‐hold abnormal returns of single‐listed Chinese ADRs following their IPO are not significantly different from the typical post‐IPO performance of stocks in U.S. exchanges, including that of traditional dual‐listed Chinese ADRs. Nevertheless, over the longer horizon, the excess returns of a portfolio composed solely of single‐listed Chinese ADRs outperform a portfolio of dual‐listed Chinese ADRs, but underperform a benchmark portfolio composed of U.S. firms matched on the basis of their IPO date. We also find that the portfolio formed solely of single‐listed Chinese ADRs exhibits significantly distinct loadings on the common portfolio factors from the portfolio formed of dual‐listed Chinese ADRs and from the benchmark portfolio of U.S. stocks.     

The economic value of patent portfolios

Patent holders may choose to protect innovations with single patents or to develop portfolios of multiple, related patents. We propose a decision‐making model in which patent holders allocate resources to either expanding the number of related patents or investing in higher value of patents in the portfolio. We estimate the derived value equation using portfolio value data from an inventor survey at the level of individual inventions rather than the firm as a whole. We find that investments in individual inventions exhibit diminishing returns, and that a good part of the value of a portfolio depends on adding new patented inventions. Also, while diminishing returns to individual inventions are stable across subsamples, the returns to portfolio size vary between complex and discrete industries, and between inventions that are science‐based or driven by customer information. When firms seek to strengthen appropriability, the returns to an increase of portfolio size are not different from the sample average. Thus, a higher number of inventions in a portfolio may reflect both stronger appropriability via patents and genuine creation of value.     

Selecting a discrete portfolio

We study the problem of selecting an optimal portfolio out of a finite set of available assets. Assets are characterized by their expected returns and the covariance matrix, and investors are assumed to have a mean–variance utility, that is, their utility function is linear in the mean and variance of the portfolio they hold.When assets are negatively correlated, or even when a slightly more general condition is satisfied, we provide an algorithm for selecting an optimal portfolio. We illustrate the usefulness of this algorithm by some comparative statics result. When assets can be positively correlated, we deliver a negative result regarding the existence of useful algorithms for selecting an optimal portfolio.     

Fractal statistical measure and portfolio model optimization under power-law distribution

An effective portfolio selection model is constructed on the premise of measuring accurately the risk and return on assets. According to the reality that the tail of returns on assets obey power-law distribution, this paper firstly builds two fractal statistical measures, fractal expectation and fractal variance, to measure the asset returns and risks, inspired by the method of measuring curve length in the fractal theory. Then, by incorporating the fractal statistical measure into the return-risk criterion, a portfolio selection model based on fractal statistical measure is established, namely the fractal portfolio selection model, and the closed-form solution of the model is given. Finally, through empirical analysis we find that the fractal portfolio selection model is effective and can improve investment performance.     

Stochastic volatility models: conditional normality versus heavy‐tailed distributions

Most of the empirical applications of the stochastic volatility (SV) model are based on the assumption that the conditional distribution of returns, given the latent volatility process, is normal. In this paper, the SV model based on a conditional normal distribution is compared with SV specifications using conditional heavy‐tailed distributions, especially Student's t‐distribution and the generalized error distribution. To estimate the SV specifications, a simulated maximum likelihood approach is applied. The results based on daily data on exchange rates and stock returns reveal that the SV model with a conditional normal distribution does not adequately account for the two following empirical facts simultaneously: the leptokurtic distribution of the returns and the low but slowly decaying autocorrelation functions of the squared returns. It is shown that these empirical facts are more adequately captured by an SV model with a conditional heavy‐tailed distribution. It also turns out that the choice of the conditional distribution has systematic effects on the parameter estimates of the volatility process. Copyright © 2000 John Wiley & Sons, Ltd.     

Land of addicts? an empirical investigation of habit‐based asset pricing models

This paper studies the ability of a general class of habit‐based asset pricing models to match the conditional moment restrictions implied by asset pricing theory. We treat the functional form of the habit as unknown, and estimate it along with the rest of the model's finite dimensional parameters. Using quarterly data on consumption growth, assets returns and instruments, our empirical results indicate that the estimated habit function is nonlinear, that habit formation is better described as internal rather than external, and the estimated time‐preference parameter and the power utility parameter are sensible. In addition, the estimated habit function generates a positive stochastic discount factor (SDF) proxy and performs well in explaining cross‐sectional stock return data. We find that an internal habit SDF proxy can explain a cross‐section of size and book‐market sorted portfolio equity returns better than (i) the Fama and French ( 1993 ) three‐factor model, (ii) the Lettau and Ludvigson ( 2001b ) scaled consumption CAPM model, (iii) an external habit SDF proxy, (iv) the classic CAPM, and (v) the classic consumption CAPM. Copyright © 2009 John Wiley & Sons, Ltd.     

Acquirer Characteristics and Method of Payment: Evidence from Chinese Mergers and Acquisitions

This study examines the effects of acquirer characteristics on method of payment of Chinese acquirers on the basis of a sample of 1370 mergers and acquisitions that occurred between 1998 and 2008. Using both buy and hold abnormal returns and calendar time abnormal returns approaches, we find that Chinese acquirers experience pre‐acquisition abnormal returns ranging from 14.29% to 121% over the period of 12–36 months prior to the acquisition relative to three different portfolio benchmarks. In the pre‐bid period, acquisitions financed by shares outperform acquisitions financed by cash. However, in the post‐acquisition period, we document no significant difference between cash‐financed and equity‐financed acquisitions. The study also finds that acquirer market value, Tobin's Q, state ownership and leverage have significant effects on the method of payment. Copyright © 2013 John Wiley & Sons, Ltd.     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.     

Corporate governance and the returns to acquiring firms' shareholders: an international comparison

We examine the effects of mergers on the returns to acquiring companies' shareholders for a large sample of companies from both Anglo‐Saxon and non‐Anglo‐Saxon countries over the 1980s and 1990s. With the important exception of Japan, we find similar patterns of returns across both types of countries. For a sample of 9733 acquiring companies the mean percentage gain over a short window of 21 days is 0.6%. This picture changes dramatically as the market has more time to evaluate the mergers and/or the acquiring firms. After three years, acquirers' shareholders in the United States and continental Europe lost on average 19% of their market value compared to a portfolio of non‐merging firms in their size deciles and their two‐digit industry, in Canada, Australia and New Zealand roughly 16%, and in the four Scandinavian countries almost 15%. Further analysis indicates that some mergers are consistent with the hypothesis that mergers generate synergies, but that a majority of mergers in Continental Europe are explained by the managerial discretion and/or hubris hypothesis. Our findings also suggest that corporate governance institutions in the United States and the other Anglo‐Saxon countries lead to better investment performance than in continental Europe, when one confines one's attention to mergers. Copyright © 2007 John Wiley & Sons, Ltd.     

Scale economies in electricity distribution: a semiparametric analysis

We estimate the costs of distributing electricity using data on municipal electric utilities in Ontario, Canada for the period 1993–5. The data reveal substantial evidence of increasing returns to scale with minimum efficient scale being achieved by firms with about 20,000 customers. Larger firms exhibit constant or decreasing returns. Utilities which deliver additional services (such as water/sewage), have significantly lower costs, indicating the presence of economies of scope. Our basic specifications comprise semiparametric variants of the translog cost function where output enters non‐parametrically and remaining variables (including their interactions with output) are parametric. We rely upon non‐parametric differencing techniques and extend a previous differencing test of equality of non‐parametric regression functions to a panel data setting. Copyright © 2000 John Wiley & Sons, Ltd.     

On portfolio optimization: How and when do we benefit from high‐frequency data?

We examine how the use of high‐frequency data impacts the portfolio optimization decision. Prior research has documented that an estimate of realized volatility is more precise when based upon intraday returns rather than daily returns. Using the framework of a professional investment manager who wishes to track the S&P 500 with the 30 Dow Jones Industrial Average stocks, we find that the benefits of using high‐frequency data depend upon the rebalancing frequency and estimation horizon. If the portfolio is rebalanced monthly and the manager has access to at least the previous 12 months of data, daily data have the potential to perform as well as high‐frequency data. However, substantial improvements in the portfolio optimization decision from high‐frequency data are realized if the manager rebalances daily or has less than a 6‐month estimation window. These findings are robust to transaction costs. Copyright © 2009 John Wiley & Sons, Ltd.     

Extreme returns and intensity of trading

Asymmetric information models of market microstructure claim that variables such as trading intensity are proxies for latent information on the value of financial assets. We consider the interval‐valued time series (ITS) of low/high returns and explore the relationship between these extreme returns and the intensity of trading. We assume that the returns (or prices) are generated by a latent process with some unknown conditional density. At each period of time, from this density, we have some random draws (trades) and the lowest and highest returns are the realized extreme observations of the latent process over the sample of draws. In this context, we propose a semiparametric model of extreme returns that exploits the results provided by extreme value theory. If properly centered and standardized extremes have well‐defined limiting distributions, the conditional mean of extreme returns is a nonlinear function of the conditional moments of the latent process and of the conditional intensity of the process that governs the number of draws. We implement a two‐step estimation procedure. First, we estimate parametrically the regressors that will enter into the nonlinear function, and in a second step we estimate nonparametrically the conditional mean of extreme returns as a function of the generated regressors. Unlike current models for ITS, the proposed semiparametric model is robust to misspecification of the conditional density of the latent process. We fit several nonlinear and linear models to the 5‐minute and 1‐minute low/high returns to seven major banks and technology stocks, and find that the nonlinear specification is superior to the current linear models and that the conditional volatility of the latent process and the conditional intensity of the trading process are major drivers of the dynamics of extreme returns.     

Predictability and Regional Integration of Pacific Basin Equity Markets

Between 1986 and 1990, returns on equity indices in Pacific Basin markets can be partially predicted by a set of information variables whose time series coefficients reveal at most one latent variable in the four-market system. A single-factor model implies that the reward for bearing a unit of common covariance risk is the same across markets which, in turn, implies that the markets are integrated. Total sample evidence, however, is driven by price movements in the first two subperiods and is also sensitive to other seemingly trivial adjustments to forecasting specifications. Thus, the predictability of Pacific Basin equity returns is inconsistent. To the extent such inconsistencies suggest imperfect regional integration, the costs to non-Asian firms of providing indirect diversification for their shareholders by investing in real assets in Pacific Rim countries will be high.     

An Empirical Test of Pricing Kernel Monotonicity

A large class of asset pricing models predicts that securities which have high payoffs when market returns are low tend to be more valuable than those with high payoffs when market returns are high. More generally, we expect the projection of the stochastic discount factor on the market portfolio—that is, the discounted pricing kernel evaluated at the market portfolio—to be a monotonically decreasing function of the market portfolio. Numerous recent empirical studies appear to contradict this prediction. The non‐monotonicity of empirical pricing kernel estimates has become known as the pricing kernel puzzle. In this paper we propose and apply a formal statistical test of pricing kernel monotonicity. We apply the test using 17 years of data from the market for European put and call options written on the S&P 500 index. Statistically significant violations of pricing kernel monotonicity occur in a substantial proportion of months, suggesting that observed non‐monotonicities are unlikely to be the product of statistical noise. Copyright © 2014 John Wiley & Sons, Ltd.     

Agency costs in the banking industry: An examination of ownership behavior,leverage and dividend policies

This paper examines agency theory arguments in the banking industry by analyzing the effect of four variables that proxy for agency costs—earnings volatility, managers' portfolio diversification losses, bank size, and standard deviation of bank equity returns—on the three financial policy variables of managerial stock ownership, leverage, and dividend yield. It is one of the first studies that examines the determination of financial policy variables, in light of agency concerns, in the banking industry. The study examines the largest 104 U.S. banks during the period 1985–1989. Evidence suggests that bank size and a measure of the managers' portfolio diversification opportunity set affect the bank's level of managerial stock ownership, leverage and dividends.     

Portfolio Selection under Maximum Minimum Criterion

In this paper, we studied the problem of risky portfolio selection under uncertainty. Different from risk-return analytical methodology, we formulated a model under maximum minimal criterion of uncertain decision-making theory. If the investor had no any distribution information of the returns and (s)he knew the variation scopes of the returns by his/her knowledge of the market information or experts’ evaluations of the alternative risky assets, then we showed that the optimal portfolio strategy of the model under maximal minimal criterion could be obtained by solving linear programming. If the returns were known to be normal distributed, the investor’s optimal portfolio strategy could be obtained by solving a nonlinear programming. The paper also provided an algorithm to solve this programming. At last, the paper compared this model with Markowitz’s mean-varience (M-V) model and Young’s minmax model, and pointed out the distinctions and similarities between our model and the other two. Supported in part by Program for NCET, in part by the Key Project of Chinese Ministry of Education 104053.     

Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?

This paper addresses the debate about the usefulness of high‐frequency (HF) data in large‐scale portfolio allocation. We construct global minimum variance portfolios based on the constituents of the S&P 500. HF‐based covariance matrix predictions are obtained by applying a blocked realized kernel estimator, different smoothing windows, various regularization methods and two forecasting models. We show that HF‐based predictions yield a significantly lower portfolio volatility than methods employing daily returns. Particularly during the 2008 financial crisis, these performance gains hold over longer horizons than previous studies have shown, translating into substantial utility gains for an investor with pronounced risk aversion. Copyright © 2013 John Wiley & Sons, Ltd.     

Diversified behavioral portfolio as an alternative to Modern Portfolio Theory

The traditional mean–variance approach has been complemented by alternative theories that use risk measures different from standard deviation of returns or involve additional distributional features of returns like skewness and kurtosis. We propose a portfolio choice model that combines different distributional characteristics of the returns in the decision-making making process, considering preferences of investors which are modeled as non-statistical uncertainties of investors using fuzzy theory. We use 20 stocks of the S&P500 from January 2013 to December 2017. We assess the obtained portfolios’ performance, and the diversified behavioral portfolios outperform than the mean–variance portfolio. This methodological proposal can be seen as a strong managerial tool to make investment portfolio decisions.