STRATEGIC ASSET ALLOCATION FOR LONG‐TERM INVESTORS: PARAMETER UNCERTAINTY AND PRIOR INFORMATION

We study the effect of parameter uncertainty on the long‐run risk for three asset classes: stocks, bills and bonds. Using a Bayesian vector autoregression with an uninformative prior we find that parameter uncertainty raises the annualized long‐run volatilities of all three asset classes proportionally with the same factor relative to volatilities that are conditional on maximum likelihood parameter estimates. As a result, the horizon effect in optimal asset allocations is much weaker compared to models in which only equity returns are subject to parameter uncertainty. Results are sensitive to alternative informative priors, but generally the term structure of risk for stocks and bonds is relatively flat for investment horizons up to 15 years. Copyright © 2013 John Wiley & Sons, Ltd.     

Predictability of stock returns and asset allocation under structural breaks

This paper adopts a new approach that accounts for breaks to the parameters of return prediction models both in the historical estimation period and at future points. Empirically, we find evidence of multiple breaks in return prediction models based on the dividend yield or a short interest rate. Our analysis suggests that model instability is a very important source of investment risk for buy-and-hold investors with long horizons and that breaks can lead to a negative slope in the relationship between the investment horizon and the proportion of wealth that investors allocate to stocks. Once past and future breaks are considered, an investor with medium risk aversion reduces the allocation to stocks from close to 100% at short horizons to 10% at the five-year horizon. Welfare losses from ignoring breaks can amount to several hundred basis points per year for investors with long horizons.     

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.     

A spatial dominance approach to evaluate the performance of stocks and bonds: Does the investment horizon matter?

The common advice by practitioners is to allocate a greater proportion of stocks for long-term investors than for short-term investors. However, part of the academic literature disagrees with this advice. We use a spatial dominance test which is suited for comparing alternative investments for a given time horizon. Using daily data for the US from 1962 to 2012, we test for dominance of cumulative returns series for stocks versus bonds at different investment horizons from 1 to 15 years. We find evidence that bonds second order spatially dominate stocks for horizons from 1 to 4 years. In contrast, for horizons of 6 years and longer, we find evidence that stocks dominate bonds. The conclusion that bonds dominate stocks at short horizons, while stocks dominate bonds at long horizons is consistent across different periods and international markets. When different portfolios of stocks and bonds are compared, we find that for long investment horizons only those portfolios with a sufficiently high proportion of stocks are efficient in the sense of spatial dominance.     

Value‐at‐risk for long and short trading positions

In this paper we model Value‐at‐Risk (VaR) for daily asset returns using a collection of parametric univariate and multivariate models of the ARCH class based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed density models when the left and right tails of the distribution of returns must be modelled. Thus, VaR for traders having both long and short positions is not adequately modelled using usual normal or Student distributions. We suggest using an APARCH model based on the skewed Student distribution (combined with a time‐varying correlation in the multivariate case) to fully take into account the fat left and right tails of the returns distribution. This allows for an adequate modelling of large returns defined on long and short trading positions. The performances of the univariate models are assessed on daily data for three international stock indexes and three US stocks of the Dow Jones index. In a second application, we consider a portfolio of three US stocks and model its long and short VaR using a multivariate skewed Student density. Copyright © 2003 John Wiley & Sons, Ltd.     

关于价值投资投资期限的思考

文章研究了价值投资持股期限问题,并得出结论：对价值型股票,投资者不应长期持股;对无长期竞争优势的成长型股票,长期持股绩效不佳;对有长期竞争优势的成长型股票,应坚持长期持有。     

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.     

A preliminary exploration of the effects of rational factors and behavioral biases on the managerial choice to invest in corporate responsibility

We explore a possible decision‐making process in which mixes of rational and non‐rational factors affect the choice made by a firm's management to invest in corporate responsibility. We propose that the rational factors affecting the decision‐makers' investment choice are: (a) moral choice; (b) risk management; (c) consequential changes that would be required in corporate structure or production processes; and (d) long‐term versus short‐term considerations. The non‐rational behavioral biases that we suggest affecting the decision‐makers' investment choice are: (a) attitude to risk, (b) status quo bias, (c) subjective discounting, and (d) myopic loss‐aversion. Copyright © 2011 John Wiley & Sons, Ltd.     

Prevention incentives in long‐term insurance contracts

Long‐term insurance contracts are widespread, particularly in public health and the labor market. Such contracts typically involve monthly or annual premia which are related to the insured's risk profile. A given profile may change, based on observed outcomes which depend on the insured's prevention efforts. The aim of this paper is to analyze the latter relationship. In a two‐period optimal insurance contract in which the insured's risk profile is partly governed by her effort on prevention, we find that both the insured's risk aversion and prudence play a crucial role. If absolute prudence is greater than twice absolute risk aversion, moral hazard justifies setting a higher premium in the first period but also greater premium discrimination in the second period. This result provides insights on the trade‐offs between long‐term insurance and the incentives arising from risk classification, as well as between inter‐ and intragenerational insurance.     

REVERSE REGRESSIONS AND LONG‐HORIZON FORECASTING

Long‐horizon predictive regressions in finance pose formidable econometric problems when estimated using available sample sizes. Hodrick in 1992 proposed a remedy that is based on running a reverse regression of short‐horizon returns on the long‐run mean of the predictor. Unfortunately, this only allows the null of no predictability to be tested, and assumes stationary regressors. In this paper, we revisit long‐horizon forecasting from reverse regressions, and argue that reverse regression methods avoid serious size distortions in long‐horizon predictive regressions, even when there is some predictability and/or near unit roots. Meanwhile, the reverse regression methodology has the practical advantage of being easily applicable when there are many predictors. We apply these methods to forecasting excess bond returns using the term structure of forward rates, and find that there is indeed some return forecastability. However, confidence intervals for the coefficients of the predictive regressions are about twice as wide as those obtained with the conventional approach to inference. We also include an application to forecasting excess stock returns. Copyright © 2011 John Wiley & Sons, Ltd.     

Time-frequency co-movement and network connectedness between green bond and financial asset markets: Evidence from multiscale TVP-VAR analysis

This study examines the time-frequency co-movement and network connectedness between green bonds and other financial assets in China. We propose wavelet coherence and multiscale TVP-VAR to explore the time-frequency co-movement and spillover connectedness. The empirical results are as follows. First, green bonds positively co-move with conventional bonds across time scales and negatively co-move with stocks and commodities. Second, there is a significant network connectedness of green bonds with conventional bonds in the short term, and the connectedness with stocks and commodities gradually strengthens with the increase in time scales. Third, the dynamic spillover between green bonds and other assets is much greater in the long and medium terms than in the short term. Finally, under crisis shocks, the spillovers spike temporarily in the short term, while they are persistent and at a high level in the long term. Overall, some practical implications are proposed for investors and policymakers.     

Is Bitcoin a better hedging and safe-haven investment than traditional assets against currencies? Evidence from the time-frequency domain approach

Many studies have discussed hedges and safe havens against stocks, but few studies focus on the hedging/safe-haven performance of assets against the currency market over different time horizons. This paper studies the connectedness, hedging and safe-haven properties of Bitcoin/gold/crude oil/commodities against six currencies across multiple investment horizons, placing a particular focus on the performance of these assets during the recent COVID-19 outbreak. Our findings suggest that the overall dependence between assets and the currency market is the strongest in the short term, and Bitcoin is the least dependent across all investment horizons. The dynamic relationships between the four assets and the currency market vary with timescales. Bitcoin offers better hedging capability in the long term and commodities emerge as the most favorable option for the optimal portfolio of currency over all time horizons. Further analysis shows that assets are better at helping investments reduce risk in the initial stages of the pandemic, and gold is an effective and robust safe haven for currencies.     

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.     

Corporate tax,financial leverage,and portfolio risk

We examine the theoretical implications of corporate income tax for a risky portfolio in a aggregate-endowment economy. In this model, corporate income tax affects the portfolio risk associated with the rebalancing motive during market clearance. An asset is defined as a portfolio of stocks and bonds whose portfolio weights are similar to financial leverage. Corporate tax can decrease after-tax consumption from dividends (increase leverage) and increase the tax shield that increases dividends (decrease leverage). Changes in dividends are responsible for the correlation between expected dividend growth and consumption growth and, thus, affect stock pricing and returns. Overall, the model is characterized by tax-induced portfolio risk associated with financial leverage.     

Capital allocation to alternatives with a multivariate ladder gamma return distribution

This paper considers investment decision making when returns have a multivariate gamma distribution which has the particular correlation structure of Furman’s ladder gamma distribution. With an exponential utility function It is shown that there is a subset of the candidate investment opportunities to which investment should be allocated. This subset can be readily identified in a two step process that results in a list of candidate investments that is ranked in sequence of decreasing risk. The last investment opportunity on this list will be no risk cash. Investments are then selected from this list in sequence up to a cutoff point that depends on the investors capital and degree of risk aversion. If capital and degree of risk aversion are sufficiently large so that it is optimal to allocate some to no risk cash, then the capital allocated to each risky investment is a constant fraction of the total capital allocated to risky investments irrespective of how risk averse is the investor.     

Lessons from naïve diversification about the risk-reward trade-off

Studies of naïve diversification show that average total portfolio risk declines asymptotically as number of stocks increases. Recent work shows that a significant amount of idiosyncratic risk remains, even for portfolios with large numbers of stocks. The corresponding shocks are non-trivial. For example, more than half of all equal-weighted portfolios with 100 stocks have better than a 16 percent chance of an annual shock at least as large as about half of the annualized mean excess return on the U.S. total stock market index over July 1963–June 2018. I perform a simulation analysis of portfolio reward-to-risk as well as the components of total portfolio risk. On average, investors do not appear to be rewarded for exposure to non-systematic risk. The cross-sectional distribution of the true Sharpe ratio rises and its dispersion shrinks significantly as the number of stocks in the portfolio increases, whereas the cross-sectional distribution of the true non-systematic risk falls and its dispersion shrinks significantly as the number of stocks in the portfolio increases. This pattern appears regardless of the true asset pricing model for generating security returns, the portfolio weighting method, or specification of security alphas.     

Can governance help in making an IPO “successful”? New evidence from Europe

This paper investigates the determinants of a “successful” IPO from a corporate governance perspective upon a representative sample of European listings from 2000 to 2015. We use an extensive dataset of market performance, financial data, and corporate governance characteristics to run the investigation. Differently from previous studies, our analysis embraces both a short‐term perspective and a medium–long‐term perspective, where the board of directors seems to perform different tasks, moving from a value creation to a value protection strategy. Among the others, we find that board size, board independence, and their qualifications, together with their experience in other boards, are associated with a positive performance of the IPO in a short‐term horizon and in the medium–long‐term period, although significant differences emerge among those time perspectives.     

Mean–variance econometric analysis of household portfolios

We investigate households' portfolio choice using a microeconometric approach derived from mean–variance optimization. We assume that households have heterogeneous expectations on the distribution of excess returns and that they cannot take short positions in risky assets. Assuming two such assets, we derive an explicit solution of the model characterized by four possible portfolio regimes, which are analyzed using two structural probit and tobit specifications with three latent state variables. Both specifications are estimated by weighted maximum likelihood on a cross‐section of US households drawn from the 2004 SCF. The tobit specification is simulated in order to evaluate the regressors' effects on regime probabilities and asset demands. We also assess to what extent the predicted state variables are consistent with the self‐reported expected returns and risk aversion elicited from the SCF questionnaire. Copyright © 2009 John Wiley & Sons, Ltd.     

基于自由现金流的投资组合研究

本文运用我国股市1998～2006年间的财务报表数据,选择正自由现金流、低自由现金流乘数和低财务杠杆的大公司,对其以自由现金流为基础的投资组合进行了检验。结果显示,以自由现金流为基础的投资组合回报始终优于市场指数,说明在我国股市实行基于自由现金流的投资组合是积极可行的。     

Long‐run monetary neutrality and long‐horizon regressions

A prominent test of long‐run monetary neutrality (LRMN) involves regressing long‐horizon output growth on long‐horizon money growth. We obtain limited support for LRMN with this test in long‐annual Australian, Canadian, UK and US samples. Although empirical confidence intervals yield evidence in favour of LRMN, Monte Carlo experiments reveal the power of this test is near its size. Thus, this test is unlikely to detect important deviations from LRMN. These problems arise because the long‐horizon regression test of LRMN relies on estimates of the covariance of long‐horizon output growth and long‐horizon money growth. Copyright © 2004 John Wiley & Sons, Ltd.