Optimal market timing strategies under transaction costs

In this paper, we consider optimal market timing strategies under transaction costs. We assume that the asset's return follows an auto-regressive model and use long-term investment growth as the objective of a market timing strategy which entails the shifting of funds between a risky asset and a riskless asset. We give the optimal trading strategy for a finite investment horizon, and analyze its limiting behavior. For a finite horizon, the optimal decision in each step depends on two threshold values. If the return today is between the two values, nothing needs to be done, otherwise funds will be shifted from one asset to another, depending on which threshold value is being exceeded. When investment horizon tends to infinity, the optimal strategy converges to a stationary policy, which is shown to be closely related to a well-known technical trading rule, called Momentum Index trading rule. An integral equation of the two threshold values is given. Numerical results for the limiting stationary strategy are presented. The results confirm the obvious guess that the no-transaction region increases as the transaction cost increase. Finally, the limiting stationary strategy is applied to data in the Hang Seng Index Futures market in Hong Kong. The out-of-sample performance of the limiting stationary strategy is found to be better than the simple strategy used in literature, which is based on an 1-step ahead forecast of return.     

Dividend yield and stability versus performance on the German stock market: a descriptive study

It is often examined in the literature whether the dividend yields of stocks correlate with their total returns. This paper analyzes the effect of dividend yield on return as well as on risk and on performance of stocks and stock portfolios on the German market. Not only the influence of dividend yield but also of dividend stability is subject to our analysis. Furthermore, tax aspects are considered. However, this study should rather be seen as an empirical analysis of the influence of dividends as a capital market anomaly than a theoretical based validation approach. Our data set comprises daily adjusted stock prices and dividend payment data from the German capital market over the period 3 January 2000–31 July 2008. This period was characterized by a high volatility of the stock market. In addition to the existing literature examining mainly the long-term influence of dividend yields, we also want to find out whether stocks with high and stable dividend payments are able to reduce the risk of a stock investment in short time periods characterized by extreme conditions. We use blue chips (DAX), stocks of medium-sized companies (MDAX), and stocks of technology firms (TecDAX). Our findings suggest that stock performance generally improves with an increasing dividend yield, where this result is actually based on risk reduction instead of a higher return. However, this risk reduction diminishes with an increasing degree of diversification.     

Lot Sizing for Optimal Collection and Use of Remanufacturable Returns over a Finite Life‐Cycle

Reverse supply chains process used product returns to recover value by re‐processing them via remanufacturing operations. When remanufacturing is feasible, the longer the return flows are delayed during the active (primary) market demand period of the product, the lower the value that can be recovered through these operations. In fact, in order to recover the highest value from remanufactured products, the collection rates, return timings, and reusability rates should be matched with the active market demand and supply. With these motivations, this paper is aimed at developing analytical models for the efficient use of returns in making production, inventory, and remanufacturing decisions during the active market. More specifically, we consider a stylistic setting where a collector collects used product returns and ships them to the manufacturer who, in turn, recovers value by remanufacturing and supplies products during the active market demand. Naturally, the manufacturer's production, inventory and remanufacturing decisions and costs are influenced by the timing and quantity of the collector's shipments of used product returns. Hence, we investigate the impact of the timing of returns on the profitability of the manufacturer‐collector pair by developing system‐wide cost optimization models. Analyzing the properties of the optimal shipment frequency, we observe that the fastest reverse supply chain may not always be the most efficient one.     

基于网络外部性的规模收益与产品差异化

通过构造一个纵向产品差异化的两阶段模型(先决定产品质量再决定产量) ,研究了当产 品具有网络外部性特征时,其规模收益特征对企业的产品质量差异度、利润、产品价格和需求 等因素的影响. 然后对产品的规模收益递增和递减两种情况作了比较分析并得出相应的结论. 研究表明,当不考虑网络外部性时递减规模收益假设下产品的质量(不论是高质量还是低质 量) 都要低于递增规模收益情形;而考虑网络外部性时递减规模收益假设下的高质量要远大于 递增规模收益假设下的质量;递减规模收益假设下的市场需求都要低于递增规模收益假设下 的市场需求,这与产品是否具有网络外部性特征无关.     

Returns Policy and Quality Risk in E‐Business

In this article, we investigate the interplay between returns policy, pricing strategy, and quality risk. We define quality risk as the possibility of product misfit, defect, or unconformity with the consumers’ perception. These notions of quality risks differ in return policy restriction, residual values, and whether it is possible to unambiguously reduce the probability of mismatch. Using a stylized two‐segment market setting, we demonstrate that consumer returns are offered only when the high‐segment consumers incur a higher hassle cost, and both the quality risk and the valuation of the low segment are moderate. Moreover, it is possible to wisely design the returns policy that eliminates all inappropriate returns. Furthermore, the seller with a high‐quality risk may offer a refund that exceeds the selling price, which provides a theoretical ground and specific operating regime for the satisfaction guaranteed policy used in some e‐tailers. In contrast, when the quality risk is relatively low, further improvement on mitigating the quality risk may not necessarily benefit the seller. Finally, we observe that the restocking fee may be non‐monotonic in product quality; thus, a more generous returns policy does not necessarily indicate a lower quality risk.     

尾风险度量与定价能力分析

经典资本资产定价模型假设资产收益服从正态分布或者投资者效用函数是二次型,然而实际中资产收益的分布呈现尖峰厚尾特征,因而无法在均值-方差框架下分析风险与收益的关系。与资产收益正常变化相比,极端变化带来的尾风险会对市场风险和投资者的决策行为产生更为显著的影响,因此研究尾风险的度量方法和定价能力具有一定理论和实践意义。 依据极值理论,基于Fama-French的3因子模型无法解释的收益残差构建尾风险度量模型。选取中国股票市场沪深A股作为研究对象,以历史滑动窗估计尾风险,投资组合分析和横截面回归分析的结果都表明尾风险可以稳定地负向预测股票收益,在控制规模、交易量、非流动性、短期反转、中期动量、特质波动率、特质偏度等股票特征后,尾风险的负向预测能力依然显著。引入融资融券交易,发现融资融券标的股票的尾风险显著低于非融资融券标的股票的尾风险,而且可以消除尾风险的负向溢价,因而中国资本市场尚未完全开放融资融券交易机制可能是产生尾风险负向溢价的主要原因。 通过买多-卖空组合构建尾风险定价因子,发现尾风险因子比投资因子和盈利因子有更高的风险价格,而且并不是相对于Fama-French的5因子的冗余因子。与Fama-French的3因子模型和5因子模型相比,Fama-French的3因子或5因子基础上包含尾风险因子的定价模型对股票横截面收益有更好的解释能力,因而有更高的定价效率。 2008年全球金融危机爆发后,对尾风险定价能力的忽略使已有资产定价模型受到广泛批评。因此,探究尾风险的度量方法以及分析尾风险的定价能力,不仅有助于完善和拓展资产定价理论,而且有助于降低投资者对股票价格的认知偏差,提升市场资源配置效率和维护市场稳定性。     

考虑退货风险的制造商双渠道供应链定价决策研究

针对事前不确定性导致的退货问题,从消费者行为出发,建立考虑定价差异和退货风险双重因素的市场需求函数,通过Stackelberg博弈模型研究四种不同市场需求情形在不同定价模式下双渠道供应链的最优决策。在数值仿真部分,对不同情形决策之间比较、供应链收益进行比较分析。研究表明：电子渠道销售价格与电子渠道的市场基本需求成正比,批发价和传统渠道销售价格与传统渠道的市场基本需求成正比,实体店体验服务水平和传统渠道的市场基本需求的关系（线性关系）则根据某些条件而定;退货风险对供应链决策的影响与定价模式、市场需求影响因素相关;制造商偏好于定价不相等模式,零售商偏好于定价相等模式;退货风险与定价差异对供应链成员收益的影响与定价模式相关,对整体供应链收益的影响与定价模式和退货率大小有关。     

基于波动择时绩效的高维波动率估计量与预测模型研究

对协方差矩阵高频估计量和预测模型的选择，共同影响协方差的预测效果，从而影响波动择时投资组合策略的绩效。资产维数很高时，协方差矩阵高频估计量的构建会因非同步交易而丢弃大量数据，降低信息利用效率。鉴于此，将可以充分利用资产日内价格信息的KEM估计量用于估计中国股市资产的高维协方差矩阵，并与两种常用协方差矩阵估计量进行比较。进一步地，将三种估计量分别用于多元异质自回归模型、指数加权移动平均模型以及短、中、长期移动平均模型进行样本外预测，并比较在三种基于风险的投资组合策略下的经济效益。采用上证50指数中20只不同流动性成份股逐笔高频数据的实证研究发现：（1）无论是在市场平稳时期还是市场剧烈震荡期，长期移动平均模型都是高维协方差估计量预测建模的最优选择，在应用于各种波动择时策略时都可以实现最低成本和最高收益。（2）在市场平稳时期，KEM估计量是高维协方差估计的最优选择，应用于各种波动择时策略时基本都可以实现最低成本和最高收益；在市场剧烈震荡期，使用KEM估计量进行波动择时仍然可以在成本方面保持优势，但在收益上并不占优。（3）无论是在市场平稳时期还是市场剧烈震荡期，最低的成本都是在采用等风险贡献投资组合时实现的，而最高的收益则都是在采用最小方差投资组合时实现的。研究不仅首次检验了KEM估计量在常用波动择时策略中的适用性，而且首次实证了实现最为简单的长期移动平均模型在高维协方差矩阵预测中的优越性，对投资决策和风险管理等实务应用都具有重要意义。     

Leverage and the performance of buyouts

In this paper we investigate the effects of debt usage on (risk-adjusted) equity returns in private equity-backed buyout transactions. We use a proprietary data set of more than 700 realized European and North American buyouts acquired between 1990 and 2006 to empirically assess theoretical predictions. Our results show a positive relationship between debt levels and equity returns indicating the return-enhancing effects related to leverage. However, our results also provide evidence that it is possible to ‘over-leverage’ a company, i.e. at very high levels of leverage (debt to total capital of approximately 90%) the increased company default risk outweighs the positive effects related to leverage resulting in decreasing equity returns. On the other side, we find no significant relationship between leverage and risk-adjusted equity returns which implies that the return increase through higher debt ratios comes along with increasing risk. In addition, we show that private equity sponsors successfully conduct debt-equity-arbitrage in times of favorable debt market conditions. This implies that these investors successfully time debt markets.     

The impact of investor sentiment on the German stock market

This paper develops a broad-based sentiment indicator for Germany and investigates whether investor sentiment can explain stock returns on the German stock market. Based on a principal component analysis, we construct a sentiment indicator that condenses information of several well-known sentiment proxies. We show that this indicator explains the return spread between sentiment sensitive stocks and stocks that are not sensitive to sentiment fluctuations. Specifically, stocks that are difficult to arbitrage and hard to value are sensitive to the indicator. However, we do not find much predictive power of sentiment for future stock returns.     

Consumer return policies in presence of a P2P market

Retailers selling products with valuation uncertainty often offer return policies to consumers to stimulate demand. However, some products that do not meet consumers’ expectations cannot be returned to the retailers either because of retailers’ strict restrictions on returns or because of short trial period. With the development of e-commerce, consumers who cannot return their products to retailers can resell them directly to others through electronic peer-to-peer (P2P) second-hand goods markets. This paper examines the effect of the presence of a P2P market on a retailer’s optimal return policy when the consumers are strategic and uncertain about their valuations. As a benchmark, we first examine the retailer’s optimal return policy when there is only a retailer-run resale market. Then, we analyze the retailer’s optimal return policy in presence of both the retailer-run resale market and the P2P market. Theoretical and numerical results show that, first, the presence of the P2P market is detrimental to the retailer in most cases. The presence of the P2P market is beneficial to the retailer only when the unit purchasing cost is very high, the consumers’ acceptance of products in the P2P market is relatively high and the transaction cost in the P2P market is relatively low; second, the consumer surplus is improved by the presence of P2P market; third, when the retailer-run resale market is the only second-hand products market, returned products are sold out; while in presence of the P2P market, the retailer will hold some inventory when the unit purchasing cost is very low; fourth, the selling price of new products is increased and the selling price of second-hand products in the retailer-run resale market is decreased with the emergence of the P2P market while the refund amount is increased in most cases.     

不同态势下β特征及其与收益关系研究

分析了中国证券市场中不同态势下各行业β值的不同表现特征,检验了中国证券市场成立以来至2003年12月13年间β与收益的关系,发现引入市场态势后,条件CAPM的解释能力显著增强;当市场处于牛市(熊市)时,β与收益呈显著正(负)相关;得出了β仍然是度量资产风险的重要方法的结论.     

耦合风险视角下基于GARCH-Copula模型的基金组合风险研究

在资本市场不断多样化的投资方式中, 投资组合以其相对稳定的风险与收益而得到广泛应用, 其中基金组合凭借在收益一定的情况下的低风险成为投资者关注的热门品种.传统的投资组合研究大多只考虑市场风险的影响, 忽略了信用风险的耦合效应, 从而往往导致对组合总体风险的低估.首先借助于GARCH模型获得边缘分布, 然后选择Copula函数刻画各基金之间的相关结构, 建立联合分布模型, 进而采用Monte Carlo方法模拟生成基金组合中各基金的收益率序列, 最后根据损失函数计算基金组合的风险价值.实证结果表明, 市场风险大的基金组合其信用风险不一定大, 并且基金组合能有效分散基金风险.同时, 耦合风险视角下基金组合的CVaR值大于市场风险视角下的CVaR值, 耦合风险能更好地衡量基金组合的风险.另外, Student t-Copula模型较之其它模型能更好地刻画耦合风险的联合相依结构.     

基于β系数优化的动态投资组合策略研究

本文提出一种股票动态投资组合策略，首先通过上升和下降贝塔来优选行业，然后在选择的行业中构造股票投资组合。对于股票投资组合，利用均值方差投资组合模型作为内核，通过引入参考时间窗口和持有期限窗口两个外生参数构建动态的均值-方差模型，并实证检验了模型的可行性。然后再经过多项业绩评价指标对比分析得出动态投资组合策略的收益明显优于被动投资策略，这种动态投资组合策略能够获得部分超额收益并且具有更好的可靠性。本研究为投资者提供了一种定量的投资组合管理方法，并从侧面验证了我国股市的非有效性。     

基于分位数Granger因果的网络情绪与股市收益关系研究

 行为金融学理论认为,股票市场的价格变动除受宏观基本因素影响外,还在很大程度上受众多个体投资者或噪音交易者行为左右。中国股票市场拥有庞大的个人投资者群体,且股民群体与网民群体之间具有高度耦合性,使用网络情绪等信息能够探索中国股市收益变动基本规律。        为揭示个体投资者行为对股市收益的影响,以个体投资者情绪为视角,以网络环境中个体投资者的情绪信息为切入点,检验投资者情绪与股市收益的关联关系,评估网络情绪信息价值。使用中文文本情感分析方法,从新浪微博文本中提取出网络情绪时间序列;分别运用均值Granger因果和分位数Granger因果检验方法,探讨网络情绪波动与股市收益之间是否存在因果关系;将股票市场发展阶段进行细致划分,研究不同市场阶段下网络情绪波动与股市收益之间的因果关系。        对沪深300指数收益进行实证研究,结果表明,尽管在均值框架下网络情绪波动与股市收益之间因果关系并不明显,但基于分位数Granger因果分析却发现两者在极端分位点区间处存在广泛且显著的因果关系。数据显示,在40个因果关系检验中,分位数Granger因果检验的因果关系发现了23个显著的因果关系,发现率为57.5%,远高于均值Granger因果检验的7.5%。此外,股市收益受到网络情绪波动影响的程度和方式在不同市场阶段下有所不同。        研究结果具有一定的理论意义和应用价值。在一些特定分位点区间网络情绪波动对股市收益存在显著因果关系影响,这为在特定条件下股市收益的可预测性提供了佐证。网络情绪能够预测股市收益的尾部(上尾或下尾)行为特征,可以为金融风险防范提供决策参考。研究结果为股票市场的定价、收益预测和波动率估计等相关研究提供了新的研究思路,也为网络情绪信息使用提供了新的方向。     

考虑退货的定价和订货优化及信息发布美化策略

考虑顾客由感知差异引起的退货行为，以及促销期与日常销售期不同的行为特点，对日常销售期和促销期顾客购买率和退货率分别进行了解析表述.在此基础上分析了确定需求下信息发布美化策略对最大利润的影响.建立了随机需求下在线零售商的期望利润模型，对定价和订货量进行联合优化.最后通过算例分析了在感知价值和感知差异服从更为一般的分布形式且存在相关性条件下，最优策略下的预期利润随信息发布美化程度的变化规律，以及相关性对信息发布美化策略有效性的影响.研究结果表明，基于感知价值和感知差异构建的购买率和退货率模型可较好解释已有实证研究结果，以此为基础对定价和库存进行联合优化更符合实际，优化条件下的最大利润随信息发布美化程度的提高先增后降，取得最大利润的信息美化值促销期大于日常销售期，且线性相关程度越高的产品取得最大利润的信息美化值越大。     

我国投资者情绪对股票收益影响——基于面板数据的研究

基于基金的资金流量构造投资者情绪指标,应用面板数据模型对我国投资者情绪与股票收益之间的关系展开研究,结果表明情绪对股票收益具有显著影响,其中乐观情绪的影响高于悲观情绪,而极端情绪在我国市场中具有独特的预测能力。利用行为金融学理论建立合理的情绪指标,可以作为预测市场未来波动的可靠指针,为我国证券投资者及监管者提供决策参考依据。     

基于EVT-POT-SV-MT模型的极值风险度量

针对金融资产收益的异常变化,采用SV-MT模型对风险资产的预期收益做风险补偿并捕捉收益序列的厚尾性、波动的异方差性等特征,将收益序列转化为标准残差序列,通过SV-MT模型与极值理论相结合拟合标准残差的尾部分布,建立了一种新的金融风险度量模型——基于EVT-POT-SV-MT的动态VaR模型.通过该模型对上证综指做实证分析,结果表明该模型能够合理有效地度量上证综指收益的风险.     

Applying the Partitioned Multiobjective Risk Method (PMRM) to Portfolio Selection

The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model. However, under extremely unfavorable market conditions, results indicate that f(4) can be a more valid measure of risk than volatility.     

资产定价与劳动成本占比

根据经典生产函数可知与劳动要素相关的变化应反映到企业价值层面，那么劳动要素对股票市场具有怎样的影响呢？本文以沪深A股为研究对象，基于劳动成本占比探索劳动要素对股票市场定价的影响。通过模型推理和实证分析相结合的方法证明劳动成本占比与企业风险正相关，劳动成本占比具有放大企业风险的杠杆作用。基于风险与收益相补偿的原理，通过回归分析和投资组合分析发现劳动成本占比具有正的股票收益预测能力；采用买多-卖空组合构建劳动成本占比风险因子，通过Fama-MacBeth回归证明劳动成本占比是影响股票收益的风险因子，并且相对Fama-French的五因子不是冗余因子；在三因子模型和五因子模型基础上包含劳动成本占比风险因子的定价模型对股票横截面收益的解释力更好，模型的定价效率更高。本文不仅丰富了股票收益影响因子和劳动成本经济后果领域的文献，而且对投资者投资决策、企业制定薪酬制度、政府颁布劳动保护相关政策和收入分配改革方面具有参考价值。