一种基于规则化方法的最优稀疏指数追踪模型设计

本文将目前流行的规则化方法加入到传统指数追踪模型中,得到若干种稀疏而且稳定的资产组合,用于复制指数的收益率,并构建样本内外预测、模型一致性、资产组合稀疏性和BIC准则进行模型效果评价。基于对上证综指、沪深300指数和中证500指数的实证发现:图结构约束可以提升模型的样本外预测能力、模型一致性和资产组合稀疏性;ITM-adaL₁在资产组合稀疏性上表现远好于其他模型;结合三种指数追踪,含有自适应L₁罚函数以及图结构约束的指数追踪模型总体表现优于其他模型。本文的研究方法和结果对指数型基金管理公司、个人和投资机构者有较为重要的实际意义。     

VaR方法与资产组合分析

本文针对风险方差度量方法投资收益正态分布假设的缺陷，引入了考察投资绩效对资产组合影响的VaR方法，在探讨VaR定义以及计算方法的基础上，求解了VaR约束下的资产组合问题。在VaR框架下，建立了形同于Sharpe指数的单位风险超额收益指数，并提出了类似于均值－方差分析中存在无风险资产的两基金分离定理，从而弥补了方差度量方法的不足，提高了资产配置模型的应用效率。     

CAPM模型在上海股票市场的有效性检验

根据资本资产定价模型(CAPM),从上海A股市场随机抽取100支股票,计算它们的收益率,选择上证综合指数为市场组合的市场指数,并利用双层回归分析方法对2007年1月1日至2011年12月31日这段时间的100支股票进行实证检验。虽然很多国外研究表明,CAPM模型在一定程度上能够解释市场收益,并在资产估价、资本预算、投资风险分析方面已经得到了广泛应用,同时也有利于投资者构建最优的证券投资组合,但本文实证研究结果发现,CAPM模型并不适合中国的股票市场,股票预期收益率和系统风险之间不仅不存在正相关的关系,而且也不存在线性关系,除了系统风险外,非系统风险在解释股票收益上也具有一定的作用。     

通货膨胀条件下我国股票收益变化的实证研究

本文构建了资产组合均衡框架下的联立方程模型,对通货膨胀条件下我国股票收益的变化特征进行了分析.结果表明,通货膨胀对股票市场不存在系统性的正面影响.     

通货膨胀条件下我国股票收益变化的实证研究

本文构建了资产组合均衡框架下的联立方程模型,对通货膨胀条件下我国股票收益的变化特征进行了分析。结果表明,通货膨胀对股票市场不存在系统性的正面影响。     

基于对冲基金的投资组合新架构研究

本文在分析传统的投资对冲基金组合架构存在不足的基础上,提出了新的资产组合构架模型,并通过美国市场的数据论证了对冲基金为何不是一个纯粹超额收益的制造者而更多是一个风险溢价的提供者以及将对冲基金与传统资产有机整合到一起的好处,为投资者优化资产投资组合提供了新的方法。     

CAPM模型在我国证券市场的运用性及其不足探讨——基于上海证券市场的实证研究

资本资产定价模型,即CAPM模型,是针对金融资产的定价而建立的理论模型。由于资产组合投资理论需要复杂的数学理论计算,而CAPM模型大大简化了计算过程,更易于理解,因此获得更多的运用。国内很多学者已经针对此模型在中国股票市场的实用性进行研究。本文主要通过选取2012年至2016年上证50指数中的21只股票进行实证研究,发现该模型现在对我国的证券市场的解释能力有所提高,但还相当有限。     

序贯非负最小方差模型平均组合及应用

研究目标:构建改进全局非负最小方差模型的序贯最小方差模型平均组合。研究方法:在大样本下,给出序贯非负最小方差模型组合的期望方差和收益的理论近似,在此基础上探讨其平均组合的期望收益和期望方差的性质,并通过数值模拟和沪深300成分股数据在有限样本下进行验证。研究发现:与全局非负最小方差模型组合相比,序贯非负最小方差模型平均组合在样本外风险更小、收益更高,且保持组合稀疏性。研究创新:在卖空限制下,给出了一种比全局非负最小方差模型更优的组合构建方法。研究价值:序贯非负最小方差模型平均组合方法在国内市场具有较强的适用性,同时丰富了目前投资组合方法论的研究。     

构建中国金融压力指数

本文通过银行部门风险、股票市场收益、股票市场波动、主权债务利差和外汇市场五个指标来构建中国的金融压力指数,用以衡量金融市场所承受的压力。     

基于系统性风险和夏普指数的社保基金投资绩效分析

本文利用资本资产定价模型(CAPM)中的贝塔系数来刻画全国社保基金投资组合的系统性风险,通过与市场收益系统性风险的大小做对比来说明投资组合的整体风险状况。并且以CVa R代替Va R计算夏普指数的变形形式(RAROC)来反映投资组合的风险收益状况。而CVa R则是利用Pair-Copula拟合投资组合的分布之后,通过蒙特卡罗模拟求出的。     

A kind of new time-weighted nonnegative lasso index-tracking model and its application

This study describes improved index-tracking methods to replicate the target index’s market performance in a high-dimensional sparse linear regression with nonnegative constraints on the coefficients. The main objective of this study is to construct a sparse portfolio with a better prediction effect and robustness. Considering the influence of time factors on index tracking, we propose a time-weighted nonnegative lasso index tracking model under different market constraints and define two new time-weighted construction methods. This index tracking model is an extension of Lasso and has variable selection consistency and estimation consistency under time-weighted nonnegative irrepresentable conditions similar to the irrepresentable condition in Lasso. We use the multiplicative updates algorithm to obtain the model’s solution since it is faster and simpler. The constrained index tracking problem in the stock market without short sales is studied in the latter part. The empirical results indicate that the optimized time-weighted nonnegative lasso index tracking model can obtain a smaller out-of-sample tracking error. The constructed portfolio has a better prediction effect and robustness, and we find that the exponential time-weighted method is better than the linear time-weighted method in capturing time information.     

Two new mean–variance enhanced index tracking models based on uncertainty theory

The enhanced index tracking (EIT) problem is concerned with selecting a tracking portfolio that achieves an excess return over a given benchmark with a minimum tracking error. This paper explores the EIT problem by providing two new mean–variance EIT models based on uncertainty theory where stock returns are treated as uncertain variables instead of random variables and stock return distributions are estimated by experts instead of from historical data. First, this paper formulates an uncertain enhanced index tracking (UEIT) model and analyzes the characteristic of the UEIT frontier. Then to reduce the tracking portfolio’s risk, this paper adds a risk index (RI) constraint to the UEIT model and proposes a UEIT-RI model. Next, by comparing the UEIT and UEIT-RI models this paper gives the advantages of the two models. Investors can choose the model according to their preferences. Finally, this paper conducts numerical examples to illustrate the application of the two models and the analysis results.     

A novel LASSO – TLBO – SVR hybrid model for an efficient portfolio construction

In this study a LASSO – TLBO – SVR hybrid model is used for portfolio construction. Relevant economic parameters are determined and used for stock selection. Along with stock selection, weights for the stocks are obtained by solving a portfolio optimization problem using three methods: GRG Nonlinear, Evolutionary method based on Genetic Algorithm, and Equal weight method. The portfolio return in the proposed model is compared with the return of the Indian market portfolio (NSE and BSE). It is observed that the proposed model outperforms the market portfolio.     

Jump risk and cross section of stock returns: evidence from China’s stock market

Various studies have confirmed the existence of jumps in different financial markets. However, there is sparse theoretical or empirical effort to examine the dynamic relation between jump risk and cross-sectional expected stock returns. We follow a stylized SDF-based diffusion-jump model to examine its testable implications about the relation between cross-section expected excess returns and variations in jump intensities across stocks. The zero-cost portfolio, exploiting the return spreads between the top and bottom decile portfolios formed on jump intensity, could earn an annualized return as high as 24% with an annualized Sharpe ratio of 1.67. A Fama-MacBeth test shows that stock excess returns monotonically decrease in jump intensity even after controlling for other common risk factors.     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective

The risk–return trade-off refers to the compensation required by investors for bearing risks, which can be viewed as the risk preference of investors in a market. The current study investigates the dynamic interdependence of risk–return trade-offs between China’s stock market and the crude oil market from the perspective of risk preference of investors, which is designed to explore the transmission process of investors’ risk preference in both markets. Specifically, this study applies the time-varying parameter GARCH-M model, namely TVP-GARCH-M model, to characterize the time-dependent risk–return trade-offs (investors’ risk preferences) in the crude oil and China’s stock markets, then examines their relationship through Granger causality tests. Results show that a variation in risk preferences of the oil market investors can dramatically cause a variation in risk preferences of the Chinese stock market investors, while the risk preference of investors in the Chinese stock market does not lead to that in the crude oil market, which is in accordance with expectations. The dynamic effect of investors’ risk appetite in the crude oil market is further examined by the TVP-VAR model. The findings of this work suggest that there generally exists a positive impact of investors’ risk preference in the oil market and that the effect is time-varying to a greater degree during the short and medium term. Moreover, responses of the Chinese stock market investors’ risk preference were more significant during the 2008 financial crisis. Additionally, the empirical results remain robust when applying alternative crude oil prices and China’s stock prices.     

一种改进的不完全指数复制方法

本文在考虑交易成本和投资组合动态调整的基础上,建立混合整数线性规划模型,引入内核搜索分析框架进行近似求解,并利用沪深300进行实证研究。研究发现,一是相比于基本内核搜索法,增强型内核搜索法仅在基准指数成分股数量很大时才会较大幅度提高求解质量;二是考虑投资组合动态调整的模型不仅更稳健,而且跟踪的继承性和保持性更好,尤其适用于单边市场;三是过度刻画现实交易特征一定程度上会降低不完全指数复制模型的复制和预测效果。     

我国上市公司房地产与公司绩效关系的实证研究

房地产能否增加公司的股票市场价值、提高其收益率和公司的整体赢利能力是公司房地产领域一个很重要的问题。文章采取深圳股票市场非房地产上市公司的股票以及其公司房地产信息,结合三因素模型,对该问题做了实证分析。实证结果表明,公司房地产比率对所有行业整体的平均股票收益率影响并不显著,却对信息技术行业影响显著;同时公司房地产的规模对公司的赢利能力有比较显著的影响。     

Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence

This study investigates the level of risk due to fat tails of the return distribution and the changes of tail fatness (TF) through portfolio diversification. TF is not eliminated through portfolio diversification, and, interestingly, the positive tail has declining fatness until a certain level is reached, while the negative tail has rising fatness. This indicates that fat tails are highly relevant to common factors on systematic risk and that the relevance of common factors is higher for the negative tail compared to the positive tail. In the portfolio diversification effect, the declining fatness of the positive tail further reduces risk, but the rising fatness of the negative tail does not contribute to this effect. The asymmetry between the fatness of the positive and negative tails in the return distribution corresponds to the asymmetry of the trade-off relationship between loss avoidance and profit sacrifice that is expected as a consequence of portfolio diversification. Investors use portfolio diversification to reduce their risk of suffering high losses, but following this strategy means sacrificing high-profit potential. Our study provides empirical confirmation for the practical limitation of portfolio diversification and explains why investors with diversified portfolios suffer high losses from market crashes. An examination of the Northeast Asian stock markets of China, Japan, Korea, and Taiwan show identical results.     

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.