Statistical inference and modelling of momentum in stock prices

The following results are obtained, (i) It is possible to obtain a time series of market data {y(t)} in which the fluctuations in fundamental value have been compensated for. An objective test of the efficient market hypothesis (EMH), which would predict random correlations about a constant value, is thereby possible, (ii) A time series procedure can be used to determine the extent to which the differences in the data and the moving averages are significant. This provides a model of the form y(t)-y(t-l)=0.5{y(t- l)-y(t-2)}+ε(t)+0.8ε(r-1) where ε(t) is the error at time t, and the coefficients 0.5 and 0.8 are determined from the data. One concludes that today's price is not a random perturbation from yesterday's; rather, yesterday's rate of change is a significant predictor of today's rate of change. This confirms the concept of momentum that is crucial to market participants. (iii) The model provides out-of-sample predictions that can be tested statistically. (iv) The model and coefficients obtained in this way can be used to make predictions on laboratory experiments to establish an objective and quantitative link between the experiments and the market data. These methods circumvent the central difficulty in testing market data, namely, that changes in fundamentals obscure intrinsic trends and autocorrelations. This procedure is implemented by considering the ratio of two similar funds (Germany and Future Germany) with the same manager and performing a set of statistical tests that have excluded fluctuations in fundamental factors. For the entire data of the first 1149 days beginning with the introduction of the latter fund, a standard runs test indicates that the data is 29 standard deviations away from that which would be expected under a hypothesis of random fluctuations about the fundamental value. This and other tests provide strong evidence against the efficient market hypothesis and in favour of autocorrelations in the data. An ARIMA time series finds strong evidence (9.6 and 21.6 standard deviations in the two coefficients) that the data is described by a model that involves the first difference, indicating that momentum is the significant factor. The first quarter's data is used to make out-of-sample predictions for the second quarter with results that are significant to 3 standard deviations. Finally, the ARIMA model and coefficients are used to make predictions on laboratory experiments of Porter and Smith in which the intrinsic value is clear. The model's forecasts are decidedly more accurate than that of the null hypothesis of random fluctuations about the fundamental value.     

解析对冲基金经理人的收费最大化问题

由于近年来对冲基金发展迅速,对冲基金经理人收费最大化问题也成为学者关注的焦点。文中将研究对冲基金经理人价值最大化的2篇文章作对比,其次考虑在只收取激励费和高水位线有增长率的情况下,分析对冲基金经理人的价值最大化问题。     

投资者情绪对基金羊群效应的影响研究

大量研究表明我国证券投资基金存在羊群效应,然而却很少对影响基金羊群效应的因素展开进一步的实证研究。基于此,本文利用主成分分析法测量投资者情绪,利用前十大流通股东中证券投资基金的家数来测量基金羊群效应的程度,研究投资者情绪指标是否对基金羊群效应产生影响。研究结果表明,投资者情绪是证券投资基金形成羊群效应的重要因素之一,基金经理在观察到上一期投资者悲观情绪(乐观情绪)时会做出负反馈策略。     

开放式基金流动性指标研究

本文主要介绍了开放式投资基金的流动性概念和指标体系设计，并运用指标进行实证检验，分析基金的流动性及其实际意义。     

Leaders and followers in mutual funds: A dynamic Bayesian approach

This article proposes a dynamic Bayesian framework to analyze the leadership relationships between mutual funds. To this end, a two‐step procedure is proposed. First, a Bayesian rolling window based on the Capital Asset Pricing Model is used to estimate the evolution of mutual funds' market exposure over time. Then, a vector autoregressive (VAR) model is used to analyze the leader‐follower relationship between pair of mutual funds. Several leadership measures are studied. An application to Spanish mutual funds is carried out. In addition, the study examines the determining factors of mutual fund leadership.     

DEA模型在资金分配和管理中的应用

资金的合理使用,是经济活动中的一个非常重要的问题.利用DEA的理论、方法模型,探讨资金的使用效率、分配的合理性,以及最佳资金预算的确定方法.涉及的DEA模型结构属于非参数的最优化DEA模型,以及DEA平行网络结构.模型中所使用的生产可能集是可以评价是否呈现"拥挤"迹象的.     

基金与基金组合投资

本根据组合证券投资理论了基金的投资原理,建立了基金管理决策模型与基金组合投资决策模型,并进行了静态分析和动态分析。     

Multi-period defined contribution pension funds investment management with regime-switching and mortality risk

Using mean–variance criterion, we investigate a multi-period defined contribution pension fund investment problem in a Markovian regime-switching market. Both stochastic wage income and mortality risk are incorporated in our model. In a regime-switching market, the market mode changes among a finite number of regimes, and the market state process is modeled by a Markov chain. The key parameters, such as the bank interest rate, or expected returns and covariance matrix of stocks, will change according to the market state. By virtue of Lagrange duality technique, dynamic programming approach and matrix representation method, we derive expressions of efficient investment strategy and its efficient frontier in closed-form. Also, we study some special cases of our model. Finally, a numerical example based on real data from the American market sheds light on our theoretical results.     

带偏好DEA在“科学研究基金”管理中的应用研究

将带偏好锥DEA理论引入科学研究基金管理中,在包含"拥挤"迹象的生产可能集基础上建立了三个带偏好锥的平行网络结构DEA模型,对科研基金投入后产生的"成效"进行评价.这些模型分别从三个层面探讨了科研基金使用效率、分配合理性,以及最佳基金预算的确定方法.     

Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method

Socially Responsible Investing (SRI) is broadly defined as an investment process that integrates not only financial but also social, environmental, and ethical (SEE) considerations into investment decision making. SRI has grown rapidly around the world in the last decades. In the last years, given the causes of the 2008 financial crisis, ethical, social, environmental and governance concerns have become even more relevant investment decision criteria. However, while a diverse set of models have been developed to support investment decision-making based on financial criteria, models including also social responsibility criteria are rather scarce.