Information-based multi-assets artificial stock market with heterogeneous agents

In this paper, an artificial stock market characterized by heterogeneous and informed agents is presented. The heterogeneous agents are seen as nodes of sparsely connected graphs. The agents trade risky assets and are characterized by sentiments, amount of cash and stocks owned. Agents share information and sentiments by means of interactions determined by graphs. A central market maker (clearing house mechanism) determines the price processes for each stock at the intersection of the demand and supply curves. In this framework, the statistical properties of the univariate and multivariate process of prices and returns are studied. Importantly, concerning univariate price processes, the proposed model is able to reproduce unit root, volatility cluster and fat tails of returns. The multivariate price process exhibits both static and dynamic stylized facts, in particular the presence of static factors and common trends. Static factors are studied making reference to the cross-correlation between returns of different stocks, whereas the common trends are investigated considering the variance–covariance matrix of prices. The proposed approach allows to endogenously reproduce the multivariate stylized facts.     

The informational content of insider trading disclosures: empirical results for the Polish stock market

In this paper, we try to answer the question as to whether insider trading disclosures convey valuable information to market participants, valuable in the sense of the profitability of an investment strategy that faithfully mirrors insider behaviour. Our interest in this subject is limited to the case of announcements concerning insider transactions issued over a 6 year-period on the Warsaw Stock Exchange (WSE). Initially, we use event study methodology to check whether insider trading disclosures are accompanied by a performance of stock returns as well as trading volume. Two different models generating expected returns (expected volume) are employed to verify the robustness of our results. The first of these is the regime switching model, with the results then being recalculated by using a GARCH-type model which seem to be most useful for dealing with some of the inconvenient statistical properties of stock return and trading volume data. Afterwards, a technique based on the reference return strategies is used to examine whether or not outsiders who imitate insider behaviour are able to profit from it. The major findings are as follows: firstly, announcements about the sale of stocks by insiders convey no information to market participants. Secondly, a statistically significant market response to insider disclosures of purchases of stocks in their own company can be observed in the three days prior to the announcement release for both return as well as trading volume series, and finally, outsiders who purchased stocks previously bought by insiders experience negative returns whereas outsiders disposing of stocks previously sold by insiders earned a return of 8.57% over the 6 month-period.      

Dynamic Calibration of Price-Trend Parameters for Commodity Futures Trading

This paper addresses the problem of buying commodities through the futures markets and deals specifically with a heuristic rule developed for the scenario described as `purchasing under a deadline'. The rule is based on a short-term forecasts produced by Taylor's price-trend model. In a previous study applied to the Chicago Board of Trade (CBOT) corn futures market the price-trend parameters of the stochastic process generating the daily returns were shown to be nearly stable over time and hence could be estimated using a static procedure. However, the analysis presented in this paper concerning the CBOT soybean futures market strongly suggests that those parameters were unstable, impairing the successful application of the purchasing rule. The authors recommend the continuous CUSUM monitoring of the purchasing results and propose a procedure for dynamically calibrating the price-trend and buying parameters. Under this procedure the price-trend parameter estimates are derived from exponentially smoothed sample autocorrelation coefficients of the rescaled daily returns. The procedure was developed and tested using the 1972-87 series of CBOT daily soybean futures closing prices. The results suggest that it leads to an improvement on the purchasing results derived from the static parameter calibration procedure formerly adopted.     

Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

This paper makes use of spot and futures market data to carry out a thorough analysis of the dynamics of carbon price returns in the European Union Emission Trading Scheme for the whole first commitment period from 2008 to 2012. Understanding the properties of carbon price returns is especially crucial for industries which have to comply with an emission trading system and other market participants such as risk managers and speculators. We therefore seek to develop accurate models which capture the behavior of carbon price returns comprehensively. We apply a broad spectrum of GARCH model specifications, using different distributions for model innovations. As both time series, spot and futures price returns, exhibit asymmetric behavior in their variance, we additionally take Markov regime switching models for the variance equation into consideration. Empirical results demonstrate that AGARCH, NARCH and GJR fit the data best. We further show that, in the error term of any model, fat-tailed distributions—in particular the generalized error distribution—significantly improve the fit. Additionally, as futures returns seem to carry informational content concerning subsequent spot returns, we propose a sound, yet parsimonious, spot returns model, well-suited to capturing the dynamics. Finally, the most appropriate models for spot and futures price returns are tested in an out-of-sample environment, and further checked for robustness in data subsets. Subsequently a model for each market is proposed.     

Microfoundations for diffusion price processes

We study microeconomic foundations of diffusion processes as models of stock price dynamics. To this end, we develop a microscopic model of a stock market with finitely many heterogeneous economic agents, who trade in continuous time, giving rise to an endogeneous pure-jump process describing the evolution of stock prices over time. When the number of agents in the market is large, we show that the price process can be approximated by a diffusion, with price-dependent drift and volatility coefficients that are determined by small excess demands and trading volume in the microscopic model. We extend the microscopic model further by allowing for non-market interactions between agents, to model herd behavior in the market. In this case, price dynamics can be approximated by a process with stochastic volatility. Finally, we demonstrate how heavy-tailed stock returns emerge when agents have a strong tendency towards herd behavior.     

投资者情绪和市场利率对证券市场指数收益影响特点的动态刻画

本文基于2006年10月到2015年6月市场层面的投资者情绪和上证综指收益率,刻画了投资者情绪和市场利率对证券市场指数收益率的影响。首先,本文通过误差修正模型研究了短期层面投资者情绪对证券市场收益的影响特点,补充了以往在长期层面和整体收益水平上投资者情绪对市场收益影响的研究。由于市场层面的投资者情绪会受到宏观政策影响,之后本文将市场利率作为政策因素,通过分位数回归分析了不同市场收益水平下,市场利率和剔除了宏观政策因素的投资者情绪对市场收益的影响。研究结果表明:投资者情绪和证券市场收益之间的关系在短期层面上更为显著;当我国的证券市场环境处于“牛市”时,市场利率和投资者情绪均会对证券市场指数收益产生显著的影响,且随着市场收益水平的逐步上升,市场利率的反向作用和投资者情绪的正向作用均会逐渐加强。     

The impact of US macroeconomic news on the Polish stock market

This paper investigates the impact of US macroeconomic news announcements on the intraday returns of Warsaw Stock Exchange indices. The WSE is the largest and the most liquid stock market among the new European Union member countries. By means of an event study we examine the response of three indices, namely the WIG20, the mWIG40 and the sWGI80, describing the stock price behavior of the largest, medium-sized and small firms, respectively. The results of the empirical analysis show that the stock prices of the largest firms react in the first minute after a news release. This indicates the relatively high efficiency of the WSE. The response of smaller firms’ stock returns is slower but more persistent. The most influential are the announcements of Nonfarm Payrolls describing the US labor market. The indices of the WSE react similarly to good and bad news about the US economy.     

ASV-T模型研究及其在中国创业板市场中的应用

本文提出了T分布的带杠杆效应的随机波动模型,该模型同时兼顾了股票市场的杠杆效应和厚尾效应,并对模型进行了统计结构分析,证明了模型的有效性,基于贝叶斯分析,给出了对ASV-T模型的MCMC估计方法,其中对参数采取Gibbs抽样。利用该模型,通过对中国创业板指数的实证研究,证明了ASV-T模型对创业板市场的回报和波动性特征有更好的拟合效果,并且模型能够较好地描述金融数据的杠杆效应和厚尾效应。     

Nonparametric prediction of stock returns based on yearly data: The long-term view

One of the most studied questions in economics and finance is whether empirical models can be used to predict equity returns or premiums. In this paper, we take the actuarial long-term view and base our prediction on yearly data from 1872 through 2014. While many authors favor the historical mean or other parametric methods, this article focuses on nonlinear relationships between a set of covariates. A bootstrap test on the true functional form of the conditional expected returns confirms that yearly returns on the S&P500 are predictable. The inclusion of prior knowledge in our nonlinear model shows notable improvement in the prediction of excess stock returns compared to a fully nonparametric model. Statistically, a bias and dimension reduction method is proposed to import more structure in the estimation process as an adequate way to circumvent the curse of dimensionality.     

Consumer returns reduction and information revelation mechanism for a supply chain

In this paper, we develop two revelation mechanism models of a supply chain consisting of one manufacturer and one retailer under asymmetric information, where the retailer provides store assistance (SA) to reduce consumer returns rate and increase demand. Under full information, we find that a higher returns rate or returns handling cost increases the SA level if the market scale is sufficiently high. In the demand information asymmetry model, we find that: (i) the low-type retailer (facing a low demand) has no incentive to distort demand information while the high-type retailer may report wrong information; (ii) the manufacturer would like to design a menu of wholesale price-order quantity contract to induce truthful demand information and the manufacturer pays an information rent to the high-type retailer if the returns rate or returns handling cost for the retailer is sufficiently low; (iii) asymmetry of information does not change the monotonicity of the unit wholesale price in the retailer’s type, and information asymmetry decreases the retail price but increases the SA level. Unlike the demand information asymmetry model, a higher retailer’s returns handling cost expands the effects of information asymmetry on the retail price and the SA level, and using revelation mechanism decreases the channel profit if the retailer’s returns handling cost is sufficiently high under the returns rate information asymmetry model.     

媒体信息量异常或有害股票收益

本文借助一个独特的数据样本,运用媒体对股票的剩余关注度模型,实证研究异常媒体信息量与股票收益之间的关系,以期为投资者进行投资决策提供一定的参考和指导。研究发现:异常媒体信息量越大,该股票在下一个月的平均收益率越低,存在媒体效应;由此所构造的零投资组合经CAPM模型、FF三因素模型和Car-hart四因素模型调整后,均能获取显著的超额收益,结果具有稳健性。此外,实证结果还表明媒体效应所带来的超额收益源于媒体信息量异常大的股票组合的显著低收益,本文认为,这种不对称现象产生的原因可能更多的是由投资者情绪导致的股票价格对媒体报道的过度反应,并进而导致较低的期望收益。     

考虑投资者风险态度的随机模糊投资组合模型及实证

考虑投资者面临证券市场随机和模糊的双重不确定性,把证券收益率视为随机模糊变量。在前景理论下考虑投资者的风险态度,建立不同的随机模糊收益率、期望收益隶属度函数和目标权重,构建考虑投资者风险态度的随机模糊投资组合模型。采用实证方法把市场分为下降和上升两个阶段,研究不同风险态度投资者的投资组合差异及模型表现。结果表明:投资者的风险态度会影响投资组合的结构;考虑投资者风险态度的随机模糊投资组合模型,能够满足不同风险态度投资者对投资收益和风险的差异需求,且在实际投资决策中具有可行性。     

CED model for asset returns and fractal market hypothesis

A new general model for asset returns is studied in the framework of the Fractal Market Hypothesis (FMH). To accommodate markets with arbitrage opportunities, it concerns capital market systems in which the Conditionally Exponential Dependence (CED) property can be attached to each investor on the market. Employing the limit theorem for the CED systems, the universal characteristics for the distribution of asset returns are derived. This explains the special role of the Weibull distribution in modeling of global asset returns for market with no arbitrage and the two-power laws property of the density of global returns, evident in the empirical data. Finally, the link with two-parameter Pareto distributions is established.     

Mixed effect models for absolute log returns of ultra high frequency data

Considering absolute log returns as a proxy for stochastic volatility, the influence of explanatory variables on absolute log returns of ultra high frequency data is analysed. The irregular time structure and time dependency of the data is captured by utilizing a continuous time ARMA(p,q) process. In particular, we propose a mixed effect model class for the absolute log returns. Explanatory variable information is used to model the fixed effects, whereas the error is decomposed in a non‐negative Lévy driven continuous time ARMA(p,q) process and a market microstructure noise component. The parameters are estimated in a state space approach. In a small simulation study the performance of the estimators is investigated. We apply our model to IBM trade data and quantify the influence of bid‐ask spread and duration on a daily basis. To verify the correlation in irregularly spaced data we use the variogram, known from spatial statistics. Copyright © 2006 John Wiley & Sons, Ltd.     

我国期货市场违约风险的研究

引入违约距离的概念,建立了期货市场违约风险评估模型,采用GARCH-M模型对期货合约价格收益的波动率进行估计.运用此模型研究了郑州商品交易所上市品种小麦的违约风险,所得结果与实际市场结果相吻合.因此,可以运用本文提出的期货市场违约风险评估模型能预测临近交割月时期货市场发生违约的概率,实时捕捉期货市场发生违约事件的信息.     

投资组合模型的改进研究:基于企业社会责任视角的实证分析

在尊重和借鉴前人对企业社会责任研究,尤其是在企业社会责任评价研究基础之上,本文从投资者的角度在投资组合过程中研究企业社会责任。在Markowitz(均值—方差)理论模型上添加企业社会责任的三个一级指标期望作为目标函数,由此将传统的投资组合模型扩展为五个目标函数的投资组合选择模型,而且我们根据经济学中经典的效用函数理论证明了此模型的正确性。本文引入主流的企业社会责任评价标准,并对一些典型公司进行打分量化。在此基础之上建立了以期望回报率、回报率的方差、核心利益相关者期望、蛰伏利益相关者期望和边缘利益相关者期望为目标函数的投资组合选择模型,在最小方差曲面上选取10个点构造投资组合,并以样本外的数据验证了模型的有效性。研究发现:根据此模型计算出来的部分投资组合回报率显著高于同期的市场指数。研究结果表明,这种关注企业社会责任的多目标投资组合选择模型,不仅让投资者可以直接控制企业社会责任,而且实际数据证明了此模型的优势之处,从而为关注企业社会责任的投资者提供一种投资的方法和思路。     

A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans

A data-driven Neural Network (NN) optimization framework is proposed to determine optimal asset allocation during the accumulation phase of a defined contribution pension scheme. In contrast to parametric model based solutions computed by a partial differential equation approach, the proposed computational framework can scale to high dimensional multi-asset problems. More importantly, the proposed approach can determine the optimal NN control directly from market returns, without assuming a particular parametric model for the return process. We validate the proposed NN learning solution by comparing the NN control to the optimal control determined by solution of the Hamilton–Jacobi–Bellman (HJB) equation. The HJB equation solution is based on a double exponential jump model calibrated to the historical market data. The NN control achieves nearly optimal performance. An alternative data-driven approach (without the need of a parametric model) is based on using the historic bootstrap resampling data sets. Robustness is checked by training with a blocksize different from the test data. In both two and three asset cases, we compare performance of the NN controls directly learned from the market return sample paths and demonstrate that they always significantly outperform constant proportion strategies.     

An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast

In this paper, we propose an adaptive investment strategy (AIS) based on a dynamic portfolio selection model (DPSM) that uses a time-varying investment target according to the market forecast. The DPSM allows for flexible investments, setting relatively aggressive investment targets when market growth is expected and relatively conservative targets when the market is expected to be less attractive. The model further allows investments to be liquidated into risk-free assets when the market forecast is pessimistic. By dynamically determining the investment target, the DPSM allows construction of portfolios that are more responsive to market changes, while eliminating the possibility of the model becoming infeasible under certain market conditions. When the proposed DPSM is implemented in real-life investment scenarios using the AIS, the portfolio is rebalanced according to a predefined rebalancing cycle and the model’s input parameters are estimated on each rebalancing date using an exponentially weighted moving average (EWMA) estimator. To evaluate the performance of the proposed approach, a 7-year investment experiment was conducted using historical stock returns data from 10 different stock markets around the world. Performance was assessed and compared using diverse measures. Superior performance was achieved using the AIS proposed herein compared with various benchmark approaches for all performance measures. In addition, we identified a converse relationship between the average trading volume of a market and the value of the weighting parameter prescribed to the EWMA estimator, which maximizes cumulative returns in each market.     

欧盟碳市场期货价格分布特征分析

市场价格的收益率特征是研究市场特点的出发点.从各类常见分布入手,检验了欧盟碳市场的真实分布特性,最终发现稳态分布最适宜于描述碳市场的收益率分布.据此,讨论了欧盟碳市场的信息传递与风险评价特点.     

A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

GARCH models are commonly used for describing, estimating and predicting the dynamics of financial returns. Here, we relax the usual parametric distributional assumptions of GARCH models and develop a Bayesian semiparametric approach based on modeling the innovations using the class of scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution. The proposed specification allows for greater flexibility in capturing the usual patterns observed in financial returns. It is also shown how to undertake Bayesian prediction of the Value at Risk (VaR). The performance of the proposed semiparametric method is illustrated using simulated and real data from the Hang Seng Index (HSI) and Bombay Stock Exchange index (BSE30).