APPLICATION OF LEAST MEDIAN OF SQUARED ORTHOGONAL DISTANCE (LMD) AND LMD BASED REWEIGHTED LEAST SQUARES (RLS) METHODS ON THE STOCK RECRUITMENT RELATIONSHIP

Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.

A Stock-Recruitment Relationship Applicable to Pacific Bluefin Tuna and the Pacific Stock of Japanese Sardine

This study shows that the stock-recruitment relationship (SRR) for Pacific bluefin tuna and the Pacific stock of Japanese sardine can be expressed by the same SRR model. That is, (environmental factors), where Rt and St-1 denote the recruitment in year t and spawning stock biomass in year t - 1, and f(.) is a function that evaluates the effect of environmental factors in year t. The simulations showed that when the fluctuation in environmental factors cyclically changed, 1) the shape of the apparent SRR assumed clockwise loops for the shorter maturity age of fish, and 2) the apparent SRR comprised scattered anticlockwise loops for the longer maturity age of fish. These features coincided well with those observed. This finding gives us a new paradigm in SRR, which is far different from the concept that has predominated in the field for more than 60 years.

Analysis of the Relationship between Monetary Policy and Stock Market Liquidity

After the 19th National Congress of the Chinese Communist Party,the introduction of the economic theory has promoted the integration of the global socialist market economy.Thereafter,this integration of the domestic and international market has been preliminarily completed,the role of the factor market in resource allocation has been improved,and a sturdy environment has been established for the development of Chinese enterprises.With the effective implementation of a series of policies after the financial system reform,the roles of the financial market in regulating macro-economy and revitalizing the market have become increasingly prominent.In regard to that,it has effectively promoted the financial market as a trade to"enrich people."This paper analyzes the relationship between monetary policy and stock market liquidity in terms of the influence of the former on the latter and suggests strategies to enhance the liquidity effect of monetary policy.

Research on the Relationship Between Macroeconomic Indicators and Stock Market Value

The article first addresses the following questions:“Why does gross domestic product(GDP)rises,but the stock market value falls?”;“Among the macroeconomic factors,which factor has a greater impact on the promotion of investment value in the securities market?”.With these questions in mind,we put forward a hypothesis emphasizing on the impact of macroeconomic factors on the value of the stock market based on existing research and used the regression method to verify this hypothesis.The following conclusions were drawn:(1)variables that have a positive nonlinear relationship with stock market value include balance of payments surplus,rising GDP level,M1,the whole society’s fixed asset investment,and national per capita disposable income;(2)variables that have a negative nonlinear relationship with stock market value include deposit,loan interest rate,new RMB loan amount,consumer price index(CPI),and producer price index;(3)deposit reserve ratio has an S-shaped curve relationship with stock market value;(4)exchange rate has an inverted U-shaped curve relationship with stock market value.

城市存量用地再开发中的供需关系转型与广州实践

为深入探讨城市存量用地再开发的供需逻辑与实现机制,指出当前我国城市存量用地再开发实践中的需求方是渴望持续升值的市场资本,而供给方则是同时受到“现状、规划、适用政策”制约的存量地块。在此基础上,以广州为例开展实证研究,进一步剖析受政策制度演进影响的供需关系曲线对存量用地再开发规模解释的内在规律,并基于供需关系视角,重新审视我国的城市更新问题,针对存量转型的发展前景与空间生产特征提出若干思考。

基金经理激励机制与股票错误定价——一项实验研究

委托代理背景下的激励机制设计是管理科学研究的重要主题,然而现有研究较少关注如何对从事风险决策的代理人进行激励.本研究设计了一个委托代理框架下的股票交易实验,通过引入投资者和基金经理两类角色模拟现实基金经理决策环境,研究基金经理激励机制与股票市场错误定价的因果关系.结果发现,在对基金经理分别采用线性激励(按比例提成)、凸性激励(奖励好的)和凹性激励(惩罚差的)的三种情况下,市场都出现了股票错误定价现象.与线性激励相比,凸性激励和凹性激励导致了显著更高的错误定价.基金经理风险偏好水平和过度自信水平均为激励机制影响股票错误定价的作用机制.此外,实验发现激励机制是通过影响基金经理风险选择行为而非羊群行为,进而影响股票错误定价.本研究探索了微观层面基金经理激励机制设计和宏观层面市场错误定价之间的关系,对基金公司激励制度设计,以及监管部门加强对机构投资者投资组合风险的审慎监管、维持金融市场稳定具有一定现实意义.

基于改进Transformer和超图模型的股票趋势预测方法研究

股票预测是一项令人痴迷又极具挑战的任务。近年来,融合关系信息的股票时序预测方法取得一些进展,但仍存在如下问题:首先,基于图神经网络的方法仅考虑股票之间简单的成对关系,而未考虑股票间的高阶协同关系。其次,现有方法采用预定义图的方式直接给出股票间的静态关系,无法建模股票间潜在的动态变化关系。为了解决上述问题,提出一种端到端的动态超图卷积神经网络股票趋势预测框架。该框架基于改进的Transformer提取股票的时序信息,通过静态超图和动态超图将股票间的协同关系信息引入到时序建模中。在中国A股和美股市场数据集上的实验结果表明,与当前先进模型相比,本文模型的预测性能具有显著优势。

ESG投资提高了基金业绩吗?——兼论ESG投资对“业绩-资金流量关系”的影响

ESG投资将环境、社会责任与公司治理因素纳入考虑范畴,重新定义了投资与市场的边界,是新经济形势下投资领域的热点话题。而ESG投资行为是否具有超额收益,更是在实业界和学术界引发了广泛关注和讨论。本研究以我国2016—2021年股票型和偏股混合型基金为研究对象,从投资组合优化的角度探讨了基金ESG投资行为与业绩的关系,研究发现,基金开展ESG投资能够获得超额收益,并且这一效果在疫情发生后和经济政策不确定性较卡时更加明显。究其原因,在于开展ESG投资的基金对绩优个股的识别和选择能力更好,使得投资组合在个股配置方面得到优化。此外,基金虽然能够通过ESG投资获取超额收益,但也会导致基金业绩与资金流入的正相关性变弱。以上结论不仅深化和丰富了当前关于ESG投资与投资业绩关系的研究,也为我国完善ESG投资体系,进一步推动高质量可持续发展战略提供了政策参考。

客户集中度与控股股东股权质押

控股股东股权质押具有担保增信机制与引发系统性金融风险的两面性,而关系交易作为一种中国传统“关系信任”的交易模式存在“合作”与“掠夺”的双重性,考察两者的关系对于科学合理安排关系交易与股权质押具有重要意义。以2010—2022年沪深A股上市公司为研究对象,以客户集中度表征关系交易,考察以下三个问题:①客户集中度的双重性是否影响股权质押行为?②影响机制是什么?③具有怎样的经济后果?研究发现:客户集中度与控股股东股权质押呈显著的倒“U”型关系。机制检验发现,信息不对称、代理成本与风险承担发挥了部分中介效应。经济后果检验发现,在关系治理情景下,控股股东股权质押降低了股价崩盘风险。研究表明,关系交易这种利益相关者共同治理模式具有极强的灵活性与适应性,在约束与监督控股股东股权质押私利行为上具有突出优势,科学合理地处理客户集中度与股权质押限制性规定之间的关系,能避免陷入关系交易失灵和股权质押限制“一刀切”的两难窘境,有利于推动资本市场实现长期稳定发展。

基于业务内容构建股票关联关系的股价预测

传统的股价预测方法大多基于单只股票的时间序列,而忽视了股票间复杂的相互影响关系。针对该问题,从构建更有效的股票组合角度出发,提出一种基于业务内容构建股票关联关系的股价预测方法。模型包含3个组件:关联关系构建组件、时序特征提取组件和关联关系捕捉组件。关联关系构建组件通过改进的TF-IDF提取上市公司年报中业务内容关键字的相似度来构建股票关联关系;时序特征提取组件利用LSTM提取股票交易数据的时序特征;关联关系捕捉组件利用GCN捕获股票间相互作用的高维特征,最后通过全连接层输出预测的股价。在中国A股市场的实验结果表明,该模型与用单只股票和基于行业关系的预测方法相比误差最小,拟合度最优,能更有效地预测股价,是一种能更充分捕捉股票间相互影响关系的股价预测模型。

Detecting the lead–lag effect in stock markets:definition,patterns,and investment strategies

Human activities widely exhibit a power-law distribution.Considering stock trading as a typical human activity in the financial domain,the first aim of this paper is to validate whether the well-known power-law distribution can be observed in this activity.Interestingly,this paper determines that the number of accumulated lead–lag days between stock pairs meets the power-law distribution in both the U.S.and Chinese stock markets based on 10 years of trading data.Based on this finding this paper adopts the power-law distribution to formally define the lead–lag effect,detect stock pairs with the lead–lag effect,and then design a pure lead–lag investment strategy as well as enhancement investment strategies by integrating the lead–lag strategy into classic alpha-factor strategies.Tests conducted on 20 different alpha-factor strategies demonstrate that both perform better than the selected benchmark strategy and that the lead–lag strategy provides useful signals that significantly improve the performance of basic alpha-factor strategies.Our results therefore indicate that the lead–lag effect may provide effective information for designing more profitable investment strategies.

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

This study proposes a simulation model that well reproduces the spawning stock biomass of Pacific bluefin tuna. Environmental factors were chosen to estimate the recruitment per spawning stock biomass, and a simulation model that well reproduced the spawning stock biomass was developed. Then, effects of various fisheries regulations were evaluated using the simulation study. The results were as follows: 1) arctic oscillations, Pacific decadal oscillations and the recruitment number of the Pacific stock of Japanese sardine were chosen as the environmental factors that determined the recruitment per spawning stock biomass;2) spawning stock biomass could be well reproduced using a model that reproduced the recruitment per spawning stock biomass and the survival process of the population that included the effect of fishing;and 3) the effects of various fisheries regulation could be evaluated using the simulation model mentioned above. The effective regulation in the simulations conducted in this paper was a prohibition of fishing for 0- and 1-year-old fish in terms of recovering the spawning stock biomass. The reduction of fishing mortality coefficients for all age fish to 50% of actual values also showed a good performance. The recent reductions of the recruitment and spawning stock biomass were likely caused by heavy harvesting, especially of immature fish, since 2004.

A recruitment forecasting model for the Pacific stock of the Japanese sardine (<i>Sardinops melanostictus</i>) that does not assume density-dependent effects

This study developed a recruitment forecasting model based on a new concept of the stock recruitment relationship. No density-dependent effect in the relationship was assumed in the model, which showed that fluctuations in recruitment and spawning stock biomass of Japanese sardine in the northwestern Pacific can be explained mainly by environmental factors and the effects of fishing. The February Arctic Oscillation (AO) and sea surface temperature over the southern area of the Kuroshio Extension (30 - 35°N and 145 - 180°E;KEST) were used as the environmental factors. The recruitment forecasting model is proposed: The values for recruitment (), spawning stock biomass, (), in year t, forecast by this model accurately reproduced those estimated by tuning virtual population analysis (VPA), and the pattern of variability in the stock recruitment relationship was also reproduced well. In conclusion, a density-dependent effect does not necessarily have to be included to explain the large variations in recruitment and the spawning stock biomass of the Japanese sardine.

Hot Events Detection of Stock Market Based on Time Series Data of Stock and Text Data of Network Public Opinion

With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.

货币供给、股票价格波动与物价水平的联动关系——基于VAR模型的实证分析

物价指数作为宏观经济健康运行的诊断指标之一,与股票市场、货币供给有着系统的联动关系。文章选取我国2007年1月—2021年10月的178期月度数据资料,运用VAR模型对货币供给、股票价格波动和物价水平三者之间的联动关系进行实证分析。研究表明:其一,货币供给、股票价格波动和物价水平之间存在不对称的联动关系;其二,货币供给、股票价格波动、物价水平之间存在长期稳定的均衡关系,当受到外界冲击影响后较难回到初始状态;其三,货币供给增加会冲击股票市场繁荣,也会推动物价水平上升,但这种影响具有时滞性,股票价格波动会反作用于物价水平。今后,政府应进一步加强对货币供给的宏观调控,加强对股票市场的监管和调控,继续实施稳健的货币政策,更好地引导市场投资和消费。

基于时空注意力机制的双向长短期记忆神经网络的股指预测研究

股票市场是一个高噪音的混沌系统,其外部属性之间的相关性问题以及在长期预测时外部影响对股价波动的加剧,导致对股票市场进行准确预测是一项富有挑战性的工作。为解决上述问题,本文利用基于注意力机制的双向长短期记忆神经网络(BiLSTM)对香港地区恒生指数收盘价进行有效性的实证检验。其中,空间注意力机制用于捕捉输入指标之间的相关性并为其赋予区别权重,时间注意力机制用于描述数据的时间相关性以解决长期预测中的时间依赖问题并为时间步赋予区别权重,BiLSTM神经网络用于拟合数据并构建预测模型。本文还比较了四种基于注意力机制的神经网络方法和六种基线方法,实验结果表明,与当下流行的股票指数预测方法相比,基于双维度注意力机制的BiLSTM可以在短、中、长期预测中均实现更准确的股票指数收盘价预测。

利率变动对银行股价的波动影响研究——以A股上市银行为例

本文选择2007—2021年银行板块指数年数据和银行间同业拆放利率SHIBOR的年数据,建立VAR模型,并进行实证检验来尝试研究银行业股价和利率变动之间的相互关系。结果发现,利率与银行板块指数之间存在长期负向的均衡关系且利率能够影响银行股价。本文结合相关理论及实证检验结论提出相应的政策建议,继续推进利率市场化进程、加强政府对投资市场的监管、推进银行内部风控体系建设、提高投资者风险防范意识等。

城际铁路动车组修造合一车间调度优化方法与应用系统

针对城际铁路动车组生产基地在新造车产能降低后的资源浪费问题,利用修造合一调度优化方法实现动车组的高级修和新造的工艺过程共享生产设备。分析了动车组新造和高级修工艺过程,梳理了设施共享方案。基于检修/新造工艺拓扑和检修/新造物料清单(BOM),提出了一类基于修造拓扑关系约束的车间调度问题,设计修造合一拓扑关系的构造方法。以最小化动车组新造和高级修流程无效时间碎片为优化目标,以修造合一调度拓扑图和基地的设备资源为约束条件,建立修造合一调度优化模型。利用最大最小蚁群系统进行迭代优化,获得拓扑节点的最优调度序列,并将最优调度序列转化为修造合一调度方案。以某城际铁路动车组生产基地的新造和三级检修一体化调度问题为算例,验证了模型和算法的有效性。以修造合一调度模型为核心组件,设计实现了动车组修造基地生产管控系统,并在某城际铁路修造基地以及7个高铁动车组高级修检修基地上线应用。

实体企业金融化对股票流动性的影响

以2008-2020年A股上市公司为样本,探究实体企业金融化对股票流动性的影响及其作用机制。研究发现,企业金融化与股票流动性呈现显著的倒“U”型关系,企业价值在这一关系中承担着部分中介作用。基于企业金融化的利弊进行分析,不仅有助于深刻认识过度金融化对股票流动性的危害,引导实体企业回归主营业务,也为实体企业提高股票流动性提供决策依据。

我国股指期货市场与股票现货市场的价格关系——来自中国资本市场的经验证据

本文基于沪深300股指期货自2010年4月推出以来的日收盘价数据,运用协整检验、格兰杰因果检验、脉冲响应函数等方法分析股指期货市场与现货市场价格的领先滞后关系,结果发现:沪深300股指期货和沪深300指数现货价格之间存在长期稳定的协整关系,股指期货的价格引导着现货的价格,股指期货与现货指数之间存在着单向的因果关系,脉冲响应的结果也印证了期货价格对现货价格具有更大的冲击效应的结论。这说明股指期货的上市加强了现货市场的信息传导机制,股指期货市场的推进和健康发展有利于优化我国的资本市场结构。