基于灰色系统和神经网络的创业板股票价格预测研究

股票价格预测是投资领域的一个重点关注课题。由于股票价格受到诸多非线性因 素的影响,得到精确的预测结果较为困难。为了消除股票指标的多重共线性,采用Adaptive- Lasso算法对指标变量进行筛选,实现了数据降维。之后,利用灰色预测对股票价格影响指标 进行预测,并在此基础上利用神经网络模型对股票收盘价进行预测。结果表明,利用灰色系统 和BP神经网络结合的模型所得预测结果平均相对误差为0.095,且运行效率较高,对股票预测 具有一定的积极意义。     

基于灰色预测模型的股价预测研究

灰色预测模型是根据灰色系统理论创建的预测方法。利用灰色预测模型对部分股票收盘价进行的实证研究表明，该模型对股票价格预测的准确度较高，可用于股票价格的短期预测。     

灰色预测在股票价格中的研究应用

股票价格的预测对于投资者的投资决策有着实质性的影响,因此,通过建立正确的股票预测价格模型对于投资者的投资决策有一定的指导作用。本文运用股票价格的灰色预测方法,并且通过MATLAB程序,研究灰色GM(1,1)模型以及它的修正模型Verhulst模型在股票市场中的应用。在短期内,GM(1,1)模型可以做出股价的预测,但是由于在实际应用中没有考虑到其他因素对于其自身的影响,因此对于长期的股票价格预测,由于其数据变动序列庞大,模型的精度有所降低,而修正的Verhulst模型则更适合研究实际长期股价变化的预测。     

卖空交易与股票价格稳定性--来自中国融资融券市场的自然实验北大核心CSSCISSCI

基于2009年4月-2013年12月我国殷票市场的数据,本文研究了融资融券标的股票和非标的股票、以及股票被列入和剔出融资融券标的前后的价格波动特征。结果表明,融资融券交易机制的推出有效提高了我国股票价格的稳定性,融资融券标的股票的价格波动率和振幅均出现了显著性下降。我们还发现,融资融券交易显著降低了股票价格的跳跃风险,有利于防止股票价格的暴涨暴跌和过度投机。此外,融资融券交易在抑制股票价格异质性波动上也起到了实质性作用,从而有助于增加上市公司信息透明度和市场信息效率。     

基于马尔科夫链对股票价格预测

合理地对股票价格进行预测是众多股票研究者所追求的目标。而随着知识学习的不断深入,利用数学模型的方法进行股票价格预测近年来更加受到人们的关注。在研究股票市场时,我们常常利用马尔科夫链的方法预测股票价格趋势,以期为投资者及股票市场管理者提供一些决策依据。本文先向大家简单介绍了马尔科夫链,接着建立数学模型,并利用实例检验了所建立的马尔科夫链模型在进行股价预测时的可行性,为以后股价预测方面的研究提供借鉴。     

公司股票和债券价格中的信用风险信息效率研究北大核心CSSCISSCI

有效市场假说认为,金融市场上金融资产的价格变化是对各种信息的反应,如果这种反应是即刻而充分的,那么市场就是有效的。本文基于我国上市公司股票和债券的价格数据,应用KMV模型和信用价差模型度量上市公司股票和债券价格变化所反映的公司信用风险大小,在此基础上检验并比较上市公司股票和债券价格对其信用风险信息反应的效率。结果表明,从短期信息有效性方面看,股票价格能够更加及时有效地反映公司信用风险信息,公司债券价格对信用风险信息的反应存在滞后性,但随着实际信用风险增加,滞后时差缩短;从长期来看,股票价格和债券价格在反映公司信用风险信息上存在一致性,公司的实际信用风险大小是影响两者存在一致性的重要因素,实际的信用风险越大,两者的一致性越强。     

关于金融危机中股价决定理论的实证检验

实证研究了两次金融危机中股票价格的决定因素,实证结果表明传统股票定价理论不能解释股票价格的大幅下降,在金融危机中股票价格由市场中的现金量决定,人民币的升值有助于股票价格的提高。我们运用两组面板数据,支持了cash-in-market pricing理论模型的结论。     

一种股票价格与股票需求之间数量关系的估计方法

股票作为一种特殊的商品,其价格会受到投资者需求的影响.本文以股票的商品性质作为切入点,分析了股票价格与投资者对股票需求之间的定性关系,而后介绍了一种估计股票价格与股票需求之间数量关系的方法.选取代表性股票上海机场(600009)进行了实证分析,建立了理论模型,利用上海机场股票的相关数据对模型参数进行了估计,并对模型参数的意义进行了解释,说明了这种方法在股票价格分析中的作用.     

盈余公告后债券价格反应对股票未来回报的预测研究

本文研究了我国A股上市公司发布盈余公告后,债券价格反应对于关联股票未来回报的预测能力。研究发现,盈余公告前1日至后1日债券收益率的变动可以预测公告公布后窗口期为20日的股票持有超额累计回报;实证结果显示,这种预测能力不会因为盈余信息的好坏而存在显著的差异;最后基于似无相关模型SUR的检验后,发现上市公司的机构持股比例越低,债券价格反应对于股票回报的预测能力越强。本文研究表明,我国被富有经验的投资者所主导的债券市场中债券价格相对于股票价格会更迅速地吸收消化盈余公告信息。     

我国股票市场投资者异质期望与股票收益的实证研究

传统的资产定价理论要求投资人对资产未来收益保持一致的预期和判断。然而,现实的金融市场中,因为未来收益不能确定、私有信息的存在以及先验异质等原因,投资人对未来收益的预测存在分歧。同时,卖空成本存在会限制投资者卖出股票,使股票价格只反映乐观投资者预期,由此导致短期内股票价格偏高,未来收益率降低。投资者的收益预期分歧程度越大,相应股票的当期价格越高,未来收益率越低。本文以我国沪深A股市场为研究对象,以投资者异质期望和股票市场的卖空限制导致股票溢价为理论框架,对投资者异质期望与股票未来收益率的关系进行了实证研究。本文选证券公司卖方分析师业对股票的绩预测的分散程度来反映投资者异质期望的程度,利用2014年到2015年卖方分析师业对股票的绩预测和相关上市股票交易数据进行实证分析。实证结果证实了"高异质期望导致低后期收益率"这一假设。     

Volatility of stock price as predicted by patent data: An MGARCH perspective

This paper proposes to model stock price volatility and variations in innovation effort using a Multivariate GARCH structure designed to extract information for risk prediction. The salient feature is that the model order, alongside other parameters, is endogenously determined by the estimation procedures. Using stock prices of U.S. computer firms, it is found that the model can pick up the correlation between the two variables and aid in producing accurate Value-at-Risk estimates.     

投资者理性预期、流动性约束与股价崩盘传染研究

“传染性”是股价崩盘三大基本特征之一,会加剧股价崩盘负面影响,甚至引发系统性金融风险,因此,本文重点关注股价崩盘传染机制研究。首先,本文基于两阶段理性预期均衡模型,提出股价崩盘传染两大假设,即投资者理性预期与流动性约束导致传染;其次,基于2000-2016年全球28个国家或地区资本市场数据,实证检验股价崩盘传染机制和传染渠道。研究显示:(1)投资者理性预期、流动性约束会导致股价崩盘发生传染;(2)股价崩盘事件会在资本市场关联国家或地区传染;(3)提高资本市场信息透明度、加强金融管制有助于降低受关联国家或地区股价崩盘传染。     

去伪存真:油价趋势与股票市场——来自“一带一路”35国的经验证据

油价波动深刻影响全球经济,严重时会造成全球股市动荡,甚至引发系统性金融风险。然而油价中的信息噪音严重阻碍国际油价对股票市场的预测效果。本文提出的移动平均法可有效减弱信息噪音,研究表明,本文基于移动平均法构建的油价趋势因子对“一带一路”沿线国家股票市场具有良好的样本内和样本外可预测性。进一步研究发现,国际油价波动对产油国和非产油国股票市场的影响存在非对称性。本文为国际油价冲击股票市场提供了新的有力证据,同时本文研究成果提示了油价风险,对维持我国股票市场稳定,保持金融稳定具有一定意义。     

The Rationality and Efficiency of Stock Price Relative to Money Announcement Information

Recent studies find that stock price reacts only to unanticipated changes in the money supply. These studies assume a joint hypothesis of rationality and efficiency in their tests. This paper formulates a model in which stock price depends both upon anticipated and unanticipated money supply forecasts. From this model, an econometric model that separates the hypotheses of rationality and efficiency is estimated. The results show that investors rationally incorporate forecasts of the weekly current money announcement into stock price during the pre-October 6, 1979, sample period. However, efficiency with respect to money information cannot be corroborated in this period. Cross-equation restrictions implied by rationality are rejected during the post-October 6, 1979, period. In this period, efficiency again cannot be corroborated. Alternative money prediction specifications indicate the robustness of these results.     

Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mechanism in Long–Short-Term Memory (LSTM) network based stock price movement prediction. Our proposed model significantly enhances the LSTM prediction performance in the Hong Kong stock market. The attention LSTM (AttLSTM) model is compared with the LSTM model in Hong Kong stock movement prediction. Further parameter tuning results also demonstrate the effectiveness of the attention mechanism in LSTM-based prediction method.     

Does Voluntary Disclosure Improve Stock Price Informativeness?

According to theory, comovement in stock prices reflects comovement in the fundamental factors underlying the values of stocks. Recent theory contends that stock price comovement can be driven by information markets or the informational opacity of the firm. To the extent that voluntary disclosure reduces information acquisition cost and enhances firm transparency, we predict that enhanced voluntary disclosure reduces stock price comovement. We provide evidence in support of this prediction using analyst evaluation of firm disclosure policy. Overall, our evidence supports the effectiveness of firm disclosure policy in increasing the amount of firm‐specific information contained in stock returns.     

股票价格具有货币政策指示器功能吗——来自中国1997～2006年的经验证据

本文运用VAR模型考察了以股票价格为代表的金融资产价格对我国通货膨胀的影响。实证分析表明,我国股票价格的变动对产出缺口存在一定的正向影响,但是这种影响不太稳定,说明我国股票价格通过总需求渠道对未来通货膨胀产生的影响比较微弱。同时,我国股票价格的变动能引起未来CPI和WPI的同向变化,尤其与CPI的关系非常稳定,说明股票价格在一定程度上包含了我国未来通货膨胀的信息。因此,我国股票价格可以作为一个帮助判断未来经济走势和通货膨胀变动趋势的货币政策指示器。     

Short-sale refinancing and price adjustment speed to bad news: Evidence from a quasi-natural experiment in China

Short selling may accelerate stock price adjustment to negative news. However, the literature provides mixed evidence for this prediction. Using short-sale refinancing and a staggered difference-in-differences (DID) model, this paper explores the effect of short selling on stock price adjustment. Our results show that (1) short-sale refinancing improves the speed of stock price adjustment to negative news. This result holds after we control for endogeneity. (2) The positive relationship between short-sale refinancing and stock price adjustment speed is significant in subsamples of stocks with higher earnings management or lower accuracy of analyst forecasts, indicating that firms with more opaque information are more likely to be targeted by short sellers. In subsamples of stocks with a higher ownership concentration or lower ownership by institutional investors, short selling is more likely to increase the speed of stock price adjustment, indicating that ownership structure may influence negative news mining. (3) As short-sale refinancing exacerbates the absorption of bad news by stock prices, it increases crash risk. This study enriches the research on the economic consequences of short selling and provides empirical evidence supporting regulations on short selling in China.     

PREDICTING NEXT TRADING DAY CLOSING PRICE OF QATAR EXCHANGE INDEX USING TECHNICAL INDICATORS AND ARTIFICIAL NEURAL NETWORKS

Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well‐established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility. Copyright © 2014 John Wiley & Sons, Ltd.