融合投资者情绪的S＿AM＿BiLSTM股价预测模型

股票价格预测一直是金融领域的研究热点之一。然而,股票价格的形成机制是相当复杂的,各种因素都可能会导致股票价格的变化。为此,提出了一种基于深度学习方法并融合多源数据和投资者情绪的股票价格预测混合模型（S＿AM＿BiLSTM）。利用文本卷积神经网络（TextCNN）对从股票论坛中提取的投资者评论进行情绪分析,并计算情绪指数。将情绪指数（sentiment）、技术指标和股票历史交易数据作为股价预测模型的特征集,采用双向长短时记忆神经网络（BiLSTM）对股票的收盘价进行预测,并在此基础上加入注意力机制（attention mechanism）,提高预测精度。为了证明模型的有效性和适用性,随机选取4个重点行业的股票进行实证研究。实验结果表明,与其他单一模型和不含情绪因子的模型相比,所提出的混合模型的效果更优越。     

直觉模糊层次分析法下投行股票估值模型选择

基于直觉模糊集的模糊逼近理论,给出了将直觉模糊互补判断矩阵转换为模糊逼近矩阵的方法,提出了直觉模糊环境下的AHP方法(简记作IFAHP),并将其应用于投行股票估值模型选择问题,得到了股票估值模型中指标的优劣排序的权重值,是一种实用性较强的股票估值模型评价方法。     

基于FinBERT-CNN的股吧评论情感分析方法

我国股市波动受投资者情绪变化影响较大,通过对股吧等金融交流平台上投资者的评论进行情感分析,能够帮助投资者更好地了解股票市场的变化.现有的情感分析方法是利用模型对股票评论集进行分析,但缺少优质的股票评论标注数据集用于模型训练,且单一模型提取股票评论特征较为片面,模型的准确性有待提高.该文针对股吧平台上的评论数据,提出一种...     

基于经验模态分解与投资者情绪的长短期记忆网络股票价格涨跌预测模型

针对传统的基于统计学的回归股票预测模型难以表征多个变量之间的关系,预测出的股票价格趋势误差较大,提出一种基于经验模态分解（EMD）与投资者情绪的长短期记忆（LSTM）神经网络股票价格涨跌预测模型。首先,将股票收盘价通过EMD分解得到若干个具有不同时间尺度的局部特征信号的本征模函数（IMF）;其次,通过引入改进的股票领域情感词典,对东方财富网股吧的帖子,进行上一个股票交易日收盘后和下一个股票交易日开盘前的投资者情感分析,得到下一个股票交易日的投资者情绪指标;最后,将基础的股票基本行情数据、经过EMD得到的IMF以及投资者情绪指标加入LSTM神经网络预测下一个交易日的股票涨跌。仿真实验结果表明,在2019年1月至2021年9月的牧原股份（002714）股票数据上,与单独使用LSTM模型相比,改进后的LSTM模型的预测准确率提高了12.25个百分点,在预测为涨的F1值和预测为跌的F1值上分别提高了1.2个百分点和25.21个百分点。由此可见,基于EMD与投资者情绪的LSTM股票价格涨跌预测模型有效提高了预测精度,为股票市场的涨跌预测提供了一种有效的实验方法。     

多粒度覆盖粗糙模糊集模型不确定性研究

针对覆盖粗糙模糊集中存在的上下近似不一致问题.引入一种更为合理的覆盖粗糙模糊集模型,讨论了该模型的结构与相关性质,定义了基于此模型的粗糙度度量方法.基于覆盖粗糙模糊集中粗糙度相等的情形,提出模糊集中极大模糊集的概念,并利用模糊集与极大模糊集的距离问题定义了模糊集的优劣次序,从而有效解决了模糊集在覆盖粗糙模糊集中粗糙度的度量问题.通过引入粗糙熵等相关概念,证明了此模型中仍然存在随最简覆盖变细,两种度量单调减少的规律,并通过实例进行了验证.从而为进一步揭示粗糙集、粗糙模糊集及覆盖粗糙模糊集之间的不确定性度量规律提供了理论依据.     

空间分布系统的空间模糊建模

本文对空间分布系统提出了一种新的模糊建模方法.首先,在3-D模糊集的基础上给出一种改进的空间模糊集,包含了传统的模糊集和添加的一维空间信息.在空间轴方向上,将传统模糊隶属度函数沿输入变量的随空间的物理变化曲线进行扩展,通过隶属度的连续变化描述输入变量在空间中的变化.其次,基于空间模糊集,采用Mamdani模糊模型形式,设计了对空间分布系统的空间模糊模型的建模方法.最后,通过仿真算例对方法进行了验证.     

基于时空特征的社交网络情绪传播分析与预测模型

社交网络用户情绪传播与用户的空间距离和时间跨度有关,并且受到多种交互机制的影响.从大规模社交网络数据中提取情绪传播的时空特征,研究用户行为对情绪传播的影响,对预测情绪传播趋势具有实际意义.利用线性回归获取的各行为子层的情绪传输率之间存在差异.提出一种基于多层社交网络的情绪传播模型,被称为ECM模型（Emotional contagion model）.该模型包括三个行为子层,每层的拓扑结构各不相同,由该行为的交互历史决定.在真实数据上对ECM模型进行仿真分析,可以获得社交网络中情绪传播的过程与规律:1）中性情绪用户所占比例随时间逐渐增大,接近57.1%,而正向情绪与负向情绪比例始终接近.2）情绪传输率越大,用户情绪更容易受到其他用户的影响而发生变化;初始情绪越中立的用户,在演化过程中情绪波动越小,而初始情绪极性越大的用户情绪波动越大.此外,通过实验对比该模型与其他情绪传播模型,表明ECM模型更加接近真实数据,对社交网络中情绪传播具有较好的预测效果,预测准确率相比其他模型可以提高1.8%~7.8%.     

基于微博的股票投资者未来情感倾向识别研究

近年来,微博越来越受到网络用户的青睐,成千上万的用户通过发布微博共享他们的观点和情感。其中,有大量带有情感倾向(认为某事物“好”或“坏”)的微博,这些微博反映了作者的情绪。投资者情绪(investor sentiment)是研究经济市场走向的重要指标,行为金融学认为股票投资者情绪影响投资者决策,进而影响股票市场,而反映股票投资者情绪的重要指标是投资者对股票市场未来行情的情感倾向(认为股票市场未来行情“好”或“坏”)。通过对新浪微博(目前最大的中文微博平台)上股票投资者发布的文本进行情感信息方面的分析与研究,提出了一种自动识别股票投资者未来情感倾向的方法。该方法分为两级识别,第一级是:识别出微博中包含未来情感的句子;第二级是:将第一级识别出来的包含未来情感的句子分为正面评论(看涨)和负面评论(看跌)。实验结果表明,所提方法对自动识别股票投资者的未来情感倾向达到了非常好的效果。     

基于模糊等价的毕达哥拉斯模糊集相似度构造方法

毕达哥拉斯模糊集是Zadeh模糊集的一种推广形式,其相似度刻画方法是毕达哥拉斯模糊集理论的重要研究内容.现有的毕达哥拉斯模糊集相似度大多针对具体问题而提出.为推广毕达哥拉斯模糊集理论的应用范围,文中基于模糊等价研究毕达哥拉斯模糊集相似度的一般构造方法.将模糊等价概念推广至毕达哥拉斯模糊数,提出了PFN(Pythagor...     

一种广义直觉模糊粗糙集模型

Atanassov直觉模糊集是对Zadeh模糊集最有影响的一种扩充和发展。为进一步拓展Pawlak粗糙集对多重不确定性信息的处理能力,将直觉模糊集引入粗糙集,采用构造性方法提出了一种广义直觉模糊粗糙集模型。首先,介绍了直觉模糊集在一个特殊格上的等价定义,对直觉模糊近似空间的两个基本要素(直觉模糊逻辑算子和直觉模糊关系)进行了研究,证明了一些重要的性质定理;在此基础上,建立了等价关系下的直觉模糊粗糙集模型;最后,对所提模型的性质进行了分类验证与讨论。     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.     

基于Agent的投资者情绪对于股市演化行为仿真研究

基于双向拍卖机制作为价格生成机制,应用遗传算法来进化预测规则,建立了中国股市的人工金融市场模型,并在此基础上研究了投资者情绪对于市场演化行为的影响。研究结果表明人工市场能够产生真实市场演化过程中的混沌动力学行为,并且市场演化行为随着投资者情绪的变化而变动。这一研究对挖掘中国股票市场的演化规律具有重要意义。     

多源异构数据融合驱动的股票指数预测研究

现代信息技术的广泛应用使得资本市场投资者能够获得更及时、更有价值的信息,也更容易受到金融论坛、专业投资网站的影响。融合资本市场的多源异构数据对股票指数进行预测成为该领域的研究热点。提出了一种基于多源异构数据的长短期神经网络（Long Short-Term Memory,LSTM）模型,通过对融合资本市场交易数据、技术指标数据、投资者情绪三种源数据的量化来预测股票指数的走势。提出了一种可以提取深度情感特征的卷积神经网络（Convolutional Neural Networks,CNN）情感分析模型,构建了投资者情绪特征模型。利用“上证50指数”数据进行实验,结果显示：LSTM模型的预测准确率比传统模型更为优秀,数据源的增加也对模型准确率的提升有较大贡献,验证了该方法的可行性和有效性。     

Market sentiment dispersion and its effects on stock return and volatility

Behavioral economics has revealed that investor sentiment can profoundly affect individual behavior and decision-making. Recently, the question is no longer whether investor sentiment affects stock market valuation, but how to directly measure investor sentiment and quantify its effects. Before the era of big data, research uses proxies as a mediator to indirectly measure investor sentiment, which has proved elusive due to insufficient data points. In addition, most of extant sentiment analysis studies focus on institutional investors instead of individual investors. This is despite the fact that United States individual investors have been holding around 50% of the stock market in direct stock investments. In order to overcome difficulties in measuring sentiment and endorse the importance of individual investors, we examine the role of individual sentiment dispersion in stock market. In particular, we investigate whether sentiment dispersion contains information about future stock returns and realized volatility. Leveraging on development of big data and recent advances in data and text mining techniques, we capture 1,170,414 data points from Twitter and used a text mining method to extract sentiment and applied both linear regression and Support Vector Regression; found that individual sentiment dispersion contains information about stock realized volatility, and can be used to increase the prediction accuracy. We expect our results contribute to extant theories of electronic market financial behavior by directly measuring the individual sentiment dispersion; raising a new perspective to assess the impact of investor opinion on stock market; and recommending a supplementary investing approach using user-generated content.     

A type-2 fuzzy rule-based expert system model for stock price analysis

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is tested on stock price prediction of an automotive manufactory in Asia. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.     

Effective options trading strategies based on volatility forecasting recruiting investor sentiment

This study investigates an algorithm for an effective option trading strategy based on superior volatility forecasts using actual option price data for the Taiwan stock market. The forecast evaluation supports the significant incremental explanatory power of investor sentiment in the fitting and forecasting of future volatility in relation to its adversarial multiple-factor model, especially the market turnover and volatility index which are referred to as the investors’ mood gauge and proxy for overreaction. After taking into consideration the margin-based transaction cost, the simulated trading indicates that a long or short straddle 15 days before the options’ final settlement day based on the 60-day in-sample-period volatility forecasting recruiting market turnover achieves the best average monthly return of 15.84%. This study bridges the gap between option trading, market volatility, and the signal of the investors’ overreaction through the simulation of the option trading strategy. The trading algorithm based on the volatility forecasting recruiting investor sentiment could be further applied in electronic trading and other artificial intelligence decision support systems.     

The hasty wisdom of the mob: How market sentiment predicts stock market behavior

We explore the ability of sentiment metrics, extracted from micro-blogging sites, to predict stock markets. We also address sentiments’ predictive time-horizons. The data concern bloggers’ feelings about five major stocks. Taking independent bullish and bearish sentiment metrics, granular to two minute intervals, we model their ability to forecast stock price direction, volatility, and traded volume. We find evidence of a causal link from sentiments to stock price returns, volatility and volume. The predictive time-horizon is minutes, rather than hours or days. We argue that diverse and high volume sentiment is more predictive of price volatility and traded volume than near-consensus is predictive of price direction. Causality is ephemeral. In this sense, the crowd is more a hasty mob than a source of wisdom.     

Applying BERT to analyze investor sentiment in stock market

Neural Computing and Applications - This paper is an analysis of investor sentiment in the stock market based on the bidirectional encoder representations from transformers (BERT) model. First, we...     

基于模糊近似支持向量回归的股价预测研究

股价预测是投资策略形成和风险管理模型发展的基础。为了降低股价变化趋势中的噪声信息和投资者关于两种股价预测误差的不同偏好对股价预测的影响,提出了基于信噪比的模糊近似支持向量回归(FPSVR)的股价预测模型。首先构建信噪比输入变量,然后引入模糊隶属度和双边权重测量方法对支持向量回归(SVR)模型进行改进,最后借助沪深300成份股2008至2019年的股票时间序列日数据,按照股市的波动情况将其分为三个阶段(牛市、熊市、震荡市),并建立三个基准模型进行对比分析。研究结果表明:与三个基准模型相比,所提出的股价预测模型的预测误差最低;与原有的SVR模型相比,FPSVR模型可以更好地对处于牛市和震荡市阶段的股票时间序列进行股价预测。