股票趋势预测分析研究 ——基于百度指数

针对股票价格无规律、复杂的跌涨预测问题,考虑到投资者关注度可能对股票产生的影响,文章将百度旗下的百度指数作为投资者关注度的衡量标准,并确定与预测股票具有相关性的关键词.结合用于股票市场预测的神经网络模型——长短期记忆模型(LSTM模型),在对数据进行相关性分析、数据清理、数据归一化后,带入模型进行预测.实验结果表明:在不考虑宏观因素的情况下,找到有效的关键词作为投资者关注度的衡量指标,并带入模型中预测,不仅可以预测股票趋势,还能准确预测股票价格,让投资者在了解实际股价的情况下,作出适合的股票投资决策.     

基于内幕交易下的中国股市量价因果关系分析

本文对内部交易下的中国股票市场的价格和交易量进行了因果关系分析，通过基于Taylor展开的非线性因果关系检验发现，内幕交易股票存在由收益到成交量的单向因果关系，说明内幕交易者以低的成本通过信息传播影响股价，操纵市场交易来获得利润。     

TS模糊模型在个股股价预测中的应用研究

个股价格短期内的变化量一般可通过股票技术指标间接反映出来,且这些指标间有着一定的相关性;另外,股票价格模型具有Takagi-Sugeno(TS)模糊模型所研究问题的非线性、时变性特点,基于此,本研究将TS模糊模型与分析出的股票常用技术指标相结合进行股价预测。结果显示预测出的价格与股票实际价格近似一致,精度高,因此研究具有一定的实用价值。     

内幕交易对公司股价的影响——基于新潮实业等四只股票的实证研究

内幕交易破坏了证券市场的正常秩序,影响了正常股价,这不仅使得大多数股民在一个非公平的市场环境下进行交易,而且使得中小股民的利益受到损失。对证监会查处的涉及内幕交易的四只股票在未发生内幕交易时期的股价和发生内幕交易时期的股价进行实证分析后发现：在发生内幕交易期间,实际股价明显偏离正常预测股价,差异率的绝对值也呈明显上升趋势,而未发生内幕交易时期的实际股价基本符合正常预测股价走势,差异率的绝对值也呈随机游走状态,这说明内幕交易对股价有着很大的影响。     

中国股票市场流动性的实证研究

本文根据我国A股市场股票的日交易数据计算出市场非流动性比率的时间序列,采用ARMA-GARCH族模型建立我国股票市场流动性AR(1-TARCH(1,1))模型。结果表明,我国股票市场流动性具有持久性,流动性成本可以预测收益。此外,我国股票市场流动性的波动具有非对称性。     

价格波动性与股票预期回报——基于中国市场的初步证据

基于国际资本市场数据的研究发现,股票价格的波动率和股票未来的回报率负相关,而且风险差异不能解释这个现象,文章使用中国股票市场的数据发现了相同的结论。在1998年1月到2003年12月期间内,基于过去一个月内股价波动率的对冲组合在未来六个月内能够取得0.32%的月风险调整超额回报率。M iller(1977)认为股价波动性代表了投资者对股票价值评估的不确定性和异质性,因为卖空限制的存在,波动性高的股票的价格更多地反映了乐观投资者的看法,因而出现高估价值的错误定价。文章分析认为M iller的错误定价理论能够解释股价波动率与未来回报率之间的负相关关系。     

基于ARIMA类模型的农业股票价格实证研究——以隆平高科为例

运用ARIMA-GARCH的模式来对中国股价波动作出预测，选择现代化农业代表企业隆平高科收盘价指数的时间序列作为研究对象，对该企业3年来股票收盘价进行分析，并利用ARIMA模型进行股价预测，同时加入波动性影响，利用GARCH模型对风险率建立模型，研究发现所选择的ARIMA-GARCH模型对收盘价时间序列具有较好的拟合作用，股票价格整体呈上升趋势，具有一定震荡性，但总体风险不大。     

基于买卖强度的交易策略及其实证分析

文章引入了点过程中的Hawkes过程来进行股票买卖强度的拟合与预测,并提供了基于该有效预测的交易策略。文中首先对所使用的Hawkes过程进行了介绍,并从理论上说明其在高频金融数据拟合中的优势;之后叙述了极大似然方法在Hawkes模型参数估计中的具体应用;最后,结合由wind数据库中选取的内地股票市场中的股票实例,使用Hawkes过程进行强度预测、策略构建与盈利情况分析,证实了该模型在实际拟合中的优势与策略的有效性。     

美式股票期权的定价研究:平安股票期权的例证

上证50ETF期权的问世开启了中国股票期权的时代,中国股票期权市场发展潜力巨大,未来的几年将会迅速发展壮大,投资者可以通过购买股票期权进行风险规避或投机获利。本文采用GARCH模型进行参数估计,将股票价格的波动率用该模型预测的波动率代替,以此预测股票价格上升或是下降的概率。并运用二叉树模型对中国平安股票的美式看涨期权和看跌期权进行定价。最后,从扩大股票期权模拟交易规模、提升股票期权参与者的认知水平、提供完备的监管机制和应急方案三个层面提出了中国股票期权市场顺利起步和发展之对策。     

基于MACD指标的智能选股系统设计与开发

文章讨论了股票市场上MACD技术指标及应用场合,使用SQLServer2000作为后台数据库,Java和.NET作为前台开发工具,使用IKVM实现Java和.NET两种开发工具之间的互通性,设计和开发了基于MACD指标的智能选股系统,并对系统进行了测试。结果表明,系统选出的股票股价成长性相比同期沪深大盘指数的成长性要高得多。     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Forecasting stock index price using the CEEMDAN-LSTM model

This paper uses a mixture model that Long Short-Term Memory (LSTM) combines with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to forecast stock index price of Standard & Poor's 500 index (S&P500) and China Securities 300 Index (CSI300). CEEMDAN decomposes original data to obtain several IMFs and one residue. The LSTM forecasting model utilizes the decomposed data to obtain the prediction sequences. The prediction sequences are reconstructed to gain final prediction. The paper introduces contrast models such as Support Vector Machine (SVM), Backward Propagation (BP), Elman network, Wavelet Neural Networks (WAV) and their mixture models combined with the CEEMDAN. The MCS test is used as evaluation criterion and empirical results present that forecasting effects of CEEMDAN-LSTM is optimal in developed and emerging stock market.     

基于自回归条件异方差模型的上证指数收益波动分析

股票市场的股价波动会引起股价指数的涨跌。本文基于计量经济学的自回归条件异方差模型,对上证指数2007年以来的1276个交易日的样本数据进行实证研究。结果表明:上证指数收益序列具有尖峰厚尾特征和波动的时段集群特征,适合利用自回归条件异方差模型进行分析及预测。研究结果为各方从不同角度把握上证指数收益波动的规律提供了股票投资理论和方法。     

Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading

With the rapid growth of carbon trading, the development of carbon financial derivatives such as carbon options has become inevitable. This paper established a model based on GARCH and fractional Brownian motion (FBM), hoping to provide reference for China's upcoming carbon option trading through carbon option price forecasting research. The fractal characteristic of carbon option prices indicates that it is reasonable to use FBM to predict option prices. The GARCH model can make up for the lack of fixed FBM volatility. In this paper, the daily closing prices of EUA option contracts on the European Energy Exchange are selected as samples for price prediction. The GARCH model was used to determine the return volatility, and then the FBM was used to calculate the forecast price for the next 60 days. The results showed that the predicted price can better fit the actual price. This paper further compares the price prediction results of this model with the other three models through line graphs and error evaluation indicators such as MAPE, MAE and MSE. It is confirmed that the prediction results of the model in this paper is the closest to the actual price.     

Stock trading dynamics and pedestrian counterflows: Analogies and differences

This paper contrasts stock trading dynamics with pedestrian counterflow movements. We apply the social force model built on pedestrian movement patterns to examine micro characteristics of the Chinese stock market. Utilizing one-minute high frequency stock trading data of the Shanghai Composite Index between 2014 and 2017, we find that stock trading dynamics under loose, prudent and austerity monetary policies closely resemble pedestrian movement patterns under wide, moderate, and narrow door width, respectively. In addition, we find that stock trading patterns with unbalanced buyers and sellers correspond to pedestrian counterflows with unbalanced flows from one side of the door to the other. Our results also show that stock trading patterns under various trading volumes are similar to pedestrian counterflows with different flow rates. In general, our results indicate that stock trading patterns are influenced by investor behaviors and conflicting interests similar to those present in the social force model of pedestrian counterflows. Thus, examining the behavioral mechanism at play in these self-driven systems will generate important insights for the behavioral foundation of financial markets.     

交易量适合作为股价波动信息的代理变量吗?——来自中国沪深股市的证据

本文利用中国沪深股市日交易数据,采用多元GARCH模型从信息传递的角度进行实证研究,结果表明:股价对交易量具有显著的波动溢出效应,但交易量对股价的波动溢出效应不明显。这种波动的单向溢出说明在应对信息的冲击上股价比交易量能更快地做出反应,其后才通过波动溢出在交易量上得到反映,股价波动对成交量波动具有先导作用。因此,从波动冲击传导和信息传递的角度看,单纯地将交易量视为股价变动信息的代理变量还缺乏稳健的统计证据。     

基于RBF神经网络的股票价格预测

由于股票的价格是非线性的时间序列,文章提出了基于RBF神经网络的个股价格预测模型,该模型优于传统的股市技术分析方法,又避免了BP算法容易陷入局部极小点和收敛速度慢的缺点。根据实验的仿真结果显示,该模型对于个股价格的短期预测效果较好。     

Forecasting stock market returns: New technical indicators and two-step economic constraint method

The goal of our paper is to improve the accuracy of stock return forecasts by combining new technical indicators and a new two-step economic constraint forecasting model. Empirical results indicate the stock return forecasts generated by new technical indicators and new economic constraint forecasting model is statistically and economically significant both in-sample and out-of-sample prediction performance. In addition, the prediction performance of new technical indicators and new economic constraint forecasting model is robust for some extension and robustness analysis.     

Macroeconomic influences on the stock market

The paper studies the dynamic interactions among indicators of economic activity, such as industrial production, interest rate and exchange rate, the performance of the foreign stock market, oil prices, and stock returns to examine whether economic activity movements affect the performance of the stock market for Greece. The empirical evidence suggests that stock returns do not lead changes in real economic activity while the macroeconomic activity and foreign stock market changes explain only partially stock market movements. Oil price changes explain stock price movements and have a negative impact on macroeconomic activity.