Scale-Free Behavior in Weighted Stock Network

A weighted stock network model of stock market is presented based on the complex network theory. The model is a weighted random network, in which each vertex denotes a stock, and the weight assigned to each edge is the cross-correlation coefficient of returns. Analysis of A shares listed at Shanghai Stock Exchange finds that the influence-strength （IS） follows a power-law distribution with the exponent of 2.58. The empirical analysis results show that there are a few stocks whose price fluctuations can powerfully influence the price dynamics of other stocks in the same market. Further econometric analysis reveals that there are significant differences between the positive IS and the negative IS.

General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

To examine the interdependency and evolution of Pakistan’s stock market,we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange(KSE-100)index.Using the minimum spanning tree network-based method,we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan.Our results reveal a star-like structure after the general elections of 2018 and before those in 2008,and a tree-like structure otherwise.We also highlight key nodes,the presence of different clusters,and compare the differences between the three elections.Additionally,the sectorial centrality measures reveal economic expansion in three industrial sectors—cement,oil and gas,and fertilizers.Moreover,a strong overall intermediary role of the fertilizer sector is observed.The results indicate a structural change in the stock market network due to general elections.Consequently,through this analysis,policy makers can focus on monitoring key nodes around general elections to estimate stock market stability,while local and international investors can form optimal diversification strategies.

The Prediction of Stock Prices Based on PCA and BP Neural Networks

There are many factors to influence stock prices indeed. The research method combining models and examples is applied to study how the factors affect stock prices here. Firstly, the principal component analysis is used to deal with a set of variables as the input of a BP Neural Network. Therefore, not only is the number of variables less, but also most of the information of original variables is kept. Then, the BP Neural Network is established to analyze and predict stock prices. Finally, the analysis of Chinese stock market illustrates that the method predicting stock prices is satisfying and feasible.

Predicting Stock Movement Using Sentiment Analysis of Twitter Feed with Neural Networks

External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this paper, we show the effectiveness of using Twitter posts to predict stock prices. We start by training various models on the Sentiment 140 Twitter data. We found that Support Vector Machines (SVM) performed best (0.83 accuracy) in the sentimental analysis, so we used it to predict the average sentiment of tweets for each day that the market was open. Next, we use the sentimental analysis of one year’s data of tweets that contain the “stock market”, “stocktwits”, “AAPL” keywords, with the goal of predicting the corresponding stock prices of Apple Inc. (AAPL) and the US’s Dow Jones Industrial Average (DJIA) index prices. Two models, Boosted Regression Trees and Multilayer Perceptron Neural Networks were used to predict the closing price difference of AAPL and DJIA prices. We show that neural networks perform substantially better than traditional models for stocks’ price prediction.

Theoretical analyses of stock correlations affected by subprime crisis and total assets: Network properties and corresponding physical mechanisms

In the field of statistical mechanics and system science, it is acknowledged that the financial crisis has a profound influence on stock market. However, the influence of total asset of enterprise on stock quote was not considered in the previous studies. In this work, a modified cross-correlation matrix that focuses on the influence of total asset on stock quote is introduced into the analysis of the stocks collected from Asian and American stock markets, which is different from the previous studies. The key results are obtained as follows. Firstly, stock is more greatly correlated with big asset than with small asset. Secondly, the higher the correlation coefficient among stocks, the larger the eigenvector is. Thirdly, in different periods, like the pre-subprime crisis period and the peak of subprime crisis period, Asian stock quotes show that the component of the third eigenvector of the cross-correlation matrix decreases with the asset of the enterprise decreasing.Fourthly, by simulating the threshold network, the small network constructed by 10 stocks with large assets can show the large network state constructed by 30 stocks. In this research we intend to fully explain the physical mechanism for understanding the historical correlation between stocks and provide risk control strategies in the future.

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.

Prediction of the Bombay Stock Exchange (BSE) Market Returns Using Artificial Neural Network and Genetic Algorithm

Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

基于LSTM模型的股票价格预测

股票市场的价格波动被视为经济发展的晴雨表。对股票价格的精准预测一直是众多研究学者努力的方向。随着人工智能技术与大数据技术的不断应用与发展以及疫情防控期间国内经济变化和国际形势变换给股价带来的巨大波动,如何对股价进行精准预测变得越来越重要。本文根据股票市场的特点和LSTM(Long Short-Term Memory)递归神经网络的特性,对浦发银行(600000)股价进行预测。实验结果表明,LSTM模型预测股价,结果误差小,精准度高,具有良好的预测效果。

Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Integrating Strategic and Tactical Rolling Stock Models with Cyclical Demand

In the transportation industry, companies position rolling stock where it is likely to be needed in the face of a pronounced weekly cyclical demand pattern in orders. Strategic policies based on assumptions of repetition of cyclical weekly patterns set rolling stock targets;during tactical execution, a myriad dynamic influences cause deviations from strategically set targets. We find that optimal strategic plans do not agree with results of tactical modeling;strategic results are in fact suboptimal in many tactical situations. We discuss managerial implications of this finding and how the two modeling paradigms can be reconciled.

BASIC EQUATIONS, THEORY AND PRINCIPLES OF COMPUTATIONAL STOCK MARKET (Ⅱ)——BASIC PRINCIPLES

In this paper, three basic principles for computational stock market are proposed namely,“the Nearest_Time Principle” (NTP),“the Following Tendency Principle” (FTP),and “the Variational Principle on Difference of Supply and Demand” (VPDSD). The issue, expression, mathematical description and applications of these principles are stated. These applications involve the use in neural networks, basic equations of computational stock market, and the prediction of equilibrium price of stocks etc.

Application of Support Vector Machines Regression in Prediction Shanghai Stock Composite Index

The SVMs for regression is used to forecast Shanghai stock composite index (SSCI). Implementing structural risk minimization principle, SVMs can overcome the over-fitting problem. The regression uses ε-insensitive loss function. The training of SVMs leads to a quadratic programming problem and it has a global unique solution. The experiment uses BP neural networks as benchmark for comparison. The results demonstrate that the prediction figure of SSCI can help to find timing for buy or sell, the forecasting variation of SVMs is smaller than that of BP, and the direction forecasting of SVMs is more accurate than that of BP.

基于动态异构网络的股价预测

股票预测通常被形式化为非线性的时间序列预测任务,但很少有研究者试图通过技术面数据去系统地揭示股票市场内在结构,例如股票上涨或下跌背后的原因可能是业务领域之间的合作或冲突,这些额外信息的增加有助于判断股票的未来趋势。为了充分真实刻画股票市场的交易状态,表达股票之间显式或隐式的关系,提出一种基于动态异构网络的股价预测模型sDHN(stock dynamic heterogeneous network),综合股票以及所属行业和地域,将其建模为动态异构网络。该模型在网络上引入动态时序特征,创新融合股票节点的四种不同技术层面的相似性图,生成富信息异构图,最后聚合不同元路径中隐含的语义信息生成嵌入,从异构图的角度充分探索股票之间的潜在关联。此外,在三个真实世界的股票数据集上进行了大量实验,所提出的模型准确率比所有基线模型均高出5%~34%,F_(1)-score则高出11.5%~37%,并且在图解释上证明了该方法的有效性。

Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators

While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.

序列稀疏自回归方法及其在美股做空数据分析上的应用

采用序列稀疏回归的思路来处理向量自回归模型,并设计适用于大规模时间序列数据分析的序列稀疏自回归方法。研究表明:从因子角度刻画向量自回归模型可以有效地将稀疏矩阵估计问题分解成稀疏奇异向量的估计问题,从而极大地提高了计算效率。以1523家美股上市公司1973年1月—2014年12月的做空数据为例,利用此方法探索公司之间的大规模做空关联网络。研究发现:此方法可以有效地恢复股票做空份额(即某一公司的空头股份数量)与股票收益率之间隐藏的关联网络,对于股票风险溢价研究具有一定启发意义。

基于深度卷积神经网络的股票交易模型研究

为探讨卷积神经网络模型(CNN模型)的有效性,构建基于卷积神经网络模型的沪深300选股策略。首先通过分层法和IC测试法对CNN模型预测得到的上涨因子进行有效性测试;其次基于年化收益率、最大回撤等风险与收益指标,判断上涨因子选股策略的具体表现,进一步验证CNN选股模型的有效性。随后构建了基于宽基指数的择时策略,结果表明CNN模型在上证50指数上预测表现最佳。卷积神经网络的量化选股和择时模型的研究结论证实,卷积神经网络不仅能在沪深300中选出表现更好的股票,而且在量化择时方面也同样有效。

基于定性网络模型评价生态调控情景对海洋牧场生态系统的影响——以獐子岛海洋牧场为例

在海洋牧场建设过程中,生态调控方式实施后对海洋牧场生态系统的影响通常难以预测,这对海洋牧场的生态安全和高质量发展提出了严峻挑战。为此,建立了一种基于定性网络模型(Qualitative network model,QNM)的海洋牧场生态系统模拟评价方法,并以獐子岛海洋牧场近岸增殖海域作为研究区域,构建以增殖目标种为核心的定性网络模型,模拟评估海洋牧场3种不同类型的生态调控情景(增殖目标种、移除捕食者、海藻场修复)及其复合条件下,牧场群落范围内的响应,分析海洋牧场生态调控策略与生物功能群变化之间潜在关系。结果显示:目标种增殖(仿刺参和虾夷扇贝)产生的上行效应导致其捕食者呈现积极响应,产生的下行效应导致其它底栖动物、浮游植物和有机碎屑等功能群呈现消极响应,移除捕食者海星产生的下行效应导致虾夷扇贝呈现积极响应,表明在增殖区清除敌害生物的重要性,海藻场修复对整个群落有明显的积极影响,体现了海藻场在养护近岸生态系统的重要生态意义。研究表明:QNM可有效识别生态系统潜在的营养级联效应,评估生物功能群的响应,基于QNM的海洋牧场生态调控模拟评价方法,突破了定量食物网模型在数据有限系统中使用的局限性,可为海洋牧场建设的生态调控策略制定提供科学参考。

机构投资者博彩行为的传染——基金竞争网络与彩票型股票资产配置

本文以我国2005—2021年开放式股票型与混合型基金重仓持股数据为研究样本,通过三维空间模型构建基金竞争网络,实证检验了基金竞争网络对基金经理彩票型股票资产配置的影响。研究发现,同业基金在彩票型股票资产配置上呈现出显著的一致性,意味着基金经理倾向于学习与模仿同业基金的彩票型股票资产配置策略。上述结论在考虑了外围基金的持仓变动、市场信息等因素的影响后依旧成立。潜在机制表明,在竞争激烈的环境中,基金经理面临更大的解雇风险,促使其博彩偏好上升,从而趋向于学习与模仿同业基金的彩票型资产配置策略。异质性分析表明,同业基金对彩票型资产配置策略的模仿行为存在“家族效应”与“同城效应”。该模仿行为与基金经理的性别、学历等个人特征相关,且在调仓情景下更显著。拓展性分析发现,同业竞争引致的彩票型资产配置可能进一步引发股价崩盘风险。本文为监管层理解机构投资者的博彩行为、制定合理的市场竞争机制提供了有益参考。

灰狼算法优化BP神经网络的股价预测

探讨使用灰狼算法改进BP神经网络的方法,旨在提高BP神经网络的训练效果和性能。首先,介绍了BP神经网络的基本原理和灰狼算法的基本概念。然后,将灰狼算法应用于BP神经网络的权重和偏置值的优化过程中,通过调整这些参数来降低误差函数,从而提高网络的准确性和收敛速度。实验结果表明:灰狼算法优化的BP神经网络具有较好的性能和泛化能力。其次,还用股票数据进行了实证分析,该模型在股票价格预测方面具有较高的准确性和稳定性,可为投资者提供有效的决策参考。最后,总结了本研究的贡献和未来的研究方向。