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...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.展开更多
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 Chines...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.展开更多
利用复杂系统的能量特性,引入影响力概念,研究动态复杂网络的社团划分方法,以有效地发现股票网络的社团结构.利用股票收盘价,通过引入影响力和结点中心性定义,构建以影响力为权值的股票网络,并提出一种基于影响力计算模型的股票网络中...利用复杂系统的能量特性,引入影响力概念,研究动态复杂网络的社团划分方法,以有效地发现股票网络的社团结构.利用股票收盘价,通过引入影响力和结点中心性定义,构建以影响力为权值的股票网络,并提出一种基于影响力计算模型的股票网络中心结点层次聚类算法(based on the center node hierarchical clustering algorithm about the influence calculation model of stock network,BCNHC).BCNHC算法首先引入结点活跃性和影响力的定义,并给出网络中结点的影响力计算模型;然后,基于所引入的结点中心性的度量准则,选取结点中心性大的结点为中心结点,并利用结点间的亲密性和影响力模型确定相邻结点之间影响力关联度;进而,通过优先选择度值最小的结点向中心结点聚集,以降低因相邻结点所属社团不确定而导致的错误聚类;在此基础上,利用社团平均影响力关联度对相邻社团进行聚类,保证社团内所有结点的影响力关联度最大化,直至整个网络模块度最大.最后,在构建的股票网络上的实验比较和分析,验证BCNHC算法的可行性.展开更多
首先利用时间序列中的AR IM A模型和人工神经网络建立了两类股票价格预测模型并对一定时期的股票价格进行了预测,然后用4种广为使用的统计评价方法对两类模型的预测性能进行了比较。结果表明,两种模型都取得了很好的整体预测效果,而在...首先利用时间序列中的AR IM A模型和人工神经网络建立了两类股票价格预测模型并对一定时期的股票价格进行了预测,然后用4种广为使用的统计评价方法对两类模型的预测性能进行了比较。结果表明,两种模型都取得了很好的整体预测效果,而在趋势预测方面,神经网络则得到了比AR IM A模型更准确的结果。展开更多
文摘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.
文摘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.
文摘利用复杂系统的能量特性,引入影响力概念,研究动态复杂网络的社团划分方法,以有效地发现股票网络的社团结构.利用股票收盘价,通过引入影响力和结点中心性定义,构建以影响力为权值的股票网络,并提出一种基于影响力计算模型的股票网络中心结点层次聚类算法(based on the center node hierarchical clustering algorithm about the influence calculation model of stock network,BCNHC).BCNHC算法首先引入结点活跃性和影响力的定义,并给出网络中结点的影响力计算模型;然后,基于所引入的结点中心性的度量准则,选取结点中心性大的结点为中心结点,并利用结点间的亲密性和影响力模型确定相邻结点之间影响力关联度;进而,通过优先选择度值最小的结点向中心结点聚集,以降低因相邻结点所属社团不确定而导致的错误聚类;在此基础上,利用社团平均影响力关联度对相邻社团进行聚类,保证社团内所有结点的影响力关联度最大化,直至整个网络模块度最大.最后,在构建的股票网络上的实验比较和分析,验证BCNHC算法的可行性.
