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1.
Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.  相似文献   
2.
Natal philopatry is important to the structure of fish populations because it can lead to local adaptations among component stocks of a mixed population, reducing the risk of recruitment failure. By contrast, straying between component stocks may bolster declining populations or allow for colonization of new habitat. To examine rates of natal philopatry and straying among western Lake Erie walleye (Sander vitreus) stocks, we used the concentration of strontium [Sr] in otolith cores to determine the natal origin of adults captured at three major spawning sites: the Sandusky (n = 62) and Maumee (n = 55) rivers and the Ohio reef complex (n = 50) during the 2012–2013 spawning seasons. Mean otolith core [Sr] was consistently and significantly higher for individuals captured in the Sandusky River than for those captured in the Maumee River or Ohio reef complex. Although logistic regression indicates that no individuals with a Maumee River or Ohio reef complex origin were captured in the Sandusky River, quadratic discriminant analysis suggests low rates of straying of fish between the Maumee and Sandusky rivers. Our results suggest little straying and high rates of natal philopatry in the Sandusky River walleye stock. Similar rates of natal philopatry may also exist across western Lake Erie walleye stocks, demonstrating a need for stock-specific management.  相似文献   
3.
采用旋转氧弹试验、烘箱氧化试验和极压润滑油氧化性能测定法研究不同来源150BS光亮油的抗氧化性能。结果表明:采用加氢工艺制取的进口150BS-1和国产150BS-3基础油对抗氧剂的感受性比溶剂精制基础油150BS-2好;饱和烃中多环环烷烃的存在不利于提高加氢150BS基础油的氧化安定性; 150BS基础油的氧化安定性越好,用其调合的工业齿轮油抗氧化性能越优异。  相似文献   
4.
债务转为资本是债务重组的方式之一,是指债务人将债务转为资本,同时债权人将债权转为股权的债务重组方式。本文将分析这一重组方式在重组运作过程中存在的问题,并提出一系列的解决对策。  相似文献   
5.
为了进一步推动矿产资源、资产和资本三者之间的顺利流转,为风险勘查企业融资创造更好的条件,推动矿产资源的找矿发现,构建风险勘查资本市场是一重要途径。在对国际上主要风险勘查资本市场的上市标准、资金来源、中介机构体系、政府支持措施等因素进行充分研究的基础上,对我国风险勘查资本市场的构建进行了分析,提出了要推动"三资"相互转化及顺畅流转就应该建立风险勘查资本市场,并在产权制度、市场主体、监管体系等方面进一步完善,并重视诚信体系的建设和政府主管部门间的协调和沟通。  相似文献   
6.
This paper presents the development of a bottom-up stock model to perform a holistic energy study of the Mexican non-domestic sector. The current energy and exergy flows are shown based on a categorisation by climatic regions with the aim of understanding the impact of local characteristics on regional efficiencies. Due to the limited data currently available, the study is supported by the development of a detailed archetype-based stock model using EnergyPlus as a first law analysis tool combined with an existing exergy analysis method. Twenty-one reference models were created to estimate the electric and gas use in the sector. The results indicate that sectoral energy and exergy annual input are 95.37 PJ and 94.28 PJ, respectively. Regional exergy efficiencies were found to be 17.8%, 16.6% and 23.2% for the hot-dry, hot-humid and temperate climates, respectively. The study concludes that significant potential for improvements still exists, especially in the cases of space conditioning, lighting, refrigeration, and cooking where most exergy destructions occur. Additionally, this work highlights that the method described may be further used to study the impact of large-scale refurbishments and promote national regulations and standards for sustainable buildings that takes into consideration energy and exergy indicators.  相似文献   
7.
Stock selection is an important decision making problem. Trading strategies and rules based on fundamental and technical analysis can be used for decision making process. In this paper, we propose an intelligent stock selection method, which is called case-based reasoning (CBR). This technique uses the fundamental and technical indicators to identify the winning stocks around the earning announcements. CBR method is compared with other artificial intelligence techniques such as multi layer perceptron (MLP), decision trees (QUEST, Classification and Regression Trees, C5), generalized rule induction (GRI) and logistic regression. We show that the performance of CBR is better than the performance of other techniques in terms of classification accuracy, average return, Sharpe ratio and ideal profit.  相似文献   
8.
9.
Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.  相似文献   
10.
Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).  相似文献   
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