A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

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.     

原油基压裂液研究

研制了一种适用于低压、低渗、水敏性储集层的新型原油基压裂液体系。根据基液原油的组分合成相应的增稠剂，增稠剂在原油中与交联剂反应，形成网状结构，使原油成为黏度可以调控的冻胶，从根本上改变了油基压裂液以柴油、煤油为基液的现状，降低了油基压裂液的成本，同时还提高了压裂液的抗温、抗剪切和破胶性能，并将交联时间缩短到2h以内，可以满足现场压裂施工的要求，大大降低了施工强度和压裂改造的综合成本。在青海油田进行的现场先导试验取得成功。图9表1参5     

新型涂料增稠剂的化学成分鉴定

本文用红外光谱法对一种新型涂料增稠剂进行了化学成分鉴定,其主要成分为羟丙基甲基纤维素与蒙脱土。     

黄原胶接枝共聚物降滤失剂应用性能评价

黄原胶（XG）具有优良的耐盐性和增稠降滤失性能，但耐温性较差。对XG分子结构的分析认为，可利用XG链上的活泼基团接枝丙烯酰胺等乙烯基单体，增强其耐温性能，用作耐温抗盐钻井液处理剂。对比了合成的黄原胶丙烯酰胺等接枝共聚物（XGG）降滤失剂与XG在淡水、4％盐水和饱和盐水钻井液中的增粘降滤失性能和抗温性能，并通过岩心膨胀试验对比了XGG、XG和KCl对页岩的抑制性能。试验结果表明，在淡水钻井液中XGG具有极强的增粘效果和降滤失能力，远远超过XG1随着钻井液矿化度的提高，XGG的性能有一定下降，但XGG在各种钻井液中均具有比XG更好的高温增稠和控制失水的能力；XG已具有优良的抑制性，XGG的抑制性得到进一步提高。     

Stock Price Forecasting: An Echo State Network Approach

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.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

PAAD-16酸液稠化剂的合成与性能

以二甲氨基丙基甲基丙烯酰胺和溴代十六烷为原料,丙酮为溶剂,合成了一种疏水单体——二甲基十六烷基(2-甲基丙烯酰胺基丙基)溴化铵(DHAB),并以DHAB、2-甲基-2-丙烯酰胺基丙磺酸(AMPS)和丙烯酰胺(AM)为单体,以自由基水溶液聚合法合成了一种疏水缔合聚合物P(AM-AMPS-DHAB)(PAAD-16),用IR、荧光光谱(FL)和SEM对其进行了结构表征。结果表明:PAAD-16的酸溶时间约为90 min;以该疏水缔合聚合物为主要添加剂配制而成的稠化酸在30℃、170s~(–1)下的表观黏度为59m Pa×s,在60、90℃下的热稳定性(ω)分别为85.0%和66.1%,在该条件下连续剪切120 min后剪切稳定性(ω')为81.4%,具有良好的增黏性、耐温抗剪切性和缓速性能。     

超支化含苯基聚硅氧烷亲二氧化碳特性及其分子模拟

通过水解缩聚法制备含有不同苯基含量的超支化聚硅氧烷，探究苯基的引入对超支化聚合物在CO2中溶解性能的影响。浊点压力测试得出苯基的引入在超支化结构中对聚合物在CO2中的溶解度影响不大，超支化聚合物中苯基含量的增加没有导致浊点压力的明显升高，有望实现作为CO2增稠剂兼具较好的溶解度和增稠性能。分子模拟计算分析了超支化聚硅氧烷和直链聚硅氧烷与CO2分子间的相互作用以及超支化聚硅氧烷分子间的相互作用，发现含苯基的超支化聚硅氧烷具有更低的内聚能密度（CED）和溶解度参数（δ），表现出更弱的聚合物分子间相互作用，有利于超支化有机硅氧烷在CO2体系中的溶解。