关于石油企业资本经营问题的探讨

资本经营是企业经营管理的高级经营形式,实行资本经营有利于深化国企改革,加快企业转机建制、建立现代企业制度的步伐。本文提出了石油企业实行资本经营的对策与思路泡括实施资本整合战略,进行股份制改造,建立健全石油产权市场,加强资本的有效重置,实现存量资本的低成本扩张,全方位开拓国际石油市场,从而提高整个石油行业资本运营效率。     

股份合作制企业收入分配制度及其模式

对股份合作制企业收人分配制度的基本原则、逻辑基础及其框架构造进行了初步探讨,并构建了劳动者和经营者的收入分配模式．     

灰色算法在股票价格预测中的应用

股市投资已经成为人们生活中的重要组成部分,在股票市场中人们最关心的就是股票价格的变化.为了更精确的预测股票价格,得到更合理的股票投资意见.文章中提出了利用灰色系统理论对股票价格进行预测,并且利用残差修正预测结果的方法.根据灰色系统理论建立数学模型,利用得到的股票价格求得具体的预测模型及其预测结果,然后对所得结果进行残差修正以得到更精确的股票价格.文章中对华工股票价格进行预测后,发现利用灰色理论对股票价格预测,具有较高的精确度和应用价值.     

基于三角面模型的虚拟轮廓铣削仿真研究

针对基于三角面片构建的带内腔毛坯模型,在轮廓铣削仿真时,由于在不完全被切除的三角面片中未被切除顶点的不移动,所造成被切削轮廓面不光顺的问题,提出了沿刀具切削包络面的法向,未被切除的顶点移动到包络面上的方法,解决了轮廓面不光顺的问题;而对于仿真铣削带内腔毛坯时,就内外表面如何关联的问题,首先提出了判断刀具进出模型内外表面的算法,然后给出了轮廓面与内腔面关联的方法,完善地解决了这一问题.基于本研究提出的方法,通过VirtoolsDev开发平台,对带内腔的三角面片模型,获得了具有光顺切削表面的仿真效果.同时,使得铣削仿真精度也得到了根本性的改善.     

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.     

Leveraging enterprise knowledge graph to infer web events’ influences via self-supervised learning

Knowledge graph (KG) techniques have achieved successful results in many tasks, especially in semantic web and natural language processing domains. In recent years, representation learning on KG has been successfully applied to e-business applications, such as event-driven automatic investment strategies. However, there is still limited research about learning events’ influence on KG for modern quantitative investment. In this paper, we propose a novel event influence learning framework to predict stock market trends, called ST-Trend, leveraging enterprise knowledge graph to represent company correlation relationships, for mining the deep background knowledge of web events, with three self-supervised learning tasks. In particular, we devise two jointly self-supervised tasks to identify the relations between web events and companies. The first task is for generating ground-truth event-company correlation labels based on the enterprise knowledge graph. The second task is used to train how to identify the correlated companies of an event based on the generated correlation labels, with the encoding of web events, company features, and technical sequential data. We then design the prediction network to infer an event’s influence on stock price trends of the identified correlated companies based on the enterprise KG. Finally, we perform extensive experiments on a massive real-life dataset to validate the effectiveness of our proposed framework, and the experimental results demonstrate its superior performance in predicting stock market trends via considering events’ influences with the enterprise knowledge graph.     

Otolith microchemistry shows natal philopatry of walleye in western Lake Erie

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.     

浅谈债务转为资本存在的问题及解决对策

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。     

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.     

Stock loan valuation based on the Finite Moment Log-Stable process

The empirical test suggests that the log-return series of stock price in US market reject the normal distribution and admit instead a subclass of the asymmetric distribution. In this paper, we investigate the stock loan problem under the assumption that the return of stock follows the finite moment log-stable process (FMLS). In this case, the pricing model of stock loan can be described by a space-fractional partial differential equation with time-varying free boundary condition. Firstly, a penalty term is introduced to change the original problem to be defined on a fixed domain, and then a fully-implicit difference scheme has been developed. Secondly, based on the fully-implicit scheme, we prove that the stock loan value generated by the penalty method cannot fall below the value obtained when the stock loan is exercised early. Thirdly, the numerical experiments are carried out to demonstrate differences of stock loan model under the FMLS and the standard normal distribution. Optimal redemption strategy of stock loan has been achieved. Furthermore the impact of key parameters in our model on the stock loan evaluation are analyzed, and some reasonable explanation are given.