Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm

In this paper, we propose a Weighted Stochastic Mesh (WSM) algorithm for approximating the value of discrete‐ and continuous‐time optimal stopping problems. In this context, we consider tractability of such problems via a useful notion of semitractability and the introduction of a tractability index for a particular numerical solution algorithm. It is shown that in the discrete‐time case the WSM algorithm leads to semitractability of the corresponding optimal stopping problem in the sense that its complexity is bounded in order by with being the dimension of the underlying Markov chain. Furthermore, we study the WSM approach in the context of continuous‐time optimal stopping problems and derive the corresponding complexity bounds. Although we cannot prove semitractability in this case, our bounds turn out to be the tightest ones among the complexity bounds known in the literature. We illustrate our theoretical findings by a numerical example.     

基于双层规划模型的冷链物流网络多目标优化

针对冷链物流时效性强这一特性,应用软时间窗反映客户满意度,并结合T.T.T理论换算货损成本。在考虑满足客户时间窗的条件下,寻找配送中心建设及操作成本、车辆成本、惩罚成本及货损成本所构成的总成本最小时的最优配送方案,建立冷链物流配送选址及路径优化的双层规划模型,并将改进的遗传算法运用到该模型中,借助Matlab软件对具体实例进行求解,得到最优结果,为冷链物流网络多目标优化问题提供理论依据。     

Economic forecasting with evolved confidence indicators

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.     

南太行綦村岩体中辉石的成因矿物学研究

通过对不同岩石中的辉石进行了详细的成因矿物学研究,结果表明綦村岩体不同岩性中的辉石可以分为两类:一类是岩浆系统中早期结晶形成的晶体,被后期的岩浆携带至地壳浅部,另一类是岩浆定位以后结晶的自生晶。在辉石的分类中大部分辉石属于透辉石,有少量是普通辉石。尽管在不同岩性中辉石的成分存在一定差异,但是所有辉石成分都指示綦村岩体的母岩浆属于亚碱性系列,其在演化过程中经历了辉石和角闪石的结晶分离过程,这一过程导致岩浆中的镁元素在早期形成的辉石和角闪石中富集,铁元素在残余熔体中富集,并在岩浆定位后随流体出熔成矿。     

基于图论的认知无线网络频谱动态分配

在认知无线电网络中，图论与量子遗传算法相结合的频谱分配策略能够提高频谱利用率，但存在早熟和收敛精度不够等缺点。为了解决该问题而实现算法的优化，对图着色理论的频谱分配模型进行数学建模，并针对该模型提出了改进的量子遗传算法。首先，通过使用小生境技术初始化种群，使种群分布更加广泛、算法的收敛度更高；其次，根据进化代数对量子旋转角进行实时动态调整，对染色体进行阈值变异，防止个体陷入早熟，跳出局部解；然后，对干扰约束条件进行重新设计，有效地避免盲目性，提高了网络的公平性和网络效益。仿真结果表明，所提算法有效地提高了频谱利用率，极大地增强了网络系统的性能。     

Payments for agrobiodiversity conservation services: An overview of Latin American experiences,lessons learned and upscaling challenges

Many developing countries face a major challenge today: how to safeguard the biodiversity maintained in the fields of the rural poor - which constitute a national and global public good - whilst meeting those same people's development needs and rights? A solution to this dilemma has thus been sought in adapting the design and implementation of Payments for Ecosystem Services (PES) concepts to the conservation of agrobiodiversity.Here we review the application of nine such Payments for Agrobiodiversity Conservation (PACS) schemes that have been applied to date in four Latin American countries over the period 2010–2018. These covered 130 threatened varieties across a number of major food crops, and involved over 100 farming communities and 1,100 farmers (45 % of which were women). Conservation service offers were received through a competitive tender mechanism. Average bid offers revealed high heterogeneity, varying between US$675/ha. to ten times as much.In relation to issues identified as key to PES, such as spatial targeting, differentiated payments and conditionality, the underlying design of the PACS schemes may be considered solid. PACS-related prioritisation processes allow for the a priori identification of sites with high ecosystem service densities and high threat levels. The use of competitive tenders permits accounting for cost heterogeneity in the provision of conservation services and for payments to be differentiated. Conditionality is strong.In terms of implementation, a “back of the envelope” calculation based on the results of the competitive tenders suggests that conservation costs are modest. For a priority conservation portfolio of 100 varieties (which may be from different crops) each with a target area of five hectares, costs would amount to just under US$860,000 over twenty years or US$70,000 p.a. at a 5 % discount rate. The small-scale and one-off nature of the interventions realised to date, along with threatened crop variety seed availability constraints, have however meant that environmental effectiveness has been incomplete in the short-term (area cultivated with specific threatened varieties increased, but still below the “not at risk” threshold). The establishment of systematic monitoring systems is required to determine longer-term impacts and inform more regular PACS interventions within a dynamically evolving systems context.     

基于两阶段法的多车型卷烟配送路径优化研究

在烟草商业企业中,物流是其重要的环节,是连接客户与烟草企业间的纽带,其卷烟配送线路设计的合理与否直接影响到卷烟的配送效率和配送成本,就如何合理的规划线路成为卷烟配送的关键问题。文章以Q烟草公司为例,运用先分组后路线的两阶段法对问题进行求解,先利用K-means聚类算法对区域进行划分,再考虑工作量均衡的条件下引入遗传算法对区域进一步调整,最后利用混合遗传算法对各配送区域进行线路优化,通过实例从线路数、车辆数、空载率、配送里程及总成本等指标上可以看出两阶段法的配送效果更优。     

采用遗传算法的企业动态联盟风险分析与评价研究

企业动态联盟风险体系包括内生体系和外生体系两个方面 ,根据企业动态联盟不同的风险 ,采用不同的分析与评价方法 ,在某种程度上提高了企业动态联盟风险分析与评价的准确性 ,同时也降低了企业动态联盟风险评价的难度。对动态联盟中承担生产任务的成员企业风险因素进行分析 ,采用遗传算法对其风险进行评价 ,旨在为企业动态联盟的具体实施及其风险分析与评价提供技术上的支持     

信用风险测量指标体系研究

在现代商业银行经营中,信用风险是影响其安全高效运营的主要原因。信用风险管理中最重要的就是信用风险测量。自从20世纪80年代末期以来,人工智能技术如神经网络、专家系统也被应用于商业银行信用风险测量中。但预测指标的研究则相对滞后,成为研究的一个难点。