A PSO Approach to Search for Adaptive Trading Rules in the EUA Futures Market

AbstractThe carbon emission futures markets become more and more important in worldwide. More and more counties begin to emphasize environmental protection in the economicdevelopment. Carbon emission trading has become an important part of the energy finance. How to make more profits in the carbon emission futures market is concern by more and more traders and scholars. This paper proposed an approach to search for optimal trading rules in the CO2 allowance futures markets. A group of different moving average trading rules with different weights are used to constitute an integrated trading rule. This is better than a single fixed moving average trading rule.Similarity of trading rules, a parameter we designed, is used to help select basic rules. The authors use static particle swarm optimization process to find the best weights distributions of the selected basic trading rules. After the initial weight distribution is determined, the weights of the basic trading rules will adjusted dynamically every day in the trading process using particle swarm optimization algorithms. Experiments using the EUA Futures Market price data were conducted to find out best adaptive trading rules in the carbon emission futures market. According to our results, it is not necessary to use two moving average trading rules that making same investment advice at a probability higher than 70%. The results show this approach have good performance in adjusting the weights according to the price changes. We found that the adaptive trading rules can help traders make profit in the EUA Futures Marketexcept extreme specialcircumstancesafter price change significantly. This approach might be helpful for traders to make scientificdecision in actual investments

Environmental and economic sustainability of key sectors in China's steel industry chain: An application of the Emergy Accounting approach

Abstract Increasing urbanization day–by–day requires new housing and transportation infrastructures. As a consequence, demand for steel – a basic material for buildings construction as well as for vehicles and railroads – would also increases. This study applies Emergy Accounting (EMA) to assess the Chinas steel industry environmental performance and to identify key application sectors. Subsequently, this study calculates emergy–based indicators capable to assess the present economic performance, environmental sustainability, and land resource appropriate utilization. Building on these indicators, changes of sustainability scenarios in key application sectors are also investigated, with special focus on increased use of recycled steel. The results show that the environmental impacts of steel use in downstream sectors, specially in the Housing and Vehicles Sectors, are significantly higher. Furthermore, the downstream sectors also have a very large requirement for embodied land. Additionally, the Emergy Benefit Ratio (EBR) shows non-negligible advantages to China derived from importing raw iron from abroad at international market prices. Finally, when the recycling rate of scrap steel increases, the performance of downstream sectors improves, with the Vehicle sector showing the most significant changes. Although the benefits of steel-based economy to society are clear, multidimensional sustainability concerns and international competition for primary resources necessitate a transition towards increased recycling and innovative materials within a strictly enforced "circular economy" policy

Existence Result to Boundary Value Problem for Fractional Differential Equations with Integral Boundary Conditions

Abstract: By the means of the Green's function, the boundary value problem of fractional differential equation can be reduced to the equivalent integral equation. Recently, this method is used successfully to discuss the existence of the solution to boundary value problem of nonlinear fractional differential equation. By applying Carathéodory conditions and continuous condition on the nonlinear terms f , we obtain an existence results for solution. Our analysis relies on the concept of measures of noncompactness, the Mönch ′ s fixed point theorem and the Schaefer's fixed point theorem. Example is provided to illustrate the theory

Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

The crude oil futures market plays a critical role in energy finance. To gain greater investment return, scholars and traders use technical indicators when selecting trading strategies in oil futures market. In this paper, the authors used moving average prices of oil futures with genetic algorithms to generate profitable trading rules. We defined individuals with different combinations of period lengths and calculation methods as moving average trading rules and used genetic algorithms to search for the suitable lengths of moving average periods and the appropriate calculation methods. The authors used daily crude oil prices of NYMEX futures from 1983 to 2013 to evaluate and select moving average rules. We compared the generated trading rules with the buy-and-hold (BH) strategy to determine whether generated moving average trading rules can obtain excess returns in the crude oil futures market. Through 420 experiments, we determine that the generated trading rules help traders make profits when there are obvious price fluctuations. Generated trading rules can realize excess returns when price falls and experiences significant fluctuations, while BH strategy is better when price increases or is smooth with few fluctuations. The results can help traders choose better strategies in different circumstances

Doping and dimensionality effects on the core-level spectra of layered ruthenates

Core-level spectra of the Mn-doped Sr3Ru2O7 and Srn+1RunO3n+1 (n = 1, 2 and 3) crystals are investigated with X-ray photoelectron spectroscopy. Doping of Mn to Sr3Ru2O7 considerably affects the distribution of core-level spectral weight. The satellite of Ru 3d core levels exhibits a substantial change with doping, indicating an enhanced electron localization across the doping- induced metal-insulator transition. However, the Ru 3p core levels remain identical with Mn-doping, thus showing no sign of doping-induced multiple Ru valences. In the Srn+1RunO3n+1 (n = 1, 2 and 3), the Ru 3d core-level spectra are similar, indicating that the chemical bonding environment around Ru ions remains the same for different layered compounds. Meanwhile the Sr 3d shallow core levels shift to higher binding energy with increasing n, suggesting their participation in Sr-O bonding with structural evolution.Comment: 6 pages with 6 figures, to be published in PR

Sex-specific transcription and DNA methylation landscapes of the Asian citrus psyllid, a vector of huanglongbing pathogens

The relationship of DNA methylation and sex-biased gene expression is of high interest, it allows research into mechanisms of sexual dimorphism and the development of potential novel strategies for insect pest control. The Asian citrus psyllid, Diaphorina citri Kuwayama, is a major vector for the causative agents of Huanglongbing (HLB), which presents an unparalleled challenge to citrus production worldwide. Here, we identify the X chromosome of D. citri and investigate differences in the transcription and DNA methylation landscapes between adult virgin males and females. We find a large number of male-biased genes on the autosomes and a depletion of such on the X chromosome. We have also characterized the methylome of D. citri, finding low genome-wide levels, which is unusual for an hemipteran species, as well as evidence for both promoter and TE methylation. Overall, DNA methylation profiles are similar between the sexes but with a small number of differentially methylated genes found to be involved in sex differentiation. There also appears to be no direct relationship between differential DNA methylation and differential gene expression. Our findings lay the groundwork for the development of novel epigenetic-based pest control methods, and given the similarity of the D. citri methylome to some other insect species, these methods could be applicable across agricultural insect pests.</p

Multi-Angle Fast Neural Tangent Kernel Classifier

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement