Factor pricing of cryptocurrencies

In this paper, we study the cryptocurrency pricing factors. We review the literatures which state that the cryptocurrency market is weakly efficient. We use the Fama–MacBeth method to investigate the pricing factors. The classical equity-based risk factors including size, momentum, and value to growth from the Fama–French three factor model are studied. We use crypto-unique coin-to-token as a proxy for value-to-growth. For volatility risk factor category, we investigate realized volatility, skewness and jump. We also investigate liquidity factors including bid–ask, volume growth and Roll’s measure. The macro factors are found not to be an explanatory factor. The attention factor works sometimes. The factor model constructed by the significant factors explain most of the excess return of cryptocurrencies.     

融合反距离加权和矩阵填充的位置指纹库构建

为解决位置指纹定位在离线阶段构建位置指纹库时耗费的人力和时间成本较大，构建指纹库效率低和利用空间插值法构建的指纹库精度不高的问题，提出了一种融合反距离加权和矩阵填充的位置指纹库构建算法。该算法仅需人工采集定位区域内少量参考点的接收信号强度值用作信标点指纹信息，结合反距离加权算法特性计算出次信标点指纹信息，根据位置指纹库数据矩阵的低秩性，应用奇异值阈值矩阵填充算法构建出位置指纹数据库。仿真实验结果表明，所提算法有效降低了矩阵填充算法构建位置指纹库所需的人工和时间成本，构建出的位置指纹库定位性能优于反距离加权和克里金空间插值法，接近传统人工采集法，显著地提高了位置指纹库的构建效率。     

一种基于Radon变换的微表情识别算法

微表情是人们处在一些与平时生活环境不同的高强度环境下试图控制和掩饰的情感表现，也是一种不曾意识到的瞬时脸部表情,持续时间短，强度弱。为了提高其准确率，提出了基于Radon变换的微表情识别算法。首先，对数据库中的视频序列进行灰度归一化、尺寸归一化和二维主成分分析法（Two-dimensional Principal Component Analysis，2DPCA）降维预处理，使用光流法对降维后图像提取运动特征；然后使用Radon变换算法对光流图像进行处理，得到对应微表情的特征值和特征图像；最后使用支持向量机进行微表情分类识别。实验结果表明，使用Radon变换后得到的微表情特征图像得到了较好的识别效果，在微表情数据集CASME和CASMEⅡ上识别率分别为81.48%和82.17%，通过与选取的其他方法对比说明了该方法具有更好的识别性能。     

Research on a stock-matching trading strategy based on bi-objective optimization

In recent years, with strict domestic financial supervision and other policy-oriented factors, some products are becoming increasingly restricted, including nonstandard products, bank-guaranteed wealth management products, and other products that can provide investors with a more stable income. Pairs trading, a type of stable strategy that has proved efficient in many financial markets worldwide, has become the focus of investors. Based on the traditional Gatev–Goetzmann–Rouwenhorst (GGR, Gatev et al., 2006) strategy, this paper proposes a stock-matching strategy based on bi-objective quadratic programming with quadratic constraints (BQQ) model. Under the condition of ensuring a long-term equilibrium between pairedstock prices, the volatility of stock spreads is increased as much as possible, improving the profitability of the strategy. To verify the effectiveness of the strategy, we use the natural logs of the daily stock market indices in Shanghai. The GGR model and the BQQ model proposed in this paper are back-tested and compared. The results show that the BQQ model can achieve a higher rate of returns.     

沈阳城市周边耕地多功能评价

[目的]为探索城市化影响下城市周边耕地多功能的现状,分析其空间差异,为耕地保护及城市周边耕地发展提供参考。[方法]从耕地的生产、生态、社会和景观4个功能出发构建评价指标体系,利用熵权法确定指标权重,加权求和法计算单一功能和多功能值,并用ArcGIS进行空间分析,对沈阳城市周边50个乡镇(街道)的耕地多功能性进行综合评价。[结果](1)沈阳城市周边耕地的多功能性空间差异较为明显。生产功能远郊平原区最强,远郊山区和近郊区较弱;生态功能总体偏弱,个别乡镇较好;距离城市越远,社会功能越强;景观功能近郊区较强,远郊区较弱。(2)耕地的生态、社会和景观功能主要受距市中心距离的影响,生产功能受地形和距市中心距离的双重影响,而多功能则受地形、距市中心距离及各乡镇(街道)耕地主导功能等的综合影响。(3)沈阳城市周边的大多数耕地以农业生产为主导,景观文化功能整体相对较弱。近郊区耕地生态功能所占比重较大,但社会功能比重较小,远郊区则与之相反。[结论]沈阳城市周边耕地多功能性空间差异较为明显且不均衡,应科学规划城市周边耕地功能,优化空间布局,协调耕地各功能的关系,促进耕地多功能综合发展。     

生物质能源可利用潜力评价*——以黑龙江省重点国有林区为例

[目的]生物质能源具有污染少、可再生等特点,合理地对其开发利用是黑龙江省重点国有林区经济转型发展的一种探索。通过对黑龙江省重点国有林区生物质能源资源量的计算,明确了生物质能源的可利用潜力及变化趋势,以期为其产业发展的政策制定提供理论参考。[方法]文章利用自下而上估算法,以林木生物质的资源量和农作物秸秆的产量为原始数据,对2005—2015年黑龙江省重点国有林区生物质能源可利用潜力进行评价。[结果](1)研究期间,黑龙江省重点国有林区林木生物质资源可利用潜力小幅下降,农作物秸秆资源可利用潜力稳步上升。(2)林木生物质资源中,商品林采伐剩余物可利用潜力骤降,森林抚育剩余物可利用潜力增加;农作物秸秆资源中,粮食作物秸秆可利用潜力逐年上升。(3)黑龙江省重点国有林区生物质能源可利用潜力随着国家"天保工程"和"重点国有林区全面禁止天然林商业性采伐"政策的实施而发生变化。[结论]黑龙江省重点国有林区生物质能源丰富,可利用潜力较大,其中农作物秸秆资源可利用潜力增速较快。     

毛竹水溶性多糖提取工艺的优化及结构表征

采用响应面法优化毛竹水溶性多糖的提取工艺,分析其成分,并进行结构表征。研究结果表明,优化的毛竹多糖(BSP)提取条件为:料液比1∶27(g/mL)、提取温度78 ℃、提取时间4 h,该条件下BSP提取率为(5.18±0.07)%,提取物中多糖含量为(72.14±0.02)%,糖醛酸含量为(9.76±0.03)%;BSP具有酸性多糖典型的官能团特征吸收峰,其单糖组成及摩尔比为n_甘露糖∶n_葡萄糖∶n_阿拉伯糖∶n_木糖∶n_鼠李糖∶n_半乳糖=62.52∶23.05∶10.35∶2.78∶0.79∶0.51;BSP由两个主峰组成,相对分子质量分别为91.11、4.55 kDa。BSP是一种混合酸性多糖。     

广西农业生态脆弱性评价及区划研究

［目的］随着经济的快速发展，我国各地区都面临着不同程度的农业生态脆弱性，通过研究特殊地形和气候下的农业生态脆弱性，有助于为农业生态环境的综合治理提供参考。［方法］文章以广西为例，通过构建评价指标体系，采用层次分析法和熵权法确定综合权重，通过构建综合评价模型对广西农业生态脆弱性程度进行评价，利用ArcGIS对生态脆弱性进行分级，以期探究广西农业生态脆弱性的空间分布情况。［结果］权重分析结果表明水蚀面积的权重值最高为0134，年平均降水量的权重值为0113，自然环境指标相对于农业生产和经济指标对农业生态脆弱性的影响更大。区划研究结果表明广西农业生态脆弱性区域大致可分为5个区域，分别是桂西峰丛洼地带、桂东山地丘陵地带、桂中低山丘陵地带、桂东北山地、桂南沿海丘陵地带。［结论］广西农业生态环境整体来看较为脆弱，处于轻度和中度脆弱等级，但生态环境问题仍不容忽视。为了改善农业生态脆弱性，政府应实行区域的联合防治，加大对农业生态环境的监督和治理，积极探寻生态、环保的农业发展方式，调整产业结构，改善农业生态环境的状态，促进广西农业的可持续发展。     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.