Statistical inference and modelling of momentum in stock prices

The following results are obtained, (i) It is possible to obtain a time series of market data {y(t)} in which the fluctuations in fundamental value have been compensated for. An objective test of the efficient market hypothesis (EMH), which would predict random correlations about a constant value, is thereby possible, (ii) A time series procedure can be used to determine the extent to which the differences in the data and the moving averages are significant. This provides a model of the form y(t)-y(t-l)=0.5{y(t- l)-y(t-2)}+ε(t)+0.8ε(r-1) where ε(t) is the error at time t, and the coefficients 0.5 and 0.8 are determined from the data. One concludes that today's price is not a random perturbation from yesterday's; rather, yesterday's rate of change is a significant predictor of today's rate of change. This confirms the concept of momentum that is crucial to market participants. (iii) The model provides out-of-sample predictions that can be tested statistically. (iv) The model and coefficients obtained in this way can be used to make predictions on laboratory experiments to establish an objective and quantitative link between the experiments and the market data. These methods circumvent the central difficulty in testing market data, namely, that changes in fundamentals obscure intrinsic trends and autocorrelations. This procedure is implemented by considering the ratio of two similar funds (Germany and Future Germany) with the same manager and performing a set of statistical tests that have excluded fluctuations in fundamental factors. For the entire data of the first 1149 days beginning with the introduction of the latter fund, a standard runs test indicates that the data is 29 standard deviations away from that which would be expected under a hypothesis of random fluctuations about the fundamental value. This and other tests provide strong evidence against the efficient market hypothesis and in favour of autocorrelations in the data. An ARIMA time series finds strong evidence (9.6 and 21.6 standard deviations in the two coefficients) that the data is described by a model that involves the first difference, indicating that momentum is the significant factor. The first quarter's data is used to make out-of-sample predictions for the second quarter with results that are significant to 3 standard deviations. Finally, the ARIMA model and coefficients are used to make predictions on laboratory experiments of Porter and Smith in which the intrinsic value is clear. The model's forecasts are decidedly more accurate than that of the null hypothesis of random fluctuations about the fundamental value.     

Distribution of trace elements in flowing surface waters: Effect of seasons and anthropogenic practices in India

This study reports the distribution of Cr, Ni, Cu and Pb in waters, sediments, macrophytes and fish of the Yamuna River’s patch in Delhi (India). This is one of the most polluted stretches of rivers in the world. Water from this river is used fosr irrigation, industrial and domestic purposes, including drinking water. The effects of season and anthropogenic practices were identified. Almost zero dissolved oxygen and high levels of lead and phosphate indicate the polluted state of the aquatic system. The enrichment factors in sediments (with respect to reference site) vary in the range of 0.60–82.9% (Cr), 1.40–90.5% (Ni), 1.00–85.3% (Cu) and 3.80–86.6% (Pb). The toxic effects due to Ni and Pb could frequently be visible in aquatic life as their values fall above the probable effect level (36 mg Kg^?1 for Ni and 91.3 mg Kg^?1 for Pb). The metal contents in the macrophyte (Eichhornia crassipes) and the fish (Oreochromis niloticus) generally increase in the summer season. Cu and Pb accumulate preferentially in E. crassipes. The fish from these waters is unsuitable for human consumption as the levels of Cr, Cu and Pb are above the recommended limits. Significant spatial variations in parameters of water and sediments were confirmed via one-way analysis of variance. The Pearson’s correlation analysis suggests a common source of some of the metals. Principal component analysis highlighted domestic, and industrial waste and coal-fired thermal power plants as the metal sources. This study might act as a benchmark for other investigations focused on similarly affected surface aquatic systems due to agricultural and industrial activities.     

Evidence for the Chain‐Growth Synthesis of Statistical π‐Conjugated Donor–Acceptor Copolymers

The synthesis of a series of dithienosilole–benzotriazole donor–acceptor statistical copolymers with various donor–acceptor ratios is reported, prepared by Kumada catalyst‐transfer polymerization. Statistical copolymer structure is verified by ¹H NMR and optical absorption spectroscopy, and supported by density functional theory (DFT) calculations. The copolymers exhibit a single optical absorption band that lies between dithienosilole and benzotriazole homopolymers, which shifts with varying donor–acceptor content. A chain extension experiment using a partially consumed benzotriazole solution as a macroinitiator followed by addition of dithienosilole leads to the synthesis of a statistical dithienosilole–benzotriazole block copolymer from a pure benzotriazole block, demonstrating that both chain extension and simultaneous monomer incorporation are possible using this methodology.

     

Use of Kirkwood–Buff Integrals for Extracting Distinct Diffusion Coefficients in Macromolecule–Solvent Mixtures

The possibility to extract velocity correlation quantities from fluctuation thermodynamic properties is explored in the case of macromolecule–solvent mixtures. Indeed, Kirkwood–Buff integrals, G_ij, together with self‐diffusion and viscosity data can provide an approximation for distinct diffusion coefficients (DDCs), D^d_ij. Herein, D^d_ij for binary polyethyleneglycol (PEG)(i)–water(0) systems is calculated. These systems show positive values of D^d_ii coefficients, indicating strong PEG–PEG interaction, and providing marker of water mediated PEG–PEG networks. The efficiency of several standard DDCs present in literature for D^d_ij analysis is compared, summarizing the usefulness of each one, depending on the nonideality degree.

     

固体中微裂纹系统统计演化的基本描述

本文建议了一种在外界载荷作用下,在固体中产生的微损伤的统计演化的理论描述方法。特别是,作为一个例子,讨论了理想微裂纹系统,以便了解在微裂纹成核和扩展过程中,损伤演化的基本行为。本文也简单讨论了有关的平均损伤函数。     

大散射角散斑场中有关相位奇异新特性的研究

利用散斑场和参考光的干涉提取散斑场复振幅和相位, 研究了不同散射角下散斑场相位的分布规律以及相位奇异处光波复振幅实部零值线和虚部零值线夹角、光强等值线离心率的统计特性. 在大散射角散斑中发现了一种新的相位奇异现象, 即相位奇异线; 研究了相位在跨越奇异线时的突变规律及涡旋状相位的分布特征, 发现在相位奇异线处存在着呈双曲线或抛物线状的光强等值线.     

基于高级统计能量分析的周期加筋板振动特性研究

统计能量分析(statistical energy analysis, SEA)是复杂耦合系统中、高频动力学特性计算的有力工具. 本文以波传播理论和SEA的基本原理为基础, 研究周期加筋板中弯曲波传播特性. 分析了周期结构的频率带隙特性和加强筋对板上弯曲波的滤波特性对SEA计算结果的影响规律, 发现经典SEA由于忽视了加筋板中物理上不相邻子系统间存在的能量隧穿效应, 而导致响应预测结果产生最高近 40 dB的误差. 为了解决这一问题, 本文应用高级统计能量分析(advanced statistical energy analysis, ASEA)方法, 考虑能量在不相邻子系统间的传递、转移和转化的物理过程, 从而大幅提高子系统响应的预测精度, 将误差在大部分频段降低至小于5 dB. 设计了模拟简支边界条件的加筋板振动测试实验装置, 实验测试结果与有限元结果符合较好, 对理论模型进行了验证.     

反对称分子动力学模型中的冻结概念（英文）

给出了反对称分子动力学模型(AMD)计算的50 Me V/nucleon112Sn+112Sn反应的分析结果。该研究是反对称分子动力学模型中统计冻结概念的部分研究结果。利用自洽法结合修正的Fisher模型,提取了发射源的温度和密度分别为T=(6.1±0.2)Me V,ρ/ρ0=0.69±0.03。通过与AMD模型计算的系统在时间演化过程中的最大密度比较,得出碎片发射源的密度远小于系统的最大密度。利用自洽法提取的温度和密度与35 Me V/nucleon的40Ca+40Ca反应系统及40 Me V/nucleon的64Zn+112Sn反应系统所提取的温度和密度非常接近。该结果表明反对称分子动力学模型中,系统在中等质量碎片形成时刻处于统计冻结体积。