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.     

Nitrile Rubber Reactor Operation Troubleshooting with Principal Component Analysis

Principal Component Analysis (PCA) is employed as a tool in order to demonstrate yet another application of the technique, and, most importantly, to show that results from the statistical multivariate technique do make physico-chemical sense. The operation of a typical emulsion copolymerization of acrylonitrile and butadiene (nitrile butadiene rubber, NBR) is used as an example of process troubleshooting. In more general terms, a statistical tool is used to aid process data analysis and process operation (recipe, product property) troubleshooting.

The goal is to produce consistent Mooney Viscosity (MV) among different batches. The observation is that varying induction times lead to Mooney Viscosity inconsistencies. Firstly, we show results from the application of PCA to process data. Secondly, we deal with an even more important (and often ignored) question by examining whether the trends indicated by PCA make process sense.     

Reasoning about proof and knowledge

In previous work [15], we presented a hierarchy of classical modal systems, along with algebraic semantics, for the reasoning about intuitionistic truth, belief and knowledge. Deviating from Gödel's interpretation of IPC in S4, our modal systems contain IPC in the way established in [13]. The modal operator can be viewed as a predicate for intuitionistic truth, i.e. proof. Epistemic principles are partially adopted from Intuitionistic Epistemic Logic IEL [4]. In the present paper, we show that the S5-style systems of our hierarchy correspond to an extended Brouwer–Heyting–Kolmogorov interpretation and are complete w.r.t. a relational semantics based on intuitionistic general frames. In this sense, our S5-style logics are adequate and complete systems for the reasoning about proof combined with belief or knowledge. The proposed relational semantics is a uniform framework in which also IEL can be modeled. Verification-based intuitionistic knowledge formalized in IEL turns out to be a special case of the kind of knowledge described by our S5-style systems.     

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.     

Water PMF for predicting the properties of water molecules in protein binding site

Water is an important component in living systems and deserves better understanding in chemistry and biology. However, due to the difficulty of investigating the water functions in protein structures, it is usually ignored in computational modeling, especially in the field of computer‐aided drug design. Here, using the potential of mean forces (PMFs) approach, we constructed a water PMF (wPMF) based on 3946 non‐redundant high resolution crystal structures. The extracted wPMF potential was first used to investigate the structure pattern of water and analyze the residue hydrophilicity. Then, the relationship between wPMF score and the B factor value of crystal waters was studied. It was found that wPMF agrees well with some previously reported experimental observations. In addition, the wPMF score was also tested in parallel with 3D‐RISM to measure the ability of retrieving experimentally observed waters, and showed comparable performance but with much less computational cost. In the end, we proposed a grid‐based clustering scheme together with a distance weighted wPMF score to further extend wPMF to predict the potential hydration sites of protein structure. From the test, this approach can predict the hydration site at the accuracy about 80% when the calculated score lower than ?4.0. It also allows the assessment of whether or not a given water molecule should be targeted for displacement in ligand design. Overall, the wPMF presented here provides an optional solution to many water related computational modeling problems, some of which can be highly valuable as part of a rational drug design strategy. © 2012 Wiley Periodicals, Inc.     

KiSThelP: A program to predict thermodynamic properties and rate constants from quantum chemistry results†

Kinetic and Statistical Thermodynamical Package (KiSThelP) is a cross‐platform free open‐source program developed to estimate molecular and reaction properties from electronic structure data. To date, three computational chemistry software formats are supported (Gaussian, GAMESS, and NWChem). Some key features are: gas‐phase molecular thermodynamic properties (offering hindered rotor treatment), thermal equilibrium constants, transition state theory rate coefficients (transition state theory (TST), variational transition state theory (VTST)) including one‐dimensional (1D) tunnelling effects (Wigner, and Eckart) and Rice‐Ramsperger‐Kassel‐Marcus (RRKM) rate constants, for elementary reactions with well‐defined barriers. KiSThelP is intended as a working tool both for the general public and also for more expert users. It provides graphical front‐end capabilities designed to facilitate calculations and interpreting results. KiSThelP enables to change input data and simulation parameters directly through the graphical user interface and to visually probe how it affects results. Users can access results in the form of graphs and tables. The graphical tool offers customizing of 2D plots, exporting images and data files. These features make this program also well‐suited to support and enhance students learning and can serve as a very attractive courseware, taking the teaching content directly from results in molecular and kinetic modelling. © 2013 Wiley Periodicals, Inc.     

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.