Analysis of Earnings, Stock Prices and Bond Yields: The Fed Model Approach

Prediction of the stock market valuation is a common interest to all market participants. Theoretically sound market valuation can be achieved by discounting future earnings of equities to present. Competing valuation models seek to find variables that affect the equity market valuation in a way that the market valuation can be explained and also variables that could be used to predict market valuation. In this paper we test the contemporaneous relationship between stock prices, forward looking earnings and long-term government bond yields. We test this so-called Fed model in a long- and short-term time series analysis. In order to test the dynamics of the relationship, we use the cointegration framework. The data used in this study spans over four decades of various market conditions between 1964-2007, using data from United States. The empirical results of our analysis do not give support for the Fed model. We are able to show that the long-term government bonds do not play statistically significant role in this relationship. The effect of forward earnings yield on the stock market prices is significant and thus we suggest the use of standard valuation ratios when trying to predict the future paths of equity prices. Also, changes in the long-term government bond yields do not have significant short-term impact on stock prices.

Monitoring Fatigue Status with HRV Measures in Elite Athletes : An Avenue Beyond RMSSD?

Among the tools proposed to assess the athlete's "fatigue," the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global "fatigue" level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of "fatigue." Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes.

Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?

Among the tools proposed to assess the athlete's "fatigue," the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global "fatigue" level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of "fatigue." Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes.

The Association of H1N1 Pandemic Influenza with Congenital Anomaly Prevalence in Europe: An Ecological Time Series Study.

BACKGROUND: In the context of the European Surveillance of Congenital Anomalies (EUROCAT) surveillance response to the 2009 influenza pandemic, we sought to establish whether there was a detectable increase of congenital anomaly prevalence among pregnancies exposed to influenza seasons in general, and whether any increase was greater during the 2009 pandemic than during other seasons. METHODS: We performed an ecologic time series analysis based on 26,967 pregnancies with nonchromosomal congenital anomaly conceived from January 2007 to March 2011, reported by 15 EUROCAT registries. Analysis was performed for EUROCAT-defined anomaly subgroups, divided by whether there was a prior hypothesis of association with influenza. Influenza season exposure was based on World Health Organization data. Prevalence rate ratios were calculated comparing pregnancies exposed to influenza season during the congenital anomaly-specific critical period for embryo-fetal development to nonexposed pregnancies. RESULTS: There was no evidence for an increased overall prevalence of congenital anomalies among pregnancies exposed to influenza season. We detected an increased prevalence of ventricular septal defect and tricuspid atresia and stenosis during pandemic influenza season 2009, but not during 2007-2011 influenza seasons. For congenital anomalies, where there was no prior hypothesis, the prevalence of tetralogy of Fallot was strongly reduced during influenza seasons. CONCLUSIONS: Our data do not suggest an overall association of pandemic or seasonal influenza with congenital anomaly prevalence. One interpretation is that apparent influenza effects found in previous individual-based studies were confounded by or interacting with other risk factors. The associations of heart anomalies with pandemic influenza could be strain specific.

Analysis of outliers in electricity spot prices with exampleof New England and New Zealand markets

Electricity spot prices have always been a demanding data set for time series analysis, mostly because of the non-storability of electricity. This feature, making electric power unlike the other commodities, causes outstanding price spikes. Moreover, the last several years in financial world seem to show that ’spiky’ behaviour of time series is no longer an exception, but rather a regular phenomenon. The purpose of this paper is to seek patterns and relations within electricity price outliers and verify how they affect the overall statistics of the data. For the study techniques like classical Box-Jenkins approach, series DFT smoothing and GARCH models are used. The results obtained for two geographically different price series show that patterns in outliers’ occurrence are not straightforward. Additionally, there seems to be no rule that would predict the appearance of a spike from volatility, while the reverse effect is quite prominent. It is concluded that spikes cannot be predicted based only on the price series; probably some geographical and meteorological variables need to be included in modeling.

Uso da RMN como um sensor online em processos industriais

Nuclear magnetic resonance (NMR) is one of the most versatile analytical techniques for chemical, biochemical and medical applications. Despite this great success, NMR is seldom used as a tool in industrial applications. The first application of NMR in flowing samples was published in 1951. However, only in the last ten years Flow NMR has gained momentum and new and potential applications have been proposed. In this review we present the historical evolution of flow or online NMR spectroscopy and imaging, and current developments for use in the automation of industrial processes.

Online Ultrasound Measurements of Membrane Compaction

There are several filtration applications in the pulp and paper industry where the capacity and cost-effectiveness of processes are of importance. Ultrafiltration is used to clean process water. Ultrafiltration is a membrane process that separates a certain component or compound from a liquid stream. The pressure difference across the membrane sieves macromolecules smaller than 0.001-0.02 μm through the membrane. When optimizing the filtration process capacity, online information about the conditions of the membrane is needed. Fouling and compaction of the membrane both affect the capacity of the filtration process. In fouling a “cake” layer starts to build on the surface of the membrane. This layer blocks the molecules from sieving through the membrane thereby decreasing the yield of the process. In compaction of the membrane the structure is flattened out because of the high pressure applied. The higher pressure increases the capacity but may damage the structure of the membrane permanently. Information about the compaction is needed to effectively operate the filters. The objective of this study was to develop an accurate system for online monitoring of the condition of the membrane using ultrasound reflectometry. Measurements of ultrafiltration membrane compaction were made successfully utilizing ultrasound. The results were confirmed by permeate flux decline, measurements of compaction with a micrometer, mechanical compaction using a hydraulic piston and a scanning electron microscope (SEM). The scientific contribution of this thesis is to introduce a secondary ultrasound transducer to determine the speed of sound in the fluid used. The speed of sound is highly dependent on the temperature and pressure used in the filters. When the exact speed of sound is obtained by the reference transducer, the effect of temperature and pressure is eliminated. This speed is then used to calculate the distances with a higher accuracy. As the accuracy or the resolution of the ultrasound measurement is increased, the method can be applied to a higher amount of applications especially for processes where fouling layers are thinner because of smaller macromolecules. With the help of the transducer, membrane compaction of 13 μm was measured in the pressure of 5 bars. The results were verified with the permeate flux decline, which indicated that compaction had taken place. The measurements of compaction with a micrometer showed compaction of 23–26 μm. The results are in the same range and confirm the compaction. Mechanical compaction measurements were made using a hydraulic piston, and the result was the same 13 μm as obtained by applying the ultrasound time domain reflectometry (UTDR). A scanning electron microscope (SEM) was used to study the structure of the samples before and after the compaction.

Compton scattering tomography for agricultural measurements

This paper presents a new approach in tomographic instrumentation for agriculture based on Compton scattering, which allows for the simultaneous measurements of density and moisture of soil samples. Compton tomography is a technique that can be used to obtain a spatial map of electronic density of samples. Quantitative results can be obtained by using a reconstruction algorithm that takes into account the absorption of incident and scattered radiation. Results show a coefficient of linear correlation better than 0.81, when comparison is made between soil density measurements based on this method and direct transmission tomography. For soil water contents, a coefficient of linear correlation better than 0.79 was found when compared with measurements obtained by time domain reflectrometry (TDR). In addition, a set of Compton scatter images are presented to illustrate the efficacy of this imaging technique, which makes possible improved spatial variability analysis of pre-established planes.

Sensibility analysis of the pearl model for pesticide leaching in the State of Mato Grosso do Sul, Brazil

For an accurate use of pesticide leaching models it is necessary to assess the sensitivity of input parameters. The aim of this work was to carry out sensitivity analysis of the pesticide leaching model PEARL for contrasting soil types of Dourados river watershed in the state of Mato Grosso do Sul, Brazil. Sensitivity analysis was done by carrying out many simulations with different input parameters and calculating their influence on the output values. The approach used was called one-at-a-time sensitivity analysis, which consists in varying independently input parameters one at a time and keeping all others constant with the standard scenario. Sensitivity analysis was automated using SESAN tool that was linked to the PEARL model. Results have shown that only soil characteristics influenced the simulated water flux resulting in none variation of this variable for scenarios with different pesticides and same soil. All input parameters that showed the greatest sensitivity with regard to leached pesticide are related to soil and pesticide properties. Sensitivity of all input parameters was scenario dependent, confirming the need of using more than one standard scenario for sensitivity analysis of pesticide leaching models.

LVDC power distribution system: computational modelling

In the doctoral dissertation, low-voltage direct current (LVDC) distribution system stability, supply security and power quality are evaluated by computational modelling and measurements on an LVDC research platform. Computational models for the LVDC network analysis are developed. Time-domain simulation models are implemented in the time-domain simulation environment PSCAD/EMTDC. The PSCAD/EMTDC models of the LVDC network are applied to the transient behaviour and power quality studies. The LVDC network power loss model is developed in a MATLAB environment and is capable of fast estimation of the network and component power losses. The model integrates analytical equations that describe the power loss mechanism of the network components with power flow calculations. For an LVDC network research platform, a monitoring and control software solution is developed. The solution is used to deliver measurement data for verification of the developed models and analysis of the modelling results. In the work, the power loss mechanism of the LVDC network components and its main dependencies are described. Energy loss distribution of the LVDC network components is presented. Power quality measurements and current spectra are provided and harmonic pollution on the DC network is analysed. The transient behaviour of the network is verified through time-domain simulations. DC capacitor guidelines for an LVDC power distribution network are introduced. The power loss analysis results show that one of the main optimisation targets for an LVDC power distribution network should be reduction of the no-load losses and efficiency improvement of converters at partial loads. Low-frequency spectra of the network voltages and currents are shown, and harmonic propagation is analysed. Power quality in the LVDC network point of common coupling (PCC) is discussed. Power quality standard requirements are shown to be met by the LVDC network. The network behaviour during transients is analysed by time-domain simulations. The network is shown to be transient stable during large-scale disturbances. Measurement results on the LVDC research platform proving this are presented in the work.

Minimizing Conducted RF-Emissions in Switch Mode Power Supplies Using Spread-Spectrum Techniques

Switching power supplies are usually implemented with a control circuitry that uses constant clock frequency turning the power semiconductor switches on and off. A drawback of this customary operating principle is that the switching frequency and harmonic frequencies are present in both the conducted and radiated EMI spectrum of the power converter. Various variable-frequency techniques have been introduced during the last decade to overcome the EMC problem. The main objective of this study was to compare the EMI and steady-state performance of a switch mode power supply with different spread-spectrum/variable-frequency methods. Another goal was to find out suitable tools for the variable-frequency EMI analysis. This thesis can be divided into three main parts: Firstly, some aspects of spectral estimation and measurement are presented. Secondly, selected spread spectrum generation techniques are presented with simulations and background information. Finally, simulations and prototype measurements from the EMC and the steady-state performance are carried out in the last part of this work. Combination of the autocorrelation function, the Welch spectrum estimate and the spectrogram were used as a substitute for ordinary Fourier methods in the EMC analysis. It was also shown that the switching function can be used in preliminary EMC analysis of a SMPS and the spectrum and autocorrelation sequence of a switching function correlates with the final EMI spectrum. This work is based on numerous simulations and measurements made with the prototype. All these simulations and measurements are made with the boost DC/DC converter. Four different variable-frequency modulation techniques in six different configurations were analyzed and the EMI performance was compared to the constant frequency operation. Output voltage and input current waveforms were also analyzed in time domain to see the effect of the spread spectrum operation on these quantities. According to the results presented in this work, spread spectrum modulation can be utilized in power converter for EMI mitigation. The results from steady-state voltage measurements show, that the variable-frequency operation of the SMPS has effect on the voltage ripple, but the ripple measured from the prototype is still acceptable in some applications. Both current and voltage ripple can be controlled with proper main circuit and controller design.

The importance of accurate anatomic assessment for the volumetric analysis of the amygdala

There is a wide range of values reported in volumetric studies of the amygdala. The use of single plane thick magnetic resonance imaging (MRI) may prevent the correct visualization of anatomic landmarks and yield imprecise results. To assess whether there is a difference between volumetric analysis of the amygdala performed with single plane MRI 3-mm slices and with multiplanar analysis of MRI 1-mm slices, we studied healthy subjects and patients with temporal lobe epilepsy. We performed manual delineation of the amygdala on T1-weighted inversion recovery, 3-mm coronal slices and manual delineation of the amygdala on three-dimensional volumetric T1-weighted images with 1-mm slice thickness. The data were compared using a dependent t-test. There was a significant difference between the volumes obtained by the coronal plane-based measurements and the volumes obtained by three-dimensional analysis (P < 0.001). An incorrect estimate of the amygdala volume may preclude a correct analysis of the biological effects of alterations in amygdala volume. Three-dimensional analysis is preferred because it is based on more extensive anatomical assessment and the results are similar to those obtained in post-mortem studies.

Heart rate variability in athletes and nonathletes at rest and during head-up tilt

The purpose of the present study was to determine if autonomic heart rate modulation, indicated by heart rate variability (HRV), differs during supine rest and head-up tilt (HUT) when sedentary and endurance-trained cyclists are compared. Eleven sedentary young men (S) and 10 trained cyclists (C) were studied. The volunteers were submitted to a dynamic ECG Holter to calculate HRV at rest and during a 70º HUT. The major aerobic capacity of athletes was expressed by higher values of at anaerobic threshold and peak conditions (P < 0.05). At rest the athletes had lower heart rates (P < 0.05) and higher values in the time domain of HRV compared with controls (SD of normal RR interval, SDNN, medians): 59.1 ms (S) vs 89.9 ms (C), P < 0.05. During tilt athletes also had higher values in the time domain of HRV compared with controls (SDNN, medians): 55.7 ms (S) vs 69.7 ms (C), P < 0.05. No differences in power spectral components of HRV at rest or during HUT were detected between groups. Based on the analysis of data by the frequency domain method, we conclude that in athletes the resting bradycardia seems to be much more related to changes in intrinsic mechanisms than to modifications in autonomic control. Also, HUT caused comparable changes in sympathetic and parasympathetic modulation of the sinus node in both groups.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Analytic evaluation of three-phase short circuit demagnetization and hysteresis loss risk in rotor-surface-magnet Permanent-Magnet Synchronous Machines

Permanent magnet synchronous machines (PMSM) have become widely used in applications because of high efficiency compared to synchronous machines with exciting winding or to induction motors. This feature of PMSM is achieved through the using the permanent magnets (PM) as the main excitation source. The magnetic properties of the PM have significant influence on all the PMSM characteristics. Recent observations of the PM material properties when used in rotating machines revealed that in all PMSMs the magnets do not necessarily operate in the second quadrant of the demagnetization curve which makes the magnets prone to hysteresis losses. Moreover, still no good analytical approach has not been derived for the magnetic flux density distribution along the PM during the different short circuits faults. The main task of this thesis is to derive simple analytical tool which can predict magnetic flux density distribution along the rotor-surface mounted PM in two cases: during normal operating mode and in the worst moment of time from the PM’s point of view of the three phase symmetrical short circuit. The surface mounted PMSMs were selected because of their prevalence and relatively simple construction. The proposed model is based on the combination of two theories: the theory of the magnetic circuit and space vector theory. The comparison of the results in case of the normal operating mode obtained from finite element software with the results calculated with the proposed model shows good accuracy of model in the parts of the PM which are most of all prone to hysteresis losses. The comparison of the results for three phase symmetrical short circuit revealed significant inaccuracy of the proposed model compared with results from finite element software. The analysis of the inaccuracy reasons was provided. The impact on the model of the Carter factor theory and assumption that air have permeability of the PM were analyzed. The propositions for the further model development are presented.