Systematic risk in the biopharmaceutical sector: a multiscale approach

AbstractIt is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.

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Assessing the Time-Frequency Co-Movements among the Five Largest Engineering Consulting Companies: A Wavelet-Base Metrics of Contagion and VaR Ratio

Mathematics ◽

10.3390/math9050504 ◽

2021 ◽

Vol 9 (5) ◽

pp. 504

Author(s):

Marcos Albuquerque Junior ◽

José António Filipe ◽

Paulo de Melo Jorge Neto ◽

Cristiano da Costa da Silva

Keyword(s):

Systematic Risk ◽

Investment Horizon ◽

Time Frequency ◽

Engineering Consulting ◽

Country Factors ◽

Asset Class ◽

Asset Classes ◽

International Assets ◽

Portfolio Return

Diversification in a portfolio is an important tool for the systematic risk management that is inherent to different asset classes. The composition of a portfolio with domestic and international assets is seen as one of the main alternatives for building a diversified portfolio, as this approach tends to reduce portfolio return exposure depending on country factors. However, in scenarios where industry factors are predominant, international diversification can increase systematic risk in a portfolio centered on a single asset class. This study is a pioneer in using wavelet-based methods to identify intersectoral co-movements, based on a portfolio of shares of the world’s top five consulting engineering companies, providing an innovative way to be applied to this phenomenon. Our evidence indicates that companies share a strong pattern of co-movements among themselves, especially in cycles of 32 to 64 days, suggesting a higher exposure to risk for portfolios with an investment horizon in long-term cycles.

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An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

Open Computer Science ◽

10.2478/s13537-011-0019-8 ◽

2011 ◽

Vol 1 (3) ◽

Cited By ~ 2

Author(s):

T. Sumathi ◽

M. Hemalatha

Keyword(s):

Wavelet Transform ◽

Image Fusion ◽

Curvelet Transform ◽

Relevant Information ◽

Good Time ◽

Discrete Wavelet ◽

Fusion Algorithm ◽

Time Frequency ◽

Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

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UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

Remote Sensing ◽

10.3390/rs13061205 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1205

Author(s):

Caidan Zhao ◽

Gege Luo ◽

Yilin Wang ◽

Caiyun Chen ◽

Zhiqiang Wu

Keyword(s):

Recognition Performance ◽

Principal Component ◽

Texture Features ◽

Training Model ◽

Recognition Algorithm ◽

Discrete Wavelet ◽

Dynamic Feature ◽

Complex Environments ◽

Time Frequency ◽

Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

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Orthogonal-based wavelet analysis of wind turbulence and correlation between turbulence and forces

Vietnam Journal of Mechanics ◽

10.15625/0866-7136/29/2/5593 ◽

2007 ◽

Vol 29 (2) ◽

pp. 73-82 ◽

Cited By ~ 2

Author(s):

Le Thai Hoa ◽

Nguyen Dong Anh

Keyword(s):

Wavelet Transforms ◽

Discrete Wavelet ◽

Time Frequency ◽

Existing Problems ◽

Physical Measurements ◽

Force Relation ◽

Wind Turbulence ◽

Time Frequency Localization ◽

Physical Simulations ◽

Wavelet Decompositions

Recent models of wind turbulence and turbulence-force relation as well still contain uncertainties. Further studies on them are needed to gain the better knowledge to refine the existing problems from analytical computations to wind tunnel's physical simulations in the wind engineering. The continuous and discrete wavelet transforms have been applied as powerful transformation tools to represent time series into the time-frequency localization. This paper will apply the orthogonal-based wavelet decomposition to investigate the intermittency of the turbulence and to detect the turbulence-force correlation in the both temporal-spectral information using proposed cross energy of wavelet decompositions. Analyzing data have been obtained by physical measurements on model from the wind tunnel tests.

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Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

ISRN Signal Processing ◽

10.5402/2011/269361 ◽

2011 ◽

Vol 2011 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Timur Düzenli ◽

Nalan Özkurt

Keyword(s):

Wavelet Transform ◽

Real Time ◽

Wavelet Transforms ◽

Principal Component ◽

Discrete Wavelet ◽

Vanishing Moments ◽

Zero Crossings ◽

Time Frequency ◽

Common Time ◽

Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

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COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s021951940700225x ◽

2007 ◽

Vol 07 (02) ◽

pp. 199-214 ◽

Cited By ~ 2

Author(s):

S. M. DEBBAL ◽

F. BEREKSI-REGUIG

Keyword(s):

Wavelet Transform ◽

Heart Sound ◽

Wavelet Packet ◽

Extraction Methods ◽

Heart Sounds ◽

Discrete Wavelet ◽

Clinical Tool ◽

Prognostic Information ◽

Transform Method ◽

Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

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Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux

Energies ◽

10.3390/en12091658 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1658 ◽

Cited By ~ 7

Author(s):

Israel Zamudio-Ramirez ◽

Roque Alfredo Osornio-Rios ◽

Miguel Trejo-Hernandez ◽

Rene de Jesus Romero-Troncoso ◽

Jose Alfonso Antonino-Daviu

Keyword(s):

Induction Motors ◽

Early Stage ◽

Industrial Sector ◽

Smart Sensors ◽

Discrete Wavelet ◽

Electrical Machine ◽

Smart Sensor ◽

Time Frequency ◽

Essential Components ◽

Winding Insulation

Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.

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VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419500088 ◽

2019 ◽

Vol 19 (03) ◽

pp. 1950008

Author(s):

MONALISA MOHANTY ◽

PRADYUT BISWAL ◽

SUKANTA SABUT

Keyword(s):

Ventricular Tachycardia ◽

Decision Tree ◽

Ventricular Arrhythmias ◽

Ventricular Tachyarrhythmia ◽

Support Vector ◽

Svm Classifier ◽

Discrete Wavelet ◽

Time Frequency ◽

Tree Classifier ◽

Frequency Features

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

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