scholarly journals Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1627
Author(s):  
Lucas Schneider ◽  
Johannes Stübinger
Keyword(s):  
Stock Returns ◽  
Large Scale ◽  
Selection Process ◽  
Explanatory Power ◽  
Principal Component ◽  
Deep Dive ◽  
Trading Systems ◽  
Dispersion Trading ◽  
Out Of Sample ◽  
The Individual

This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.

Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?

2017 ◽  
Vol 30 (20) ◽  
pp. 8335-8355 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett
Keyword(s):  
North American ◽  
Large Scale ◽  
Climate Model ◽  
Principal Component ◽  
Eastern Africa ◽  
Multimodel Ensemble ◽  
Anomaly Correlation ◽  
The North ◽  
The Individual ◽  
North American Multimodel Ensemble

Abstract Canonical correlation analysis (CCA)-based statistical corrections are applied to seasonal mean precipitation and temperature hindcasts of the individual models from the North American Multimodel Ensemble project to correct biases in the positions and amplitudes of the predicted large-scale anomaly patterns. Corrections are applied in 15 individual regions and then merged into globally corrected forecasts. The CCA correction dramatically improves the RMS error skill score, demonstrating that model predictions contain correctable systematic biases in mean and amplitude. However, the corrections do not materially improve the anomaly correlation skills of the individual models for most regions, seasons, and lead times, with the exception of October–December precipitation in Indonesia and eastern Africa. Models with lower uncorrected correlation skill tend to benefit more from the correction, suggesting that their lower skills may be due to correctable systematic errors. Unexpectedly, corrections for the globe as a single region tend to improve the anomaly correlation at least as much as the merged corrections to the individual regions for temperature, and more so for precipitation, perhaps due to better noise filtering. The lack of overall improvement in correlation may imply relatively mild errors in large-scale anomaly patterns. Alternatively, there may be such errors, but the period of record is too short to identify them effectively but long enough to find local biases in mean and amplitude. Therefore, statistical correction methods treating individual locations (e.g., multiple regression or principal component regression) may be recommended for today’s coupled climate model forecasts. The findings highlight that the performance of statistical postprocessing can be grossly overestimated without thorough cross validation or evaluation on independent data.

Factors That Fit the Time Series and Cross-Section of Stock Returns

2020 ◽  
Vol 33 (5) ◽  
pp. 2274-2325 ◽  
Author(s):  
Martin Lettau ◽  
Markus Pelger
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Cross Section ◽  
Principal Component ◽  
Expected Returns ◽  
Portfolio Returns ◽  
Pricing Errors ◽  
Out Of Sample ◽  
Pricing Error ◽  
Sharpe Ratios

Abstract We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample maximum Sharpe ratio of our factors is twice as large as with PCA with substantially smaller pricing errors. Our factors imply that a significant amount of characteristic information is redundant. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Segmentation of High Dimensional Time-Series Data Using Mixture of Sparse Principal Component Regression Model with Information Complexity

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1170
Author(s):  
Yaojin Sun ◽  
Hamparsum Bozdogan
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Fitness Function ◽  
Explanatory Power ◽  
Principal Component ◽  
Predictor Variables ◽  
Series Data ◽  
High Dimensional ◽  
Information Complexity

This paper presents a new and novel hybrid modeling method for the segmentation of high dimensional time-series data using the mixture of the sparse principal components regression (MIX-SPCR) model with information complexity (ICOMP) criterion as the fitness function. Our approach encompasses dimension reduction in high dimensional time-series data and, at the same time, determines the number of component clusters (i.e., number of segments across time-series data) and selects the best subset of predictors. A large-scale Monte Carlo simulation is performed to show the capability of the MIX-SPCR model to identify the correct structure of the time-series data successfully. MIX-SPCR model is also applied to a high dimensional Standard & Poor’s 500 (S&P 500) index data to uncover the time-series’s hidden structure and identify the structure change points. The approach presented in this paper determines both the relationships among the predictor variables and how various predictor variables contribute to the explanatory power of the response variable through the sparsity settings cluster wise.

Bolt assessment of wind turbine hub using nonlinear ultrasound methods

2019 ◽  
pp. 0309524X1988773
Author(s):  
Gian Piero Malfense Fierro ◽  
Michele Meo
Keyword(s):  
Large Scale ◽  
Selection Process ◽  
Detection Accuracy ◽  
Nonlinear Ultrasound ◽  
Frequency Selection ◽  
Modal Response ◽  
The Individual ◽  
Response Method

This work evaluates various nonlinear ultrasound methods for in situ structural health monitoring of the loosened state of a four-bolt structure found on large-scale wind turbines. The aim was assessment of a four bolted structure with only two piezoelectric sensors, and determination of individual bolt loosened and the extent of loosening. Nonlinear ultrasound methods have been shown to have advantages over linear methods in terms of sensitivity, although the detection accuracy and robustness of these methods can be highly dependent on correct frequency selection. Thus, a frequency selection process based on the modal response of the structure is suggested for determination of bolt-specific frequencies, which was then used to evaluate the individual bolt loosened state. Two nonlinear ultrasound techniques were used to evaluate the bolted structure: the second- and third-order nonlinearity parameters and a nonlinear acoustic moment’s method. The modal response method used for frequency selection was able to determine specific bolt frequencies based on surface and bolt velocities. Nonlinear evaluation at these frequencies showed that specific frequencies related to individual bolts, and as the bolts loosened there was a clear increase in the production of nonlinearities. Thus, the loosened status of individual bolts could be tracked using specific pre-identified frequencies.

Freedom Indices and Capital Asset Pricing Model: Some Malaysian Evidence

2020 ◽  
Vol 1 (1) ◽  
pp. 1-21
Author(s):  
Ying Tay Lee ◽  
Devinaga Rasiah ◽  
Ming Ming Lai
Keyword(s):  
Human Rights ◽  
Asset Pricing ◽  
Stock Returns ◽  
Firm Size ◽  
Explanatory Power ◽  
Least Square ◽  
Press Freedom ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Cross Sectional

Human rights and fundamental freedoms such as economic, political, and press freedoms vary widely from country to country. It creates opportunity and risk in investment decisions. Thus, this study is carried out to examine if the explanatory power of the model for capital asset pricing could be improved when these human rights movement indices are included in the model. The sample for this study comprises of 495 stocks listed in Bursa Malaysia, covering the sampling period from 2003 to 2013. The model applied in this study employed the pooled ordinary least square regression estimation. In addition, the robustness of the model is tested by using firm size as a controlled variable. The findings show that market beta as well as the economic and press freedom indices could explain the cross-sectional stock returns of the Malaysian stock market. By controlling the firm size, it adds marginally to the explanation of the extended CAP model which incorporated economic, political, and press freedom indices.

Models, Practices and Methods of Reading: Evolution in Time and Space

2009 ◽  
pp. 59-64
Author(s):  
Yulia P. Melentyeva
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
National Program ◽  
Time And Space ◽  
Lack Of Information ◽  
The Individual

In recent years as public in general and specialist have been showing big interest to the matters of reading. According to discussion and launch of the “Support and Development of Reading National Program”, many Russian libraries are organizing the large-scale events like marathons, lecture cycles, bibliographic trainings etc. which should draw attention of different social groups to reading. The individual forms of attraction to reading are used much rare. To author’s mind the main reason of such an issue has to be the lack of information about forms and methods of attraction to reading.

THEORETICAL PRINCIPLES OF FORECASTING THE COST OF A SOFTWARE PRODUCT

2017 ◽  
Author(s):  
Pavlo Rodionov ◽  
◽  
Anna Ploskonos ◽  
Lesya Gavrutenko ◽  
◽  
...  
Keyword(s):  
Selection Process ◽  
Individual Characteristics ◽  
Successful Implementation ◽  
Software Products ◽  
Software Product ◽  
Continue Research ◽  
Cocomo Model ◽  
Factors Of Influence ◽  
The Individual ◽  
The Cost

The paper analyzes the factors that affect the amount of effort required to create a mobile application and its cost. It is established that the main factors of influence are the design of the application, its functionality, the type of mobile platform, the availability and level of testing and support, as well as the individual characteristics of the developer. Based on the analysis of information sources, the main methods and approaches to forecasting the cost of software products are identified, which include the COCOMO model, Price-to-win method, expert evaluation, algorithmic methods and the method of analogies. It is proposed to consider the method of analogies as a tool that allows you to make predictions about the cost of resources required for the successful implementation of IT projects based on the experience of similar projects. It is proved that the advantages of this method are the simplicity of its implementation and the clarity of the results obtained, which follows from the practical orientation of this tool. Among the limitations of the method of analogy is the mandatory need for reliable data relating to similar projects, as well as the difficulty of taking into account unspecified indicators. Taking into account the mentioned limitations of the method of analogies and on the basis of the analysis of scientific sources the possible directions of its optimization are determined. Thus, among the ways to improve the effectiveness of this method are those aimed at optimizing the project selection process, the data for which are used as a basis for forecasting. Attempts to improve the method of analogies by including parameters that were previously ignored by this technique seem promising. This in turn can lead to an expansion of the scope of the method of analogies and increase the accuracy of forecasts. As prospects for further research, the need to continue research in the field of optimization of the method of analogies with the subsequent practical verification of theoretical positions on the data of real projects.

Forecasting Stock Returns: New Out-of-Sample Evidence

2000 ◽  
Author(s):  
Martin Lettau ◽  
Sydney C. Ludvigson
Keyword(s):  
Stock Returns ◽  
Out Of Sample

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

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