scholarly journals Tests of two optimal incentive models for executive stock options

2011 ◽  
Vol 9 (1) ◽  
pp. 136-155
Author(s):  
Jean M. Canil ◽  
Bruce A. Rosser
Keyword(s):  
Executive Compensation ◽  
Stock Options ◽  
Abnormal Returns ◽  
Outside Directors ◽  
Data Set ◽  
Stock Volatility ◽  
Unique Data ◽  
Exercise Price ◽  
Grant Size

Using a unique data set, we test theoretical propositions relating to grant size and exercise price in determination of optimal executive compensation. For Hall and Murphy, pay-performance sensitivity does not behave as predicted with respect to CEO risk aversion and diversification, but the latter supports observed grant size while ATM grants exhibit positive abnormal returns as predicted. Consistent with Choe, exercise price is found inversely related to leverage. The unexpected positive relation between grant size and stock volatility is conjectured driven by CEOs’ influencing large grants, which are found associated with weak corporate governance but ameliorated by outside directors.

Information content of web-based stock ratings: the case of Motley fool CAPS data

2018 ◽  
Vol 15 (3) ◽  
pp. 393-410
Author(s):  
Arvind Mahajan
Keyword(s):  
Information Content ◽  
Factor Model ◽  
Abnormal Returns ◽  
Predictive Ability ◽  
Data Set ◽  
Web Based ◽  
Stock Performance ◽  
Content Type ◽  
Unique Data ◽  
Asset Pricing Models

Purpose The purpose of this paper is to answer a fundamental question – are individual stock picks by a particular internet investment community informative enough to beat the market? The author observes that the stock picks by the CAPS community are reflective of existing information and portfolios based upon CAPS community stock rankings do not generate abnormal returns. The CAPS community is good at tracking existing performance but, it lacks predictive ability. Design/methodology/approach The study uses a unique data set of stock ratings from Motley Fools CAPS community to determine the information content embedded in these ratings. Observing predictive ability of this web-based stock ratings forum will raise questions about the efficiency of the financial markets. The author forms stock portfolios based on stocks’ star ratings, and star rating changes, and test if the long-short portfolio strategy generates significant α after controlling for single, and multi-factor asset pricing models, such as Fama-French three-factor model and Carhart four-factor model. Findings The paper finds no evidence that the CAPS community ratings contain “information content,” which can be exploited to generate abnormal returns. CAPS community ratings are good at tracking existing stock performance, but cannot be used to make superior forecasts to generate abnormal returns. The findings are consistent with the efficient market hypothesis. Furthermore, the author provides evidence that CAPS community ratings are themselves determined by stock performance rather than the other way around. Originality/value The study employs a unique data set capturing the stock ratings of a very popular web-based investment community to evaluate its ability to make better than random forecasts. Besides applying well-accepted asset pricing models to generate α, the study conducts causality tests to discern a causal relation between stock ratings and stock performance.

scholarly journals The Pristine Inner Galaxy Survey (PIGS) II: Uncovering the most metal-poor populations in the inner Milky Way

2020 ◽  
Vol 496 (4) ◽  
pp. 4964-4978 ◽  
Author(s):  
Anke Arentsen ◽  
Else Starkenburg ◽  
Nicolas F Martin ◽  
David S Aguado ◽  
Daniel B Zucker ◽  
...  
Keyword(s):  
Milky Way ◽  
Dynamical Evolution ◽  
Spectral Library ◽  
Data Set ◽  
Unique Data ◽  
Horizontal Branch Stars ◽  
History Of ◽  
Low Metallicity

ABSTRACT Metal-poor stars are important tools for tracing the early history of the Milky Way, and for learning about the first generations of stars. Simulations suggest that the oldest metal-poor stars are to be found in the inner Galaxy. Typical bulge surveys, however, lack low metallicity ($\rm {[Fe/H]} \lt -1.0$) stars because the inner Galaxy is predominantly metal-rich. The aim of the Pristine Inner Galaxy Survey (PIGS) is to study the metal-poor and very metal-poor (VMP, $\rm {[Fe/H]} \lt -2.0$) stars in this region. In PIGS, metal-poor targets for spectroscopic follow-up are selected from metallicity-sensitive CaHK photometry from the CFHT. This work presents the ∼250 deg2 photometric survey as well as intermediate-resolution spectroscopic follow-up observations for ∼8000 stars using AAOmega on the AAT. The spectra are analysed using two independent tools: ULySS with an empirical spectral library, and FERRE with a library of synthetic spectra. The comparison between the two methods enables a robust determination of the stellar parameters and their uncertainties. We present a sample of 1300 VMP stars – the largest sample of VMP stars in the inner Galaxy to date. Additionally, our spectroscopic data set includes ∼1700 horizontal branch stars, which are useful metal-poor standard candles. We furthermore show that PIGS photometry selects VMP stars with unprecedented efficiency: 86 per cent/80 per cent (lower/higher extinction) of the best candidates satisfy $\rm {[Fe/H]} \lt -2.0$, as do 80 per cent/63 per cent of a larger, less strictly selected sample. We discuss future applications of this unique data set that will further our understanding of the chemical and dynamical evolution of the innermost regions of our Galaxy.

How Insiders’ Personal Incentives and Timeliness of Information Revelation are Related to Their Sales Timing

2016 ◽  
Vol 19 (02) ◽  
pp. 1650009 ◽  
Author(s):  
Yin-Hua Yeh ◽  
Pei-Gi Shu ◽  
Ya-Wei Yang
Keyword(s):  
Cash Flow ◽  
Abnormal Returns ◽  
Bank Loans ◽  
Information Revelation ◽  
Data Set ◽  
Listed Firms ◽  
Unique Data ◽  
Cumulative Abnormal Returns ◽  
Flow Deviation ◽  
Cash Flow Rights

In this paper, we investigate how insiders’ personal incentives and the timeliness of information revelation are related to the timing of their sales. We use a unique data set of 7,678 insider sales of listed firms in Taiwan, where insider sales exceeding 10,000 shares must be reported to the regulatory entity on an ex-ante basis so that the price pattern before announcement remains independent from insider sales and therefore closely captures insiders’ timing. We find evidence from insiders’ timing that the cumulative abnormal returns monotonically increase up to the announcement of insider sales, and decrease thereafter. Moreover, insiders’ timing and profitability are closely related to their personal incentives, as manifested in lower cash flow rights, a higher pledge ratio of holding shares for bank loans, higher control-cash flow deviation, and the condition that insiders simultaneously serve in managerial posts. Furthermore, the timeliness of information revelation affects insiders’ timing in the sense that optimistic news is revealed before the announcement whereas a decrease in earnings is revealed after the execution of insider sales. Finally, the interactions between the personal incentives of insiders and the timeliness of information revelation also have an effect on their timing and profitability.

scholarly journals Microfinance and Vulnerability to Seasonal Famine in a Rural Economy: Evidence from Monga in Bangladesh

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Claudia Berg ◽  
M. Shahe Emran
Keyword(s):  
Food Security ◽  
Rural Economy ◽  
Screening Process ◽  
Data Set ◽  
Estimation Strategy ◽  
Positive Effects ◽  
Unique Data ◽  
Primary Mechanism ◽  
Poor Households ◽  
Microfinance Institution

AbstractThis paper uses a unique data set on 143,000 poor households from Northern Bangladesh to analyze the effects of microfinance membership on a household’s ability to cope with seasonal famine known as Monga. We develop an identification and estimation strategy that exploits a jump and a kink at the 10-decimal land ownership-threshold driven by the Microfinance Institution screening process to ensure repayment by excluding the ultra-poor. Evidence shows that microfinance membership improves food security during Monga, especially for the poorest households who survive at the margin of one and two meals a day. The positive effects on food security are, however, not driven by higher income, as microcredit does not improve the ability to migrate for work, nor does it reduce dependence on distress sale of labor. The evidence is consistent with consumption smoothing being the primary mechanism behind the gains in food security of MFI households during the season of starvation.

scholarly journals Microfinance and Vulnerability to Seasonal Famine in a Rural Economy: Evidence from Monga in Bangladesh

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Claudia Berg ◽  
M. Shahe Emran
Keyword(s):  
Food Security ◽  
Rural Economy ◽  
Screening Process ◽  
Data Set ◽  
Estimation Strategy ◽  
Positive Effects ◽  
Unique Data ◽  
Primary Mechanism ◽  
Poor Households ◽  
Microfinance Institution

AbstractThis paper uses a unique data set on 143,000 poor households from Northern Bangladesh to analyze the effects of microfinance membership on a household's ability to cope with seasonal famine known as Monga. We develop an identification and estimation strategy that exploits a jump and a kink at the 10 decimal land ownership-threshold driven by the Microfinance Institution (MFI) screening process to ensure repayment by excluding the ultra-poor. Evidence shows that microfinance membership improves food security during Monga, especially for the poorest households who survive at the margin of one and two meals a day. The positive effects on food security are, however, not driven by higher income, as microcredit does not improve the ability to migrate for work, nor does it reduce dependence on distress sale of labor. The evidence is consistent with consumption smoothing being the primary mechanism behind the gains in food security of MFI households during the season of starvation.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

A NEW TECHNIQUE ESTIMATING LOCATION OF MEAN JOINT CENTERS OF ROTATION WITH ASSOCIATED DISPERSIONS AND ASSESSING TO FUNCTIONAL COORDINATE SYSTEM OF MOVING LIMB SEGMENTS

2006 ◽  
Vol 06 (04) ◽  
pp. 373-384
Author(s):  
ERIC BERTHONNAUD ◽  
JOANNÈS DIMNET
Keyword(s):  
Numerical Technique ◽  
New Technique ◽  
Helical Axis ◽  
Joint Movement ◽  
Time Step ◽  
Data Set ◽  
Minimization Procedure ◽  
A New Technique ◽  
Joint Center

Joint centers are obtained from data treatment of a set of markers placed on the skin of moving limb segments. Finite helical axis (FHA) parameters are calculated between time step increments. Artifacts associated with nonrigid body movements of markers entail ill-determination of FHA parameters. Mean centers of rotation may be calculated over the whole movement, when human articulations are likened to spherical joints. They are obtained using numerical technique, defining point with minimal amplitude, during joint movement. A new technique is presented. Hip, knee, and ankle mean centers of rotation are calculated. Their locations depend on the application of two constraints. The joint center must be located next to the estimated geometric joint center. The geometric joint center may migrate inside a cube of possible location. This cube of error is located with respect to the marker coordinate systems of the two limb segments adjacent to the joint. Its position depends on the joint and the patient height, and is obtained from a stereoradiographic study with specimen. The mean position of joint center and corresponding dispersion are obtained through a minimization procedure. The location of mean joint center is compared with the position of FHA calculated between different sequential steps: time sequential step, and rotation sequential step where a minimal rotation amplitude is imposed between two joint positions. Sticks are drawn connecting adjacent mean centers. The animation of stick diagrams allows clinical users to estimate the displacements of long bones (femur and tibia) from the whole data set.

Moment-tensor solutions for the 24 November 1987 Superstition Hills, California, earthquakes

1989 ◽  
Vol 79 (2) ◽  
pp. 493-499
Author(s):  
Stuart A. Sipkin
Keyword(s):  
Main Shock ◽  
Moment Tensor ◽  
Time Dependent ◽  
Origin Time ◽  
Data Set ◽  
Filter Theory ◽  
Long Period ◽  
Seismograph Network ◽  
Scalar Moment

Abstract The teleseismic long-period waveforms recorded by the Global Digital Seismograph Network from the two largest Superstition Hills earthquakes are inverted using an algorithm based on optimal filter theory. These solutions differ slightly from those published in the Preliminary Determination of Epicenters Monthly Listing because a somewhat different, improved data set was used in the inversions and a time-dependent moment-tensor algorithm was used to investigate the complexity of the main shock. The foreshock (origin time 01:54:14.5, mb 5.7, Ms 6.2) had a scalar moment of 2.3 × 1025 dyne-cm, a depth of 8 km, and a mechanism of strike 217°, dip 79°, rake 4°. The main shock (origin time 13:15:56.4, mb 6.0, Ms 6.6) was a complex event, consisting of at least two subevents, with a combined scalar moment of 1.0 × 1026 dyne-cm, a depth of 10 km, and a mechanism of strike 303°, dip 89°, rake −180°.

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