Industry Concentration and the Cross-section of Stock Returns: Evidence from the UK

In this paper, I examine the relationship between industry concentration and the cross-section of stock returns in the London Stock Exchange between 1985 and 2010. Using Multifactor asset pricing theory, I test whether industry concentration is a new asset pricing factor in addition to conventional risk factors such as beta, firm size, book-to-market ratio, momentum, and leverage. I find that industry concentration is negatively related to the expected stock returns in all Fama and MacBeth cross-sectional regressions. In addition, the negative relationship between industry concentration and expected stock returns remain significantly negative after beta, size, book-to-market, momentum, and leverage are included, while beta is never significant. The results are robust to firm- and industry-level regressions and the formation of firms into 100 size-beta portfolios. The findings indicate that competitive industries earn, on average, higher risk-adjusted returns compared to concentrated industries which is consistent with Schumpeter’s concept of creative destruction.Industry concentration, Stock returns, Multifactor asset pricing theory, Competitive industries, Concentrated industries, Creative destruction, London Stock Exchange

Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon

Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (vis-NIR) spectroscopy. This study compares the performance of local farm scale calibrations with those based on the spiking of selected local samples from both fields into an European dataset for TN and TC estimation using three modelling techniques, namely gradient boosted machines (GBM), artificial neural networks (ANNs) and random forests (RF). The on-line measurements were carried out using a mobile, fiber type, vis-NIR spectrophotometer (305-2200 nm) (AgroSpec from tec5, Germany), during which soil spectra were recorded in diffuse reflectance mode from two fields in the UK. After spectra pre-processing, the entire datasets were then divided into calibration (75%) and prediction (25%) sets, and calibration models for TN and TC were developed using GBM, ANN and RF with leave-one-out cross-validation. Results of cross-validation showed that the effect of spiking of local samples collected from a field into an European dataset when combined with RF has resulted in the highest coefficients of determination (R-2) values of 0.97 and 0.98, the lowest root mean square error (RMSE) of 0.01% and 0.10%, and the highest residual prediction deviations (RPD) of 5.58 and 7.54, for TN and TC, respectively. Results for laboratory and on-line predictions generally followed the same trend as for cross-validation in one field, where the spiked European dataset-based RF calibration models outperformed the corresponding GBM and ANN models. In the second field ANN has replaced RF in being the best performing. However, the local field calibrations provided lower R-2 and RPD in most cases. Therefore, from a cost-effective point of view, it is recommended to adopt the spiked European dataset-based RF/ANN calibration models for successful prediction of TN and TC under on-line measurement conditions

On the Information Engine of Circuit Design

This paper addresses a new approach to find a spectrum of information measures for the process of digital circuit synthesis. We consider the problem from the information engine point of view. The circuit synthesis as a whole and different steps of the design process (an example of decision diagram is given) are presented via such measurements as entropy, logical work and information vitality. We also introduce new information measures to provide better estimates of synthesis criteria. We show that the basic properties of information engine, such as the conservation law of information flow and the equilibrium law of information can be formulated.Comment: 4 pages, 1 figure, 2 tables, MWSCAS'0

Estimation of secondary soil properties by fusion of laboratory and on-line measured vis-NIR spectra

Visible and near infrared (vis-NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis-NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305-1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis-NIR spectroscopy

Augustine’s Master Argument for the Incorporeality of the Mind

In De Trinitate 10, Augustine offers an argument that seemingly proceeds from certain premises about self-knowledge to the conclusion that the mind is incorporeal. Although the argument has sometimes been compared to later Cartesian arguments, it has received relatively little philosophical attention. In this paper, I offer a detailed analysis and original interpretation of Augustine's argument and argue that it is not vulnerable to some of the main objections which have been raised against it. I go on to argue that while an important part of Augustine's argument does face several hitherto neglected objections, Augustine's ultimate case for the incorporeality of the mind is somewhat different and more successful than one might initially think

A Dream Come True -- An Academic Friend\u27s First ALA Annual Conference

Essraa Nawar describes her first time attending the American Library Association\u27s Annual Conference as the inaugural recipient of the United for Libraries/SAGE Academic Friend Conference Grant

Industry concentration and stock returns: evidence from publicly listed firms in the U.K

In this dissertation, I examine the relationship between market structure and average stock returns in the London Stock Exchange during 1985 and 2010. Using Multifactor asset pricing theory, I test whether industry concentration is a new asset pricing factor in addition to conventional risk factors such as beta, size, book-to-market equity, momentum, and leverage. I find that industry concentration is negatively related to average stock returns in all Fama-MacBeth cross-sectional regressions, even after controlling for beta, size, book-to-market equity, momentum, and leverage. In addition, there is strong evidence of a growth effect. Firms or industry portfolios with smaller book-to-market equity ratios have significantly higher returns. In contrast, beta is never statistically significant. The above results are robust to firm- and industry-level regressions, and the formation of firms into 100 size-beta portfolios. The time-series results show some evidence that industry concentration premium contains separate information compared with other risk premiums or risk factors and helps explain the time-series variation in stock returns, even after accounting for the premiums of beta, size, book-to-market, momentum, and leverage. The empirical findings indicate that competitive industries earn, on average, higher risk-adjusted returns than concentrated industries. An explanation is that investors in more competitive industries require larger return premiums for greater distress risks associated with these industries

Stubborn Optimism or Toxic Positivity

A global pandemic, 6,332,783 Covid19 cases, 376,041 deceased and counting, riots in the streets of every major American city, recession on the horizon, 40 million jobs lost, a devastated world economy and more. This is pretty much the world we are ALL living in across the globe and sometimes it is impossible to escape the news. If it is not on TV, you will catch it on any of your social media platforms, if not on any of these two, you will get it through a text from a friend or as you chat with another. Amid all of this, one may ask: How can I continue to be productive, positive or optimistic and also take care of myself, my family, friends and pets? I am writing this today not to preach optimism but to encourage us all to Stay in our lanes as my friend artist Allison Adams put it so well together

Solving the Inverse Problem for Localising the Biomagnetic Activity in the Heart

This thesis develops a comprehensive solution for forward and inverse problems in modeling the human heart, focusing on MCG and ECG datasets. The methodology includes data recording, MRI processing, and constructing a multi-regional model to segment tissues based on characteristics, solving the forward problem. The approach uses Kalman filter and state-space models, followed by the GARCH model, to solve the inverse problem, improving data analysis and source localization accuracy. This is the first attempt to apply Kalman filtering to MCG data, leveraging experience from brain research (EEG and MEG datasets). The approach has been validated with simulated and real MCG and ECG datasets, showing efficacy in heart activity analysis and potential clinical applications. The research’s significance lies in its implications for diagnosing and treating heart conditions. The methodology can precisely localize heart activity sources, aiding in diagnosis and intervention planning, such as ablation or pacemaker implantation. The non-invasive localization method using MCG and ECG datasets offers new avenues for heart condition diagnosis and treatment compared to invasive catheter methods. Simpler inverse problem methods can find source activity in MCG SQUID and ECG electrode datasets without high computational power. This thesis also aims to analyze data from sensors with lower signal-to-noise ratios, like the magnetoelectric sensor being developed in Kiel, which is cost-effective. This interdisciplinary research presents a novel methodology for analyzing MCG and ECG datasets, potentially revolutionizing heart condition diagnosis. It highlights the significant contributions interdisciplinary research with engineering can make to advancing medical science