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The origin of financial crises : central banks, credit bubbles and the efficient market fallacy
\"[This book] provides a compelling analysis of the forces behind the recent economic crisis. In a series of disarmingly simple arguments George Cooper challenges the core principles of today's economic orthodoxy, explaining why financial markets do not obey the efficient market principles but are instead inherently unstable and habitually crisis prone. First published in the summer of 2008 in the midst of the crisis, the author accurately pinpointed the fundamental problems in policy and economic theory that led to the banking crisis. Now updated to reflect the massive upheavals since then and providing even more forthright opinions, the book is essential reading for anyone looking to find the root cause of [financial crises].\"--Publisher's description.
Is CAPM Valid: Search for Efficient Market Portfolio
The selection of a proxy for market portfolio in CAPM model remains one of the most crucial steps in its empirical use. This paper aims to check the validity of CAPM in Indian market and to identify the most efficient proxy for market portfolio. Incorrect selection of this proxy may lead to inaccurate result and even rejection of CAPM model when tested empirically. This further emphasises the importance of selecting the correct proxy for market portfolio. The paper reviews why standard cap-weighted indices can be used as the proxy and then which ofsuch index is the most efficient proxy for the Indian market. A sample of 100 companies and has been taken to study the efficiency of four broad index namely BSE SENSEX, BSE 100, BSE 200, BSE 500 as proxy for market portfolio. The result shows that BSE 500 is a better index to be used as proxy for the market portfolio, instead of the most popular BSE Sensex, which is widely used for these kinds of studies.
Journal Article
Little evidence that farmers should consider abundance or diversity of arbuscular mycorrhizal fungi when managing crops
Arbuscular mycorrhizal fungi (AMF) are ubiquitous in agroecosystems and often stated to be critical for crop yield and agroecosystem sustainability. However, should farmers modify management to enhance the abundance and diversity of AMF? We address this question with a focus on field experiments that manipulated colonisation by indigenous AMF and report crop yield, or investigated community structure and diversity of AMF. We find that the literature presents an overly optimistic view of the importance of AMF in crop yield due, in part, to flawed methodology in field experiments. A small body of rigorous research only sometimes reports a positive impact of high colonisation on crop yield, even under phosphorus limitation. We suggest that studies vary due to the interaction of environment and genotype (crop and mycorrhizal fungal). We also find that the literature can be overly pessimistic about the impact of some common agricultural practices on mycorrhizal fungal communities and that interactions between AMF and soil microbes are complex and poorly understood. We provide a template for future field experiments and a list of research priorities, including phosphorus-efficient agroecosystems. However, we conclude that management of AMF by farmers will not be warranted until benefits are demonstrated at the field scale under prescribed agronomic management.
Journal Article
Climate finance and disclosure for institutional investors: why transparency is not enough
The finance sector’s response to pressures around climate change has emphasized disclosure, notably through the recommendations of the Financial Stability Board’s Task Force on Climate-related Financial Disclosures (TCFD). The implicit assumption—that if risks are fully revealed, finance will respond rationally and in ways aligned with the public interest—is rooted in the “efficient market hypothesis” (EMH) applied to the finance sector and its perception of climate policy. For low carbon investment, particular hopes have been placed on the role of institutional investors, given the apparent matching of their assets and liabilities with the long timescales of climate change. We both explain theoretical frameworks (grounded in the “three domains”, namely satisficing, optimizing, and transforming) and use empirical evidence (from a survey of institutional investors), to show that the EMH is unsupported by either theory or evidence: it follows that transparency alone will be an inadequate response. To some extent, transparency can address behavioural biases (first domain characteristics), and improving pricing and market efficiency (second domain); however, the strategic (third domain) limitations of EMH are more serious. We argue that whilst transparency can help, on its own it is a very long way from an adequate response to the challenges of ‘aligning institutional climate finance’.
Journal Article
Optimized convolutional neural network architectures for efficient on-device vision-based object detection
Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off.
Journal Article
A deep LSTM network for the Spanish electricity consumption forecasting
Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.
Journal Article
Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
In recent years the use of biomass for electricity generation in thermal and cogeneration plants has increased worldwide because it is an environmentally clean fuel whose impact measured in greenhouse gas emissions is practically zero. However, biomass bottom ash, a waste produced during combustion, has also increased considerably, which has both a negative economic and environmental impact, due to landfill transport and management of this by-product. Although biomass bottom ash has potential characteristics for application in the manufacture of construction materials, its full-scale application is difficult because of the wide range in physicochemical properties, depending on the type of biomass burned, such as wood residue, olive waste, waste paper sludge, cocoa shell, etc., and the type of combustion process in the plant. This study reviews the influence on the physicochemical properties, mechanical behavior, and durability of different cement-based materials, such as mortars, concrete, and cement-treated granular material, manufactured from biomass bottom ash. The previous studies demonstrate the feasibility of substituting natural materials for biomass bottom ash in cement-based materials, presenting adequate mechanical behavior and durability properties to comply with the required technical specifications in different building materials.
Journal Article
Suicidal ideation and mental disorder detection with attentive relation networks
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts.
Journal Article
HetConv: Beyond Homogeneous Convolution Kernels for Deep CNNs
While usage of convolutional neural networks (CNN) is widely prevalent, methods proposed so far always have considered homogeneous kernels for this task. In this paper, we propose a new type of convolution operation using heterogeneous kernels. The proposed Heterogeneous Kernel-Based Convolution (HetConv) reduces the computation (FLOPs) and the number of parameters as compared to standard convolution operation while it maintains representational efficiency. To show the effectiveness of our proposed convolution, we present extensive experimental results on the standard CNN architectures such as VGG, ResNet, Faster-RCNN, MobileNet, and SSD. We observe that after replacing the standard convolutional filters in these architectures with our proposed HetConv filters, we achieve 1.5 × to 8 × FLOPs based improvement in speed while it maintains (sometimes improves) the accuracy. We also compare our proposed convolution with group/depth wise convolution and show that it achieves more FLOPs reduction with significantly higher accuracy. Moreover, we demonstrate the efficacy of HetConv based CNN by showing that it also generalizes on object detection and is not constrained to image classification tasks. We also empirically show that the proposed HetConv convolution is more robust towards the over-fitting problem as compared to standard convolution.
Journal Article