Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Implementation of the Stewardship Code Affect Investors' Judgment and Decision-Making on Firm's Negative Information?

Since 2010, the Stewardship Code (SC) or similar regulations have been introduced to enhance the quality of institutional investors' engagement and to improve long-term firm value. Although some prior anecdotal or legal studies have expressed skepticism toward the effectiveness of the SC, there has been little empirical evidence regarding the effects of these policies. Accordingly, the purpose of this paper is to investigate how the implementation of the SC affects nonprofessional investors’ judgment and decision-making on an investee firm facing negative issues. Based on the results of a quasi-experiment with Korean nonprofessional investors, we show that the implementation of the SC may negatively impact nonprofessional investors' assessments of the firm, contrary to the intended purpose of SC adoption. This study provides timely implications for the future operation and development of the stewardship policies recently adopted in many countries.
Volatility and the Cross-Section of Equity Returns: Market Frictions vs. Arbitrage Risk

IHS Markit data reveals that stocks with high idiosyncratic volatility are far more likely to be hard-to-borrow than low idiosyncratic volatility stocks. When hard-to-borrow stocks are excluded, the relation between idiosyncratic volatility (IVOL) and stock returns disappears. The relation between idiosyncratic volatility and returns is more accurately described as a relation between being hard-to-borrow and stock returns. Arbitrage risk, on the other hand, is far too small to explain mispricing of high IVOL stocks. When the idiosyncratic volatility of portfolios rather than individual stocks is considered, Sharpe ratios of mispriced IVOL portfolios are much greater than the value-weighted market’s Sharpe ratio.
Unique Factors

It is well known that 70% of individual stocks' returns are classified as idiosyncratic returns under a conventional asset pricing model. In this study, we raise an important question as to whether majority return variations are truly influenced by idiosyncratic risks that at most affect several stocks from an empirical perspective. In other words, we explore a possible middle ground between common risk factors that influence almost all stocks and idiosyncratic risks by proposing unique factors that may only affect certain groups of stocks. In particular, we present a simple iterative approach both to extract unique factors from individual stock returns and to group stocks simultaneously. Comparing with industry groupings, this approach not only allows for a parsimonious group structure but also ensures that unique factors are indeed unique to individual groups. With the deterioration in the explanatory power of common factors such as the Fama and French factors in recent years, we find that unique factors not only are pervasive within their groups but also have played an increasingly important role in explaining individual stock returns over the past forty years. As an example, a multifactor model with two common factors and two unique factors not only provides a 10% extra explanatory power over other popular models but also outperforms the Fama and French model in out-of-sample tests. Therefore, the simple structure of unique factor model may hold the key to improve the performance of an asset pricing model in general.
Bidding and Information: Evidence from Gilt-Edged Auctions

This paper looks in detail at pricing in Gilt Auctions. It compares the prices received at all auctions between May 1987 and February 1995 with both the When-Issued price (the pre-auction market for delivery of the auction stock) and the price of a comparable parent stock (when one existed). The paper finds a marked difference in performance between non-fungible stocks (when a parent exists but the tranche cannot be traded identically with the parent until a few weeks after the auction due to part payments and different first coupons) and fully-fungible stocks (when the parent and tranche trade identically from auction day). The average auction price received for non-fungible stock was significantly below both the When-issued price (about 13p per #100) and the adjusted parent price (about 24p per #100). In the case of fully fungible auctions there was no discernable price difference between auction stock, When-issued, or adjusted parent stock price.Auction prices below comparable secondary market ones is a common result for Government debt auctions (similar results have been found for the US, Germany and Mexico). The explanation usually given is that uncertainty about the correct price leads bidders to shade down their bids. The paper examines this explanation and finds that although it can help explain the results for non-fungible auctions (measures of uncertainty seem to be correlated with the degree of price difference) it does not explain the markedly different results observed for fully-fungible auctions since the dispersion of bids for these auctions is no less than for non-fungible ones suggesting that some uncertainty still exists. Note: This is a slightly revised version of a paper that was published in July 1995 as part of the Debt Management Review.
AndroAnalyzer: android malicious software detection based on deep learning

Background Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods. Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector. Results Accuracy of 98.16% was achieved by presenting a better performance compared to traditional machine learning techniques. Values of recall, precision, and F-measure were 98.78, 99.24 and 98.90, respectively. The results showed that deep learning models using trace-based feature vectors outperform current cutting-edge technology approaches.
LinkPred: a high performance library for link prediction in complex networks

The problem of determining the likelihood of the existence of a link between two nodes in a network is called link prediction. This is made possible thanks to the existence of a topological structure in most real-life networks. In other words, the topologies of networked systems such as the World Wide Web, the Internet, metabolic networks, and human society are far from random, which implies that partial observations of these networks can be used to infer information about undiscovered interactions. Significant research efforts have been invested into the development of link prediction algorithms, and some researchers have made the implementation of their methods available to the research community. These implementations, however, are often written in different languages and use different modalities of interaction with the user, which hinders their effective use. This paper introduces LinkPred, a high-performance parallel and distributed link prediction library that includes the implementation of the major link prediction algorithms available in the literature. The library can handle networks with up to millions of nodes and edges and offers a unified interface that facilitates the use and comparison of link prediction algorithms by researchers as well as practitioners.
Layer-by-Layer Oxidation for Decreasing the Size of Detonation Nanodiamond

Diamond nanoparticles attract much attention as they combine outstanding mechanical properties with biocompatibility and are available in large quantities. Control and tunability of the particle size is very important for any nanomaterial. Although oxidation can burn carbon and lead to a particle size decrease, this technique could not be successfully employed for nanodiamond size reduction on the nanoscale. In this work, two commercial nanodiamond powders are used to demonstrate separation of the oxidation reaction into two steps (i) the oxygen chemisorption and (ii) the CO and/or CO2 desorption. This allows for an effective control of the oxidation process. In situ thermogravimetric analysis suggests that the oxidation is thermodynamically rather than kinetically controlled, and that the carbon burn off can be adjusted by repeating chemisorption/desorption steps to remove carbon layer after layer. Small-angle X-ray scattering (SAXS) characterization of the diamond nanoparticles showed a continuous size .
EyeDiseases: an integrated resource for dedicating to genetic variants, gene expression and epigenetic factors of human eye diseases

Abstract Eye diseases are remarkably common and encompass a large and diverse range of morbidities that affect different components of the visual system and visual function. With advances in omics technology of eye disorders, genome-scale datasets have been rapidly accumulated in genetics and epigenetics field. However, the efficient collection and comprehensive analysis of different kinds of omics data are lacking. Herein, we developed EyeDiseases (https://eyediseases.bio-data.cn/), the first database for multi-omics data integration and interpretation of human eyes diseases. It contains 1344 disease-associated genes with genetic variation, 1774 transcription files of bulk cell expression and single-cell RNA-seq, 105 epigenomics data across 185 kinds of human eye diseases. Using EyeDiseases, we investigated SARS-CoV-2 potential tropism in eye infection and found that the SARS-CoV-2 entry factors, ACE2 and TMPRSS2 are highly correlated with cornea and keratoconus, suggest that ocular surface cells are susceptible to infection by SARS-CoV-2. Additionally, integrating analysis of Age-related macular degeneration (AMD) GWAS loci and co-expression data revealed 9 associated genes involved in HIF-1 signaling pathway and voltage-gate potassium channel complex. The EyeDiseases provides a valuable resource for accelerating the discovery and validation of candidate loci and genes contributed to the molecular diagnosis and therapeutic vulnerabilities with various eyes diseases.
Monetary Incentives and Knowledge Spillover: Evidence from a Natural Experiment

We examine how the introduction of monetary incentives by a knowledge-sharing platform affects the nonrewarded knowledge activity on the platform. Our setting is a question-and-answer platform that provides monetary incentives for holding live talks. Using a combination of coarsened exact matching and difference-in-differences estimation techniques, we find that the launch of the paid feature creates a positive spillover effect on the hosts’ free contributions, specifically, 9.4%–40.8% more answers in the short run when compared with nonhosts. The paid feature did not result in any significant change in the quality of answers. We suggest reputation building is one plausible mechanism underlying the spillover. Additional analyses reveal the spillover effect is negative for short-lived hosts in the long run, indicating possible crowding out of free contributions. The positive spillover effect for long-lived hosts lasts longer but is also reduced over time. Our findings suggest that introducing monetary incentives can be a viable business model for knowledge platforms to stimulate user contributions in both the paid and related unpaid activities. Yet platform owners should be cautious about the potential negative spillover after users stop contributing to the paid activity and develop effective strategies to maintain users’ long-term interests in the paid program. This paper was accepted by Kartik Hosanagar, information systems.
The Impact of Fake Reviews on Reputation Systems and Efficiency

Online interactions are frequently governed by reputation systems that allow users to evaluate each other after an interaction. Effective reputation systems can increase trust and may improve efficiency in market settings. In recent years, however, fake reviews have become increasingly prevalent. Since it is difficult to clearly identify fake reviews in field studies, we design a laboratory experiment. Using a repeated public good game with a reputation system, we study (i) how feedback manipulation influences the reliability of average ratings and (ii) whether the existence of manipulated ratings reduces efficiency. We find that feedback manipulation generally decreases the reliability of average ratings in comparison to a control treatment where cheating is not possible. When manipulation is possible and free, average ratings become less reliable, expectations are lower and both cooperation and efficiency are significantly reduced. When there are costs of manipulation, however, average ratings are more reliable and contributions and efficiency are not impaired. Interestingly, this is the case even when costs of manipulation are comparatively low.
Design and Synthesis of New Tripod-Chromogenic Sensor Based on S-Triazine and Thiazolidine-2,4-Dione Ring (TCST) for Naked-Eye Detection of Li-Ions

A novel tripod-chromogenic sensor contains central nucleus s-triazine and thiazolidine-2,4-dione ring (TCST) was designed, synthesized and applied as colorimetric probes in aqueous solutions of dimethyl sulfoxide (DMSO). The probe showed a highly sensitive and selective colorimetric sensor for naked-eye detection of Li+ ion, with a different color change from colorless to yellow. The probe’s detection limit (LOD) toward Li+ was found to be 1.2 μM. The result of the Job’s plot showed 1:1 stoichiometry for the interaction between tripod-chemosensor and Li-ion that this result was confirmed by 1H NMR titration experiments. The probe can also be used for biological activities depending on the results of microbial tests.
On the IASB Comprehensive Income Project: An Analysis of the Case for Dual Income Display

The IASB is presently involved in a project on reporting comprehensive income. Since the IASB accounting model mixes two income determination systems, namely, historical cost accounting and fair value accounting, an interesting question pertains to whether the display of comprehensive income should reflect the existence of these two paradigms. This article scrutinizes, from both the points of view of accounting theory and a valuation perspective, the typical arguments made by proponents of historical cost net income and comprehensive fair value income. It finds that claims for exclusive reliance on a single concept of income are untenable. The analysis provides arguments in defence of an income display that explicitly features both income concepts. Such a dual income display would correspond to a categorization of comprehensive income that is currently investigated in the IASB performance reporting project. However, given the importance of summarization in financial analysis, as is most extremely reflected in the focus on the earnings per share (EPS) number, the case for a dual income display should also be considered at the highest level of summarization of financial performance reporting. In other words, mandatory publication of two EPS numbers, one for net income, and one for comprehensive income, should be considered. Possible effects on perception and actual use of financial reports that should enter such a consideration are suggested as topics for experimental research.
Institutional Quality Causes Social Trust: Experimental Evidence on Trusting Under the Shadow of Doubt

Social trust underlies virtually any social and economic interaction and is a crucial ingredient for successful collective action. What causes social trust to develop, however, remains poorly understood. Institutional quality has been proposed as a candidate driver and has been shown to correlate with social trust. We provide experimental evidence for the causal direction of this relationship. We first exogenously expose the participants to institutions of different quality, defined as their ability to prevent corrupt behaviours on behalf of administrators. We then measure social trust among the participants using a trust game. We find that individuals exposed to settings with low institutional quality trust others significantly less. Moreover, using novel survey data we show that our experimental results correspond to correlational patterns usually found across countries. The paper makes a step forward in the decades-long search for the causality between institutional quality and social trust.
Common Component Structural VARs

Small scale VAR models are subject to two major issues: first, the information set might be too narrow; second, many macroeconomic variables are measured with error. The two features produce distorted estimates of the impulse response functions. We propose a new procedure, called Common Components Structural VARs (CC-SVAR), which solves both problems. It consists in (a) treating the variables, prior to estimation, in order to extract their common components; this eliminates measurement errors; (b) estimating a VAR with m &gt; q common components, that is a singular VAR, where q is the number of shocks driving the economy; this solves the fundamentalness problem. SVARs and CC-SVARs are compared in the empirical analysis of monetary policy and technology shocks. The results obtained by SVARs are not robust, in that they strongly depend on the choice and the treatment of the variables considered. On the contrary, using CCSVARs (i) contractionary monetary shocks produce a decrease of prices independently of the variables included in the model, (ii) irrespective of whether hours worked enter the model in log-levels or growth rates, technology improvements produce an increase in hours worked.
Integrating Extended Experiential Group Learning Projects into Undergraduate Marketing Classes: Observations, Processes and Lessons Learned

For a decade and a half, the author has integrated experiential learning into marketing and business classes with the goal of deepening student learning of concepts and theories, as well as, promote the use by students of theories and concepts into situational analyses and development of relevant resulting recommendations. The focus of this article is to discuss the purposes, processes, outcomes and challenges of integrating an extended group experiential learning project – one lasting approximately 12 weeks – into an upper division undergraduate semester-based marketing class. The results of these experiential projects were well received by both students and their business clients.
A novel no-sensors 3D model reconstruction from monocular video frames for a dynamic environment

Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance in practical applications due to the absence of accurate depth, camera pose, and object motion.This paper proposes a new framework that builds a full 3D model reconstruction that overcomes the occlusion problem in a complex dynamic scene without using sensors’ data. Popular devices such as a monocular camera are used to generate a suitable model for video streaming applications. The main objective is to create a smooth and accurate 3D point-cloud for a dynamic environment using cumulative information of a sequence of RGB video frames. The framework is composed of two main phases. The first uses an unsupervised learning technique to predict scene depth, camera pose, and objects’ motion from RGB monocular videos. The second generates a frame-wise point cloud fusion to reconstruct a 3D model based on a video frame sequence. Several evaluation metrics are measured: Localization error, RMSE, and fitness between ground truth (KITTI’s sparse LiDAR points) and predicted point-cloud. Moreover, we compared the framework with different widely used state-of-the-art evaluation methods such as MRE and Chamfer Distance. Experimental results showed that the proposed framework surpassed the other methods and proved to be a powerful candidate in 3D model reconstruction.
Perspectives of Lipid Extraction by Different Biomass Phases of Microalgae Aphanothece Halophytica Cultivated in Commercial Scale

Worldwide, people are accepting a sustainable, renewable and economical source for biofuel production without affecting the environmental concerns. Microalgae can overcome problems with reasonable solutions along with higher lipid content, carbon neutrality, biomass productivity and utilization of feed supplements. Picking a distinctive strain is vital and the wealth depends on its micro, macro nutrients and its photosynthesis efficiency. Extracting oil from the different phases of microalgal biomass is a tedious process. Presently, halophilic Aphanothece halophytica was mass cultivated in raceway ponds using organic and inorganic nutrients. Microalgal biomass was harvested using novel organic flocculant and oilil extraction was done through wet and dry basis based on solvent polarity. The process parameters were optimized for maximum lipid productivity using RSM and the empirical model was significantly analysed by analysis of variance. In wet basis, maximum lipid yield was 29.3%, where the reaction temperature, reaction time, solvent biomass ratio and mixing intensity are 68ºC, 190 min, 9:1 and 300 rpm respectively. In dry basis, lipid yield 27.5% was achieved using 12:1 solvent biomass ratio and mixing intensity was 300 rpm for 190 min at 68ºC. Then, the lipid was characterized by GCMS to identify the fatty acid composition to confirm its compatibility.
Artificial Neural Networks' Robustness; Foretelling Financial Turmoil

The research focuses on the financial turmoil, pursuing different methods to foretell such turmoil. Besides, the methods are undertaken from (McCulloch and Pitts 1943) and ended till (Hosaka 2019). The evidence from such a comprehensive analysis pointed to the use of various ratios using multiple models of Neural Networks, thus incorporating the economy and scale of the business into the research. Consequently, the goal during the research is to reach the gaps and move research to the next predicting stage.
Partitional Estimation Using Partial Moments

Function approximation is at the heart of machine learning. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning algorithms to approximate this function. We highlight the identical objective of multivariate nonparametric regressions, for both continuous and discrete outputs (dependent variables), using both numerical and categorical inputs (regressors). Underlying the multivariate nonparametric technique is the use of partitional based conditional estimation. <br><br>We examine 3 methods of partitioning and their resulting conditional estimates in evaluating unknown functional forms. We find the iterated means partitioning technique employed by the NNS R-package achieves superior mean-squared errors from the true forms in multivariate simulations.