Miracle-dz/UniFAR-FTUs huggingface dataset:
Does CSR Engender Trust? Evidence From Investor Reactions to Corporate Disclosures

We investigate whether a firm's corporate social responsibility (CSR) activities engender investor trust. Motivated by the observation that investor trust facilitates greater informational price efficiency, we address our question by examining the relation between CSR and three dimensions of stock price discovery: (1) the speed with which stock prices reflect earnings news, (2) the level of investor uncertainty around earnings announcements, and (3) earnings response coefficients. We find robust evidence that CSR enhances investor trust in firms. Specifically, we find that firms with more CSR enjoy faster incorporation of earnings news into stock prices, lower investor uncertainty around earnings announcements, and higher earnings response coefficients. Using a regression discontinuity design, we identify the causal effect of CSR on the speed with which stock prices reflect earnings news. Our inferences are robust to controls for characteristics of reported earnings and firms' information environment, as well as alternative measures of CSR.
Delta Hedging of Derivatives using Deep Reinforcement Learning

Building on previous work of Kolm and Ritter (2019) and Cao et al. (2019), this paper explores the novel application of Deep Reinforcement Learning for Delta Hedging of options in an utility based framework where an agent is faced with a trade-off between hedging error and transaction costs while aiming at maximizing the expected profit and loss and minimizing its variance. In the presence of transaction costs we compare the performance of two state-of-the-art Reinforcement Learning algorithms with two simple benchmark strategies widely used in practice. We perform the analysis on synthetic data for different market characteristics, transaction costs, option maturities and hedging frequencies, and find that the agents deliver a strong performance in markets characterized by stochastic volatility and jumps in asset prices, as well as for high transaction costs, high hedging frequency and for options with long maturities. Furthermore, we apply trained algorithms to similar (but not seen before) options and present a way of improving the robustness of the algorithms to different levels of volatility. Finally, we transfer the hedging strategies learned on simulated data to empirical option data on the S&amp;P500 index, and demonstrate that transfer learning is successful: hedge costs encountered by reinforced learning decrease by as much as 30% compared to the Black- Scholes hedging strategy. Our results indicate that the hedging strategies based on Reinforcement Learning outperform the benchmark strategies and are suitable for traders taking real-life hedging decisions, even when the networks are trained on synthetic (but versatile) data.
Predictably Satisfied: Contributions of Artificial Intelligence to Intra-Organizational Communication

Artificially intelligent (AI) applications make data-driven predictions that enable personalization on a large scale. As such, recent advances in AI's predictive power might have the potential to create more productive work environments. Using a principal-agent model and understanding AI as signal production technology, I show that, within an organization, higher accuracy of an AI's predictions reduces information asymmetries and fosters truthful communication. Detailed information about employees allows for individually tailored management which ultimately raises production and profits. Using observational data, I test the main implications of the theoretical model concerning optimal behavior and heterogeneity in employee satisfaction. I exploit a unique individual-level panel dataset from Attuned, a Japanese startup that developed an AI tool measuring employees' intrinsic motivation. Empirical results show that when communicating via the AI tool, employee satisfaction increases over time. The effect is particularly pronounced in the long run, for initially dissatisfied individuals, in small teams, and for those whose motivational profile resembles that of their teammates. This heterogeneity suggests personalized work experiences due to managers' better targeting.
Effect of Entrepreneurial Marketing Dimensions on Small and Medium Enterprises Performance in Nasarawa State

Entrepreneurial Marketing (EM) Dimensions play a critical role in small and medium enterprises (SMEs) performance. The extant study explored the effect of EM dimensions on the performance of SMEs operating within Nasarawa State, Nigeria. EM was conceptualized as innovativeness, risk-taking, and value creation. The research population was 1979 registered SMEs in Nasarawa state, Nigeria. The sample size was 322 which was determined using Raosoft sample size calculator. Out of 322 sets of questionnaire distributed, 136 were validly filled and returned. The data collected were analyzed using Regression Analysis technique to test the study’s hypotheses using Statistical Product and Service Solutions software. Results specify that all three of the EM dimensions under study have significant effect on SME performance. In terms of contribution to the model, the explanatory variables were able to contribute 62.1 percent to the variable of interest (SME performance). On individual basis, innovativeness explained the most to the criterion variable. The findings of this study offer important insights for owners and managers of SMEs, researchers, and policymakers to further understand the effects of EM dimensions on SMEs’ performance. SMEs should be encouraged to embrace the entrepreneurial dimensions of innovativeness, risk taking, and value creation to increase business performance.
Excess Cash and Equity Option Liquidity

We examine the relation between excess corporate cash holdings and equity option market liquidity from January 3, 2005 to December 31, 2019. We show that the level of cash reserve in excess of what can be captured by firm characteristics significantly explains stock option liquidity. Trading volume and option open interest increase in companies with a higher magnitude of excess cash, whereas the bid–ask spreads of stock options decline in excess cash. Our findings confirm the theoretical prediction that excess cash improves option market liquidity as it reduces adverse selection problems caused by uncertainty in firm valuations. This relation remains more pronounced with put options, out‐of‐the‐money contracts, and short‐maturity contracts. In addition, excess cash has a stronger impact on option liquidity within firms that have a greater degree of informed trading and during high‐volatility periods in financial markets. Our results show that when uncertainty about firm prospects rises, excess cash becomes more valuable and affects option market liquidity.This article is protected by copyright. All rights reserved.
Greenwashing: Evidence from Hedge Funds

Hedge funds whose management companies endorse the United Nations Principles for Responsible Investment (PRI) underperform other hedge funds but attract larger flows, accumulate more capital, and harvest greater fee revenues. Consonant with an agency explanation, the underperformance is driven by PRI signatories with low ESG exposures. By exploiting environmental, temperature, and policy shocks, we provide causal evidence that relate incentive misalignment to signatory underperformance. Besides not walking the talk, low-ESG signatories also commit more regulatory, investment, and severe infractions, and report more suspicious returns. The results suggest that some fund managers opportunistically embrace responsible investment to pander to investor preferences.
A Novel Greedy-Step Bellman Optimality Equation for Efﬁcient Value Propagation

Efﬁciently propagating credit to responsible actions is a central and challenging task in reinforcement learning. To accelerate information propagation, this paper presents a new method that bridges a highway that allows unimpeded information to ﬂow across long horizons. The key to our method is a newly proposed Bellman equation, called Greedy-Step Bellman Optimality Equation, through which the high-credit information can fast propagate across a long horizon. We theoretically show that the solution of the new equation is exactly the optimal value function and the corresponding operator converges faster than the classical operator. Besides, it leads to a new multi-step off-policy algorithm, which is capable of safely utilizing any off-policy data collected by the arbitrary policy. Experiments reveal that the proposed method is reliable, easy to implement. Moreover, without employing additional components of Rainbow except Double DQN, our method achieves competitive performance with Rainbow on the benchmark tasks.
Mechanical metamaterials: does toughness characterize fracture?

Rapid progress in additive manufacturing methods has created a new class of ultralight and strong architected metamaterials that resemble periodic truss structures. The mechanical performance of these metamaterials with a very large number of unit cells is ultimately limited by their tolerance to damage and defects, but an understanding of this sensitivity has remained elusive. Using a stretching-dominated micro-architecture and metamaterial specimens comprising millions of unit-cells we show that not only is the stress intensity factor, as used in conventional elastic fracture mechanics, insufficient to characterize fracture but also that conventional fracture testing protocols are inadequate. Via a combination of numerical calculations and asymptotic analyses, we extend the ideas of fracture mechanics and develop a general test and design protocol for the failure of metamaterials.
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention

Emotion recognition from speech is a challenging task. Recent advances in deep learning have led bi-directional recurrent neural network (Bi-RNN) and attention mechanism as a standard method for speech emotion recognition, extracting and attending multi-modal features - audio and text, and then fusing them for downstream emotion classification tasks. In this paper, we propose a simple yet efficient neural network architecture to exploit both acoustic and lexical information from speech. The proposed framework using multi-scale convolutional layers (MSCNN) to obtain both audio and text hidden representations. Then, a statistical pooling unit (SPU) is used to further extract the features in each modality. Besides, an attention module can be built on top of the MSCNN-SPU (audio) and MSCNN (text) to further improve the performance. Extensive experiments show that the proposed model outperforms previous state-of-the-art methods on IEMOCAP dataset with four emotion categories (i.e., angry, happy, sad and neutral) in both weighted accuracy (WA) and unweighted accuracy (UA), with an improvement of 5.0% and 5.2% respectively under the ASR setting.
Double Descent and Other Interpolation Phenomena in GANs

We study overparameterization in generative adversarial networks (GANs) that can interpolate the training data. We show that overparameterization can improve generalization performance and accelerate the training process. We study the generalization error as a function of latent space dimension and identify two main behaviors, depending on the learning setting. First, we show that overparameterized generative models that learn distributions by minimizing a metric or $f$-divergence do not exhibit double descent in generalization errors; specifically, all the interpolating solutions achieve the same generalization error. Second, we develop a new pseudo-supervised learning approach for GANs where the training utilizes pairs of fabricated (noise) inputs in conjunction with real output samples. Our pseudo-supervised setting exhibits double descent (and in some cases, triple descent) of generalization errors. We combine pseudo-supervision with overparameterization (i.e., overly large latent space dimension) to accelerate training while performing better, or close to, the generalization performance without pseudo-supervision. While our analysis focuses mostly on linear GANs, we also apply important insights for improving generalization of nonlinear, multilayer GANs.
Self-Supervised Structure-from-Motion through Tightly-Coupled Depth and Egomotion Networks

Much recent literature has formulated structure-from-motion (SfM) as a self-supervised learning problem where the goal is to jointly learn neural network models of depth and egomotion through view synthesis. Herein, we address the open problem of how to optimally couple the depth and egomotion network components. Toward this end, we introduce several notions of coupling, categorize existing approaches, and present a novel tightly-coupled approach that leverages the interdependence of depth and egomotion at training and at inference time. Our approach uses iterative view synthesis to recursively update the egomotion network input, permitting contextual information to be passed between the components without explicit weight sharing. Through substantial experiments, we demonstrate that our approach promotes consistency between the depth and egomotion predictions at test time, improves generalization on new data, and leads to state-of-the-art accuracy on indoor and outdoor depth and egomotion evaluation benchmarks.
A Family of Bounded and Analytic Hyper-Operators

This is a summation of research done in the author’s second and third year of undergraduate mathematics at The University of Toronto. As the previous details were largely scattered and disorganized; the author decided to rewrite the cumulative research. The goal of this paper is to construct a family of analytic functions α ↑ z : (1, e) × CR(z)>0 → CR(z)>0 using methods from fractional calculus. This family satisfies the hyper-operator chain, α ↑ α ↑ z = α ↑ (z + 1); with the initial condition α ↑ z = α · z.
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model

Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce, and the annotation ability of existing distant or weak supervision approaches is very limited. To remedy this problem, in this paper, we propose to obtain training data for ultra-fine entity typing by using a BERT Masked Language Model (MLM). Given a mention in a sentence, our approach constructs an input for the BERT MLM so that it predicts context dependent hypernyms of the mention, which can be used as type labels. Experimental results demonstrate that, with the help of these automatically generated labels, the performance of an ultra-fine entity typing model can be improved substantially. We also show that our approach can be applied to improve traditional fine-grained entity typing after performing simple type mapping.
Occode: an end-to-end machine learning pipeline for transcription of historical population censuses

Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end machine learning pipeline that scales to the dataset size, and a model that achieves high accuracy with few manual transcriptions. In addition, the correctness of the model results must be verified. This paper describes our lessons learned developing, tuning, and using the Occode end-to-end machine learning pipeline for transcribing 7,3 million rows with handwritten occupation codes in the Norwegian 1950 population census. We achieve an accuracy of 97% for the automatically transcribed codes, and we send 3% of the codes for manual verification. We verify that the occupation code distribution found in our result matches the distribution found in our training data which should be representative for the census as a whole. We believe our approach and lessons learned are useful for other transcription projects that plan to use machine learning in production. The source code is available at: https://github.com/uit-hdl/rhd-codes
Augmenting Teleportation in Virtual Reality With Discrete Rotation Angles

Locomotion is one of the most essential interaction tasks in virtual reality (VR) with teleportation being widely accepted as the state-of-the-art locomotion technique at the time of this writing. A major draw-back of teleportation is the accompanying physical rotation that is necessary to adjust the users' orientation either before or after teleportation. This is a limiting factor for tethered head-mounted displays (HMDs) and static body postures and can induce additional simulator sickness for HMDs with three degrees-of-freedom (DOF) due to missing parallax cues. To avoid physical rotation, previous work proposed discrete rotation at fixed intervals (InPlace) as a controller-based technique with low simulator sickness, yet the impact of varying intervals on spatial disorientation, user presence and performance remains to be explored. An unevaluated technique found in commercial VR games is reorientation during the teleportation process (TeleTurn), which prevents physical rotation but potentially increases interaction time due to its continuous orientation selection. In an exploratory user study, where participants were free to apply both techniques, we evaluated the impact of rotation parameters of either technique on user performance and preference. Our results indicate that discrete InPlace rotation introduced no significant spatial disorientation, while user presence scores were increased. Discrete TeleTurn and teleportation without rotation was ranked higher and achieved a higher presence score than continuous TeleTurn, which is the current state-of-the-art found in VR games. Based on observations, that participants avoided TeleTurn rotation when discrete InPlace rotation was available, we distilled guidelines for designing teleportation without physical rotation.
SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning

Rescaling the backpropagated gradient of contrastive loss has made significant progress in descriptor learning. However, current gradient modulation strategies have no regard for the varying distribution of global gradients, so they would suffer from changes in training phases or datasets. In this paper, we propose a dynamic gradient modulation, named SDGMNet, for contrastive local descriptor learning. The core of our method is formulating modulation functions with dynamically estimated statistical characteristics. Firstly, we introduce angle for distance measure after deep analysis on backpropagation of pair-wise loss. On this basis, auto-focus modulation is employed to moderate the impact of statistically uncommon individual pairs in stochastic gradient descent optimization; probabilistic margin cuts off the gradients of proportional triplets that have achieved enough optimization; power adjustment balances the total weights of negative pairs and positive pairs. Extensive experiments demonstrate that our novel descriptor surpasses previous state-of-the-art methods in several tasks including patch verification, retrieval, pose estimation, and 3D reconstruction.
Infinite-color randomly reinforced urns with dominant colors

We define and prove limit results for a class of dominant Polya sequences, which are randomly reinforced urn processes with color-specific random weights and unbounded number of possible colors. Under fairly mild assumptions on the expected reinforcement, we show that the predictive and the empirical distributions converge almost surely (a.s.) in total variation to the same random probability measure $\tilde{P}$; moreover, $\tilde{P}(\mathcal{D})=1$ a.s. , where $\mathcal{D}$ denotes the set of dominant colors for which the expected reinforcement is maximum. In the general case, the predictive probabilities and the empirical frequencies of any $\delta$-neighborhood of $\mathcal{D}$ converge a.s. to one. That is, although non-dominant colors continue to be regularly observed, their distance to $\mathcal{D}$ converges in probability to zero. We refine the above results with rates of convergence. We further hint potential applications of dominant Polya sequences in randomized clinical trials and species sampling, and use our central limit results for Bayesian inference.
Relative rank and regularization

We introduce a new concept of rank – relative rank associated to a filtered collection of polynomials. When the filtration is trivial, our relative rank coincides with Schmidt rank (also called strength). We also introduce the notion of relative bias. The main result of the paper is a relation between these two quantities over finite fields (as a special case, we obtain a new proof of the results in [21]). This relation allows us to get an accurate estimate for the number of points on an affine variety given by a collection of polynomials which is of high relative rank (Lemma 3.2). The key advantage of relative rank is that it allows one to perform an efficient regularization procedure which is polynomial in the initial number of polynomials (the regularization process with Schmidt rank is far worse than tower exponential). The main result allows us to replace Schmidt rank with relative rank in many key applications in combinatorics, algebraic geometry, and algebra. For example, we prove that any collection of polynomials $\mathcal P=(P_i)_{i=1}^c$ of degrees $\le d$ in a polynomial ring over an algebraically closed field of characteristic $>d$ is contained in an ideal $\mathcal I({\mathcal Q})$ , generated by a collection ${\mathcal Q}$ of polynomials of degrees $\le d$ which form a regular sequence, and ${\mathcal Q}$ is of size $\le A c^{A}$ , where $A=A(d)$ is independent of the number of variables.
Learning Riemannian Manifolds for Geodesic Motion Skills

For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly. This demands learning models that capture relevant motion patterns, while offering enough flexibility to adapt the encoded skills to new requirements, such as dynamic obstacle avoidance. We introduce a Riemannian manifold perspective on this problem, and propose to learn a Riemannian manifold from human demonstrations on which geodesics are natural motion skills. We realize this with a variational autoencoder (VAE) over the space of position and orientations of the robot end-effector. Geodesic motion skills let a robot plan movements from and to arbitrary points on the data manifold. They also provide a straightforward method to avoid obstacles by redefining the ambient metric in an online fashion. Moreover, geodesics naturally exploit the manifold resulting from multiple--mode tasks to design motions that were not explicitly demonstrated previously. We test our learning framework using a 7-DoF robotic manipulator, where the robot satisfactorily learns and reproduces realistic skills featuring elaborated motion patterns, avoids previously unseen obstacles, and generates novel movements in multiple-mode settings.