Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machine-learning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts’ scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse next-quarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machine-powered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data

Does Reputation Management on Social Media Boost Career? Evidence from the Market for Executives

Our paper studies the impact of reputation management (RM) on executives\u27 career using their usage of Twitter. This self-promoting behavior has an influence on the bargaining powers in negotiating compensation and sorting during the hiring process, which increases their job acquisition chances. Our structural model which is based on a two sided matching model, is able to separately identify the two influences. We modeled the matching and the pay as endogenously determined. We find both effects to be significant. While RM only increases the compensations of successful candidates, in the recruiting process of CEO and CMO markets, both outstanding and outperformed applicants benefit from it. Our analysis sheds light upon the pricing scheme of social media for the use of self-promotion

Research and Practice on the Training Mode of Master of Finance Under the Background of Fin Tech

In the trend of Fin Tech, the training mode of Master of Finance(MF) is in urgent need of reform and innovation. In this paper, the author proposes to build a three in one postgraduate training model of "science and technology, application and internationalization" to strengthen the training quality of MF, through the establishment of a curriculum system that integrates Fin Tech and finance; organic embedding of cross courses such as artificial intelligence; building a multi faculty, implementing the dual tutor system; building an international training platform, cultivating the international vision of teachers and students; exploring the training mechanism of professional qualification embedding, encouraging students to obtain high-end professional qualification certificates such as CFA;developing practice bases for in-depth cooperation

A causal fused lasso for interpretable heterogeneous treatment effects estimation

We propose a novel method for estimating heterogeneous treatment effects based on the fused lasso. By first ordering samples based on the propensity or prognostic score, we match units from the treatment and control groups. We then run the fused lasso to obtain piecewise constant treatment effects with respect to the ordering defined by the score. Similar to the existing methods based on discretizing the score, our methods yields interpretable subgroup effects. However, the existing methods fixed the subgroup a priori, but our causal fused lasso forms data-adaptive subgroups. We show that the estimator consistently estimates the treatment effects conditional on the score under very general conditions on the covariates and treatment. We demonstrate the performance of our procedure using extensive experiments that show that it can outperform state-of-the-art methods

2D score based estimation of heterogeneous treatment effects

Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatment effects, especially in the case when there are a large number of features in estimation. To make efforts to address the issue, we propose a score-based framework for estimating the Conditional Average Treatment Effect (CATE) function in this paper. The framework integrates two components: (i) leverage the joint use of propensity and prognostic scores in a matching algorithm to obtain a proxy of the heterogeneous treatment effects for each observation, (ii) utilize non-parametric regression trees to construct an estimator for the CATE function conditioning on the two scores. The method naturally stratifies treatment effects into subgroups over a 2d grid whose axis are the propensity and prognostic scores. We conduct benchmark experiments on multiple simulated data and demonstrate clear advantages of the proposed estimator over state of the art methods. We also evaluate empirical performance in real-life settings, using two observational data from a clinical trial and a complex social survey, and interpret policy implications following the numerical results

Discussion On Device Structures And Hermetic Encapsulation For SiOx Random Access Memory Operation In Air

An edge-free structure and hermetic encapsulation technique are presented that enable SiOx-based resistive random-access memory (RRAM) operation in air. A controlled etch study indicates that the switching filament is close to the SiOx surface in devices with an exposed SiOx edge. Electrical test of encapsulated, edge-free devices in 1 atmosphere air indicates stable switching characteristics, unlike devices with an edge. This work demonstrates that SiOx RRAM is able to operate in air with proper encapsulation and an edge-free structure. The resistive switching failure mechanism when operating in air is explained by the oxidation of hydrogen-complexed defects in the switching filament. (C) 2014 AIP Publishing LLC.National Science Foundation IIP-1127537Microelectronics Research Cente

μ-Oxido-bis­({4,4′-dibromo-2,2′-ethane-1,2-diylbis(nitrilo­methyl­idyne)]diphenolato}iron(III))

In the title compound, [Fe2(C16H12Br2N2O2)2O], the complete mol­ecule is generated by twofold symmetry, with the bridging O atom, which links the iron centres, lying on the roatation rotation axis. The Fe(III) ion is chelated by the N,N,O,O-tetra­dentate Schiff base dianion, resulting in an FeN2O3 square-based pyramid, with the two N atoms in the basal plane

"Why Should I Review This Paper?" Unifying Semantic, Topic, and Citation Factors for Paper-Reviewer Matching

As many academic conferences are overwhelmed by a rapidly increasing number of paper submissions, automatically finding appropriate reviewers for each submission becomes a more urgent need than ever. Various factors have been considered by previous attempts on this task to measure the expertise relevance between a paper and a reviewer, including whether the paper is semantically close to, shares topics with, and cites previous papers of the reviewer. However, the majority of previous studies take only one of these factors into account, leading to an incomprehensive evaluation of paper-reviewer relevance. To bridge this gap, in this paper, we propose a unified model for paper-reviewer matching that jointly captures semantic, topic, and citation factors. In the unified model, a contextualized language model backbone is shared by all factors to learn common knowledge, while instruction tuning is introduced to characterize the uniqueness of each factor by producing factor-aware paper embeddings. Experiments on four datasets (one of which is newly contributed by us) across different fields, including machine learning, computer vision, information retrieval, and data mining, consistently validate the effectiveness of our proposed UniPR model in comparison with state-of-the-art paper-reviewer matching methods and scientific pre-trained language models