Language Commonality and Sell-Side Information Production

2021 ◽  
Author(s):  
Ruishen Zhang
Keyword(s):  
Financial Markets ◽  
Native Language ◽  
Side Information ◽  
Information Production ◽  
Geographic Proximity ◽  
Data Set

I study the effects of language commonality (i.e., sharing a native language) on information production in financial markets. Using a hand-collected data set on the prevalent dialects for 2,091 cities (counties) in China, I identify the effects of language commonality separately from those of shared hometown and geographic proximity. In in-sample tests, language commonality between analysts and CEOs increases the return of trading on analysts’ recommendations by 5.5%. The results mainly stem from less intelligible dialects. Broadly speaking, language commonality can alleviate communication frictions when nonnative languages are used in professional settings. This paper was accepted by Gustavo Manso, finance.

Sell-Side Information Production in Financial Markets

2012 ◽  
Vol 47 (4) ◽  
pp. 763-794 ◽  
Author(s):  
Zhaohui Chen ◽  
William J. Wilhelm
Keyword(s):  
Financial Markets ◽  
Market Efficiency ◽  
Financial Market ◽  
Production Technology ◽  
Private Information ◽  
Side Information ◽  
Investment Banking ◽  
Information Production ◽  
Advisory Services ◽  
Proprietary Trading

AbstractWe study decisions to sell nonexcludable private information in the presence of a trading opportunity. Sell-side agents heighten competition among agents who buy their signals to combine with their own for proprietary trading purposes and thereby promote financial market efficiency. This result holds even when the sell-side production technology is not unique. But sell-side information is subject to underinvestment if producers do not internalize the benefits. The model suggests that fee-based compensation for corporate advisory services diminishes this problem and that market efficiency is undermined by forces steering investment-banking resources toward proprietary trading.

scholarly journals An Economic Examination of Collateralization in Different Financial Markets

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Risk Factors ◽  
Credit Risk ◽  
Financial Markets ◽  
Counterparty Risk ◽  
Financial Contracts ◽  
Theoretical Justification ◽  
Data Set ◽  
Unique Data ◽  
Economic Significance ◽  
The Difference

This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets.

scholarly journals Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit

2020 ◽  
Author(s):  
Mengying Zhu ◽  
Xiaolin Zheng ◽  
Yan Wang ◽  
Qianqiao Liang ◽  
Wenfang Zhang
Keyword(s):  
Financial Markets ◽  
Transaction Costs ◽  
Portfolio Selection ◽  
Financial Engineering ◽  
Side Information ◽  
Cardinality Constraint ◽  
Real World Datasets ◽  
Online Portfolio ◽  
Online Portfolio Selection ◽  
Practical Constraints

Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.

Affective Ties That Bind: Investigating the Affordances of Social Networking Sites for Commemoration of Traumatic Events

2018 ◽  
Vol 37 (3) ◽  
pp. 333-354 ◽  
Author(s):  
Yunya Song ◽  
Ran Xu
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Traumatic Event ◽  
Preferential Attachment ◽  
Chinese History ◽  
Geographic Proximity ◽  
Data Set ◽  
Mass Trauma ◽  
Nanjing Massacre ◽  
Negative Feelings

Social networking sites (SNSs) facilitate self-expression and promote social connections. There has been growing scholarly attention to the affect-charged collectivities created online in the aftermath of disasters and mass traumas. This study was designed to examine how individuals affiliate in SNS-based commemoration of a mass trauma, taking advantage of a large Weibo (the Chinese equivalent of Twitter) data set which captures users’ responses over 4 years to the anniversary of the Nanjing massacre, a major traumatic event in Chinese history. Machine learning–based content analysis was combined with dyadic-level network analysis to examine the content Weibo users create and the conversational structures they formed. The results reveal that homophily, geographic proximity, and preferential attachment work in tandem with displays of emotion to influence the formation of online conversational ties. Expressions of negative emotions were found to facilitate or inhibit the homophily effect. Being exposed to the display of anger amplifies the homophily effect among the users, while sadness weakens it. The findings point to the importance of examining specific emotions rather than global (positive–negative) feelings in understanding the dynamics of SNS-based interaction.

Farm income and output and lending by the farm credit system

2017 ◽  
Vol 77 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Denis Nadolnyak ◽  
Xuan Shen ◽  
Valentina Hartarska
Keyword(s):  
Real Estate ◽  
Financial Markets ◽  
Fixed Effects ◽  
Balance Sheet ◽  
State Level ◽  
Farm Income ◽  
Full Time ◽  
Data Set ◽  
Content Type ◽  
Rural Financial Markets

Purpose The purpose of this paper is to provide evidence of the positive impact of the FCS lending on farm incomes which should be useful to policymakers as they consider reforms and further support for this 100-year-old major agricultural lender. Design/methodology/approach The authors construct a panel for the 1991-2010 period from the FCS financial statements and evaluate how lending by the FCS institutions has affected farm incomes and farm output. The authors use fixed effects estimations and control for credit by other agricultural lenders as well as the stock of capital, prices, and interest rates. Since previous work suggests that rural financial markets are segmented and the FCS serves larger full-time farmers with mostly real-estate backed loans, the authors evaluate the impacts of farm real-estate backed loans and of short-term agricultural loans separately for a shorter period for which the data is available. The authors also perform robustness checks with alternative estimation techniques. Findings The authors found a positive association between credit by the FCS institutions and farm income and output. The magnitude of the estimated impact is larger during the 1990s than in the 2000s. Research limitations/implications The positive link between the FCS institutions’ credit and farm incomes and output supports the notion that the FCS lending was beneficial to farmers. The evidence also supports the segmentation hypothesis of rural financial markets. The financial reports data for 1991-2010 are from the ACAs and FLCAs aggregated on the regional level because there is no clear way to classify FCS lending to a more disaggregate level like the state. The authors also assemble and analyze a state-level data set that contains state-level balance sheet data for the period 1991-2003. Originality/value The authors are not aware of another work that directly links (real estate and non-real estate) credit by FCS institutions to agricultural output and farm incomes.

scholarly journals Board size and firm performance: A comparative and comprehensive analysis by using organizational theories and correct proxies

2010 ◽  
Vol 6 (2) ◽  
pp. 35-52
Author(s):  
Kashif Rashid ◽  
Sardar M. N. Islam
Keyword(s):  
Financial Markets ◽  
Comprehensive Analysis ◽  
Stewardship Theory ◽  
Governance Mechanism ◽  
Board Size ◽  
Data Set ◽  
Organizational Theories ◽  
Industry Professionals ◽  
Selection Of

An organization’s board is an important governance mechanism to incorporate corporate governance provisions in financial markets. Previous studies on board size and the value of a firm relationship (BVF) are inconclusive and lack a comparative and comprehensive analysis of this relationship which incorporates the role of additional factors present in the developing financial market. This study bridges the gap in the literature by providing some additional empirical evidence about the BVF relationship. This evidence is provided by performing a comparative and comprehensive analysis of the firms in developing and developed financial markets. Based on a sophisticated data set for the selected markets, two separate models are run and their results are compared. The results for this study suggest that in the developing market a bigger board improves the value of a firm, supporting the relevance of stewardship theory. On the contrary, in the developed market a smaller board improves shareholders’ value, supporting the agency theory. The study has reflected the differences in the efficiency of institutional framework and the sophistication of financial development in a selection of countries, in the results on the BVF relationship. Furthermore, these results make the applicability of different business theories explaining market operations in these markets different from each other. The results are innovative and valuable to academics, analysts and industry professionals in both developing and developed financial markets.

scholarly journals GRAPH BASED CLUSTERING WITH CONSTRAINTS AND ACTIVE LEARNING

2021 ◽  
Vol 37 (1) ◽  
pp. 71-89
Author(s):  
Vu-Tuan Dang ◽  
Viet-Vu Vu ◽  
Hong-Quan Do ◽  
Thi Kieu Oanh Le
Keyword(s):  
Active Learning ◽  
Clustering Algorithm ◽  
Side Information ◽  
Clustering Algorithms ◽  
Real Data ◽  
Data Sets ◽  
Data Set ◽  
Supervised Clustering ◽  
Class Labels ◽  
Graph Based Clustering

During the past few years, semi-supervised clustering has emerged as a new interesting direction in machine learning research. In a semi-supervised clustering algorithm, the clustering results can be significantly improved by using side information, which is available or collected from users. There are two main kinds of side information that can be learned in semi-supervised clustering algorithms: the class labels - called seeds or the pairwise constraints. The first semi-supervised clustering was introduced in 2000, and since that, many algorithms have been presented in literature. However, it is not easy to use both types of side information in the same algorithm. To address the problem, this paper proposes a semi-supervised graph based clustering algorithm that tries to use seeds and constraints in the clustering process, called MCSSGC. Moreover, we introduces a simple but efficient active learning method to collect the constraints that can boost the performance of MCSSGC, named KMMFFQS. In order to verify effectiveness of the proposed algorithm, we conducted a series of experiments not only on real data sets from UCI, but also on a document data set applied in an Information Extraction of Vietnamese documents. These obtained results show that the proposed algorithm can significantly improve the clustering process compared to some recent algorithms.

scholarly journals Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors

2021 ◽  
Vol 3 ◽  
Author(s):  
Marco Cox ◽  
Bert de Vries
Keyword(s):  
Mixture Model ◽  
Pure Tone ◽  
Information Gain ◽  
Side Information ◽  
Hearing Threshold ◽  
Pure Tone Audiometry ◽  
Threshold Estimation ◽  
Data Set ◽  
Hearing Thresholds ◽  
Gp Model

Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inaudible” responses to tones of varying frequency and intensity—is the basis for diagnosing and quantifying hearing loss. By taking a probabilistic modeling approach, both optimal tone selection (in terms of expected information gain) and hearing threshold estimation can be derived through Bayesian inference methods. The performance of probabilistic model-based audiometry methods is directly linked to the quality of the underlying model. In recent years, Gaussian process (GP) models have been shown to provide good results in this context. We present methods to improve the efficiency of GP-based audiometry procedures by improving the underlying model. Instead of a single GP, we propose to use a GP mixture model that can be conditioned on side-information about the subject. The underlying idea is that one can typically distinguish between different types of hearing thresholds, enabling a mixture model to better capture the statistical properties of hearing thresholds among a population. Instead of modeling all hearing thresholds by a single GP, a mixture model allows specific types of hearing thresholds to be modeled by independent GP models. Moreover, the mixing coefficients can be conditioned on side-information such as age and gender, capturing the correlations between age, gender, and hearing threshold. We show how a GP mixture model can be optimized for a specific target population by learning the parameters from a data set containing annotated audiograms. We also derive an optimal tone selection method based on greedy information gain maximization, as well as hearing threshold estimation through Bayesian inference. The proposed models are fitted to a data set containing roughly 176 thousand annotated audiograms collected in the Nordic countries. We compare the predictive accuracies of optimized mixture models of varying sizes with that of an optimized single-GP model. The usefulness of the optimized models is tested in audiometry simulations. Simulation results indicate that an optimized GP mixture model can significantly outperform an optimized single-GP model in terms of predictive accuracy, and leads to significant increases the efficiency of the resulting Bayesian audiometry procedure.

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