As independent financial advisors, securities firms are the core intermediaries in major asset reorganization(MAR) of listed companies.Furthermore, they play the dual roles of transaction and authentication.Based on t...As independent financial advisors, securities firms are the core intermediaries in major asset reorganization(MAR) of listed companies.Furthermore, they play the dual roles of transaction and authentication.Based on this institutional background, this paper studies how listed companies choose between industry experience(‘‘meritocracy') and relationships(‘‘nepotism').Using the MAR of A-share listed companies from 2008 to 2013 as the sample, this paper shows that higher transaction costs(i.e., greater demand for the transaction function of advisors) are related to the higher possibility of advisors with weaker relationships and more industry experience being hired.It also shows that higher suspicion of tunneling(i.e., greater demand for the signal of fairness associated with advisors’ authentication function) is related to the higher possibility of advisors with weaker relationships being hired, but it is not significantly related to whether advisors have more or less industry experience.This paper also shows that reputation has a certain governance effect on the negative consequences of relationship.For the most part, listed companies reward meritocracy but not nepotism when appointing independent financial advisors.展开更多
Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for users.In real-world scenarios,identifying...Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for users.In real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the portfolios.Thus,extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent.However,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data sparsity.In this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related tasks.Then,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems simultaneously.In addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature fields.Finally,we conduct extensive experiments on a real-world dataset provided by a famous fintech bank.Experimental results clearly demonstrate the effectiveness of MFEM.展开更多
基金the financial support of the National Natural Science Foundation of China (Nos.71802095, 71672204, 71702038)Cen Wu’s China Postdoctoral Science Foundation Grant (No.2018M640888)
文摘As independent financial advisors, securities firms are the core intermediaries in major asset reorganization(MAR) of listed companies.Furthermore, they play the dual roles of transaction and authentication.Based on this institutional background, this paper studies how listed companies choose between industry experience(‘‘meritocracy') and relationships(‘‘nepotism').Using the MAR of A-share listed companies from 2008 to 2013 as the sample, this paper shows that higher transaction costs(i.e., greater demand for the transaction function of advisors) are related to the higher possibility of advisors with weaker relationships and more industry experience being hired.It also shows that higher suspicion of tunneling(i.e., greater demand for the signal of fairness associated with advisors’ authentication function) is related to the higher possibility of advisors with weaker relationships being hired, but it is not significantly related to whether advisors have more or less industry experience.This paper also shows that reputation has a certain governance effect on the negative consequences of relationship.For the most part, listed companies reward meritocracy but not nepotism when appointing independent financial advisors.
基金partially supported by the National Key Research and Development Program of China(No.2018YFC0832101)the National Natural Science Foundation of China(Nos.71802068,61922073,and U20A20229)+1 种基金the financial supports of Tianjin University(No.2020XSC-0019)the support of USTC-CMB Joint Laboratory of Artificial Intelligence
文摘Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for users.In real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the portfolios.Thus,extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent.However,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data sparsity.In this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related tasks.Then,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems simultaneously.In addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature fields.Finally,we conduct extensive experiments on a real-world dataset provided by a famous fintech bank.Experimental results clearly demonstrate the effectiveness of MFEM.
