Implications of Market Structure on the Financial Advisory Industry Öffentlichkeit
Kumar, Ishitha (Summer 2024)
Abstract
This dissertation investigates how the evolution of market structure, in terms of technology adoption and market competition, is impacting the financial advisory industry. First, I study the effect of advent of robo-advisors (financial technology) on the labor market outcomes for financial advisors. I use hand-collected data on robo-advisors, and an instrumental variable approach for this purpose. I find that robo-advisors and financial advisors are complements. This complementarity can be explained by the expansion in market for financial services through (1) an increase in financial advisors at firms that directly compete with robo-advisors in terms of services provided (relative to the rest of the firms) and (2) an increase in investor-level demand for financial advisors. I also find that the observed increase in the number of financial advisors is due to a reduction in separations and an increase in hirings. This is associated with an increase in the average experience of a financial advisor with no effect on the misconduct behavior of a financial advisor at the firm.
Next, I study the effect of market competition among financial advisors on the misconduct of financial advisors. For this purpose, I construct a competition measure using an exogenous shock that affects both supply of financial advisors and demand for financial advisors. Using this plausibly exogenous competition measure, I find that a one-unit standard deviation increase in competition measure increases the probability of misconduct complaint by 0.3%. Further, an increase in competition increases the total number of complaints at the financial advisor level. I also find a reduction in the probability of termination after a misconduct complaint with an increase in competition. I examine the severity of misconduct complaints with an increase in competition and find that the observed results are driven by an increase in misconduct complaints with no significant material allegations.
Table of Contents
Impact of Robo-advisors on the Labor Market for Financial Advisors 1
Introduction 2
Literature 8
Data and Methodology 12
Data 12
Data on Robo-advisors 12
Data on Financial Advisory Firms 13
Data on Individual Financial Advisors 14
Combining Data on Financial Advisory Firms and Individual Advisor 14
Survey Data on Demand for Financial Advisors 15
Macro Data 16
Methodology 16
State Regulations 16
PATH Act 2016 17
Instrumental Variable 18
Alternate IV 19
Empirical Specification 20
Empirical Specification with Alternate IV 22
Results and Discussion 22
Main Result 22
Validity of the IV 24
External Validity for the Results 25
Robustness with Various Controls 25
Effect on Other Employment Types 27
Heterogeneous Response 27
Mechanism 28
Separation and Hiring 32
Composition of Financial Advisors 33
Impact on Misconduct Behavior 33
Conclusion 34
Appendix 36
Competition and Misconduct: Evidence from Financial Advisory Industry 41
Introduction 42
Literature 45
Data and Methodology 46
Data 46
Demographic Data 46
SHELDUS - Natural Disasters Data 47
Competition Measure 47
Economic Motivation 47
Measuring Competition 48
Bootstrap Measure for Competition 50
Empirical Specification 51
Results and Discussion 52
Main Result 52
Robustness of the Result 53
Effect on Aggregate Complaints 53
Termination After a Misconduct Complaint 54
Severity of Misconduct Complaint 54
Conclusion 55
Appendices 57
Tables 57
Figures 75
References 80
List of Tables
Impact of Robo-advisors on the Labor Market for Financial Advisors
Table 1. Break-down of Various Types of Firms that Provide Robo-advisory Services 57
Table 2. Summary statistics57
Table 3. Impact of Robo-advisors on the Number of Financial Advisors at State and Firm-State Levels 58
Table 4. Impact of Robo-advisors on the Number of Financial Advisors at State and Firm-State levels: Estimated Using Bartik-like Instrument over the Period 2006-201959
Table 5. Robustness of the Effect of Robo-advisors on the Number of Financial Advisors at a Firm with Additional Controls 60
Table 6. 2SLS Estimates Impact of Robo-advisors on the Number of Financial Advisors and Brokers at a Firm 61
Table 7. Heterogeneity in Response to the Number of Robo-advisory Firms 62
Table 8. Response by Firms that Provide Services Similar to Robo-advisory Services63
Table 9. Response by Firms that Cater to Individual and High-net-worth Clienetele64
Table 10. Impact of Robo-advisors on the Demand for Financial Advisory Services65
Table 11. Effect of Robo-advisors on the Number of Financial Advisors Separated and Hired at the Firm-State 65
Table 12. Effect of Robo-advisors on the Average Experience of Financial Advisors and the Misconduct Behavior of the Financial Advisors at the Firm-State66
Table A1. Correlation between Level of Internet Firms and Characteristics during 200367
Table A2. Coefficient Estimates from 2SLS Regressions with and without 2003 Level Variables Interacted with Time as Controls 67
Table A3. Coefficient Estimates from 2SLS regression using the IVs Constructed using Number of Internet Advisor Firms from various Base Years 67
Competition and Misconduct: Evidence from Financial Advisory Industry
Table 13. Effect of Competition on the Misconduct Behavior of a Financial Advisor 68
Table 14. Effect of Competition on the Misconduct Behavior of a Financial Advisor - Removing Extreme Values for Competition Measure 69
Table 15. Effect of Competition on the Misconduct Behavior of a Financial Advisor using Bootstrap Competition Measure 70
Table 16. Effect of Competition on the Total Number of Misconduct Complaints at a Financial Advisor Level 71
Table 17. Effect of Competition on the Termination of Employment After a Misconduct Behavior Complaint 72
Table 18. Effect of Competition on the Alleged Damages and Settlement Amount for a Misconduct Complaint at a Financial Advisor level 73
Table 19. Effect of Competition on the Probability that a Misconduct Complaint at a Financial Advisor level is Settled or Denied 74
List of Figures
Impact of Robo-advisors on the Labor Market for Financial Advisors
Figure 1.A. Distribution of Firms Offering Robo-advisory Services across US States during 200675
Figure 1.B. Distribution of Firms Offering Robo-advisory Services across US States during 201975
Figure 2.A. Distribution of Financial Advisory Firms across US States during 200676
Figure 2.B. Distribution of Financial Advisory Firms across US States during 201976
Figure 3. Adoption of NASAA Model Rule 202(d)-1 by Various States 77
Figure 4. Average Number of Robo-advisors in States with Different Levels of Adoption of NASAA Recommendations on Firms’ Financial Requirements77
Figure 5. Average Number of Financial Advisory Firms in States with Different Levels of Adoption of NASAA Recommendations on Firms’ Financial Requirements 78
Figure 6. Coefficient Estimates on the Instrumental Variable Plotted over Years - Obtained by Estimating Equation 3.79
Competition and Misconduct: Evidence from Financial Advisory Industry
Figure 7. Simple Supply-Demand Graphs; D and D’ Represent Demand Curves, and S and S’ Represent Supply Curves 79
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