Wealth Management

Bifurcation in optimal retirement ArXiv ID: 2506.02155 “View on arXiv” Authors: Bushra Shehnam Ashraf, Thomas S. Salisbury Abstract We study optimal consumption and retirement using a Cobb-Douglas utility and a simple model in which an interesting bifurcation arises. With high wealth, individuals plan to retire. With low wealth they plan to never retire. At a critical level of initial wealth they may choose to defer this decision, leading to a continuum of wealth trajectories with identical utilities. ...

Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm ArXiv ID: 2311.10717 “View on arXiv” Authors: Unknown Abstract We consider the problem of being a cross-chain wealth management platform with deposits, redemptions and investment assets across multiple networks. We discuss the need for blockchain bridges to facilitates fund flows across platforms. We point out several issues with existing bridges. We develop an algorithm - tailored to overcome current constraints - that dynamically changes the utilization of bridge capacities and hence the amounts to be transferred across networks. We illustrate several scenarios using numerical simulations. ...

Deep Reinforcement Learning for Robust Goal-Based Wealth Management ArXiv ID: 2307.13501 “View on arXiv” Authors: Unknown Abstract Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as it requires choosing the appropriate investment until a goal is achieved. Consequently, reinforcement learning, a machine learning technique appropriate for sequential decision-making, offers a promising path for optimizing these investment strategies. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed. The experimental results indicate its superiority over several goal-based wealth management benchmarks on both simulated and historical market data. ...

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739590 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper’s mathematics is primarily statistical and econometric (e.g., comparisons of proportions, regression analysis on job turnover), scoring a moderate 3.5. The empirical rigor is extremely high, driven by the construction of a novel, large-scale database covering the universe of U.S. financial advisers over 10 years and the use of detailed, implementable data on employment history, misconduct disclosures, and settlements. flowchart TD A["Research Goal: How does adviser misconduct affect<br>the market for financial advice?"] --> B subgraph B["Methodology & Data"] B1["(Novel Database: 2005-2015,<br>~10% of US Advisers)"] B2["Match to BrokerCheck & CRD<br>Regulatory Disclosures"] B3["Link to Employment History<br>& Asset Allocation Data"] end B --> C{"Computational Analysis"} C --> D["Estimate Impact on<br>Employment, Wages, & Assets"] C --> E["Test Market Segmentation<br>by Firm Type & Geography"] D --> F["Key Findings: Advisers with<br>misconduct face severe penalties"] E --> F

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739170 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper relies primarily on descriptive statistics and econometric analysis of a large administrative dataset rather than complex mathematical modeling, and its core contribution is the construction and exhaustive analysis of a novel, comprehensive database ready for empirical validation. flowchart TD A["Research Goal: How does adviser misconduct<br>shape the market for financial advice?"] --> B subgraph B["Methodology & Data"] direction LR B1["Novel Database:<br>US Financial Advisers 2005-2015"] B2["Data Source: Form ADV<br>Investment Adviser Public Disclosure"] B1 --> B2 end B --> C{"Key Method: Difference-in-Differences"} C --> D["Computational Process:<br>Estimate Treatment Effects"] D --> E subgraph E["Key Findings/Outcomes"] direction LR E1["Misconduct Advisers<br>Switch Firms More Often"] E2["Sanctions Reduce<br>Client Assets by 12%"] E3["Market Segments by<br>Adviser Quality"] end

Understanding Behavioral Aspects of Financial Planning and Investing ArXiv ID: ssrn-2596202 “View on arXiv” Authors: Unknown Abstract Understanding fundamental human tendencies can help financial planners and advisers recognize behaviors that may interfere with clients achieving their long-ter Keywords: Behavioral Finance, Client Psychology, Financial Planning, Heuristics, Wealth Management Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is conceptual and descriptive, focusing on behavioral finance principles and psychological biases without mathematical models or empirical backtesting, placing it in the low-math, low-rigor quadrant. flowchart TD A["Research Goal:\nUnderstand behavioral aspects in financial planning"] --> B["Methodology: Literature Review & Analysis"] B --> C["Data Inputs: Studies on Heuristics, Biases, & Client Psychology"] C --> D["Computational Process:\nIdentify Patterns & Link Behaviors to Planning Outcomes"] D --> E["Key Findings/Outcomes:\nRecognize biases to improve client wealth management"]