Labor Market

Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach ArXiv ID: 2507.01979 “View on arXiv” Authors: Adam Nelson-Archer, Aleia Sen, Meena Al Hasani, Sofia Davila, Jessica Le, Omar Abbouchi Abstract We present a deep learning approach for forecasting short-term employment changes and assessing long-term industry health using labor market data from the U.S. Bureau of Labor Statistics. Our system leverages a Long- and Short-Term Time-series Network (LSTNet) to process multivariate time series data, including employment levels, wages, turnover rates, and job openings. The model outputs both 7-day employment forecasts and an interpretable Industry Employment Health Index (IEHI). Our approach outperforms baseline models across most sectors, particularly in stable industries, and demonstrates strong alignment between IEHI rankings and actual employment volatility. We discuss error patterns, sector-specific performance, and future directions for improving interpretability and generalization. ...

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