Cross-Sectional Analysis

On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model

On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model ArXiv ID: 2510.14156 “View on arXiv” Authors: Jan Kwiatkowski, Jarosław A. Chudziak Abstract Quantitative trading strategies rely on accurately ranking stocks to identify profitable investments. Effective portfolio management requires models that can reliably order future stock returns. Transformer models are promising for understanding financial time series, but how different training loss functions affect their ability to rank stocks well is not yet fully understood. Financial markets are challenging due to their changing nature and complex relationships between stocks. Standard loss functions, which aim for simple prediction accuracy, often aren’t enough. They don’t directly teach models to learn the correct order of stock returns. While many advanced ranking losses exist from fields such as information retrieval, there hasn’t been a thorough comparison to see how well they work for ranking financial returns, especially when used with modern Transformer models for stock selection. This paper addresses this gap by systematically evaluating a diverse set of advanced loss functions including pointwise, pairwise, listwise for daily stock return forecasting to facilitate rank-based portfolio selection on S&P 500 data. We focus on assessing how each loss function influences the model’s ability to discern profitable relative orderings among assets. Our research contributes a comprehensive benchmark revealing how different loss functions impact a model’s ability to learn cross-sectional and temporal patterns crucial for portfolio selection, thereby offering practical guidance for optimizing ranking-based trading strategies. ...

Profitable Momentum Trading Strategies for Individual Investors

Profitable Momentum Trading Strategies for Individual Investors ArXiv ID: ssrn-2420743 “View on arXiv” Authors: Unknown Abstract For nearly three decades, scientific studies have explored momentum investing strategies and observed stable excess returns in various financial markets. Howeve Keywords: Momentum investing, Excess returns, Cross-sectional analysis, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper focuses on practical strategy implementation with transaction costs and dataset analysis (NYSC 1991-2010), but uses simple statistical comparisons rather than advanced mathematical derivations. flowchart TD A["Research Goal: Does momentum investing yield excess returns for individual investors?"] --> B["Data Source: US Equity Market 1926-2023"] B --> C["Methodology: Cross-Sectional Analysis"] C --> D["Computation: Sort stocks by past 6-month returns into deciles"] D --> E["Portfolio Formation: Long top decile Short bottom decile"] E --> F["Outcome: Consistent excess returns across decades"] F --> G["Key Finding: Profitable momentum strategy valid for individual investors"]

Why Did Some Banks Perform Better during the Credit Crisis? A Cross-Country Study of the Impact of Governance and Regulation

Why Did Some Banks Perform Better during the Credit Crisis? A Cross-Country Study of the Impact of Governance and Regulation ArXiv ID: ssrn-1433502 “View on arXiv” Authors: Unknown Abstract Though overall bank performance from July 2007 to December 2008 was the worst since at least the Great Depression, there is significant variation in the cross-s Keywords: Bank Performance, Financial Crisis, Cross-Sectional Analysis, Banking Sector, Asset Quality, Financials Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper primarily uses regression analysis and statistical metrics to examine cross-country bank performance during the crisis, focusing on empirical backtesting using pre-crisis data, with minimal advanced mathematical formalism or derivations. flowchart TD A["Research Goal: Cross-sectional analysis of bank performance during Credit Crisis (July 2007 - Dec 2008)"] --> B{"Data Collection"} B --> C["Bank-Level Financials Asset Quality Metrics"] B --> D["Regulatory & Governance Indices by Country"] C --> E["Cross-Sectional Regression Analysis"] D --> E E --> F{"Key Findings"} F --> G["Stronger Governance Correlates with Better Performance"] F --> H["Stricter Regulation Linked to Higher Resilience"]

The Age of Reason: Financial Decisions Over the Lifecycle

The Age of Reason: Financial Decisions Over the Lifecycle ArXiv ID: ssrn-997547 “View on arXiv” Authors: Unknown Abstract In cross-sectional data sets from ten credit markets, we find that middle-aged adults borrow at lower interest rates and pay fewer fees relative to younger and Keywords: Credit Markets, Borrowing Costs, Cross-Sectional Analysis, Financial Intermediation, Consumer Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper focuses on empirical analysis of cross-sectional credit market data with clear real-world applicability, but its mathematical depth appears limited to basic econometric models without advanced derivations. flowchart TD A["Research Goal: Identify Lifecycle Patterns in Borrowing Costs & Credit Access"] --> B["Data Source: Cross-Sectional Credit Data from 10 Markets"] B --> C["Key Methodology: Cross-Sectional Analysis Segmentation by Age Group"] C --> D{"Computational Process"} D --> E["Compare Interest Rates & Fees: Young vs. Middle vs. Old"] E --> F["Statistical Testing & Intermediation Assessment"] F --> G["Key Findings: Middle-Aged Adults Obtain Lower Rates & Fewer Fees Optimal Financial Decisions at Mid-Life"]