Asset Pricing

A Survey of Behavioral Finance ArXiv ID: ssrn-332266 “View on arXiv” Authors: Unknown Abstract Behavioral finance argues that some financial phenomena can plausibly be understood using models in which some agents are not fully rational. The field has two Keywords: Behavioral finance, Asset pricing, Rational agents, Financial phenomena, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a comprehensive literature review discussing concepts like limits to arbitrage and psychology, which are conceptual and theoretical, lacking dense mathematical derivations or empirical backtesting results. flowchart TD A["Research Goal: Review behavioral finance models with non-rational agents"] --> B["Data/Inputs: Empirical asset pricing anomalies, survey data"] B --> C["Key Methodology: Literature survey, model comparison"] C --> D["Computational Processes: Psychological bias analysis, agent-based simulations"] D --> E{"Key Findings/Outcomes"} E --> F["Deviations from rational expectations"] E --> G["Persistent equity anomalies explained"] E --> H["Limited arbitrage success"]

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2024 Edition ArXiv ID: ssrn-4751941 “View on arXiv” Authors: Unknown Abstract The equity risk premium is the price of risk in equity markets, and it is not just a key input in estimating costs of equity and capital in both corporate finan Keywords: Equity Risk Premium, Asset Pricing, Cost of Capital, Valuation Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper introduces advanced financial theory and a wide array of estimation methodologies (implied premiums, surveys) but is grounded in extensive real-world data analysis, including country-specific risk premiums and market volatility metrics. flowchart TD A["Research Goal: ERP Determinants & Estimation"] --> B["Data Inputs"] B --> C{"Methodology: Historical vs. Forward<br>Integration of Macroeconomic Variables"} C --> D["Computational Processes<br>Model Estimation & Valuation Metrics"] D --> E["Key Findings: ERP Trends & Implications<br>Cost of Capital Updates"]

In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis ArXiv ID: ssrn-3886763 “View on arXiv” Authors: Unknown Abstract Our framework allows us to give a dynamic economic structure to old and recent datasets comprising holdings and flows in various segments of the market. The mys Keywords: Asset Pricing, Market Dynamics, Holding Data Analysis, Flow Analysis, Financial Markets, Equity Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper presents a complex stochastic framework using integrals and non-linear dynamics to model price impact and liquidity, indicating high mathematical density. Empirically, it leverages extensive granular datasets on holdings and flows across various market segments, suggesting strong data backing and backtest potential. flowchart TD A["Research Goal:<br>Determine the origins of financial fluctuations<br>via the Inelastic Markets Hypothesis"] --> B["Methodology:<br>Theoretical framework integrating<br>asset pricing with holdings/flows"] B --> C["Data Inputs:<br>Portfolio holdings & trading flows<br>in various market segments"] C --> D["Computational Process:<br>Dynamic economic structure modeling<br>of supply/demand inelasticity"] D --> E["Key Findings:<br>Price volatility stems from inelastic supply/demand<br>Portfolio adjustments drive financial fluctuations"] E --> F["Outcomes:<br>Unified framework for analyzing<br>old and recent market datasets"]

Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning ArXiv ID: 2601.00593 “View on arXiv” Authors: Yan Liu, Ye Luo, Zigan Wang, Xiaowei Zhang Abstract Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We propose a simple change: sort assets using uncertainty-adjusted prediction bounds instead of point predictions alone. Across a broad set of ML models and a U.S. equity panel, this approach improves portfolio performance relative to point-prediction sorting. These gains persist even when bounds are built from partial or misspecified uncertainty information. They arise mainly from reduced volatility and are strongest for flexible machine learning models. Identification and robustness exercises show that these improvements are driven by asset-level rather than time or aggregate predictive uncertainty. ...

Mean-Field Price Formation on Trees with a Network of Relative Performance Concerns ArXiv ID: 2512.21621 “View on arXiv” Authors: Masaaki Fujii Abstract Financial firms and institutional investors are routinely evaluated based on their performance relative to their peers. These relative performance concerns significantly influence risk-taking behavior and market dynamics. While the literature studying Nash equilibrium under such relative performance competitions is extensive, its effect on asset price formation remains largely unexplored. This paper investigates mean-field equilibrium price formation of a single risky stock in a discrete-time market where agents exhibit exponential utility and relative performance concerns. Unlike existing literature that typically treats asset prices as exogenous, we impose a market-clearing condition to determine the price dynamics endogenously within a relative performance equilibrium. Using a binomial tree framework, we establish the existence and uniqueness of the market-clearing mean-field equilibrium in both single- and multi-population settings. Finally, we provide illustrative numerical examples demonstrating the equilibrium price distributions and agents’ optimal position sizes. ...

High-Dimensional Spatial Arbitrage Pricing Theory with Heterogeneous Interactions ArXiv ID: 2511.01271 “View on arXiv” Authors: Zhaoxing Gao, Sihan Tu, Ruey S. Tsay Abstract This paper investigates estimation and inference of a Spatial Arbitrage Pricing Theory (SAPT) model that integrates spatial interactions with multi-factor analysis, accommodating both observable and latent factors. Building on the classical mean-variance analysis, we introduce a class of Spatial Capital Asset Pricing Models (SCAPM) that account for spatial effects in high-dimensional assets, where we define {"\it spatial rho"} as a counterpart to market beta in CAPM. We then extend SCAPM to a general SAPT framework under a {"\it complete"} market setting by incorporating multiple factors. For SAPT with observable factors, we propose a generalized shrinkage Yule-Walker (SYW) estimation method that integrates ridge regression to estimate spatial and factor coefficients. When factors are latent, we first apply an autocovariance-based eigenanalysis to extract factors, then employ the SYW method using the estimated factors. We establish asymptotic properties for these estimators under high-dimensional settings where both the dimension and sample size diverge. Finally, we use simulated and real data examples to demonstrate the efficacy and usefulness of the proposed model and method. ...

Deep Learning for Conditional Asset Pricing Models ArXiv ID: 2509.04812 “View on arXiv” Authors: Hongyi Liu Abstract We propose a new pseudo-Siamese Network for Asset Pricing (SNAP) model, based on deep learning approaches, for conditional asset pricing. Our model allows for the deep alpha, deep beta and deep factor risk premia conditional on high dimensional observable information of financial characteristics and macroeconomic states, while storing the long-term dependency of the informative features through long short-term memory network. We apply this method to monthly U.S. stock returns from 1970-2019 and find that our pseudo-SNAP model outperforms the benchmark approaches in terms of out-of-sample prediction and out-of-sample Sharpe ratio. In addition, we also apply our method to calculate deep mispricing errors which we use to construct an arbitrage portfolio K-Means clustering. We find that the arbitrage portfolio has significant alphas. ...

To Bubble or Not to Bubble: Asset Price Dynamics and Optimality in OLG Economies ArXiv ID: 2508.03230 “View on arXiv” Authors: Stefano Bosi, Cuong Le Van, Ngoc-Sang Pham Abstract We study an overlapping generations (OLG) exchange economy with an asset that yields dividends. First, we derive general conditions, based on exogenous parameters, that give rise to three distinct scenarios: (1) only bubbleless equilibria exist, (2) a bubbleless equilibrium coexists with a continuum of bubbly equilibria, and (3) all equilibria are bubbly. Under stationary endowments and standard assumptions, we provide a complete characterization of the equilibrium set and the associated asset price dynamics. In this setting, a bubbly equilibrium exists if and only if the interest rate in the economy without the asset is strictly lower than the population growth rate and the sum of per capita dividends is finite. Second, we establish necessary and sufficient conditions for Pareto optimality. Finally, we investigate the relationship between asset price behaviors and the optimality of equilibria. ...

Can LLM-based Financial Investing Strategies Outperform the Market in Long Run? ArXiv ID: 2505.07078 “View on arXiv” Authors: Weixian Waylon Li, Hyeonjun Kim, Mihai Cucuringu, Tiejun Ma Abstract Large Language Models (LLMs) have recently been leveraged for asset pricing tasks and stock trading applications, enabling AI agents to generate investment decisions from unstructured financial data. However, most evaluations of LLM timing-based investing strategies are conducted on narrow timeframes and limited stock universes, overstating effectiveness due to survivorship and data-snooping biases. We critically assess their generalizability and robustness by proposing FINSABER, a backtesting framework evaluating timing-based strategies across longer periods and a larger universe of symbols. Systematic backtests over two decades and 100+ symbols reveal that previously reported LLM advantages deteriorate significantly under broader cross-section and over a longer-term evaluation. Our market regime analysis further demonstrates that LLM strategies are overly conservative in bull markets, underperforming passive benchmarks, and overly aggressive in bear markets, incurring heavy losses. These findings highlight the need to develop LLM strategies that are able to prioritise trend detection and regime-aware risk controls over mere scaling of framework complexity. ...

Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks ArXiv ID: 2505.01921 “View on arXiv” Authors: Shanyan Lai Abstract In this study, MLP models with dynamic structure are applied to factor models for asset pricing tasks. Concretely, the MLP pyramid model structure was employed on firm-characteristic-sorted portfolio factors for modelling the large-capital US stocks. It was further developed as a practicable factor investing strategy based on the predictions. The main findings in this chapter were evaluated from two angles: model performance and investing performance, which were compared from the periods with and without COVID-19. The empirical results indicated that with the restrictions of the data size, the MLP models no longer perform “deeper, better”, while the proposed MLP models with two and three hidden layers have higher flexibility to model the factors in this case. This study also verified the idea of previous works that MLP models for factor investing have more meaning in the downside risk control than in pursuing the absolute annual returns. ...