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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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295886115 · May 202619922001200920172026
48 results for heterogeneous investments

Investment diversification affects financial stability, depending on network connectivity.

problem Analyzing stability of financial networks with diversified portfolios.
method Random matrix dynamical model with portfolio rebalancing, considering heterogeneity and diversification effects.
result Stability/instability transition depends on the largest eigenvalue of the random matrix.

Model trains agents to optimize saving and investment strategies for diverse retirement needs.

problem Optimal saving and investment strategies for individuals in varied employment and income profiles.
method Deep reinforcement learning to train intelligent agents with heterogeneous profiles.
result Flexible methodology estimates lifetime consumption and investment choices for different profiles.

Green stocks show less factor exposure heterogeneity compared to brown stocks.

problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.

Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.

problem Understanding how heterogeneity impacts financial market dynamics.
method Agent-based model incorporating heterogeneous investment strategies and risk tolerance.
result Heterogeneity in strategies and risk tolerance suppresses price fluctuations.

Upper bounds on utility for managing heterogeneous collectivised funds.

problem Managing pension funds with diverse investor preferences and mortality.
method Axiomatic approach to define optimal management strategies.
result Asymptotically optimal strategies for maximizing investor utility.

Investment managers face harder choices in green stocks due to reduced performance variability.

problem Difficulty in deploying talent in green stocks due to reduced performance variability.
method Analysis of S&P 500 firms' greenhouse gas emission levels and peer performance ratios.
result Performance variability has decreased in green stocks, making it harder for managers to choose.

Improved forecasting of investment dynamics across heterogeneous panels using a two-stage model.

problem Forecasting investment dynamics in heterogeneous panels with varying dynamics.
method Two-stage architecture: global pooled AR(1) for shared persistence, local models for residual dynamics.
result Significant improvement in out-of-sample R2R^2 from 0.630 to 0.677, with a gain of 0.047.

Study optimal investment decisions for diverse risk-tolerant agents.

problem Optimizing investment choices for agents with varying risk preferences.
method Characterizes optimal behavior using certainty equivalents and lognormal risks.
result Derives optimal decision menus under known and uncertain preference distributions.

Investment and consumption strategies with luxury goods for retirement age.

problem Optimal investment and consumption with heterogeneous goods and retirement timing.
method PDE and stochastic control theory, variational inequality, dual transformation.
result Optimal consumption strategies and retirement policies for utility maximizers.

Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from variou…

2018-11-09abs ↗pdf ↗

Study optimal investment in large populations of competitive, heterogeneous agents.

problem Maximizing utility in a large, interacting agent system with relative performance concerns.
method Analyzes stochastic utility maximization game in finite and infinite agent settings, using graphon models and backward stochastic differential equations.
result Convergence of Nash equilibria and optimal utilities from finite to infinite agent models under specific conditions.

Study Nash equilibrium in market with relative wealth concerns under partial information and heterogeneous priors.

problem Analyzing Nash equilibrium in a market with unobservable return rates and heterogeneous priors.
method Established a Nash equilibrium through a separation result and martingale argument. Used fully-coupled linear FBSDEs and deep neural networks for numerical computation.
result Investment strategies under relative wealth concerns exhibit a herd effect, with accurate prior estimators leading the market.

We quantify the benefit of collectivised investment funds, in which the assets of members who die are shared among the survivors. For our model, with realistic parameter choices, an annuity or individual fund requires approximately 20\% more initial capital to provide as good an outcome as a collectivised investment fu…

2019-09-27abs ↗pdf ↗

Optimizes investment strategies for retirees with longevity risk.

problem Maximizing retirement savings under longevity risk for a group of investors.
method Analytic and numerical solutions for investment strategies in both discrete and continuous time models.
result Analytic formulae for optimal investment strategies in both discrete and continuous time models.

This study presents an ANWSER model (asset network systemic risk model) to quantify the risk of financial contagion which manifests itself in a financial crisis. The transmission of financial distress is governed by a heterogeneous bank credit network and an investment portfolio of banks. Bankruptcy reproductive ratio …

2012-11-22abs ↗pdf ↗

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investmen…

2013-08-01abs ↗pdf ↗

MountainLion uses LLMs to interpret financial data and generate investment strategies.

problem Challenges in integrating heterogeneous data for financial trading.
method Multi-modal LLM-based agents that process textual and visual data.
result Improves returns and investor confidence through interpretable investment framework.

A new model for heterogeneous populations optimizes consumption and investment over short horizons.

problem Optimizing consumption and investment in economies with a heterogeneous population over short time periods.
method Continuous-time general equilibrium framework with Brownian flow on a type space, solving vanishing-horizon problems under relative-income criteria.
result Existence and characterization of short-horizon Duesenberry equilibrium, with sharp asset-pricing implications.

The paper compares ML models for credit scoring and investment decisions using explainable AI.

problem The opacity of machine learning models in financial services.
method Comparison of various machine learning models (single classifiers, ensembles, neural networks) and explainability techniques (LIME, SHAP).
result Ensemble classifiers and neural networks outperform in credit scoring models.

An interbank market lets participants pool the risk arising from the combination of illiquid investments and random withdrawals by depositors. But it also creates the potential for one bank's failure to trigger off avalanches of further failures. We simulate a model of interbank lending to study the interplay of these …

2001-04-04abs ↗pdf ↗

Agents' heterogeneity is recognized as a driver mechanism for the persistence of financial volatility. We focus on the multiplicity of investment strategies' horizons, we embed this concept in a continuous time stochastic volatility framework and prove that a parsimonious, two-scale version effectively captures the lon…

2012-05-31abs ↗pdf ↗

Game-theoretic model captures investor interactions for stock price forecasting.

problem Complex market dynamics driving stock price movements.
method Game-theoretic modeling of heterogeneous investor interactions in a dynamic graph structure.
result Our method outperforms state-of-the-art stock price forecasting methods.

This paper optimizes reinsurance contracts with belief differences between insurer and reinsurer.

problem Dynamic reinsurance design with heterogeneous beliefs under mean-variance framework.
method Modeling surplus process, applying partitioned domain optimization, solving HJB system.
result Optimal reinsurance contracts with belief heterogeneity are more complex than standard contracts.

The study reveals distinct patterns in retail investors' holding periods affecting stock returns.

problem Understanding the impact of retail investors' investment horizons on stock returns.
method Using self-reported holding periods from StockTwits, the study categorizes retail investors into long-horizon and short-horizon groups and analyzes their return patterns.
result Long-horizon retail investors exhibit underreaction to earnings announcements, while short-horizon investors show overreaction.

The paper optimizes pension policies with guarantees and sustainability constraints.

problem Designing optimal pension policies with guarantees and sustainability constraints.
method Dynamic utility model, stochastic domain, overlapping generations, time-consistent decision criterion.
result Optimal investment/pension policy computed for a general framework.

How do macro-financial shocks affect investor behavior and market dynamics? Recent evidence on experience effects suggests a long-lasting influence of personally experienced outcomes on investor beliefs and investment, but also significant differences across older and younger generations. We formalize experience-based …

2016-12-30abs ↗pdf ↗

The study analyzes ETFs' portfolio optimization and tail-risk management.

problem Analyzing the performance of actively managed ETFs in managing risk and diversification.
method Daily Bloomberg data for 30 funds, evaluating various strategies under long-only and long-short constraints.
result Tangency-type portfolios generally outperform buy-and-hold benchmarks, while minimum-variance and CVaR-minimizing portfolios sacrifice upside for downside control.

Generative model learns investment strategies without explicit utility specification.

problem Challenges in modeling complex, multi-objective fund optimization.
method Generative adversarial network (GAN) framework that learns latent strategy representations.
result Framework captures diverse investment styles and realizations of optimization parameters.

This paper won 1st place in forecasting and investment challenges, improving on meta-learning and parametric models.

problem Forecasting and investment challenges in time-series data.
method Hypernetworks and adversarial portfolios to design time-series models.
result Outperformed state-of-the-art meta-learning methods and conventional parametric models.

DUET enhances multivariate time series forecasting by clustering time and channels.

problem Heterogeneous temporal patterns and complex channel correlations in multivariate time series.
method DUET uses dual clustering on temporal and channel dimensions to handle these challenges.
result DUET achieves state-of-the-art performance on 25 real-world datasets.

When investors have heterogeneous attitudes towards risk, it is reasonable to assume that each investor has a pricing kernel, and that these individual pricing kernels are aggregated to form a market pricing kernel. The various investors are then buyers or sellers depending on how their individual pricing kernels compa…

2013-01-14abs ↗pdf ↗

Study investor sentiment and disagreement on StockTwits during COVID-19.

problem Understanding investor beliefs and sentiment during the pandemic.
method Analysis of social media data (StockTwits) for investor messages.
result Sentiment and disagreement sharply decreased in early March 2020, followed by a reversal.