Financial Markets

Deep Reinforcement Learning for Portfolio Allocation ArXiv ID: ssrn-3886804 “View on arXiv” Authors: Unknown Abstract In 2013, a paper by Google DeepMind kicked off an explosion in Deep Reinforcement Learning (DRL), for games. In this talk, we show that DRL can also be applied Keywords: Deep Reinforcement Learning, Algorithmic Trading, Artificial Intelligence, Financial Markets Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 8.0/10 Quadrant: Holy Grail Why: The paper employs advanced mathematics (reinforcement learning, optimization, Shapley values) and demonstrates strong empirical rigor with detailed backtesting methodology, specific datasets, performance metrics, and sensitivity analysis for real-world implementation. flowchart TD Goal["Research Goal: Apply DRL to Portfolio Allocation"] --> Method["Methodology: Deep Q-Network (DQN) Algorithm"] Method --> Input["Data Inputs: Historical Price Data & Market Indicators"] Input --> Proc["Computational Process: Training Agent on Simulated Market"] Proc --> Find1["Outcome 1: Dynamic Asset Weighting"] Proc --> Find2["Outcome 2: Risk-Adjusted Return Optimization"] Find1 --> End["Conclusion: DRL Viable for Financial Markets"] Find2 --> End

In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis ArXiv ID: ssrn-3875134 “View on arXiv” Authors: Unknown Abstract We develop a framework to theoretically and empirically analyze the fluctuations of the aggregate stock market. Households allocate capital to institutions, whi Keywords: Stock Market Fluctuations, Household Capital Allocation, Institutional Holdings, Financial Markets, Portfolio Choice, Equity Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper introduces a novel theoretical framework with dynamic general equilibrium models and pricing kernels (high math complexity), while rigorously testing its core hypothesis using granular instrumental variables (GIV) on real financial data to estimate a precise price impact multiplier of ~5, including robustness checks (high empirical rigor). flowchart TD A["Research Goal<br>Understand aggregate stock market fluctuations"] --> B["Methodology<br>Develop theoretical & empirical framework"] B --> C["Input Data<br>Household & institutional capital allocation data"] C --> D["Computational Process<br>Estimate supply & demand elasticities"] D --> E["Key Finding<br>Markets are inelastic due to limited arbitrage"] E --> F["Outcome<br>Explains volatility puzzles & asset pricing"]

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"]

FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance ArXiv ID: 2510.15883 “View on arXiv” Authors: Yang Li, Zhi Chen Abstract Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions. ...

FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading ArXiv ID: 2502.11433 “View on arXiv” Authors: Unknown Abstract Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to improve decision-making. To address this, we propose \textsc{“FLAG-Trader”}, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization, in which a partially fine-tuned LLM acts as the policy network, leveraging pre-trained knowledge while adapting to the financial domain through parameter-efficient fine-tuning. Through policy gradient optimization driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other financial-domain tasks. We present extensive empirical evidence to validate these enhancements. ...

HedgeAgents: A Balanced-aware Multi-agent Financial Trading System ArXiv ID: 2502.13165 “View on arXiv” Authors: Unknown Abstract As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations, impeding their practical application. Hence, there is an imperative to explore a more robust and resilient framework. This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ``hedging’’ strategies. In this well-balanced system, an array of hedging agents has been tailored, where HedgeAgents consist of a central fund manager and multiple hedging experts specializing in various financial asset classes. These agents leverage LLMs’ cognitive capabilities to make decisions and coordinate through three types of conferences. Benefiting from the powerful understanding of LLMs, our HedgeAgents attained a 70% annualized return and a 400% total return over a period of 3 years. Moreover, we have observed with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/). ...

Simulation of Social Media-Driven Bubble Formation in Financial Markets using an Agent-Based Model with Hierarchical Influence Network ArXiv ID: 2409.00742 “View on arXiv” Authors: Unknown Abstract We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investor behaviour. To explore this hypothesis, we introduce an agent-based model of financial markets, where trading agents are embedded in a hierarchical network of communities, and communities influence the strategies and opinions of traders. Empirical analysis of the model shows that its behaviour conforms to several stylized facts observed in real financial markets; and the model is able to realistically simulate the effects that social media-driven phenomena, such as echo chambers and pump-and-dump schemes, have on financial markets. ...

Multiple-bubble testing in the cryptocurrency market: a case study of bitcoin ArXiv ID: 2401.05417 “View on arXiv” Authors: Unknown Abstract Economic periods and financial crises have highlighted the importance of evaluating financial markets to investors and researchers in recent decades. Keywords: financial markets, economic periods, financial crises, market evaluation, General Financial Markets Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper applies advanced statistical methods like the Right-Tail Augmented Dickey–Fuller (RTADF) test, indicating significant mathematical modeling, but the excerpt shows no implementation details, backtesting results, or data processing steps, resulting in low empirical readiness. flowchart TD A["Research Question<br>Identify & test for multiple bubbles<br>in the cryptocurrency market"] --> B["Data Input<br>Historical Bitcoin Price Data<br>across different time periods"] B --> C["Methodology<br>Advanced Bubble Testing<br>e.g., GSADF or SADF"] C --> D["Computational Process<br>Calculate Test Statistics<br>Identify Bubble Regimes"] D --> E["Key Findings<br>Detect multiple bubble periods<br>Assess crash risks<br>Market implications"]

From Data to Trade: A Machine Learning Approach to Quantitative Trading ArXiv ID: ssrn-4315362 “View on arXiv” Authors: Unknown Abstract “Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that lev Keywords: Machine Learning, Quantitative Trading, Algorithmic Trading, Time Series Forecasting, Financial Markets, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a broad, introductory survey of ML concepts in quantitative trading with minimal advanced mathematics or original derivations, and lacks any code, backtests, or specific empirical results. flowchart TD A["Research Goal"] --> B["Data Collection"] A --> C["ML Model Selection"] B --> D["Feature Engineering"] C --> D D --> E["Model Training"] E --> F["Backtesting"] F --> G["Key Findings"]

Prima de Riesgo del Mercado: Histórica, Esperada, Exigida e Implícita (Market Risk Premium: Historical, Expected, Required and Implied) ArXiv ID: ssrn-897676 “View on arXiv” Authors: Unknown Abstract Spanish Abstract: La Prima de Riesgo del Mercado es uno de los parámetros financieros más investigados y controvertidos, y también uno de los que más con Keywords: Risk Premium, Asset Pricing, Market Risk, Financial Markets, Spanish Literature, Equities / Market Risk ...