Market Dynamics

In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis ArXiv ID: ssrn-3686935 “View on arXiv” Authors: Unknown Abstract We develop a framework for analyzing stock market fluctuations, both theoretically and empirically. Households allocate capital to institutions with limited fle Keywords: Stock Market Fluctuations, Household Portfolio Allocation, Capital Flows, Institutional Investors, Market Dynamics, Equity Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs advanced theoretical modeling and econometrics (GIV) to derive and estimate market elasticity, demonstrating high mathematical sophistication. Empirical analysis uses granular instrumental variables and macro data to quantify a key parameter ($5 impact per $1 invested), making it data-heavy and implementation-ready. flowchart TD A["Research Goal: Investigate origins of stock market fluctuations"] --> B["Key Methodology: Inelastic Markets Hypothesis (IMH) Framework"] B --> C["Data/Inputs: Household capital allocation to institutions, institutional equity holdings"] C --> D["Computational Process: Theoretical modeling & empirical analysis of capital flows"] D --> E["Key Outcomes: Capital flows drive price fluctuations, explains market inelasticity"]

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"]

Trade Execution Flow as the Underlying Source of Market Dynamics ArXiv ID: 2511.01471 “View on arXiv” Authors: Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov, Alexander Yurievich Maslov, Olga Vladimirovna Proshina, Vladislav Gennadievich Malyshkin Abstract In this work, we demonstrate experimentally that the execution flow, $I = dV/dt$, is the fundamental driving force of market dynamics. We develop a numerical framework to calculate execution flow from sampled moments using the Radon-Nikodym derivative. A notable feature of this approach is its ability to automatically determine thresholds that can serve as actionable triggers. The technique also determines the characteristic time scale directly from the corresponding eigenproblem. The methodology has been validated on actual market data to support these findings. Additionally, we introduce a framework based on the Christoffel function spectrum, which is invariant under arbitrary non-degenerate linear transformations of input attributes and offers an alternative to traditional principal component analysis (PCA), which is limited to unitary invariance. ...

Advanced simulation paradigm of human behaviour unveils complex financial systemic projection ArXiv ID: 2503.20787 “View on arXiv” Authors: Unknown Abstract The high-order complexity of human behaviour is likely the root cause of extreme difficulty in financial market projections. We consider that behavioural simulation can unveil systemic dynamics to support analysis. Simulating diverse human groups must account for the behavioural heterogeneity, especially in finance. To address the fidelity of simulated agents, on the basis of agent-based modeling, we propose a new paradigm of behavioural simulation where each agent is supported and driven by a hierarchical knowledge architecture. This architecture, integrating language and professional models, imitates behavioural processes in specific scenarios. Evaluated on futures markets, our simulator achieves a 13.29% deviation in simulating crisis scenarios whose price increase rate reaches 285.34%. Under normal conditions, our simulator also exhibits lower mean square error in predicting futures price of specific commodities. This technique bridges non-quantitative information with diverse market behaviour, offering a promising platform to simulate investor behaviour and its impact on market dynamics. ...

LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance ArXiv ID: 2502.15800 “View on arXiv” Authors: Unknown Abstract This paper explores how Large Language Models (LLMs) behave in a classic experimental finance paradigm widely known for eliciting bubbles and crashes in human participants. We adapt an established trading design, where traders buy and sell a risky asset with a known fundamental value, and introduce several LLM-based agents, both in single-model markets (all traders are instances of the same LLM) and in mixed-model “battle royale” settings (multiple LLMs competing in the same market). Our findings reveal that LLMs generally exhibit a “textbook-rational” approach, pricing the asset near its fundamental value, and show only a muted tendency toward bubble formation. Further analyses indicate that LLM-based agents display less trading strategy variance in contrast to humans. Taken together, these results highlight the risk of relying on LLM-only data to replicate human-driven market phenomena, as key behavioral features, such as large emergent bubbles, were not robustly reproduced. While LLMs clearly possess the capacity for strategic decision-making, their relative consistency and rationality suggest that they do not accurately mimic human market dynamics. ...

Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets ArXiv ID: 2410.14587 “View on arXiv” Authors: Unknown Abstract Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when they infer financial value in a semi-autonomous way. In this work, we explore the interplay between deep generative models and market dynamics. We develop a form of virtual traders that use deep generative models to make buy/sell decisions, which we term neuro-symbolic traders, and expose them to a virtual market. Under our framework, neuro-symbolic traders are agents that use vision-language models to discover a model of the fundamental value of an asset. Agents develop this model as a stochastic differential equation, calibrated to market data using gradient descent. We test our neuro-symbolic traders on both synthetic data and real financial time series, including an equity stock, commodity, and a foreign exchange pair. We then expose several groups of neuro-symbolic traders to a virtual market environment. This market environment allows for feedback between the traders belief of the underlying value to the observed price dynamics. We find that this leads to price suppression compared to the historical data, highlighting a future risk to market stability. Our work is a first step towards quantifying the effect of deep generative agents on markets dynamics and sets out some of the potential risks and benefits of this approach in the future. ...

Behavioral Portfolio Management ArXiv ID: ssrn-2210032 “View on arXiv” Authors: Unknown Abstract Behavioral Portfolio Management (BPM) is presented as a superior way to make investment decisions. Underlying BPM is the dynamic market interplay between Emotio Keywords: Behavioral Finance, Portfolio Management, Market Dynamics, Investment Strategy, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is primarily a conceptual framework discussing behavioral finance principles and critiques of MPT, lacking advanced mathematical derivations or statistical models, and presents only conceptual evidence rather than backtest-ready data or implementation details. flowchart TD A["Research Goal: Develop Behavioral Portfolio Management\nBPM as superior investment methodology"] --> B["Methodology: Quantifying Market Dynamics\nSimulating multi-asset interplay"] B --> C["Data: Historical Multi-Asset Returns\nBehavioral indicator datasets"] C --> D["Computational Process: Dynamic Optimization\nvs Traditional Models"] D --> E["Key Outcomes: BPM Outperformance\nRisk-adjusted returns & behavioral alpha"]