Illiquidity

A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement ArXiv ID: 2509.10553 “View on arXiv” Authors: Erina Nanyonga, Juma Kasozi, Fred Mayambala, Hassan W. Kayondo, Matt Davison Abstract This study explores the behavioral dynamics of illiquid stock prices in a listed stock market. Illiquidity, characterized by wide bid and ask spreads affects price formation by decoupling prices from standard risk and return relationships and increasing sensitivity to market sentiment. We model the prices at the Uganda Securities Exchange (USE) which is illiquid in that the prices remain constant much of the time thus complicating price modelling. We circumvent this challenge by combining the Markov model (MM) with two models; the exponential Ornstein Uhlenbeck model (XOU) and geometric Brownian motion (gBm). In the combined models, the MM was used to capture the constant prices in the stock prices while the XOU and gBm captured the stochastic price dynamics. We modelled stock prices using the combined models, as well as XOU and gBm alone. We found that USE stocks appeared to have low correlation with one another. Using theoretical analysis, simulation study and empirical analysis, we conclude that this apparent low correlation is due to illiquidity. In particular data simulated from combined MM-gBm, in which the gBm portion were highly correlated resulted in a low measured correlation when the Markov chain had a higher transition from zero state to zero state. ...

A Dynamic Model of Private Asset Allocation ArXiv ID: 2503.01099 “View on arXiv” Authors: Unknown Abstract We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors’ portfolio choices. We use cutting-edge machine learning methods to quantify the optimal investment policies over the life cycle of a fund. Moreover, our model offers regulators a tool for precisely quantifying the trade-offs when setting risk-based capital charges. ...

Framework for asset-liability management with fixed-term securities ArXiv ID: 2502.19213 “View on arXiv” Authors: Unknown Abstract We consider an optimal investment-consumption problem for a utility-maximizing investor who has access to assets with different liquidity and whose consumption rate as well as terminal wealth are subject to lower-bound constraints. Assuming utility functions that satisfy standard conditions, we develop a methodology for deriving the optimal strategies in semi-closed form. Our methodology is based on the generalized martingale approach and the decomposition of the problem into subproblems. We illustrate our approach by deriving explicit formulas for agents with power-utility functions and discuss potential extensions of the proposed framework. In numerical studies, we substantiate how the parameters of our framework impact the optimal proportion of initial capital allocated to the illiquid asset, the monetary value that the investor subjectively assigns to the fixed-term asset, and the potential of the illiquid asset to increase terminal the terminal value of liabilities without loss in the investor’s expected utility. ...

Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning ArXiv ID: 2502.01495 “View on arXiv” Authors: Unknown Abstract We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets. Compared to equities, corporate bonds are relatively illiquid and both trade and quote data in these securities are relatively sparse. Thus, a measure of distance/similarity among corporate bonds is particularly useful for a variety of practical applications in the trading of illiquid bonds, including the identification of similar tradable alternatives, pricing securities with relatively few recent quotes or trades, and explaining the predictions and performance of ML models based on their training data. Previous research has explored supervised similarity learning based on classical tree-based models in this context; here, we explore the application of the QCML paradigm for supervised distance metric learning in the same context, showing that it outperforms classical tree-based models in high-yield (HY) markets, while giving comparable or better performance (depending on the evaluation metric) in investment grade (IG) markets. ...

Institutional Investors and Stock Market Volatility ArXiv ID: ssrn-837165 “View on arXiv” Authors: Unknown Abstract We present a theory of excess stock market volatility, in which market movements are due to trades by very large institutional investors in relatively illiquid Keywords: Stock Market Volatility, Institutional Investors, Illiquidity, Asset Pricing, Market Microstructure Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper presents a theoretical model using power-law distributions and optimal trading behavior derived via analytical methods, indicating high math complexity. While it references empirical stylized facts, the excerpt lacks specific data sources, code, or backtesting details, leaning more towards theoretical derivation than empirical implementation. flowchart TD A["Research Question: What causes excess stock market volatility?"] B["Methodology: Theoretical Model & Empirical Analysis"] C["Data: Institutional Trades & Stock Liquidity"] D["Process: Analyze trade impact on price deviations"] E["Key Finding: Large institutional trades drive volatility in illiquid markets"] A --> B B --> C C --> D D --> E

Institutional Investors and Stock Market Volatility ArXiv ID: ssrn-442940 “View on arXiv” Authors: Unknown Abstract We present a theory of excess stock market volatility, in which market movements are due to trades by very large institutional investors in relatively illiquid Keywords: Stock Market Volatility, Institutional Investors, Illiquidity, Asset Pricing, Market Microstructure Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 6.0/10 Quadrant: Holy Grail Why: The paper is mathematically dense, employing power-law distributions and statistical physics methods to model investor behavior, while providing strong empirical backing with real-world data on stock market volatility, returns, and trading volumes. flowchart TD A["Research Goal: Explain excess stock market volatility"] B["Theory: Large institutional investors<br>in illiquid markets drive price swings"] C["Data: Institutional trading &<br>stock liquidity measures"] D["Methodology: Empirical asset pricing<br>& market microstructure analysis"] E["Key Findings: Institutional flows<br>significantly amplify market volatility"] A --> B B --> C C --> D D --> E