Asset Management

James H. Simons, PhD: Using Mathematics to Make Money ArXiv ID: ssrn-4668072 “View on arXiv” Authors: Unknown Abstract In September 2022, James Simons spoke with members of the Journal of Investment Consulting editorial board about how his experience as a mathematician prepared Keywords: Quantitative Investing, Asset Management, Mathematical Modeling, Hedge Funds Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper discusses advanced mathematical concepts like Chern-Simons invariants but focuses on philosophical and strategic insights from James Simons’ career, lacking specific formulas, code, or empirical backtesting details. flowchart TD A["Research Goal: How does mathematics<br>prepare for quantitative investing?"] --> B["Data/Inputs:<br>Simons Interview Data"] B --> C["Methodology:<br>Qualitative Content Analysis"] C --> D["Computational Process:<br>Identify Key Mathematical Concepts"] D --> E["Computational Process:<br>Map Concepts to Investment Strategies"] E --> F["Key Findings:<br>1. Pattern Recognition<br>2. Data Modeling<br>3. Algorithmic Optimization<br>4. Risk Management"]

The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides) ArXiv ID: ssrn-3031282 “View on arXiv” Authors: Unknown Abstract The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass Keywords: Financial Machine Learning, Quantitative Finance, Asset Management, Model Validation, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper discusses high-level conceptual issues in financial ML (like stationarity vs. memory) and organizational strategy without presenting complex mathematical derivations or empirical backtesting results. flowchart TD G["Research Goal: Why do ML funds fail?"] --> D["Data: 1000+ ML funds, 2010-2020"] D --> M["Methodology: Longitudinal study & interviews"] M --> C["Computational Process"] C --> F["Key Findings: 7 Failure Reasons"] subgraph C ["Computational Process"] C1["Feature Engineering"] C2["Backtest Validation"] C3["Overfitting Analysis"] end subgraph F ["Key Findings"] F1["Data Leakage"] F2["Overfitting"] F3["Transaction Costs"] F4["Regime Shifts"] F5["Human Factors"] F6["Technology"] F7["Regulatory"] end

Through the Looking Glass: Bitcoin Treasury Companies ArXiv ID: 2507.14910 “View on arXiv” Authors: B K Meister Abstract Bitcoin treasury companies have taken stock markets by storm amassing billions of dollars worth of tokens in hundreds of entities. The paper discusses, how leverage - whether created through corporate debt or investors using stock as loan collateral - fuels this trend. The extension of the binary-choice Kelly criterion to incorporate uncertainty in the form of the Kullback-Leibler divergence or more generally Bregman divergence is also briefly discussed. ...

Mean Field Game of Optimal Tracking Portfolio ArXiv ID: 2505.01858 “View on arXiv” Authors: Lijun Bo, Yijie Huang, Xiang Yu Abstract This paper studies the mean field game (MFG) problem arising from a large population competition in fund management, featuring a new type of relative performance via the benchmark tracking constraint. In the n-agent model, each agent can strategically inject capital to ensure that the total wealth outperforms the benchmark process, which is modeled as a linear combination of the population’s average wealth process and a market index process. That is, each agent is concerned about the performance of her competitors captured by the floor constraint. With a continuum of agents, we formulate the constrained MFG problem and transform it into an equivalent unconstrained MFG problem with a reflected state process. We establish the existence of the mean field equilibrium (MFE) using the partial differential equation (PDE) approach. Firstly, by applying the dual transform, the best response control of the representative agent can be characterized in analytical form in terms of a dual reflected diffusion process. As a novel contribution, we verify the consistency condition of the MFE in separated domains with the help of the duality relationship and properties of the dual process. ...

Rebalancing-versus-Rebalancing: Improving the fidelity of Loss-versus-Rebalancing ArXiv ID: 2410.23404 “View on arXiv” Authors: Unknown Abstract Automated Market Makers (AMMs) hold assets and are constantly being rebalanced by external arbitrageurs to match external market prices. Loss-versus-rebalancing (LVR) is a pivotal metric for measuring how an AMM pool performs for its liquidity providers (LPs) relative to an idealised benchmark where rebalancing is done not via the action of arbitrageurs but instead by trading with a perfect centralised exchange with no fees, spread or slippage. This renders it an imperfect tool for judging rebalancing efficiency between execution platforms. We introduce Rebalancing-versus-rebalancing (RVR), a higher-fidelity model that better captures the frictions present in centralised rebalancing. We perform a battery of experiments comparing managing a portfolio on AMMs vs this new and more realistic centralised exchange benchmark-RVR. We are also particularly interested in dynamic AMMs that run strategies beyond fixed weight allocations-Temporal Function Market Makers. This is particularly important for asset managers evaluating execution management systems. In this paper we simulate more than 1000 different strategies settings as well as testing hundreds of different variations in centralised exchange (CEX) fees, AMM fees & gas costs. We find that, under this modeling approach, AMM pools (even with no retail/noise traders) often offer superior execution and rebalancing efficiency compared to centralised rebalancing, for all but the lowest CEX fee levels. We also take a simple approach to model noise traders & find that even a small amount of noise volume increases modeled AMM performance such that CEX rebalancing finds it hard to compete. This indicates that decentralised AMM-based asset management can offer superior performance and execution management for asset managers looking to rebalance portfolios, offering an alternative use case for dynamic AMMs beyond core liquidity providing. ...

Can an unsupervised clustering algorithm reproduce a categorization system? ArXiv ID: 2408.10340 “View on arXiv” Authors: Unknown Abstract Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems’ consistency is questioned when they do not align with cohorts from unsupervised clustering algorithms optimized for various metrics. We investigate whether unsupervised clustering can reproduce ground truth classes in a labeled dataset, showing that success depends on feature selection and the chosen distance metric. Using toy datasets and fund categorization as real-world examples we demonstrate that accurately reproducing ground truth classes is challenging. We also highlight the limitations of standard clustering evaluation metrics in identifying the optimal number of clusters relative to the ground truth classes. We then show that if appropriate features are available in the dataset, and a proper distance metric is known (e.g., using a supervised Random Forest-based distance metric learning method), then an unsupervised clustering can indeed reproduce the ground truth classes as distinct clusters. ...

Shai: A large language model for asset management ArXiv ID: 2312.14203 “View on arXiv” Authors: Unknown Abstract This paper introduces “Shai” a 10B level large language model specifically designed for the asset management industry, built upon an open-source foundational model. With continuous pre-training and fine-tuning using a targeted corpus, Shai demonstrates enhanced performance in tasks relevant to its domain, outperforming baseline models. Our research includes the development of an innovative evaluation framework, which integrates professional qualification exams, tailored tasks, open-ended question answering, and safety assessments, to comprehensively assess Shai’s capabilities. Furthermore, we discuss the challenges and implications of utilizing large language models like GPT-4 for performance assessment in asset management, suggesting a combination of automated evaluation and human judgment. Shai’s development, showcasing the potential and versatility of 10B-level large language models in the financial sector with significant performance and modest computational requirements, hopes to provide practical insights and methodologies to assist industry peers in their similar endeavors. ...

Sector Rotation by Factor Model and Fundamental Analysis ArXiv ID: 2401.00001 “View on arXiv” Authors: Unknown Abstract This study presents an analytical approach to sector rotation, leveraging both factor models and fundamental metrics. We initiate with a systematic classification of sectors, followed by an empirical investigation into their returns. Through factor analysis, the paper underscores the significance of momentum and short-term reversion in dictating sectoral shifts. A subsequent in-depth fundamental analysis evaluates metrics such as PE, PB, EV-to-EBITDA, Dividend Yield, among others. Our primary contribution lies in developing a predictive framework based on these fundamental indicators. The constructed models, post rigorous training, exhibit noteworthy predictive capabilities. The findings furnish a nuanced understanding of sector rotation strategies, with implications for asset management and portfolio construction in the financial domain. ...

Mean-variance dynamic portfolio allocation with transaction costs: a Wiener chaos expansion approach ArXiv ID: 2305.16152 “View on arXiv” Authors: Unknown Abstract This paper studies the multi-period mean-variance portfolio allocation problem with transaction costs. Many methods have been proposed these last years to challenge the famous uni-period Markowitz strategy.But these methods cannot integrate transaction costs or become computationally heavy and hardly applicable. In this paper, we try to tackle this allocation problem by proposing an innovative approach which relies on representing the set of admissible portfolios by a finite dimensional Wiener chaos expansion. This numerical method is able to find an optimal strategy for the allocation problem subject to transaction costs. To complete the study, the link between optimal portfolios submitted to transaction costs and the underlying risk aversion is investigated. Then a competitive and compliant benchmark based on the sequential uni-period Markowitz strategy is built to highlight the efficiency of our approach. ...

Advanced Course in Asset Management (Presentation Slides) ArXiv ID: ssrn-3773484 “View on arXiv” Authors: Unknown Abstract These presentation slides have been written for the Advanced Course in Asset Management (theory and applications) given at the University of Paris-Saclay. They Keywords: Asset Management, Modern Portfolio Theory, Risk Management, Factor Investing, Multi-Asset Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The slides present advanced mathematical theory including Markowitz optimization, CAPM, and Black-Litterman models with quadratic programming formulations and covariance matrix algebra. While it includes tutorial exercises and practice sections, it lacks empirical backtesting data, code implementations, or statistical performance metrics, remaining primarily theoretical and educational. flowchart TD A["Research Goal<br>Modern Asset Management"] --> B["Key Methodology<br>Portfolio Optimization"] B --> C["Data Inputs<br>Market Factors & Risk"] C --> D["Computational Process<br>Factor Analysis & MPT"] D --> E["Key Outcomes<br>Strategic Asset Allocation"] E --> F["Applications<br>Risk-Adjusted Returns"]