Hedge Funds

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"]

Unveiling Hedge Funds: Topic Modeling and Sentiment Correlation with Fund Performance ArXiv ID: 2512.06620 “View on arXiv” Authors: Chang Liu Abstract The hedge fund industry presents significant challenges for investors due to its opacity and limited disclosure requirements. This pioneering study introduces two major innovations in financial text analysis. First, we apply topic modeling to hedge fund documents-an unexplored domain for automated text analysis-using a unique dataset of over 35,000 documents from 1,125 hedge fund managers. We compared three state-of-the-art methods: Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic. Our findings reveal that LDA with 20 topics produces the most interpretable results for human users and demonstrates higher robustness in topic assignments when the number of topics varies, while Top2Vec shows superior classification performance. Second, we establish a novel quantitative framework linking document sentiment to fund performance, transforming qualitative information traditionally requiring expert interpretation into systematic investment signals. In sentiment analysis, contrary to expectations, the general-purpose DistilBERT outperforms the finance-specific FinBERT in generating sentiment scores, demonstrating superior adaptability to diverse linguistic patterns found in hedge fund documents that extend beyond specialized financial news text. Furthermore, sentiment scores derived using DistilBERT in combination with Top2Vec show stronger correlations with subsequent fund performance compared to other model combinations. These results demonstrate that automated topic modeling and sentiment analysis can effectively process hedge fund documents, providing investors with new data-driven decision support tools. ...

PolyModel for Hedge Funds’ Portfolio Construction Using Machine Learning ArXiv ID: 2412.11019 “View on arXiv” Authors: Unknown Abstract The domain of hedge fund investments is undergoing significant transformation, influenced by the rapid expansion of data availability and the advancement of analytical technologies. This study explores the enhancement of hedge fund investment performance through the integration of machine learning techniques, the application of PolyModel feature selection, and the analysis of fund size. We address three critical questions: (1) the effect of machine learning on trading performance, (2) the role of PolyModel feature selection in fund selection and performance, and (3) the comparative reliability of larger versus smaller funds. Our findings offer compelling insights. We observe that while machine learning techniques enhance cumulative returns, they also increase annual volatility, indicating variability in performance. PolyModel feature selection proves to be a robust strategy, with approaches that utilize a comprehensive set of features for fund selection outperforming more selective methodologies. Notably, Long-Term Stability (LTS) effectively manages portfolio volatility while delivering favorable returns. Contrary to popular belief, our results suggest that larger funds do not consistently yield better investment outcomes, challenging the assumption of their inherent reliability. This research highlights the transformative impact of data-driven approaches in the hedge fund investment arena and provides valuable implications for investors and asset managers. By leveraging machine learning and PolyModel feature selection, investors can enhance portfolio optimization and reassess the dependability of larger funds, leading to more informed investment strategies. ...

Crisis Alpha: A High-Performance Trading Algorithm Tested in Market Downturns ArXiv ID: 2409.14510 “View on arXiv” Authors: Unknown Abstract Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1,000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions. ...

Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer ArXiv ID: 2408.03320 “View on arXiv” Authors: Unknown Abstract When constructing portfolios, a key problem is that a lot of financial time series data are sparse, making it challenging to apply machine learning methods. Polymodel theory can solve this issue and demonstrate superiority in portfolio construction from various aspects. To implement the PolyModel theory for constructing a hedge fund portfolio, we begin by identifying an asset pool, utilizing over 10,000 hedge funds for the past 29 years’ data. PolyModel theory also involves choosing a wide-ranging set of risk factors, which includes various financial indices, currencies, and commodity prices. This comprehensive selection mirrors the complexities of the real-world environment. Leveraging on the PolyModel theory, we create quantitative measures such as Long-term Alpha, Long-term Ratio, and SVaR. We also use more classical measures like the Sharpe ratio or Morningstar’s MRAR. To enhance the performance of the constructed portfolio, we also employ the latest deep learning techniques (iTransformer) to capture the upward trend, while efficiently controlling the downside, using all the features. The iTransformer model is specifically designed to address the challenges in high-dimensional time series forecasting and could largely improve our strategies. More precisely, our strategies achieve better Sharpe ratio and annualized return. The above process enables us to create multiple portfolio strategies aiming for high returns and low risks when compared to various benchmarks. ...

Is the annualized compounded return of Medallion over 35%? ArXiv ID: 2405.10917 “View on arXiv” Authors: Unknown Abstract It is a challenge to estimate fund performance by compounded returns. Arguably, it is incorrect to use yearly returns directly for compounding, with reported annualized return of above 60% for Medallion for the 31 years up to 2018. We propose an estimation based on fund sizes and trading profits and obtain a compounded return of 31.8% before fees. Alternatively, we suggest using the manager’s wealth as a proxy and arriving at a compounded growth rate of 25.6% for Simons for the 33 years up to 2020. We conclude that the annualized compounded return of Medallion before fees is probably under 35%. Our findings have implications for correctly estimating fund performance. ...

Optimal fees in hedge funds with first-loss compensation ArXiv ID: 2310.19023 “View on arXiv” Authors: Unknown Abstract Hedge fund managers with the first-loss scheme charge a management fee, a performance fee and guarantee to cover a certain amount of investors’ potential losses. We study how parties can choose a mutually preferred first-loss scheme in a hedge fund with the manager’s first-loss deposit and investors’ assets segregated. For that, we solve the manager’s non-concave utility maximization problem, calculate Pareto optimal first-loss schemes and maximize a decision criterion on this set. The traditional 2% management and 20% performance fees are found to be not Pareto optimal, neither are common first-loss fee arrangements. The preferred first-loss coverage guarantee is increasing as the investor’s risk-aversion or the interest rate increases. It decreases as the manager’s risk-aversion or the market price of risk increases. The more risk averse the investor or the higher the interest rate, the larger is the preferred performance fee. The preferred fee schemes significantly decrease the fund’s volatility. ...

Hedge Funds, Systemic Risk, and the Financial Crisis of 2007-2008: Written Testimony for the House Oversight Committee Hearing on Hedge Funds ArXiv ID: ssrn-1301217 “View on arXiv” Authors: Unknown Abstract This document is the written testimony submitted to the House Oversight Committee for its hearing on hedge funds and the financial crisis, held November 13, 200 Keywords: Hedge Funds, Financial Crisis, Systemic Risk, Regulatory Policy, Hedge Funds Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The document is a policy-oriented testimony with no mathematical formulas, derivations, or backtesting; it focuses on conceptual discussions of systemic risk and regulatory proposals rather than quantitative modeling or empirical data analysis. flowchart TD A["Research Goal: Assess hedge fund<br>role in the 2007-2008 crisis"] --> B["Data Collection & Methodology"] B --> C["Regulatory Analysis<br>Existing Frameworks"] B --> D["Empirical Analysis<br>Market Stress Events"] C & D --> E["Computational Processes<br>Systemic Risk Modeling"] E --> F{"Key Findings/Outcomes"} F --> G["Regulatory Gaps Identified"] F --> H["Policy Recommendations<br>for Oversight"]