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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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48 results for endowment fund

We determine the optimal investment strategy in a Black-Scholes financial market to minimize the so-called {\it probability of drawdown}, namely, the probability that the value of an investment portfolio reaches some fixed proportion of its maximum value to date. We assume that the portfolio is subject to a payout that…

2015-05-30abs ↗pdf ↗

Investors adjust spending based on a social norm, spending less during losses and more during gains.

problem Managing spending and portfolio decisions while adhering to a social norm.
method Formulated a preference ordering with two CRRA preference orderings, solved analytically and numerically.
result Annual spending should be lower than expected financial return and procyclical, with spending cuts following losses.

The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.

problem Analyzing performance of competing fund managers in Ito-diffusion markets.
method Developed forward relative performance criteria and forward Nash equilibrium for passive and competitive cases.
result Extended performance criteria for investment problems in Ito-diffusion markets.

Bielecki and Rutkowski (2014) introduced and studied a generic nonlinear market model, which includes several risky assets, multiple funding accounts and margin accounts. In this paper, we examine the pricing and hedging of contract both from the perspective of the hedger and the counterparty with arbitrary initial end…

2014-10-02abs ↗pdf ↗
Ponzi Fundsq-fin.GN

Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.

problem Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
method Decomposed fund returns into price pressure and fundamental components, and identified the impact of price chasing on fund flows.
result Funds' self-inflated returns lead to wealth reallocation and market crashes, and can be predicted by fund illiquidity.

This report was originally written as an industry white paper on Hedge Funds. This paper gives an overview to Hedge Funds, with a focus on risk management issues. We define and explain the general characteristics of Hedge Funds, their main investment strategies and the risk models employed. We address the problems in H…

2009-04-17abs ↗pdf ↗

Deep learning models predict mutual funds' performance better than traditional methods.

problem Predicting mutual funds' performance accurately.
method Deep learning models (LSTM, GRUs) trained with Bayesian optimization and ensemble methods.
result Ensemble method of LSTM and GRUs achieves the highest accuracy in forecasting mutual funds' Sharpe ratios.

This paper optimizes perpetual contract liquidity by accounting for funding rates.

problem Optimal liquidity provision for perpetual contracts with stochastic funding rates.
method Formulated a control problem, solved with a HJB scheme, and calibrated on real data.
result Funding-aware market making improves performance and reduces inventory risk.

Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.

problem Active liquidity management by mutual funds in India.
method Examined cash holdings and stock purchases of Indian equity mutual funds.
result Funds with active liquidity choices outperform, highlighting the importance of this strategy.

Blockchain protocol improves traditional mutual funds with performance fees and investor protection.

problem Operational issues and performance fees in traditional mutual funds.
method Developed a blockchain protocol that integrates features of mutual funds and hedge funds.
result Blockchain can simplify performance fee calculations and protect investors.

Study uses machine learning and PolyModel to improve hedge fund performance.

problem Improving hedge fund investment performance with machine learning.
method Integration of machine learning techniques, PolyModel feature selection, and analysis of fund size.
result Machine learning enhances cumulative returns but increases annual volatility.

In this paper we investigate the relationship between Funding Value Adjustment (FVA) and Net Stable Funding Ratio (NSFR). FVA is defined in a consistent way with NSFR such that the new framework of FVA monitors the costs due to keeping NSFR at an acceptable level, as well. In addition, the problem of choosing the optim…

2017-01-01abs ↗pdf ↗

Study examines Indian equity mutual funds' investment style and risk-shifting.

problem Understanding how Indian equity mutual funds' investment styles affect their returns.
method Estimating size and style beta coefficients, identifying breakpoints, analyzing investment styles, and assessing risk-shifting intensity.
result Funds can enhance returns by shifting to high-return styles like Small Value and Small Blend.

Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.

problem Building a sparse portfolio from mutual funds' favorite stocks in a market with limited fund information.
method Analyzed mutual fund favorite stocks, used portfolio optimizer with constraints, and compared different methods.
result Sparse portfolios consistently outperform the benchmark index 930950.CSI.

A fund manager invests both the fund's assets and own private wealth in separate but potentially correlated risky assets, aiming to maximize expected utility from private wealth in the long run. If relative risk aversion and investment opportunities are constant, we find that the fund's portfolio depends only on the fu…

2012-08-23abs ↗pdf ↗

Study uses topic modeling and sentiment analysis to uncover hedge fund performance insights.

problem Hedge fund opacity and limited disclosure make them hard to analyze.
method Applied topic modeling and sentiment analysis to hedge fund documents using DistilBERT and Top2Vec.
result Automated topic modeling and sentiment analysis can predict hedge fund performance.

This paper examines the relationship between Inverse Perpetual Swap contracts, a Bitcoin derivative akin to futures and the margin funding interest rates levied on BitMEX. This paper proves the Heteroskedastic nature of funding rates and goes onto establish a causal relationship between the funding rates and the Bitcoi…

2019-11-26abs ↗pdf ↗

Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity bu…

2013-10-12abs ↗pdf ↗

In this note we sketch an initial tentative approach to funding costs analysis and management for contracts with bilateral counterparty risk in a simplified setting. We depart from the existing literature by analyzing the issue of funding costs and benefits under the assumption that the associated risks cannot be hedge…

2014-10-08abs ↗pdf ↗

New methods for equity fund selection and portfolio construction using mutual fund top holdings.

problem Classic equity fund selection and portfolio construction problems.
method Propose an easy-to-implement framework to produce a long-short portfolio from mutual fund top holdings.
result Generate impressive results and show statistical evidence.

Upper bounds on utility for managing heterogeneous collectivised funds.

problem Managing pension funds with diverse investor preferences and mortality.
method Axiomatic approach to define optimal management strategies.
result Asymptotically optimal strategies for maximizing investor utility.

The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a Fund of Hedge Funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which…

2010-05-27abs ↗pdf ↗

The study quantifies the impact of fund miscategorization using machine learning.

problem The impact of fund miscategorization on investment decisions.
method Formulated as a distance-based outlier detection problem, used Random Forest based distance metric learning.
result Identified funds with strong relationship to future returns as outliers.

AI platforms disrupt investment by personalizing deal sourcing and insights.

problem Lack of scalable, personalized, and privacy-compliant deal sourcing and insights solutions.
method Development of in-house AI platforms that interact directly with funds and learn from interactions.
result AI platforms provide smarter, personalized use cases for funds, offering a competitive advantage.

The paper uses clustering and integer programming to optimize stock selection for investment funds.

problem Maximizing profits and minimizing risk in stock markets.
method Data-oriented analysis and clustering techniques with integer programming.
result Reconstructed NASDAQ 100 index fund example demonstrates effectiveness.

Study on pooled annuity funds and how initial savings affect income stability.

problem Analyzing the stability of income payments in pooled annuity funds.
method Examining the influence of initial savings on income fluctuations and developing a criterion for pooling funds.
result Identification of a term, the 'implied number of homogeneous members', linking initial savings to income fluctuations.

We quantify the benefit of collectivised investment funds, in which the assets of members who die are shared among the survivors. For our model, with realistic parameter choices, an annuity or individual fund requires approximately 20\% more initial capital to provide as good an outcome as a collectivised investment fu…

2019-09-27abs ↗pdf ↗

Study optimizes funding rates for cryptocurrency perpetual futures to maintain price alignment.

problem Maintaining alignment between perpetual future prices and target values in cryptocurrency markets.
method Developed replicating portfolios and path-dependent funding rates using path-dependent infinite-horizon BSDEs and arbitrage pricing theory.
result Appropriate funding rate design can keep perpetual future prices aligned with target values.

Analyzes how many people can receive stable income in a pooled annuity fund.

problem Quantifying the trade-off between income stability and the number of members in a pooled annuity fund.
method Investment returns held constant, systematic longevity risk omitted. Derived an analytical expression for income stability.
result The number of fund members who receive stable income is independent of the mortality model.

We discuss the binary nature of funding impact in derivative valuation. Under some conditions, funding is either a cost or a benefit, i.e., one of the lending/borrowing rates does not play a role in pricing derivatives. When derivatives are priced, considering different lending/borrowing rates leads to semi-linear BSDE…

2017-03-01abs ↗pdf ↗

Study categorizes mutual funds using natural language processing from unstructured data.

problem Categorizing mutual funds using unstructured data for financial analysis.
method Used natural language processing models to classify mutual funds from their investment strategy descriptions.
result High accuracy in categorizing mutual funds using NLP from unstructured data.

Study shows mutual funds add little value for uninformed investors.

problem Understanding the performance of actively managed equity mutual funds for uninformed investors.
method Constructed a reference portfolio using prices and supply information, analyzed various subsets of funds, and compared to market index.
result Mutual funds provide insignificant alpha for uninformed investors, with negative and significant alpha when compared to the market index.