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.
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.
Study finds stock selection ability of Chinese mutual funds is better than asset allocation ability.
problem Evaluating the performance of actively managed mutual funds in China.
method Developed performance measures for asset allocation and selection using holding-based models and compared them with Fama-French and Treynor-Mazuy models.
result Stock selection ability from holding-based models is positively correlated with Fama-French model, while industry allocation is positively correlated with Treynor-Mazuy model.
Knockoffs method selects financial factors, controlling false discoveries.
problem Controlling false discoveries in financial factor selection.
method Apply knockoff procedure to build fake factors.
result Shows versatility in fund replication and network inference.
The study designs a green investment fund and a hedging strategy for insurance policies linked to it.
problem Hedging unit-linked life insurance policies with an environmentally sensitive investment fund.
method Developed a carbon-intensity-driven portfolio selection rule and a quadratic hedging approach.
result The hedging strategy minimizes the variance of hedging costs, as demonstrated through numerical analysis.
Improved estimation of hedge fund tail risks using a novel model.
problem Estimation inefficiencies and need for manual threshold selection in extreme value regression models.
method Extended tail regression model with automatic threshold selection and artificial censoring.
result Significant link between tail risks and factors like equity momentum and financial stability index.
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.
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.
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.
Model predicts activist fund targets with 78.2% accuracy.
problem Predicting activist fund targets to mitigate risks and inform investments.
method Evaluated 123 model configurations using machine learning techniques.
result Best model achieved AUC-ROC of 0.782.
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.
This paper considers the problem of isolating a small number of exchange traded funds (ETFs) that suffice to capture the fundamental dimensions of variation in U.S. financial markets. First, the data is fit to a vector-valued Bayesian regression model, which is a matrix-variate generalization of the well known stochast…
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
problem Evaluating the impact of algorithmic policy decisions.
method Developed a treatment-effect estimator using algorithmic decisions as instruments.
result Funding from an algorithmic relief rule had little effect on COVID-19-related hospital activities.
This paper improves fund net value prediction using ARIMA-LSTM hybrid model.
problem Inaccurate prediction of fund net value due to linear characteristics of models.
method ARIMA-LSTM hybrid model: ARIMA for linear characteristics, LSTM for nonlinear.
result Hybrid model shows higher accuracy in predicting fund net value.
Paper proposes a novel metric learning algorithm using Riemannian optimization.
problem Optimizing a smooth, convex function in Riemannian space with constraints.
method Developed a primal-dual algorithm with proximal operator for iterative optimization.
result Demonstrated the efficacy of the proposed metric learning algorithm on fund selection.
Exchange Traded Funds (ETFs) have been gaining increasing popularity in the investment community as is evidenced by the high growth both in the number of ETFs and their net assets since 2000. As ETFs are in nature similar to index mutual funds, in this paper we examined if this growing demand for ETFs can be explained …
A new model selects low-carbon mutual funds considering ESG criteria, risk, and investor preferences.
problem Aligning financial investments with a low-carbon economy.
method Tri-criterion portfolio selection model using a preference-based multi-objective genetic algorithm (ev-MOGA).
result The model successfully incorporates carbon risk exposure and loss-adverse attitudes into portfolio construction.
This paper discusses the financial risks faced by the UK Pension Protection Fund (PPF) and what, if anything, it can do about them. It draws lessons from the regulatory regimes under which other financial institutions, such as banks and insurance companies, operate and asks why pension funds are treated differently. It…
The study examines biases in Kiva.org's microfinance platform and proposes methods to mitigate them.
problem Identifying and mitigating hidden biases in lender preferences for different sectors on Kiva.org.
method The study uses causal inference and regression models with Bayesian variable selection methods to investigate and quantify biases. Fairness constraints are then incorporated to maintain comparable results.
result The influence of economic factors and loan attributes on lender preferences varies by sector, and models can be adjusted to mitigate biases.
The paper studies problem of continuous time optimal portfolio selection for a incom- plete market diffusion model. It is shown that, under some mild conditions, near optimal strategies for investors with different performance criteria can be constructed using a limited number of fixed processes (mutual funds), for a m…
Paper classifies institutions based on credit, debit, and funding adjustment paradigms.
problem Classifying institutions based on credit, debit, and funding adjustment paradigms.
method Mathematical framework based on the principle of invariance.
result Improved solution of principle of invariance equations for accurate metrics calculation.
A framework for goal-based investing with penalties for fund transfers.
problem Investors' mental accounting and multiple investment goals.
method Continuous-time portfolio selection with mental costs and penalties.
result The value function is the unique solution to a complex system of equations.
PHBench predicts Series A funding from Product Hunt launch signals with 7.8% accuracy.
problem Predicting startup Series A funding from launch signals on Product Hunt.
method Constructed PHBench from 67,292 Product Hunt posts, linked to funding records, and used a three-component ensemble model.
result Best-performing model achieved F0.5 = 0.097 and AP = 0.037, with a statistically significant advantage over logistic regression.
PolyModel theory and iTransformer improve hedge fund portfolio construction.
problem Sparse financial time series data makes portfolio construction challenging.
method Identify asset pool, select risk factors, create quantitative and classical measures, and use iTransformer for trend capture.
result Improved Sharpe ratio and annualized return compared to benchmarks.
This paper uses SARIMA models to forecast Nifty 50 index.
problem Forecasting the movement of Nifty 50 index.
method Applied Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.
result The most appropriate SARIMA model was selected based on AIC criteria.
We consider and extend the adversarial agent-based learning approach of Gy{ö}rfi {\it et al} to the situation of zero-cost portfolio selection implemented with a quadratic approximation derived from the mutual fund separation theorems. The algorithm is applied to daily sampled sequential Open-High-Low-Close data and se…
We propose a novel approach for analysis of the composition of an equity mutual fund based on the time series decomposition of the price movements of the individual stocks of the fund. The proposed scheme can be applied to check whether the style proclaimed for a mutual fund actually matches with the fund composition. …
Optimal portfolios are found for a wide range of utility functions under hyperbolic returns.
problem Portfolio optimization under expected utility criterion for large portfolios.
method Analytical expressions for optimal portfolios under hyperbolic return distributions and various utility functions.
result The two-fund separation holds true for a broad class of utility functions.
Blockchain funds balance risk and return for various investors.
problem Creating diversified portfolios with risk parity for different risk appetites.
method Developed three funds (Alpha, Beta, Gamma) with distinct risk and return profiles, setting weights inversely proportional to risk.
result Blockchain enables investors to select their preferred risk-return combination and allocate wealth accordingly.
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.
PEARL uses AI to replicate private equity performance with liquid assets.
problem Lack of access to private equity due to high costs and complexity.
method Combines AI with liquid assets, incorporating asymmetry for better performance.
result Model outperforms liquid proxies and aligns with private equity benchmarks.
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…
Study on CEF discount in Bangladesh, finds size and maturity impact, turnover negative.
problem Exploring the discount puzzle in closed-end mutual funds in Bangladesh.
method Fixed effects panel regression with diagnostic tests.
result Fund size and maturity positively impact CEF discount, turnover negatively impacts.
Dynamic retirement glidepaths evolve over time based on some measure such as the retiree's funded status or current market valuations. Conversely, static glidepaths are fixed at a starting point and selected under the assumption that they will not change. In practice, new static glidepaths may be derived periodically m…
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.
This paper examines the risk-adjusted performance and differential fund flows for socially responsible mutual funds (SRMF). The results show that SRMF rated high on ESG, perform better than lower rated ESG funds during the period of economic crisis. The findings also show that low ESG rated SRMF had higher differential…
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.
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…
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.
A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.
problem Selecting projects with uncertain long-term benefits.
method Combining Quicksort and Bradley-Terry model for ranking projects based on uncertain long-term benefits.
result Proposed methods outperform existing aggregation methods and can be combined with sampling techniques.
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…
Estimates growth loss in fund models and proposes a shrinkage method.
problem Estimating growth loss in fund models under frequentist and Bayesian estimation.
method Proposes a shrinkage method to target maximal growth with minimal deviation.
result Empirical evidence shows shrinkage gives a stable estimate closer to growth potential.
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…
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…
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…