arXiv research

The paper models SOFR and EFFR dynamics, reconciling diffusive and piecewise paths.

Model estimates LIBOR rates and finds COVID-19 spread spike due to credit risk.

Kalshi prediction markets forecast cryptocurrency volatility through monetary policy and inflation signals.

Examines SOFR derivatives pricing and hedging post-LIBOR discontinuation.

D2P-Fed improves privacy and communication in federated learning.

LDP-Fed protects privacy in federated learning with neural networks.

Fed-FEARE model extracts rules from multiple agencies' data securely.

Fed-TDA augments federated tabular data to improve performance and privacy.

Fed-ensemble improves FL by averaging predictions from multiple models.

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. …

Using the descriptive method of log-periodic power laws (LPPL) based on a theory of behavioral herding, we use a battery of parametric and non-parametric tests to demonstrate the existence of an antibubble in the yields with maturities larger than 1 year since October 2000. The concept of ``antibubble'' describes the e…

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

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.

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

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

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…

This paper analyzes error feedback in compressed federated learning for non-convex optimization problems.

Fed-MIWAE improves federated learning by imputing missing data.

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

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

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

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.

Wavelet denoised-ResNet with LightGBM predicts Forex rate of change.

Study examines Fed's pandemic communication strategies.

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

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.

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

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…

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

Upper bounds on utility for managing heterogeneous collectivised funds.

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…

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 …

The paper derives market-based correlations between asset prices and returns.

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

Machine learning categorizes mutual funds for better investment strategies.

A new method identifies similar mutual funds using graph learning.

The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.

Investors in Target Date Funds are automatically switched from high risk to low risk assets as their retirements approach. Such funds have become very popular, but our analysis brings into question the rationale for them. Based on both a model with parameters fitted to historical returns and on bootstrap resampling, we…

By exploiting a bipartite network representation of the relationships between mutual funds and portfolio holdings, we propose an indicator that we derive from the analysis of the network, labelled the Average Commonality Coefficient (ACC), which measures how frequently the assets in the fund portfolio are present in th…

AI platforms disrupt investment by personalizing deal sourcing and insights.

Federated UCBVI reduces communication costs while minimizing regret in multi-agent settings.

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

Unified approach to federated learning improves robustness and personalization.