arXiv research

Study efficient rebalancing strategies for portfolio tracking error.

A new index rebalancing strategy reduces large constituent weights without undesirable effects.

Theoretical and empirical study on SMOTE rebalancing strategy for imbalanced data.

The growth-optimal portfolio optimization strategy pioneered by Kelly is based on constant portfolio rebalancing which makes it sensitive to transaction fees. We examine the effect of fees on an example of a risky asset with a binary return distribution and show that the fees may give rise to an optimal period of portf…

This paper derives a robust on-line equity trading algorithm that achieves the greatest possible percentage of the final wealth of the best pairs rebalancing rule in hindsight. A pairs rebalancing rule chooses some pair of stocks in the market and then perpetually executes rebalancing trades so as to maintain a target …

This paper introduces a new metric to improve the performance of AMMs over centralised exchanges.

Study examines strategies to reduce volatility in leveraged ETF markets.

In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain paramet…

A new approach to continuous-time universal portfolios using pathwise Itô calculus.

DeepAries optimizes rebalancing intervals and asset allocations for better portfolio performance.

Geometric Mean Market Makers super-hedge impermanent loss without models.

Dynamic-weight AMMs outperform traditional CEX rebalancing in tokenized funds, especially on L2s.

Enhances portfolio performance using deep reinforcement learning and future rewards.

This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…

Algorithm beats best constant rebalancing portfolio in long-term investment.

Hybrid classical-quantum framework optimizes portfolio rebalancing with reduced transaction costs.

Model predicts trading strategies based on latent demand and price impact.

Optimizes leveraged staking strategies in decentralized finance.

The study optimizes investment portfolios using deep learning models for variance-covariance estimation.

RAmmStein optimizes liquidity management in AMMs by learning to rebalance efficiently.

A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight. By…

This note provides a neat and enjoyable expansion and application of the magnificent Ordentlich-Cover theory of "universal portfolios." I generalize Cover's benchmark of the best constant-rebalanced portfolio (or 1-linear trading strategy) in hindsight by considering the best bilinear trading strategy determined in hin…

This paper prices and replicates the financial derivative whose payoff at $T$ is the wealth that would have accrued to a $\$1$ deposit into the best continuously-rebalanced portfolio (or fixed-fraction betting scheme) determined in hindsight. For the single-stock Black-Scholes market, Ordentlich and Cover (1998) only p…

An algorithm was recently introduced by INTECH for the purposes of estimating the trading-profit contribution of systematic rebalancing to the relative return of rules-based investment strategies. We apply this methodology to analyze the size factor through the use of equal-weighted portfolios. These strategies combine…

Optimal rebalancing strategy improves AMM pool performance by 25%.

Sharp asymptotic lower bounds of the expected quadratic variation of discretization error in stochastic integration are given. The theory relies on inequalities for the kurtosis and skewness of a general random variable which are themselves seemingly new. Asymptotically efficient schemes which attain the lower bounds a…

A flexible calendar rebalancing approach for Indian stock portfolios.

Paper introduces a new method for risk-sensitive investment management using RL.

Investors can enhance their portfolios by strategically using LETFs, especially with dynamic strategies.

We consider a financial model with permanent price impact. Continuous time trading dynamics are derived as the limit of discrete rebalancing policies. We then study the problem of super-hedging a European option. Our main result is the derivation of a quasi-linear pricing equation. It holds in the sense of viscosity so…

Bayesian approach for constructing and rebalancing sparse index-tracking portfolios.

We study T. Cover's rebalancing option (Ordentlich and Cover 1998) under discrete hindsight optimization in continuous time. The payoff in question is equal to the final wealth that would have accrued to a $\$1$ deposit into the best of some finite set of (perhaps levered) rebalancing rules determined in hindsight. A r…

New dual approach for hedging Bermudan options efficiently.

Algorithm recommends trades based on crypto asset prices and market conditions.

Diversification return is an incremental return earned by a rebalanced portfolio of assets. The diversification return of a rebalanced portfolio is often incorrectly ascribed to a reduction in variance. We argue that the underlying source of the diversification return is the rebalancing, which forces the investor to se…

We introduce a pathwise approach to analyze the relative performance of an equity portfolio with respect to a benchmark market portfolio. In this energy-entropy framework, the relative performance is decomposed into three components: a volatility term, a relative entropy term measuring the distance between the portfoli…

Hybrid model uses GNNs and pathfinding to optimize portfolio rebalancing costs.

The goal of this paper is to explore the relationship between momentum effects and liquidity in cryptocurrency markets. Portfolios based on momentum-liquidity bivariate sorts are formed and rebalanced on a varying number of cryptocurrencies through time. We find a strong momentum effect in the most liquid cryptocurrenc…

When trading incurs proportional costs, leverage can scale an asset's return only up to a maximum multiple, which is sensitive to its volatility and liquidity. In a model with one safe and one risky asset, with constant investment opportunities and proportional costs, we find strategies that maximize long term returns …

We consider a market consisting of one safe and one risky asset, which offer constant investment opportunities. Taking into account both proportional transaction costs and linear price impact, we derive optimal rebalancing policies for representative investors with constant relative risk aversion and a long horizon.

Maximizes probability of completing investment schedules with optimal portfolio weights.

This paper takes a look at the Talmudic rule aka the 1/N rule aka the uniform investment strategy from the viewpoint of elementary microeconomics. Specifically, we derive the cardinal utility function for a Talmud-obeying agent which happens to have the Cobb-Douglas form. Further, we investigate individual supply and d…

New method accurately reconstructs Russell 3000 index, revealing crowded portfolios.

Discrete time hedging in a complete diffusion market is considered. The hedge portfolio is rebalanced when the absolute difference between delta of the hedge portfolio and the derivative contract reaches a threshold level. The rate of convergence of the expected squared hedging error as the threshold level approaches z…

The paper studies a rebalanced dataset for imbalanced classification using Centered Random Forests.

This paper introduces a new process for portfolio rebalancing that is more equitable than existing methods.

This review analyzes recent advances in solving index tracking problems.

In a pathbreaking paper, Cover and Ordentlich (1998) solved a max-min portfolio game between a trader (who picks an entire trading algorithm, $θ(\cdot)$) and "nature," who picks the matrix $X$ of gross-returns of all stocks in all periods. Their (zero-sum) game has the payoff kernel $W_θ(X)/D(X)$, where $W_θ(X)$ is the…