arXiv research

New method estimates portfolio turnover using covariance matrix of returns.

Turnover-adjusted IR is always lower than classic IR, suggesting managers can improve performance by limiting turnover.

This study explains and mitigates inflated returns and turnover in SPO-based portfolio optimization.

Optimized portfolio turnover strategies enhance wealth and reduce costs.

We analyze empirical data for 4,000 real-life trading portfolios (U.S. equities) with holding periods of about 0.7-19 trading days. We find a simple scaling C ~ 1/T, where C is cents-per-share, and T is the portfolio turnover. Thus, the portfolio return R has no statistically significant dependence on the turnover T. W…

Study improves portfolio optimization by reducing estimation errors and turnover.

It is well known that combining multiple hedge fund alpha streams yields diversification benefits to the resultant portfolio. Additionally, crossing trades between different alpha streams reduces transaction costs. As the number of alpha streams increases, the relative turnover of the portfolio decreases as more trades…

We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized …

The paper calculates optimal trading turnover in terms of asset liquidity and alpha autocorrelation.

Quantum stochastic walks optimize portfolios by leveraging financial networks, improving Sharpe ratios and reducing turnover.

Internal crossing of trades between multiple alpha streams results in portfolio turnover reduction. Turnover reduction can be modeled using the correlation structure of the alpha streams. As more and more alphas are added, generally turnover reduces. In this note we use a factor model approach to address the question o…

Study compares optimal vs. naive diversification in crypto markets, finds time-varying moments improve performance.

The paper examines how trading strategies lose value due to stock turnover.

Bayesian Black-Litterman model updated to use market data for latent variables.

We advocate the use of Agnostic Allocation for the construction of long-only portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a special member of a family of risk-based portfolios that are able to mitigate certain extreme features (excess concentration, high turnover, strong exposure to low-…

Despite the availability of very detailed data on financial market, agent-based modeling is hindered by the lack of information about real trader behavior. This makes it impossible to validate agent-based models, which are thus reverse-engineering attempts. This work is a contribution to the building of a set of styliz…

HRT uses bi-level reinforcement learning to optimize stock selection and execution in multi-asset equity markets.

Average Oracle outperforms DCC+NLS in portfolio optimization.

We find that when measured in terms of dollar-turnover, and once $β$-neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of $5.1$, the "Cold-Minus-Hot" (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to marke…

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

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PM…

FR-LUX optimizes portfolio management by learning cost-aware policies robust to market conditions.

Enhances trading metrics with financially grounded loss functions.

Improved portfolio optimization method yields better risk-adjusted returns.

Portfolio managers are typically constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style (such as growth or value). In addition, portfolio managers often use multifactor stock models to choose stocks based upon their respe…

Sophisticated volatility models outperform naive portfolio strategies.

Commodity ETFs' portfolio optimization under heavy-tailed returns.

Paper develops a robust hedging framework to reduce market risk and uncertainty.

Integrates ESG data into Black-Litterman for portfolio optimization.

Unified framework combines views and optimization for better portfolio management.

A new model forecasts optimal portfolio weights from high-frequency data.

We discuss investment allocation to multiple alpha streams traded on the same execution platform with internal crossing of trades and point out differences with allocating investment when alpha streams are traded on separate execution platforms with no crossing. First, in the latter case allocation weights are non-nega…

Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.

In this short report, we discuss how coordinate-wise descent algorithms can be used to solve minimum variance portfolio (MVP) problems in which the portfolio weights are constrained by $l_{q}$ norms, where $1\leq q \leq 2$. A portfolio which weights are regularised by such norms is called a sparse portfolio (Brodie et …

This article develops the theory of risk budgeting portfolios, when we would like to impose weight constraints. It appears that the mathematical problem is more complex than the traditional risk budgeting problem. The formulation of the optimization program is particularly critical in order to determine the right risk …

Accounting for the non-normality of asset returns remains challenging in robust portfolio optimization. In this article, we tackle this problem by assessing the risk of the portfolio through the "amount of randomness" conveyed by its returns. We achieve this by using an objective function that relies on the exponential…

We introduce a financial portfolio optimization framework that allows us to automatically select the relevant assets and estimate their weights by relying on a sorted $\ell_1$-Norm penalization, henceforth SLOPE. Our approach is able to group constituents with similar correlation properties, and with the same underlyin…

Bayesian Parametric Portfolio Policies corrects overestimation of utility and risk in traditional PPP.

VNA solves large portfolio optimization problems efficiently.

We show that any immersion, which is not a covering of an embedded 2-orbifold, of a totally geodesic hyperbolic turnover in a complete orientable hyperbolic 3-orbifold is contained in a hyperbolic 3-suborbifold with totally geodesic boundary, called the "turnover core,'' whose volume is bounded from above by a function…

D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.

Different optimizer choices lead to different financial model predictions.

Proposes an efficient method for sparse index tracking with $\ell_0$-norm constraints.

BOA improves financial forecasting by combining expert models.

Study projective deformations of hyperbolic 3-orbifolds with turnover ends.

Robustifies Markowitz portfolios to reduce transaction costs and improve performance.

SBCA optimizes portfolios by fusing price data and text sentiment.

We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corr…