arXiv research

The study optimizes portfolios under transaction costs and model uncertainty, showing the effectiveness of turnover penalization.

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.

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…

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…

Predicts employee turnover and designs retention policies.

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

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…

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

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

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…

Optimized portfolio turnover strategies enhance wealth and reduce costs.

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…

Enhances trading metrics with financially grounded loss functions.

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…

We present explicit formulas - that are also computer code - for 101 real-life quantitative trading alphas. Their average holding period approximately ranges 0.6-6.4 days. The average pair-wise correlation of these alphas is low, 15.9%. The returns are strongly correlated with volatility, but have no significant depend…

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

SLOPE optimizes portfolios by grouping similar assets and estimating weights with sorted $\ell_1$-Norm.

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…

Study on CEF discount in Bangladesh, finds size and maturity impact, turnover negative.

Conformal prediction fails under severe feature turnover in COVID-19 supply chain tasks.

We classify the 3-dimensional hyperbolic polyhedral orbifolds that contain no embedded essential 2-suborbifolds, up to decomposition along embedded hyperbolic triangle orbifolds (turnovers). We give a necessary condition for a 3-dimensional hyperbolic polyhedral orbifold to contain an immersed (singular) hyperbolic tur…

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

Properties of distributions of the number of trades in different intraday time intervals for five stocks traded in MICEX are studied. The dependence of the mean number of trades on the capital turnover is analyzed. Correlation analysis using factorial and $H_q$ moments demonstrates the multifractal nature of these dist…

The Internet is known to have had a powerful impact on on-line retailer strategies in markets characterised by long-tail distribution of sales. Such retailers can exploit the long tail of the market, since they are effectively without physical limit on the number of choices on offer. Here we examine two extensions of t…

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

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…

Different optimizer choices lead to different financial model predictions.

Model predicts risk-adjusted returns across various financial markets.

Deep learning improves options trading without market assumptions.

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

Study derives a limit functional for Willmore graphs with curvature penalization.

CD converges linearly for MCP/SCAD penalized least squares.

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

Improved asset allocation strategies using penalized quantile regression.

Average Oracle outperforms DCC+NLS in portfolio optimization.

Deep Momentum Networks improve time series momentum strategies using deep learning.

Equivalence found between algorithmic regularization and convex penalization for convex losses.

Size effect persists in equity markets, with CMH portfolios less correlated to Low-Vol anomaly.

AgFlow speeds up model selection in penalized PCA.

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

There is convincing evidence showing that the probability distributions of stock returns in mature markets exhibit power-law tails and both the positive and negative tails conform to the inverse cubic law. It supports the possibility that the tail exponents are universal at least for mature markets in the sense that th…

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

Develops a method to predict stock returns with time-varying risk premia.

Sparse-penalized deep neural networks improve performance in weakly dependent processes.

LLM-based trading systems vary in execution realism and reproducibility.

Paper develops a new method for optimal stopping in American options.