arXiv research

Study improves portfolio optimization by reducing estimation errors and turnover.

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

Optimized portfolio turnover strategies enhance wealth and reduce costs.

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…

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…

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 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 paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention dis…

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…

Method prevents model divergence in rapidly changing ad markets.

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…

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

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…

Integrates ESG data into Black-Litterman for portfolio optimization.

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

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

Hybrid QAOA approach optimizes portfolios with strict constraints, outperforming classical methods.

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 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 …

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

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…

Asymmetry PRISM outperforms CPU and GPU solvers for institutional rebalancing.

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…

SBCA optimizes portfolios by fusing price data and text sentiment.

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 uses RL to optimize global equity portfolios, finds mixed results.

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

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 …

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…

VNA solves large portfolio optimization problems efficiently.

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.

Robo-advisors use MPC to create dynamic investment strategies.

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

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…

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 …

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

Develops a new framework for integrating satellite allocations in small portfolios.