turnover analysis

This study addresses the capacity constraints of trading strategies, wherein their profitability (or “edge”) deteriorates as capital scales up. Recognizing that existing observational metrics suffer from bias due to conflicting assumptions, the paper formulates strategy capacity for the first time as an identifiable causal inference problem. Leveraging a panel data design, it disentangles crowding effects from market impact by analyzing concurrent trades executed on the same day. Methodologically, the work integrates fixed-holding-period bias correction, variation in strategy exposure, and time-series variability analysis to demonstrate that conventional approaches systematically underestimate long-term crowding effects—and proposes a corrective framework. The study further provides practical guidelines for experimental design and a cost estimation framework, offering new tools for empirical asset pricing and quantitative investment research.

This study addresses the governance divergence between total portfolio approaches and strategic asset allocation in institutional investing, revealing that their fundamental difference lies in the specification of tracking error constraints. Using U.S. equity and bond data from 2004 to 2026, historical backtesting, portfolio simulations, and statistical tests demonstrate that while Sharpe ratios exhibit no significant variation across different static tracking error constraints, realized tracking error volatility can differ by up to twelvefold, and constraint-related costs surge dramatically during crises. To reconcile these frameworks, the paper proposes a dynamic tracking error mechanism as a novel governance paradigm, which substantially enhances decision-making efficiency and allocation flexibility during periods of market stress.

This study addresses the long-standing lack of systematic measurement of “implementation risk” in quantitative investment backtesting—the performance discrepancies arising from differences in backtesting engine implementations. The work formally defines this risk for the first time and proposes four metrological metrics alongside a taxonomy of five failure modes. These are derived from parallel execution of 15 benchmark strategies across five open-source backtesting engines, incorporating transaction cost modeling, non-overlapping stratified asset buckets, and source code defect analysis. Experiments reveal that while engine outputs converge under zero-cost assumptions, performance divergence can reach up to 3.71% when transaction costs are introduced. Crucially, however, the relative ranking of strategy efficacy remains unchanged across engines (conclusion stability index = 1), indicating that implementation risk affects performance attribution but does not alter investment decisions.

Current LLM-driven trading research lacks standardized execution assumptions and reproducibility criteria, hindering cross-study comparisons and economic interpretability. This work systematically reviews 30 related studies and introduces the first evidence matrix encompassing execution semantics, turnover handling, and temporal control to evaluate transparency across dimensions such as data recency, backtest partitioning, and transaction cost modeling. Through bibliometric coding, methodological analysis of backtesting practices, and friction sensitivity experiments on ten stocks, the study quantifies how execution details compress strategy returns. It reveals that most papers inadequately disclose execution assumptions and proposes a standardized reporting framework emphasizing execution transparency as critical for result credibility, advocating for stricter community-wide standards of realism and reproducibility.

Automated Market Makers (AMMs) in DeFi face adverse selection in blue-chip asset pairs: arbitrage—while essential for revenue—is also a primary source of losses due to “informed order flow.” Method: We develop a differential-equation-based arbitrage dynamics model, integrating sensitivity analysis and numerical simulation to systematically quantify how fee structures affect arbitrage behavior, uninformed trading incentives, and net revenue. Contribution/Results: We propose a directional dynamic fee mechanism—where fees adjust asymmetrically with price movement direction—to suppress toxic flow while preserving benign liquidity. Our analysis reveals that the optimal static fee lies within a narrow range; in contrast, the dynamic mechanism increases AMM net revenue by 18–32% empirically and reduces adverse selection losses by over 40%. This work provides a theoretically grounded, empirically testable framework for AMM fee design.

This study investigates the robustness of Markowitz portfolios under estimation errors in input parameters and perturbations in optimization constraints. By integrating constrained mean-variance optimization with Sobol global sensitivity analysis, it systematically evaluates how target return levels influence weight stability, risk-adjusted performance, and diversification. The work introduces the novel concept of a “fragility frontier,” revealing that low-target-return regions are primarily governed by L2 regularization, whereas high-return regions exhibit heightened sensitivity to expected return perturbations and weight caps, accompanied by a sharp decline in diversification and increased weight dispersion. Empirical validation across a multi-asset ETF universe confirms the generality of this phenomenon, and the resulting fragility map offers a structural robustness diagnostic tool for constrained optimizers without requiring modifications to allocation rules.

Traditional quantitative investment systems typically optimize a single metric—such as the information ratio—and thus struggle to meet professional investors’ multifaceted objectives, including pure alpha generation, style control, drawdown resilience, and turnover and capacity constraints. This work proposes an Objective-Oriented Quantitative Investment (OOQI) framework that formally encodes investment intent as strategy specifications and compiles them into composable, constraint-satisfying strategy assemblies. Key innovations include establishing a dual lattice structure between specifications and assemblies, designing a satisfaction-driven synthesis mechanism, and introducing rolling recertification via e-process-based validation. Empirical results demonstrate that the specification-driven approach satisfies 100% of target constraints across 32 strategies, at the cost of only a 5.5% reduction in information ratio, whereas conventional outcome-oriented methods—despite higher in-sample information ratios—fulfill merely 25% of the specified requirements.

This work proposes modeling automated market makers (AMMs) as programmable portfolio execution mechanisms capable of strictly and verifiably implementing predefined asset weights without active management intervention. Building upon the geometric mean market maker (G3M) invariant, the approach constructs target-weight portfolios augmented with multi-asset dynamic fees and achieves band-based rebalancing driven by arbitrage incentives. The mechanism enables on-chain verification of portfolio compliance, formally establishing AMMs for the first time as verifiable investment products. Simulations demonstrate that, relying solely on arbitrage-driven trading flows and with fees set within reasonable ranges, G3M-based portfolios outperform real-world benchmarks—including VBIAX, EQL, and EDOW—in both annualized returns and tracking error.

This study investigates behavioral alignment and representational dynamics of large language model (LLM) trading agents in financial markets, with a focus on early warning signals preceding failure. Using TradeArena—a novel auditable testing platform—the authors analyze LLM reasoning, position-taking, and intervention behaviors within risk reporting, execution simulation, and memory replay environments. They identify, for the first time, robust pre-failure signatures including stable representational drift, decoupling between planning and risk assessment, and contraction of the effective rank of latent manifolds. Structured risk feedback is shown to serve as an external alignment mechanism without requiring fine-tuning. Experiments across 80 rolling failure anchor points and 8 LLM trajectories confirm the robustness of these signatures. While real-world audit feedback improves calibration or returns for some models, it often exhibits myopic reward optimization and misalignment with diagnostic objectives, revealing rational blind spots in LLMs under coupled asset exposure.

Current large language model (LLM)-based trading systems lack rigorous evaluation of whether their intelligence translates into net profitability, making it difficult to ascertain if the costs of reasoning and decision-making are offset by incremental gains. This work proposes TradeLens, a novel toolkit that introduces the first “intelligence self-compensation” evaluation paradigm. By reconstructing trading trajectories, attributing costs and profits to interpretable evidence, and conducting multidimensional ablation studies across models, capital scales, trading frequencies, and architectures, TradeLens shifts the focus from mere performance ranking to diagnosing how intelligence converts into profit. The study reveals that intelligence self-compensation hinges on decision quality rather than system scale, with different models exhibiting distinct failure modes—such as asset selection bias or timing errors—while architectural factors influence profitability only indirectly through their impact on timing value.