Investigation shows CTAs and STRTs can benefit from coexistence.
problem CTAs' poor performance in recent years.
method Built an artificial market model with CTAA and STRTA agents.
result CTAA and STRTA coexistence leads to higher trading and revenue.
This paper uses Bayesian models to analyze CTA returns across short and long-term trends.
problem The relative merits and interactions of short- and long-term trend systems in CTA replication remain controversial.
method Dynamic decomposition of CTA returns into short-term trend, long-term trend, and market beta factors using a Bayesian graphical model.
result The blend of horizons shapes the strategy's risk-adjusted performance.
The influence of Commodity Trading Advisors (CTA) on the price process is explored with the help of a simple model. CTA managers are taken to be Kelly optimisers, which invest a fixed proportion of their assets in the risky asset and the remainder in a riskless asset. This requires regular adjustment of the portfolio w…
Investors trade based on shifting prices, leading to market inefficiencies.
problem Market inefficiencies due to trading behavior and financial dynamics.
method Analyzes trading strategies and market dynamics.
result Arbitrage opportunities arise from concentrated ownership and low collateral requirements.
In this paper we consider the problem of identifying a connection ∇ on a vector bundle up to gauge equivalence from the Dirichlet-to-Neumann map of the connection Laplacian ∇∗∇ over conformally transversally anisotropic (CTA) manifolds. This was proved in \cite{LCW} for line bundles in the case of t…
Paper tackles best arm identification with cost consideration.
problem Best arm identification with cost consideration in product development.
method Derives a theoretical lower bound and proposes algorithms CTAS and CO.
result Simple algorithms can deliver near-optimal performance.
The performance of trend following strategies can be ascribed to the difference between long-term and short-term realized variance. We revisit this general result and show that it holds for various definitions of trend strategies. This explains the positive convexity of the aggregate performance of Commodity Trading Ad…
New FL framework handles non-i.i.d data without strong assumptions.
problem Non-identically independent distributed (non-i.i.d) data in federated learning.
method Proposes a new algorithm design strategy from primal-dual optimization.
result Achieves optimal communication efficiency and communication complexity.
Short-term trend-following has stopped delivering profits since 2009, especially on smaller market ticks.
problem The profitability of short-term trend-following has declined since 2009.
method Cross-sectional analysis of 100 liquid futures contracts from 1995-2025, evaluating four explanations.
result The decline in short-term trend-following profits is linked to smaller market ticks, not asset class or liquidity.
Optimal trend-following strategy uses simple EMA, avoiding complex cherry-picked signals.
problem Cherry-picking signals for trend-following strategies.
method Simple EMA for trend capture, avoiding complex indicators.
result Simple EMA is optimal for capturing trend, complex indicators are risky.
We consider the anisotropic Calderon problem of recovering a conductivity matrix or a Riemannian metric from electrical boundary measurements in three and higher dimensions. In the earlier work \cite{DKSaU}, it was shown that a metric in a fixed conformal class is uniquely determined by boundary measurements under two …
DeePM is a deep-learning portfolio manager that outperforms classical strategies in diversified futures markets.
problem Maximizing risk-adjusted returns in financial markets with low signal-to-noise ratios and asynchronous data.
method Structured deep learning with a Directed Delay mechanism, Macroeconomic Graph Prior, and distributionally robust optimization.
result DeePM achieves net risk-adjusted returns roughly twice those of classical strategies and passive benchmarks.
Improved trading strategy using deep learning and changepoint detection for market changes.
problem Traditional momentum strategies struggle with rapid market changes, especially after trend reversals.
method Inserted an online changepoint detection module into a Deep Momentum Network (DMN) pipeline.
result Improvement in Sharpe ratio by one-third over 1995-2020 period, especially beneficial in nonstationary periods.