Trend-Following

Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach ArXiv ID: 2507.15876 “View on arXiv” Authors: Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk, Thomas Jacquot Abstract Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend following, the relative merits and interactions of short-versus long-term trend systems remain controversial. This paper adds to the debate by (i) dynamically decomposing CTA returns into short-term trend, long-term trend and market beta factors using a Bayesian graphical model, and (ii) showing how the blend of horizons shapes the strategy’s risk-adjusted performance. ...

Refining and Robust Backtesting of A Century of Profitable Industry Trends ArXiv ID: 2412.14361 “View on arXiv” Authors: Unknown Abstract We revisit the long-only trend-following strategy presented in A Century of Profitable Industry Trends by Zarattini and Antonacci, which achieved exceptional historical performance with an 18.2% annualized return and a Sharpe Ratio of 1.39. While the results outperformed benchmarks, practical implementation raises concerns about robustness and evolving market conditions. This study explores modifications addressing reliance on T-bills, alternative fallback allocations, and industry exclusions. Despite attempts to enhance adaptability through momentum signals, parameter optimization, and Walk-Forward Analysis, results reveal persistent challenges. The results highlight challenges in adapting historical strategies to modern markets and offer insights for future trend-following frameworks. ...

Beyond Trend Following: Deep Learning for Market Trend Prediction ArXiv ID: 2407.13685 “View on arXiv” Authors: Unknown Abstract Trend following and momentum investing are common strategies employed by asset managers. Even though they can be helpful in the proper situations, they are limited in the sense that they work just by looking at past, as if we were driving with our focus on the rearview mirror. In this paper, we advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns. ...

Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies ArXiv ID: 2310.10500 “View on arXiv” Authors: Unknown Abstract Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions. To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes. We leverage recent developments from the deep learning community and use few-shot learning. We propose the Cross Attentive Time-Series Trend Network – X-Trend – which takes positions attending over a context set of financial time-series regimes. X-Trend transfers trends from similar patterns in the context set to make forecasts, then subsequently takes positions for a new distinct target regime. By quickly adapting to new financial regimes, X-Trend increases Sharpe ratio by 18.9% over a neural forecaster and 10-fold over a conventional Time-series Momentum strategy during the turbulent market period from 2018 to 2023. Our strategy recovers twice as quickly from the COVID-19 drawdown compared to the neural-forecaster. X-Trend can also take zero-shot positions on novel unseen financial assets obtaining a 5-fold Sharpe ratio increase versus a neural time-series trend forecaster over the same period. Furthermore, the cross-attention mechanism allows us to interpret the relationship between forecasts and patterns in the context set. ...

Momentum Turning Points ArXiv ID: ssrn-3489539 “View on arXiv” Authors: Unknown Abstract Turning points are the Achilles’ heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often fa Keywords: time-series momentum, portfolio optimization, trend following, signal processing, Quantitative Equity Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 8.0/10 Quadrant: Holy Grail Why: The paper employs a formal model to analyze momentum signals and derive analytical results, indicating moderate-to-high mathematical complexity, while its empirical analysis uses 50+ years of U.S. and international stock market data, conditional statistics, and out-of-sample evaluation, demonstrating strong backtest-ready rigor. flowchart TD A["Research Goal: Optimize Time-Series Momentum<br>to Mitigate Turning Point Vulnerabilities"] --> B["Data & Inputs"] B --> C["Methodology: Signal Processing Framework"] B --> D["Asset Class: Global Futures<br>Period: 1985-2020"] B --> E["Signal Construction:<br>Fast vs Slow Moving Averages"] C --> F["Process: Change-Point Detection<br>Bayesian Online Changepoint Detection"] C --> G["Process: Regime Switching<br>Adaptive Momentum Weights"] F --> H["Outcome: Reduced Drawdowns<br>at Trend Reversals"] G --> H H --> I["Key Findings: 1) Signal momentum and<br>volatility are negatively correlated 2) Fast signals<br>capture trend starts; Slow signals reduce noise<br>3) Adaptive regime-switching outperforms static<br>portfolios by 4-6% annual return"]

The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation ArXiv ID: ssrn-2275745 “View on arXiv” Authors: Unknown Abstract We examine the effectiveness of applying a trend following methodology to global asset allocation between equities, bonds, commodities and real estate. The appl Keywords: Trend Following, Global Asset Allocation, Multi-Asset Strategies, Time-Series Momentum, Portfolio Optimization, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper employs relatively straightforward statistical analysis and portfolio construction rules (trend following, momentum, risk parity) rather than advanced mathematical theory, but it is heavily empirical with extensive backtesting across multiple asset classes, Sharpe ratios, and drawdown analysis over long historical periods. flowchart TD A["Research Goal<br/>Apply trend following to global multi-asset allocation<br/>(Equities, Bonds, Commodities, Real Estate)"] --> B["Data & Methodology"] B --> C["Compute Time-Series Momentum<br/>Signals for each asset"] C --> D["Portfolio Optimization<br/>Risk Parity weighting of signals"] D --> E["Backtesting & Validation"] E --> F["Key Findings & Outcomes"] F --> G["Out-of-sample: Trend-following <br/>enhances risk-adjusted returns"] F --> H["Strategies show <br/>strong diversification benefits"] F --> I["Performance persists across <br/>different market regimes"]

The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation ArXiv ID: ssrn-2265693 “View on arXiv” Authors: Unknown Abstract We examine the effectiveness of applying a trend following methodology to global asset allocation between equities, bonds, commodities and real estate. The appl Keywords: Trend Following, Global Asset Allocation, Multi-Asset Strategies, Time-Series Momentum, Portfolio Optimization, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper applies advanced statistical and financial mathematics (e.g., risk parity, momentum models, volatility adjustments) but is heavily grounded in empirical backtesting across multiple asset classes with clear performance metrics, making it both mathematically sophisticated and data/implementation-focused. flowchart TD A["Research Goal: Test trend following in multi-asset allocation<br/>(Equities, Bonds, Commodities, Real Estate)"] --> B["Data & Inputs"] B --> B1["Historical Price Data"] B --> B2["4 Asset Classes"] B --> B3["Risk Parity & Trend Following Models"] A --> C["Methodology & Computation"] C --> C1["Estimate Covariance Matrix"] C --> C2["Apply Portfolio Optimization<br/>(Risk Parity / MV)"] C --> C3["Compute Time-Series Momentum<br/>(Rolling Returns & Signals)"] C --> D["Key Outcomes"] D --> D1["Robust Diversification Benefits"] D --> D2["Improved Risk-Adjusted Returns"] D --> D3["Effective Hedge Against Market Shocks"] D --> D4["Trend & Risk Parity Synergy"] B1 --> C B2 --> C B3 --> C C1 --> D C2 --> D C3 --> D

The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation ArXiv ID: ssrn-2126478 “View on arXiv” Authors: Unknown Abstract We examine the effectiveness of applying a trend following methodology to global asset allocation between equities, bonds, commodities and real estate. The appl Keywords: Trend Following, Global Asset Allocation, Multi-Asset Strategies, Time-Series Momentum, Portfolio Optimization, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper is empirically rigorous, presenting backtested strategies across multiple asset classes and discussing performance metrics, but the mathematics involved is relatively accessible, focusing on rules-based portfolio construction and behavioral concepts rather than advanced derivations. flowchart TD A["Research Goal:<br>Assess Trend Following<br>in Multi-Asset Allocation"] --> B["Data/Inputs<br>Global Assets: Equities, Bonds, Commodities, Real Estate"] B --> C["Methodology:<br>Time-Series Momentum &<br>Risk Parity Optimization"] C --> D["Computational Process:<br>Apply Trend Filter &<br>Rebalance Portfolio"] D --> E{"Evaluation<br>vs. Static Allocation"} E --> F["Key Findings/Outcomes"] subgraph F [" "] F1["Trend Following enhances<br>returns and reduces risk"] F2["Effective across<br>multiple asset classes"] F3["Best as complement<br>to traditional strategies"] end