Momentum

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. ...

Heterogeneous Beliefs Model of Stock Market Predictability ArXiv ID: 2406.08448 “View on arXiv” Authors: Unknown Abstract This paper proposes a theory of stock market predictability patterns based on a model of heterogeneous beliefs. In a discrete finite time framework, some agents receive news about an asset’s fundamental value through a noisy signal. The investors are heterogeneous in that they have different beliefs about the stochastic supply. A momentum in the stock price arises from those agents who incorrectly underestimate the signal accuracy, dampening the initial price impact of the signal. A reversal in price occurs because the price reverts to the fundamental value in the long run. An extension of the model to multiple assets case predicts co-movement and lead-lag effect, in addition to cross-sectional momentum and reversal. The heterogeneous beliefs of investors about news demonstrate how the main predictability anomalies arise endogenously in a model of bounded rationality. ...

NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading ArXiv ID: 2310.00747 “View on arXiv” Authors: Unknown Abstract We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM)model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system - NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%. ...

Strategic Rebalancing ArXiv ID: ssrn-3330134 “View on arXiv” Authors: Unknown Abstract A mechanical rebalancing strategy, such as a monthly or quarterly reallocation towards fixed portfolio weights, is an active strategy. Winning asset classes are Keywords: rebalancing, portfolio weights, momentum, risk-adjusted returns, asset allocation, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper presents several analytical derivations, including a two-period model and convexity/concavity arguments, which indicate moderate mathematical density. It also includes extensive empirical backtesting on long historical datasets (1927-2017) with specific drawdown analysis and risk metrics, demonstrating strong implementation and data reliance. flowchart TD A["Research Goal"] --> B["Rebalancing<br>vs. Buy-and-Hold"] B --> C["Data Inputs<br>Multi-Asset Classes"] C --> D["Methodology<br>Strategic Rebalancing<br>Monthly/Quarterly"] D --> E["Computational Process<br>Calculate Returns &<br>Risk-Adjusted Metrics"] E --> F["Key Findings<br>Active Strategy<br>Better Risk-Adjusted Returns"]

Diversified Statistical Arbitrage: Dynamically Combining Mean Reversion and Momentum Strategies ArXiv ID: ssrn-1666799 “View on arXiv” Authors: Unknown Abstract This paper presents a quantitative investment strategy that is capable of producing strong risk-adjusted returns in both up and down markets. The strategy combi Keywords: Quantitative investment strategy, Risk-adjusted returns, Momentum, Reversal, Portfolio construction Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 6.0/10 Quadrant: Holy Grail Why: The paper employs advanced mathematical techniques like Principal Component Analysis (PCA) with eigenvalues and eigenvectors for decomposition, indicating high mathematical density. It also presents in-sample and out-of-sample performance analysis across multiple market environments (2008-2009), suggesting significant empirical testing and implementation focus. flowchart TD A["Research Goal: Develop a Quantitative Investment Strategy"] --> B["Methodology: Diversified Statistical Arbitrage"] B --> C["Data: Historical Stock Prices & Market Data"] C --> D{"Compute Signal Generation"} D --> E["Mean Reversion Strategy"] D --> F["Momentum Strategy"] E & F --> G["Dynamic Portfolio Construction"] G --> H["Key Findings: Strong Risk-Adjusted Returns"] H --> I["Outcomes: Effective in Both Up & Down Markets"]