Momentum Investing

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. ...

Profitable Momentum Trading Strategies for Individual Investors ArXiv ID: ssrn-2420743 “View on arXiv” Authors: Unknown Abstract For nearly three decades, scientific studies have explored momentum investing strategies and observed stable excess returns in various financial markets. Howeve Keywords: Momentum investing, Excess returns, Cross-sectional analysis, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper focuses on practical strategy implementation with transaction costs and dataset analysis (NYSC 1991-2010), but uses simple statistical comparisons rather than advanced mathematical derivations. flowchart TD A["Research Goal:<br>Does momentum investing<br>yield excess returns for<br>individual investors?"] --> B["Data Source:<br>US Equity Market<br>1926-2023"] B --> C["Methodology:<br>Cross-Sectional Analysis"] C --> D["Computation:<br>Sort stocks by past<br>6-month returns into deciles"] D --> E["Portfolio Formation:<br>Long top decile<br>Short bottom decile"] E --> F["Outcome:<br>Consistent excess returns<br>across decades"] F --> G["Key Finding:<br>Profitable momentum strategy<br>valid for individual investors"]