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How Fractional Differencing Revolutionized My Feature Engineering for Investment Strategies
- Fabio Capela
- Quantitative finance , Feature engineering , Machine learning , Systematic investing , Financial mathematics , Time series analysis , Advanced analytics , Algorithmic trading
As a theoretical physicist turned systematic investor, I’ve always been fascinated by the mathematical structures underlying financial markets. While most investors focus on price movements and traditional technical indicators, I discovered that the real edge comes from understanding the deeper statistical properties of market data—particularly how to extract meaningful features that preserve both trend information and stationarity.
Read MoreTrend Following Strategies: What 137 Years of Research Reveals (2024 Guide)
- Fabio Capela
- Finance , Investing strategies , Portfolio management , Risk management , Trend following , Systematic trading , Market momentum , Volatility targeting , Machine learning , Reinforcement learning , Alternative data
Trend following has long been a cornerstone strategy for traders and investors. By systematically riding market momentum, trend following strategies have historically delivered strong risk-adjusted returns across various asset classes. But how does the strategy hold up in different environments, and what does academic research say about its efficacy? Let’s explore the key insights from a wealth of scientific literature on trend following.
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