Advances in Momentum Trading Strategies

Analyze three time series momentum strategies, highlighting differences from cross sectional momentum, with 1-, 3-, 12-month horizons and rebalancing across 67 markets, using long/short positions, volatility targeting, and transaction-cost assumptions.

Explore a century of momentum performance across asset classes, highlighting Sharpe ratios for equities, fixed income, currencies, and commodities, and discuss universe selection and applying trend following with asset-specific properties.

Analyze crisis-period performance by comparing the 60/40 portfolio with trend following, noting ten worst drawdowns, recoveries, and how allocating 20% to time series momentum reduces drawdown and boosts returns.

Evaluate momentum strategy performance across recession vs boom, low vs high inflation, wartime vs peacetime, and bull vs bear markets, with timing and lookback windows shaping robust but challenged results.

Explore turning points in time series momentum, distinguishing slow and fast dynamics, and examine how noise, persistence, and a static blended model shape beta and alpha, with dynamic speed selection.

Explore turning points in time series momentum by examining expected returns, trend persistence, long and short lookback windows, noise, and false alarms across varying volatility.

Define slow and fast momentum strategies using a 12-month trailing return to set long or short positions, and calculate realized returns by multiplying the strategy signal with subsequent period returns.

Explore how noise and persistence affect momentum strategies via the Sharpe ratio, revealing turning points where slow outperforms fast under high persistence, and fast prevails with low noise.

Build a generalized momentum model that blends fast and slow strategies via a parameter a, discusses turning points and covariance-based change points, and cites deep momentum networks and related papers.

Discover dynamic speed selection that adapts weights to market cycles, optimizing momentum strategies toward the optimum Sharpe ratio across four states and their transition probabilities.

Explore momentum trading signals at fast and slow speeds, balancing type one and type two errors around turning points, boosting risk-adjusted returns with volatility managed portfolios and meta labeling insights.

Explore fast and slow momentum signals in market timing and volatility timing, using trailing 1-month and 12-month returns to construct long or short positions in the S&P 500.

This lecture surveys S&P 500 performance statistics, showing how fast and slow momentum can disagree, and suggests combining them via static intermediate speed and decision trees to improve returns.

Analyze momentum under different volatility regimes, comparing slow and fast signals in stable versus turbulent markets. Learn how attention networks and decision trees enable regime-aware switching.

Learn how volatility clusters drive risk and how to target a constant portfolio volatility by adjusting exposure and position size, with vol targets of 15–20% and limited leverage.

Findings show Sharpe ratio gains for equity and credit, with improved left-tail risk and drawdown, and note leverage effect and volatility scaling across about 50 assets.

Apply volatility targeting to scale exposure toward a target volatility, using a scaling constant K to align realized volatility with the target, and rely on exponentially weighted volatility estimates.

Volatility scaling boosts the Sharpe ratio of risk assets by leveraging the leverage effect and the negative correlation between returns and volatility, and introducing momentum when volatility rises.