Multi-Asset Strategies

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