Music as an Asset Class

Battle Count: 72

The paper is relevant to quantitative finance in terms of asset pricing, risk-return characterization, and portfolio construction. However, it focuses on an illiquid alternative asset class (music royalties) rather than tradable securities. The DCF methodology and backtesting framework are transferable to quantitative trading contexts, and the finding of low correlation with equities is relevant for portfolio diversification strategies. The high transaction costs (8%) and illiquidity limit direct applicability to algorithmic trading.

The models are relatively simple parametric DCF formulations with 1-4 parameters optimized via least squares. The backtesting methodology is straightforward (buy at model price, collect cashflows, sell at model price). The main complexity lies in data acquisition from Royalty Exchange and handling the illiquid market structure. No machine learning or complex numerical methods are required.

The paper provides detailed model specifications, parameter values, and backtesting methodology. However, the Royalty Exchange transaction data (1295 trades) is proprietary and not publicly available. The optimization procedure (least squares over 1-4 parameters) is straightforward and reproducible given the data. No code repository is mentioned.