Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns
Sparse-autoencoder crypto portfolios run at lower volatility through lower correlations. They still do not beat a simple contrarian strategy.
The claim
Sparse autoencoder portfolios hold lower volatility, but they do not beat a contrarian baseline.
The problem
Factor models drive asset pricing and portfolio construction.
Neural factors are hard to interpret.
Their effect on portfolio risk is unclear.
How it works
A sparse autoencoder describes daily returns with few active features.
The study uses cryptocurrency returns, with equities as a second test.
Evaluation is time-ordered against common benchmark strategies.
Portfolios are built from the features active on each day.
Lower volatility links to lower correlation among selected assets.
Figures
What changes
SAE portfolios show consistently lower volatility than the benchmarks.
Risk-adjusted return does not beat a simple contrarian strategy.
Factor identities are not stable across training runs.
Limits
The result supports portfolio risk reduction, not excess return claims.
The main test is cryptocurrency returns; equities serve only as a secondary check.
Performance is judged against one simple contrarian strategy for risk-adjusted return.
Source · arXiv