Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies
Portfolio optimization in crypto, studied by Chen in 2026 with a large language model. The abstract mentions a Sharpe ratio, excess returns, profits, drawdowns and a benchmark but not an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Backtesting across multiple cryptocurrencies shows that the integrated approach outperforms traditional benchmarks, including momentum strategy, Bitcoin Long-Short strategy, and an equal-weighted portfolio, achieving stronger risk-adjusted returns and more consistent cumulative growth.”
AssetCryptospot crypto
StrategyPortfolio optimizationmomentum
TechniqueLarge language model
ClaimSharpe ratioexcess return
Also stated: Long-short, Price and volume, Text and news, benchmark
Missing from abstract: out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodPortfolio optimization by year
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