Daily research update
we backtested it — Sharpe 0.66 · quant relevance 0.96
The paper restricts mean-variance portfolio targets to a numerically stable segment of the efficient frontier. It reports an out-of-sample comparison of risk-adjusted returns after transaction costs, alongside evidence on allocation stability.
we backtested it — Sharpe 0.70 · quant relevance 0.98
The paper proposes distributionally robust portfolios designed to outperform a benchmark while limiting downside risk relative to it. It reports improved out-of-sample wealth and risk-adjusted performance on real market data.
we backtested it — Sharpe 0.33 · quant relevance 0.90
The paper develops a theoretical model of anomaly crowding and a rolling-covariance detector for deciding when to trade a predictive signal. Its performance results come from model simulations, not a backtest on market data.
The rest of the batch
The paper proposes a matched-randomization audit to distinguish an earnings-trading agent’s event-selection skill from returns attributable to its mix of position changes.
and 5 more · see the full radar
Backtest and model results are research artifacts, not live trading results and not a guarantee of future performance. Informational and educational purposes only. Not individualised investment advice.