Daily research update
we backtested it — Sharpe 0.43 · quant relevance 1.00
The paper evaluates monthly, long-only dynamic allocation among SPY, AGG, GLD, and cash, using volatility targeting and constrained Markowitz optimization. It reports improved Sharpe ratios, drawdowns, and risk consistency relative to fixed-weight stock/bond and stock/bond/gold benchmarks. The platform can implement the ETF and FRED-based methodology, although its price history supports replication from approximately 2010 rather than the paper's full 2006--2026 evaluation window.
we backtested it — Sharpe -0.58 · quant relevance 0.96
The paper tests a low-rank Nyström approximation to cross-sectional Transformer attention for forecasting and ranking future stock returns. On Chinese A-share universes, it reports preserving the Rank IC of full attention while reducing attention complexity from O(N²) to O(mN), though gains from the cross-stock module did not reliably extend to the largest universe.
quant relevance 0.96
AlphaRJM uses reinforcement learning to discover interpretable formulaic equity alpha signals, with rewards tied to incremental improvements in ensemble information coefficient. The paper reports empirical gains and stability across equity universes, forecast horizons, and random seeds.
The rest of the batch
The paper derives variance-optimal stock hedges for European calls in a rough Hawkes–Heston model and provides theoretical convergence results for kernel-regularized hedges.
and 11 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.