Daily research update
quant relevance 0.94
The study compares stock-level volatility forecasts and VaR calibration using daily US equity data, with a concrete application to portfolio risk sizing. It reports forecasting accuracy and computational efficiency, not trading returns; Random Forest achieves a reported mean RMSE of 0.0892. The target description ambiguously mixes next-day rolling volatility with volatility over the next five days, so forecast alignment requires verification before replication.
quant relevance 0.94
The study tests whether technical indicators improve next-day CSI 300 direction forecasts relative to historical-return features using logistic regression and random forests. Adding indicators worsens out-of-sample accuracy, and the best model does not significantly outperform the naive baseline. The forecasting application is relevant to market timing, but the required Chinese index market is outside platform coverage.
quant relevance 0.94
QBX is compiler infrastructure for systematic alpha mining, enforcing typed strategy construction, information-availability checks, computation reuse, and auditable trial identities. Its reported benefits primarily concern candidate validity and search efficiency on Chinese A-share panels, rather than demonstrated trading profitability.
The rest of the batch
The study estimates REIT–equity volatility spillovers using multivariate GARCH models to inform sector-specific allocation and hedge ratios.
and 11 more · see the full radar
Backtests are hypothetical research results; forward-portfolio figures are a live record. Neither is a guarantee of future performance. Research and education only: not individualised investment advice, not an offer to sell or a solicitation of an offer to buy any security.