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we backtested it — Sharpe 0.37 · quant relevance 0.98
The paper constructs long-only inverse-risk-parity and equal-risk-contribution portfolios using Entropic Value-at-Risk under tempered-stable return models. Across ETF and portfolio universes, its EVaR-ERC portfolios produced positive Sharpe-ratio differences relative to equal-weight and Gaussian-EVaR benchmarks.
we backtested it — Sharpe 0.64 · quant relevance 0.96
The paper forecasts market volatility for risk management, VaR, options applications, and a reported trading strategy. A closely related volatility-targeting implementation using daily and one-minute US equity and ETF data is backtestable, but the full multi-asset sample cannot be reproduced because the required FX data and long history are unavailable.
quant relevance 0.98
The paper tests whether machine-learning return forecasts and OHLC-informed risk estimates improve portfolio allocation for NFT-ecosystem tokens. Its out-of-sample comparisons find that machine-learning forecasts add value, while intraday-informed volatility estimates do not systematically improve risk-adjusted returns.
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The paper presents a corporate-bond return signal based on ESG momentum and conditioned on issuer credibility, bond seniority, credit quality, and the SFDR regulatory regime.
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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.