Research

Published work.

Peer-reviewed papers on trading strategies, statistical method and mathematical modelling — the same approach pointed at four different subjects.

• 1 Sept 2026 Risks (MDPI) — accepted, in press Rebalancing Frequency Dominates Risk Proxy Choice: A Comparison of Mean–Variance, MAD, and Quantile-Constrained Portfolios of U.S. Sector ETFs A comparison of variance, MAD, IQR and a wider central-quantile risk proxy in constrained portfolio construction across nine U.S. sector ETFs and 27 years, finding that rebalancing cadence matters more than the risk measure.
• 8 Jul 2026 International Journal of Financial Studies 14(7): 178 Perpetual Futures in Decentralised Finance: Mechanics, Economic Claims, and the Drivers of Trading Volume A multi-asset study of DeFi perpetual futures across crypto, tokenized equities and tokenized commodities, using endogenous event detection to identify 1,797 volume anomalies and a systematic 24/7 trading premium.
• 12 Dec 2025 Metrology 5(4): 76 Curves in Archeology: Computing the Volume of a Greek Vase Advanced interdisciplinary research combining mathematical modeling, statistical analysis, and Python programming to compute volumes of ancient Greek vases using geometric principles and modern computational methods. Achieved 4.91% mean relative error.
• 1 Sept 2025 Technical Analysis of Stocks & Commodities 43(9): 20–23, September 2025 Momentum-Based Trading Strategies in Crude Oil ETFs And Futures Research on momentum-based trading strategies in crude oil ETFs and futures, developing long-short models yielding up to 19.9% annualized returns over an 18-year testing period.
• 1 Jun 2025 Technical Analysis of Stocks & Commodities 43(6): 20–22, 47, June 2025 The Silver Lining of Daily Bitcoin Trading Strategy leveraging overnight silver returns to predict Bitcoin price movements, exhibiting lower drawdowns in a 10-year backtest with 68.65% annualized returns.
• 1 Mar 2025 Machine Learning and Applications: An International Journal (MLAIJ) 12(1) Estimating the Accuracy of a Bagged Ensemble Probabilistic framework to reduce computational overhead in model fine-tuning, using various distributions to estimate Random Forest performance with less than 3% relative error.