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Last Post 23 Oct 2019 01:10 PM by  Patrick Ng
Drill down into risk and return using Hybrid model - from DCA to MPT
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Patrick Ng
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23 Oct 2019 01:10 PM
    Highlights of examples shown at the AAPG ML Workshop, Wichita KS two weeks ago.

    Hybrid model combines neural-network enabled decline curve analysis (DCA) and modern portfolio theory (MPT). We illustrate a powerful methodology to quantify risk and optimize portfolio allocation with granularity, integrity, transparency and science-based machine learning in mind.

    Examples using the same AI engine

    First quick check on Hart Energy Majors portfolio (six stocks, https://www.hartenergy.com/markets/data ). Determine the max risk-adjusted return (Sharpe ratio) is Chevron. Calibration - Barron’s “Lowest Risk and Best in Class”, August 21, 2019

    https://www.barrons.com/articles/ch...1566402158

    Next, instead of financial assets, focus on real assets - petroleum basins, Bakken, Eagle Ford, Marcelleus and Permian. Recall “Hybrid approach to Well Economics”, https://www.ogj.com/home/article/17...-economics
    published in July 2016: a) min variance portfolio - overweight Eagle Ford and Marcellus (i.e., light oil and gas rich, respectively); b) max Sharpe portfolio - all in Eagle Ford. Calibration - “Eagle Ford is weathering volatility better than most oil fields”, September 26, 2019.

    https://www.expressnews.com/busines...470238.php

    Conclusion - Hybrid model advances quantitative risk management and strategy development in energy transition.

    Relevant reading - https://superiorinvest.com/investor...f-capital/
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