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Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints

Optimization algorithms and investment portfolio analytics with machine learning techniques under... This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.Design/methodology/approachThe implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.FindingsIn particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.Originality/valueThe optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Modelling in Management Emerald Publishing

Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints

Al Janabi, Mazin A.M.
Journal of Modelling in Management , Volume 17 (3): 32 – Aug 22, 2022
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5664
eISSN
1746-5664
DOI
10.1108/jm2-10-2020-0259
Publisher site
See Article on Publisher Site

Abstract

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.Design/methodology/approachThe implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.FindingsIn particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.Originality/valueThe optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

Journal

Journal of Modelling in Management – Emerald Publishing

Published: Aug 22, 2022

Keywords: Analytics; Commodity; Liquidity risk; Optimization; Machine learning; Portfolio management; Stress testing; Liquidity-adjusted value-at-risk; Finance; Artificial intelligence; Risk analysis; Business analysis; Portfolio analysis; JEL Classifications C10; C13; G20; G28

References

  • Commodity price risk management: valuation of large trading portfolios under adverse and illiquid market settings
    Al Janabi, M.A.M.
  • Incorporating asset liquidity effects in risk-capital modeling
    Al Janabi, M.A.M.
  • A generalized theoretical modeling approach for the assessment of economic capital under liquidity risk constraints
    Al Janabi, M.A.M.
  • Optimal commodity asset allocation with a coherent market risk modeling
    Al Janabi, M.A.M.
  • Optimal and coherent economic-capital structures: evidence from long and short-sales trading positions under Illiquid market perspectives
    Al Janabi, M.A.M.
  • Optimal and investable portfolios: an empirical analysis with scenario optimization algorithms under crisis market prospects
    Al Janabi, M.A.M.
  • Is optimum always optimal? A revisit of the mean-variance method under nonlinear measures of dependence and non-normal liquidity constraints
    Al Janabi, M.A.M.
  • Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management
    Al Janabi, M.A.M.
  • Liquidity-adjusted value-at-risk optimization of a multi-asset portfolio using a vine copula approach
    Al Janabi, M.A.M.; Ferrer, R.; Shahzad, S.J.H.
  • Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios
    Al Janabi, M.A.M.; Arreola-Hernandez, J.A.; Berger, T.; Nguyen, D.K.
  • Optimal execution of portfolio transaction
    Almgren, R.; Chriss, N.
  • Liquidity adjusted value-at-risk based on the components of the bid-ask spread
    Angelidis, T.; Benos, A.
  • Forecasting of dependence, market and investment risks of a global index portfolio
    Arreola-Hernandez, J.; Al Janabi, M.A.M.
  • Time lag dependence, cross-correlation and risk analysis of U.S. energy and non-energy stock portfolios
    Arreola-Hernandez, J.; Al Janabi, M.A.M.; Hammoudeh, S.; Nguyen, D.K.
  • Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach
    Arreola-Hernandez, J.; Hammoudeh, S.; Khuong, N.D.; Al Janabi, M.A.M.; Reboredo, J.C.
  • Measuring market and credit risk under solvency II: evaluation of the standard technique versus internal models for stock and bond markets
    Asadi, S.; Al Janabi, M.A.M.
  • Machine learning and portfolio optimization
    Ban, G.Y.; El Karoui, N.; Lim, A.E.B.
  • Modeling liquidity risk with implications for traditional market risk measurement and management
    Figlewski, S.; Levich, R.M.
  • The impact of commodity price risk on firm value – an empirical analysis of corporate commodity price exposures
    Bartram, S.
  • Incorporating liquidity risk into VAR models
    Berkowitz, J.
  • On the sensitivity of mean-variance-efficient portfolios to changes in asset means: some analytical and computational results
    Best, M.J.; Grauer, R.R.
  • Commodity risk management in developing countries
    Papaioannou, M.; Tsetsekos, G.
  • Incorporating trading strategies in the Black-Litterman framework
    Fabozzi, F.J.; Focardi, S.; Kolm, P.
  • Robust portfolios: contributions from operations research and finance
    Fabozzi, F.J.; Huang, D.; Zhou, G.
  • Market risk in commodity markets: a VaR approach
    Giot, P.; Laurent, S.
  • Pricing of time-varying illiquidity within the Eurozone: evidence using a Markov switching liquidity-adjusted capital asset pricing model
    Grillini, S.; Sharma, A.; Ozkan, A.; Al Janabi, M.A.M.
  • Evaluation of value-at-risk models using historical data
    Hendricks, D.
  • International monetary fund (IMF)
  • Mopping up liquidity
    Jarrow, R.; Subramanian, A.
  • Putting Markowitz theory to work
    Jobson, J.D.; Korkie, B.M.
  • The exchange rate exposure of U.S. multinationals
    Jorion, P.
  • Bayesian and CAPM estimators of the means: implications for portfolio selection
    Jorion, P.
  • A comprehensive review of deterministic models and applications for mean-variance portfolio optimization
    Kalayci, C.B.; Ertenlice, O.; Akbay, M.A.
  • Value-at-Risk analysis: a review and the potential for agricultural applications
    Manfredo, M.; Leuthold, R.
  • Market risk and the cattle feeding margin: an application of value-at-Risk
    Manfredo, M.; Leuthold, R.
  • The Markowitz optimization enigma: is `optimized’ optimal?
    Michaud, R.O.
  • Decision-making for financial trading: a fusion approach of machine learning and portfolio selection
    Paiva, F.D.; Cardoso, R.T.N.; Hanaoka, G.P.; Duarte, W.M.
  • The role of value-at-risk in purchasing: an application to the foodservice industry
    Sanders, D.R.
  • An efficient frontier for international portfolios with commodity assets
    Satyanarayan, S.; Varangis, P.
  • Energy price risk management
    Weron, R.
  • Measuring price risk on UK arable farms
    White, B.; Dawson, P.J.
  • Value-at-risk in bakery procurement
    Wilson, W.W.; Nganje, W.E.
  • Forecasting volatility in commodity markets
    Kroner, K.; Kneafsey, K.; Claessens, S.