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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.

The 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.

In 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.

The 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.

This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.

The author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.

The author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).

The attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.

While this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

The purpose of this paper is to assess the determinants of corruption-control (CC) with freedom dynamics (economic, political, press and trade), government quality (GQ) and a plethora of socio-economic factors in 46 African countries using updated data.

A quantile regression approach is employed while controlling for the unobserved heterogeneity. Principal component analysis is also used to reduce the dimensions of highly correlated variables.

With the legal origin fundamental characteristic, the following findings have been established. First, while political freedom increases CC in a bottom quantile of English common-law countries, there is no such evidence in their French civil-law counterparts. Second, GQ consistently improves CC across all quantiles in English common-law countries but fails to exert the same effect in middle quantiles of French civil-law countries. Third, economic freedom ameliorates CC only in common-law countries with low existing CC levels (bottom quantiles). Fourth, The authors find no significant evidence of a positive “press freedom”-CC nexus and having the status of low-income English common-law (French civil law) countries decreases (increases) CC. From a religious domination scenario, the authors also find the following. First, political and trade freedoms only reduce CC in Christian-dominated countries while press freedom has a mitigation effect in both religious cultures (though more consistent across quantiles of Christian-oriented countries). Second, GQ is more pro-CC in Christian than in Muslim-dominated countries. Third, while economic freedom has a scanty negative nexus with CC in Christian-oriented countries, the effect is positive in their Muslim-dominated counterparts. Fourth, having a low-income status in countries with Christian common-law tradition improves CC.

The authors complement the literature on the fight against corruption in Africa by employing recently documented additional factors that should be considered in corruption studies.

We examine tick sizes, stock prices, and share turnover in eighteen stock markets in developed countries and find that differences in mandatory tick sizes explain a significant proportion of the variation in stock prices among markets, and that lower percentage tick sizes are not associated with higher turnover. We consider the implications of these findings for the recent decimalization of stock trading in the United States, and conclude that decimal trading is likely to result in lower stock prices (due to stock splits) with no substantial change in dollar trading volume.

This paper aims to identify user-centred facilities performance attributes of higher education buildings and how they can be used to evaluate individual learning spaces. These attributes are then consolidated for developing a post-occupancy evaluation (POE) framework in this context.

A systematic review of the literature on the POE of higher education buildings is conducted.

This study identifies 36 facility performance attributes in higher education buildings, which can be categorised into four dimensions: ambient; spatial; technology; and building support and services requirements. These facility performance attributes need to meet user requirements to achieve satisfactory feedback. It is also important to note that user requirements differ from one learning space to another; thus, it is essential to consider the characteristics of individual learning spaces.

The proposed evaluation framework is context-based and may not be suitable to evaluate other types of buildings. It may be further extended and enhanced to meet other facility management evaluation needs.

The POE framework developed in this research can be used to generate facilities management analytic to inform future design and improve existing higher education facilities.

This research has developed a holistic POE framework tool to meet user requirements in higher education buildings.