Systematic Market and Asset Liquidity Risk Processes for Machine Learning: Robust Modeling Algorithms for Multiple-Assets Portfolios

2021 ◽  
pp. 155-188
Author(s):  
Mazin A. M. Al Janabi
Keyword(s):  
Machine Learning ◽  
Liquidity Risk ◽  
Robust Modeling ◽  
Asset Liquidity ◽  
Multiple Assets ◽  
Risk Processes

Risk Management in Emerging and Islamic Markets in Light of the Subprime Global Financial Crisis

2020 ◽  
pp. 98-127
Author(s):  
Mazin A. M. Al Janabi
Keyword(s):  
Financial Crisis ◽  
Value At Risk ◽  
Global Financial Crisis ◽  
Optimization Algorithms ◽  
Liquidity Risk ◽  
Risk Capital ◽  
Practical Applications ◽  
Multiple Assets ◽  
Using Data ◽  
Definition Of

The 2007-2009 global financial crisis emphasized the need for rigorous integration of asset liquidity trading risk into value at risk (VaR) modeling algorithms. In this chapter, the author examines measures of certain kinds of liquidity risk that is useful for completing the definition of market risk and for forecasting liquidity-adjusted VaR (L-VaR) under illiquid and intricate market outlooks. This chapter proposes robust modeling algorithms for the quantification of liquidity risk for portfolios that consist of multiple-assets. The empirical testing is performed using data of emerging and Islamic Gulf Cooperation Council stock markets. To that end, the author simulates diverse portfolios and determines the risk-capital and risk-budgeting constraints. The optimization algorithms are interesting in terms of theory as well as practical applications, particularly in light of the 2007-2009 global financial meltdown. The optimization algorithms can have important uses and applications in expert systems, machine learning, and financial technology (FinTech) in big data environments.

scholarly journals Impact of Liquidity Rules on Shareholders’ Returns in Jordan Islamic Bank

2018 ◽  
Vol 13 (6) ◽  
pp. 225
Author(s):  
Abdullah Ibrahim Nazal ◽  
Fuad Al-Fasfus
Keyword(s):  
Market Price ◽  
Balance Sheet ◽  
Liquidity Risk ◽  
Annual Reports ◽  
The Real ◽  
Islamic Bank ◽  
Fixed Assets ◽  
Asset Liquidity ◽  
Study Methodology ◽  
The Impact

This paper aims to explore the impact of liquidity increases by local and international roles on shareholders’ returns in the Jordan Islamic Bank as case study. The study methodology based on financial tables annual reports of the bank from (2009-2016) in order to analysis asset liquidity risk standard and its affection on managing balance sheet, and analysis returns for common shareholders in the Bank also discuss the result of shares return reducing. The real impact is deferent because the market price of the Jordan Islamic Bank shares is not affected negatively by the rule. Its price in the market is more than the share value by the ratio (all equities/ all shares). The percentage between the market price and ratio was equal to 202% in 2014 and reduced to 155% in 2016. By discussion, the ratio there is a gap of equities impact as a result of applying depreciation on fixed assets yearly, regardless of its growth by market price. Fair result is to increase equities based on fixed assets market price increasing. This paper contributed to the knowledge by different ways, it helps leaders and managers to find the real impact of managing liquidity risk in the Islamic Bank by the Central Bank and Basel Committee.

Asset Liquidity Risk

2014 ◽  
pp. 93-107
Author(s):  
Erik Banks
Keyword(s):  
Liquidity Risk ◽  
Asset Liquidity

Asset Liquidity Risk

2005 ◽  
pp. 78-91
Author(s):  
Erik Banks
Keyword(s):  
Liquidity Risk ◽  
Asset Liquidity

scholarly journals Liquidity Stress Testing in Asset Management - Part 2. Modeling the Asset Liquidity Risk

2021 ◽  
Author(s):  
Thierry Roncalli ◽  
Amina Cherief ◽  
Fatma Karray-Meziou ◽  
Margaux Regnault
Keyword(s):  
Asset Management ◽  
Stress Testing ◽  
Liquidity Risk ◽  
Asset Liquidity

scholarly journals Towards Replication in Computational Cognitive Modeling: A Machine Learning Perspective

2019 ◽  
Author(s):  
Chris Emmery ◽  
Ákos Kádár ◽  
Travis J. Wiltshire ◽  
Andrew T Hendrickson
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Computational Modeling ◽  
Cultural Change ◽  
Cognitive Modeling ◽  
Open Science ◽  
Robust Modeling ◽  
Downstream Effects ◽  
Computational Cognitive Modeling ◽  
Modeling Practices

The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on `Robust modeling in cognitive science', we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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