An exploration of the mathematical structure and behavioural biases of 21st century financial crises

James, Nick; Menzies, Max

doi:10.1016/j.physa.2023.129256

Cited by 7 publications

(2 citation statements)

References 83 publications

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“…In 2023, James and Menzies offered novel mathematical approaches and examined the similarities in market dynamics and the ensuing implications for equity investors between different financial market crises. In order to achieve the best possible systematic risk reduction, they first examine the strength of collective dynamics during various market crises and contrast appropriate portfolio diversification strategies based on the distinct number of sectors and stocks (James & Menzies, 2023). Tolo in 2020, introduced a predicting systemic artificial model for financial crises based on neural networks, He demonstrated that the Long-Short Term Memory (RNN-LSTM) and the Gated Recurrent Unit (RNN-GRU) neural nets may be utilized to greatly enhance such predictions (Tolo, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technology to Predict the Financial Crisis in Business Companies

HAMADA,

ALHYASAT

2023

EPSTEM

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It is difficult to imagine a business in the modern world of digitization that does not use technology and artificial intelligence in some capacity. Business transformation brought a severe time of trouble for small and medium-sized enterprises all over the world. Throughout our study, we have based analysis on factual information that highlights the critical function that the Information Technology (IT) industry plays in maintaining corporate relevance and encouraging customer involvement. Our investigation goes beyond merely highlighting the value of using big data to analyze financial crises in a predictive manner. It also emphasizes the proactive incorporation of artificial intelligence (AI) into corporate operations as a preventive step to avoid them. Our study includes the examination of viewpoints from people who were actively involved in their careers before lockdown procedures were implemented and incorporated into our research the opinions of people who work for small and medium-sized businesses and government agencies. Based on the literature that already exists on the critical role that Artificial Intelligence (AI) plays in protecting companies from disasters, we looked closely at a particular case study. The case study's conclusions highlight how important corporate automation is. In this article, we provide case studies of wellknown, globally renowned companies that demonstrate how they skillfully employ new technology to maintain their competitive position. These model organizations include both internationally recognized companies like Google and Facebook and local organizations like Sberbank. In conclusion, our research adopted an artificial intelligence framework that can help business organizations to predict problems and financial crises.

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technology to Predict the Financial Crisis in Business Companies

HAMADA,

ALHYASAT

2023

EPSTEM

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“…We must acknowledge the influence of statistical physics, econophysics, and time series analysis on this work. In financial markets, these methodologies have been applied to a wide range of asset classes such as equities [45][46][47][48], foreign exchange [49], cryptocurrencies [50][51][52][53][54][55][56][57] and debt-related instruments [58]. Such methods from applied mathematics have been used in a variety of other disciplines including epidemiology [59][60][61][62][63][64][65][66][67][68], environmental sciences [69][70][71][72][73][74][75][76][77], crime [78][79][80], and other fields [81,82].…”

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confidence: 99%

Portfolio diversification with varying investor abilities

James,

Menzies

2024

EPL

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We introduce new mathematical methods to study the optimal portfolio size of investment portfolios over time, considering investors with varying skill levels. First, we explore the benefit of portfolio diversification on an annual basis for poor, average and strong investors defined by the 10th, 50th and 90th percentiles of risk-adjusted returns, respectively. Second, we conduct a thorough regression experiment examining quantiles of risk-adjusted return as a function of portfolio size across investor ability, testing for trends and curvature within these functions. Finally, we study the optimal portfolio size for poor, average and strong investors in a continuously temporal manner using more than 20 years of data. We show that strong investors should hold concentrated portfolios, poor investors should hold diversified portfolios; average investors have a less obvious distribution with the optimal number varying materially over time.

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Collective infectivity of the pandemic over time and association with vaccine coverage and economic development

James,

Menzies

2023

Chaos, Solitons & Fractals

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An exploration of the mathematical structure and behavioural biases of 21st century financial crises

Cited by 7 publications

References 83 publications

Artificial Intelligence Technology to Predict the Financial Crisis in Business Companies

Artificial Intelligence Technology to Predict the Financial Crisis in Business Companies

Portfolio diversification with varying investor abilities

Collective infectivity of the pandemic over time and association with vaccine coverage and economic development

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