The role of trust, investor sentiment, and uncertainty on bank stock return performance: Evidence from the MENA region

Shah, Syed Muhammad Hassan; Albaity, Mohamed

doi:10.1016/j.jeca.2022.e00260

Cited by 15 publications

(10 citation statements)

References 133 publications

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Section: Resultscontrasting

confidence: 70%

“…This suggests a disapproval of traditional finance or an attraction to the decentralized nature of cryptocurrencies. Interestingly, contrasting with research linking trust in the stock market to investment decisions (Balloch et al, 2015;Guiso et al, 2008;Shah and Albaity, 2022), trust in the UK financial industry does not significantly impact cryptocurrency investment, suggesting that investors, favouring decentralized assets, might find traditional financial systems less relevant, opting for more autonomous avenues. The research also reveals that higher financial satisfaction is linked with a lower inclination to invest in cryptocurrency (by 0.6 percentage points), supporting the idea that financial dissatisfaction may propel individuals towards riskier investments such as cryptocurrency to improve their socio-economic status (Delfabbro et al, 2021).…”

Section: Investment Portfolio Of Cryptocurrency Owners (H3)contrasting

confidence: 63%

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Do cryptocurrency investors in the UK need more protection?

Wang

2024

JFRC

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Purpose The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners. Design/methodology/approach This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links. Findings The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions. Originality/value This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

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Section: Resultscontrasting

confidence: 70%

Section: Investment Portfolio Of Cryptocurrency Owners (H3)contrasting

confidence: 63%

Do cryptocurrency investors in the UK need more protection?

Wang

2024

JFRC

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“…The endogenous variable BUH contained monthly stock returns generated yearly for each bank stock, and the data were obtained from the BankScope (BS) database maintained by Bureau van Dijk. BUH was selected as the endogenous variable to proxy bank stock performance (Shah and Albaity, 2022a, b).…”

Section: Sampling Methodology and Variablesmentioning

confidence: 99%

“…This negative correlation can be explained by the fact that the diffusion of information stimulated investment. As a result, traders overreacted by buying stocks based on their prior experiences and new data (Shah and Albaity, 2022a). An alternative sentiment measure could have been used; first, there is the European Economic Sentiment Indicator based on periodic surveys.…”

Section: Investor Sentiment and Stock Returnsmentioning

confidence: 99%

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Has the heterogeneity of culture, investor sentiment and uncertainty altered bank stock returns in the MENA region?

Shah

2023

JES

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PurposeThis paper has analysed the impact of cultural dimensions, investor sentiment and uncertainty on bank stock returns. Also, the study examined the influences of the interaction between cultural dimensions and individual (private) sentiment (investor sentiment).Design/methodology/approachTo meet the study's objectives, a two-step generalised method of moments estimator was applied to the study sample, which included 105 banks in the nine Middle East and North African region countries between 2010 and 2020.FindingsThe cultural dimensions of individualism and masculinity were found to have a positive and significant effect on banks' buy and hold stock return (BUH). At the same time, power distance and uncertainty avoidance were discovered to have negative effects. Besides, the findings revealed that the interactions of power distance, individual sentiment and uncertainty avoidance had positive and significant relationships with banks' BUH. However, individualism, individual sentiment and masculinity had inverse relationships with banks' BUH. Furthermore, the findings revealed that investor sentiment positively influenced banks' BUH. Finally, uncertainty influenced banks' BUH stock returns positively.Research limitations/implicationsImportant implications for participants in the financial sector and governments may be learnt from this study's conclusions. Due to cultural biases, this study's findings suggested that investors overreact in the stock market.Originality/valueAdditionally, this research comprises one of the few studies that have overviewed the link between classical and behavioural finance in MENA countries with distinctive cultural characteristics.

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Social media-based implosion of Silicon Valley Bank and its domino effect on bank stock indices: evidence from advanced machine and deep learning algorithms

Khan,

Hasan,

Anupam

2024

Soc. Netw. Anal. Min.

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Social media-driven speculations play a crucial role in triggering the collapse of the banking system and stock markets. In this paper, we investigate the effect of Twitter-based investor sentiment on the collapse of Silicon Valley Bank (SVB), the 16th largest bank in the US. Additionally, we examine the spillover effect of the social media-based investor sentiment and SVB collapse on the bank stock indices from twelve countries where Global Systemically Important Banks (G-SIBs) operate. Advanced machine and deep learning models are employed to model the social media-based investors’ sentiment regarding SVB implosion and its spillover effect on the G-SIBs’ bank stock indices. Our results reveal that social media-based negative investors’ sentiment played an important role in SVB implosion. Our results further show that the negative investors’ sentiment persisted, and its systemic shock was transmitted to the G-SIBs bank stock indices. Importantly, our results provide a lead and lag relationship between investors’ sentiment and returns of G-SIBs bank stock indices. The findings of this study offer crucial insights for policymakers to consider the external shocks associated with social media-based investors’ sentiment when devising policies related to bank runs, thus helping to prevent future financial crises and cross-border contagion.

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The role of trust, investor sentiment, and uncertainty on bank stock return performance: Evidence from the MENA region

Cited by 15 publications

References 133 publications

Do cryptocurrency investors in the UK need more protection?

Do cryptocurrency investors in the UK need more protection?

Has the heterogeneity of culture, investor sentiment and uncertainty altered bank stock returns in the MENA region?

Social media-based implosion of Silicon Valley Bank and its domino effect on bank stock indices: evidence from advanced machine and deep learning algorithms

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