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Finance, Accounting and Business Analysis 
Volume 6 Issue 2, 2024 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2024.2.05 

 

Global investor sentiment and bank performance: Evidence from African 

banks 

 

Damilola Tope Oyetade1* , Hilary Tinotenda Muguto2 , Paul-Francois Muzindutsi3  

School of Accounting, Economics and Finance, University of KwaZulu-Natal, South Africa1 

School of Accounting, Economics and Finance, University of KwaZulu-Natal, South Africa2 

School of Accounting, Economics and Finance, University of KwaZulu-Natal, South Africa3 

* Corresponding author 
 

 

Info Articles   Abstract 
 

History Article: 

Submitted 24 July 2024 

Revised 27 October 2024 

Accepted 12 November 2024 
 

 Purpose:  This study aims to address the underexplored implications of 

investor sentiment on the performance of banks operating in African 

economies. It investigates how investor sentiment affects bank 

performance across different regulatory frameworks, market conditions, 

and bank-specific attributes. 

Design/Methodology/Approach:  Using panel data from 35 

commercial banks listed on African stock exchanges from 2000 to 2022, 

this study employs a fixed effects model to assess the impact of investor 

sentiment on bank performance. 

Findings:  The findings indicate that investor sentiment positively 

impacts bank performance. This relationship is further influenced by 

bank-specific attributes, regulatory frameworks, and market contexts. 

Notably, confidence in the Basel regulatory framework enhances this 

sentiment-performance relationship, underscoring the importance of 

compliance for attracting investment. 

Practical Implications:  The results suggest several key policy 

implications: policymakers can utilize these insights to promote stable 

regulatory environments that support positive investor sentiment. Basel 

compliance further strengthens investor confidence, which contributes to 

improved bank performance in African banks. Bank managers can 

integrate sentiment analysis into their risk management strategies to 

anticipate shifts in investor confidence, thereby mitigating performance 

volatility and ensuring sustainable profitability. 

Originality/Value:  This study contributes to the literature by 

highlighting the significant role of investor sentiment in influencing bank 

performance within the African context. It emphasizes the importance 

of regulatory frameworks and sentiment-driven market dynamics in 

emerging economies, offering valuable insights for policymakers and 

bank managers aiming to enhance financial stability. 

Paper Type: Research Paper 

 

Keywords:  

Profitability, Investor 

sentiment, Behavioural 

finance, African banks, 

Bank Risk management 
 

 

JEL: G21; G32; G41; O16  

* Address Correspondence:   

E-mail: OyetadeD@ukzn.ac.za1 

  MugutoH@ukzn.ac.za2 

  MuzindutsiP@ukzn.ac.za3 
   

 

 
 

 

http://faba.bg/
https://doi.org/10.37075/FABA.2024.2.05
mailto:OyetadeD@ukzn.ac.za
mailto:MugutoH@ukzn.ac.za
mailto:MuzindutsiP@ukzn.ac.za
https://orcid.org/0000-0003-0120-5385
https://orcid.org/0000-0003-2367-3980
https://orcid.org/0000-0002-4819-8218


D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

146 

 

INTRODUCTION 

 

Background 
Over the past two decades, African banking has experienced substantial transformation driven by 

market-oriented reforms and strategic infrastructure development. These developments have played a crucial 

role in fostering economic growth and strengthening regional economies (Archibong, Coulibaly and Okonjo-

Iweala 2021), enhancing the performance of African banks. Consequently, African banks have become more 

appealing to global stakeholders seeking emerging market opportunities and collaborative ventures in today's 

interconnected financial environment (Shah and Albaity 2022). However, as these banks expand within this 

dynamic landscape, their performance is increasingly shaped by domestic factors and global forces, 

particularly shifts in global investor sentiment. Despite the importance of these dynamics, the influence of 

global and behavioural factors, such as investor sentiment, on bank performance remains underexplored, 

highlighting the need and relevance of this study. Without such explorations, it becomes challenging to 

understand the risks and opportunities banks face fully. 

Global investor sentiment is critical in influencing financial markets, and its impact is increasingly 

evident in the banking sector worldwide (Chen 2021). The interconnected nature of today's financial markets 

and systems means that shifts in global investor sentiment can have far-reaching implications, affecting 

capital flows (Muguto, Rupande and Muzindutsi 2019), credit availability (Chen 2021), and overall market 

stability (Rupande, Muguto and Muzindutsi 2019; Muzindutsi et al. 2023; Aboluwodi, Muzindutsi and 

Nomlala 2024). Many African banks are still relatively small in absolute terms, underdeveloped, and fragile 

compared to their global counterparts. Thus, these global fluctuations in investor sentiment may significantly 

influence the African banks' strategic decisions, risk and operations. Furthermore, the banking sector in 

Africa faces regulatory challenges, such as a lack of compliance with Basel capital requirements, which can 

exacerbate their vulnerability to shifts in global investor sentiment (Dayi et al. 2022; Oyetade, Obalade, and 

Muzindutsi 2023).  

African banks face challenges such as high non-performing loans and low capitalisation. However, 

some have demonstrated strong profitability, with Nigerian and South African banks achieving a return on 

equity (ROE) above 15 per cent in 2022, comparable to leading American banks (Dayi et al., 2022; Statista, 

2023). This trend extends across the continent, reflecting the resilience of certain African financial 

institutions in navigating economic and operational challenges. Nonetheless, African banks remain limited 

in offering comprehensive financial services, relying primarily on basic products such as short-term loans, 

unlike their counterparts in developed economies that provide a broader range of financial solutions, 

including short-, medium-, and long-term loans (Oyetade, Obalade and Muzindutsi 2021). As competition 

in global financial markets intensifies, these limitations could hinder African banks' ability to remain 

competitive and meet the evolving needs of their clients, especially as they strive to attract international 

investment and foster sustainable growth.  

Another challenge is the impact of shifts in global investor sentiment, which can trigger significant 

capital outflows, directly influencing bank performance (Chen 2021). These sentiment shifts can propagate 

through interconnected financial networks, amplifying their effects on banks, particularly African banks 

whose relative fragility and insufficient capitalisation make them more susceptible to external shocks. While 

favourable sentiment can attract capital inflows, boosting liquidity, fostering investment, and enhancing 

bank performance (Irresberger, Mühlnickel and Weiß 2015; Chen 2021), negative sentiment may lead to 

capital flight, increase funding costs, and exacerbate existing vulnerabilities, further weakening bank 

performance and straining the sector. This concern is heightened by the limited compliance of many African 

banks with Basel capital standards, leaving them ill-prepared for such shocks. However, the extent to which 

global investor sentiment influences African bank performance remains underexplored, highlighting the 

complexity of this relationship and the need for further research. 

The introduction of Basel III capital requirements following the 2008 financial crisis aimed to 

establish higher-quality minimum capital standards to mitigate banking risks (Bandt et al. 2018; BCBS 2017). 

Ideally, these regulations would have strengthened banks' resilience and enhanced financial stability. 

However, many African banks struggle to meet the existing Basel II requirements, while countries such as 

the USA, European nations, and South Africa have swiftly adopted Basel III. As a result, many African 

banks are left with low capital buffers, increasing their vulnerability to risks driven by fluctuating investor 

sentiment, which negatively impacts profitability. Furthermore, non-compliance with higher capital 

standards fosters negative investor sentiment, as such banks are perceived as high-risk, reducing their 

attractiveness to potential investors (Faia 2017). This lack of access to investor capital limits the funds 

available for African banks to lend and invest, further constraining profitability and weakening their financial 

standing. 

Profitability is essential for banks and is traditionally influenced by bank-specific factors, such as size 

and non-performing loans, and macroeconomic conditions (Bandt et al. 2018). However, recent research 



D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

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highlights that investor sentiment also plays a critical role in shaping bank behaviour, particularly 

influencing risk-taking decisions (Cubillas, Ferrer and Suárez 2021). Investor sentiment has further been 

shown to impact bond returns, bond yields, capital flows, and liquidity management, all closely tied to bank 

performance (Muguto et al. 2022; Chen 2021). For example, negative sentiment can drive up interest rates 

and bond yields, raising borrowing costs for banks and their customers while also heightening sovereign risk, 

ultimately deteriorating asset quality and profitability. Nevertheless, the influence of global investor 

sentiment on African banks — operating in less developed markets with unique regulatory frameworks and 

vulnerabilities — remains underexplored, revealing a critical gap in the literature that warrants further 

investigation. 

This study investigated how global investor sentiment impacts bank performance in Africa. By 

exploring this relationship, this study aimed to inform policymakers and stakeholders in developing effective 

strategies to mitigate negative sentiment risks, attract investors, and enhance bank profitability. Additionally, 

the study introduced a new global sentiment index to assess African banks' sensitivity to sentiment shifts, 

addressing a gap in the behavioural finance literature. The findings are expected to empower bank managers 

to help them leverage positive sentiment for long-term success. They also offer policy recommendations for 

policymakers and regulators to enhance the resilience and stability of the African banking sector. This 

research contributes to the limited knowledge of investor sentiment's influence on bank performance, 

particularly in Africa. The justification for choosing African banks for this study is that African banks present 

unique vulnerabilities in offering comprehensive financial services but still have growing significance in 

global markets.  

Over the past two decades, African banking has grown rapidly, attracting significant interest from 

global investors. However, many banks struggle with challenges such as limited compliance with Basel III 

capital requirements, leaving them vulnerable to shifts in global investor sentiment (Oyetade, Obalade and 

Muzindutsi 2023). Weaker capital buffers and higher non-performing loans further expose these banks to 

external shocks, amplifying the impact of sentiment-driven capital flows. Despite these challenges, research 

on the influence of global investor sentiment on African banks remains limited, highlighting a gap in the 

literature. This study aims to bridge that gap by examining how investor sentiment affects bank performance 

in Africa, providing valuable insights into how these banks can mitigate risks, capitalise on positive 

sentiment, attract investment, and enhance profitability. Investigating sentiment in the context of African 

banks also offers a deeper understanding of investor behaviour in emerging markets, an area that remains 

underexplored. 

The remainder of this article is as follows. The next two subsections present the theoretical framework 

and empirical literature. Section 2 describes the data, and the methods used to analyse such data. Section 3 

presents the results and discusses the findings. Section 4 concludes the article and provides necessary 

recommendations. 

  

Theoretical framework 
There are competing theories regarding the impact of behavioural biases, an amalgam of which 

constitutes sentiment. Market-wide investor sentiment reflects the collective attitude of investors toward 

specific assets, markets, or economies, often driven by emotions, beliefs, and perceptions rather than 

fundamental information (Shen, Yu and Zhao 2017). These biases influence investor decisions, affecting 

buying and selling behaviour and impacting asset prices and market dynamics (Kamoune and Ibenrissoul 

2022). High sentiment fosters optimism and bullish behaviour, while low sentiment triggers pessimism and 

bearishness, leading to volatility (Muguto et al. 2022). Behavioural finance argues that sentiment contributes 

to market inefficiencies, causing prices to deviate from fundamental values (Shah and Albaity 2022). These 

psychological influences drive trends that fundamental information alone cannot explain, making sentiment 

a key factor. Behavioral finance provides a valuable framework for examining how these dynamics influence 

decision-making in the banking sector, particularly in emerging markets like Africa. 

In contrast, traditional finance theory, primarily encompassing the efficient market hypothesis, 

assumes that financial markets are rational, with prices reflecting all publicly available information 

(Kamoune and Ibenrissoul 2022). Investors are presumed to make decisions based on objective evaluations 

of risk and return, unaffected by psychological biases or emotions (Shen, Yu, and Zhao, 2017). Market 

movements are considered responses to new information, with no influence from sentiment-driven factors, 

and any deviations from fundamental values are seen as temporary anomalies (Cordes, Nolte, and Schneider 

2023). Traditional finance emphasises that rational participants quickly correct inefficiencies through 

arbitrage. This perspective downplays the role of psychological biases, asserting that information and 

fundamentals drive prices and market behaviour (Faia 2017). However, these assumptions are increasingly 

questioned, particularly in emerging markets where inefficiencies persist, and behavioural influences shape 

investment decisions (Oyetade, Obalade and Muzindutsi 2021). 

Despite the incongruence between behavioural and traditional finance theories, the impact of 



D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

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sentiment on financial markets and bank performance is becoming increasingly apparent. Behavioural 

finance offers more profound insights into how psychological biases, such as optimism or fear, affect asset 

prices, capital flows, and liquidity management, especially in less developed markets (Muguto et al. 2022; 

Chen, 2021). These dynamics are critical for African banks, which operate in environments where sentiment-

driven capital inflows and outflows significantly impact profitability and stability (Oyetade, Obalade and 

Muzindutsi 2021). As African banks face challenges such as non-compliance with Basel standards, low 

capital buffers, and high non-performing loans, they are more susceptible to shifts in investor sentiment 

(Dayi et al. 2022; Statista 2023). Understanding these sentiment-driven dynamics helps banks mitigate risks, 

attract investment, and improve profitability, making behavioural finance a crucial perspective for evaluating 

bank performance in emerging markets. 

 

Empirical literature 

Investor sentiment refers to the process by which investors with high sentiment often make over-

optimistic investment decisions, while those with low sentiment lean towards pessimistic decisions (Agoraki, 

Aslanidis and Kouretas 2022). During periods of high sentiment, investors tend to act irrationally, relying 

less on fundamental analysis and more on emotions, leading to over-reaction and over-valuation of assets 

(Ali and Gurun 2009; Stambaugh, Yu and Yuan 2012). This can encourage banks to take more significant 

risks in pursuit of higher returns. Conversely, investors become more cautious and rational during low 

sentiment periods, often undervaluing assets below their fundamental values (Shen, Yu and Zhao 2017). 

These fluctuations in sentiment can have varying impacts on bank behaviour, including their lending 

practices and profitability, depending on the regional regulatory framework and market conditions. 

In developed markets, where regulatory frameworks are more robust, banks tend to be less sensitive 

to investor sentiment than those in emerging markets (Stambaugh, Yu and Yuan 2012; Di, Shaiban and 

Hasanov 2021). For example, during major financial events like the 2008 crisis and the COVID-19 

pandemic, sentiment significantly impacted bank stability and lending behaviour, with stronger creditor 

protections mitigating these effects (Cubillas, Ferrer and Suárez 2021). In markets with weaker regulatory 

frameworks, such as those in many African countries, the absence of strong legal protections amplifies the 

adverse effects of negative sentiment, increasing funding costs, non-performing loans, and asset quality risks 

(Cubillas, Ferrer and Suárez 2021). Studies show that Islamic banks are susceptible to sentiment shifts, with 

optimistic sentiment generating a stronger positive impact on performance than pessimistic sentiment (Shah 

and Albaity 2022; Di, Shaiban and Hasanov 2021). 

Investor sentiment also influences other aspects of bank performance, including liquidity, funding 

costs, and credit risk. Positive sentiment can enhance liquidity creation, but it may also encourage risky 

lending to maximise returns, leading to volatility in asset quality and higher credit risk (Cai, Pagano and 

Sedunov 2023; Agoraki, Aslanidis and Kouretas 2022). On the other hand, negative sentiment can increase 

sovereign risk, elevate funding costs, and constrain credit, reducing banks' lending capacity and profitability 

(Faia 2017; Cubillas, Ferrer and Suárez 2021). In African countries, where regulatory compliance is limited, 

investor sentiment plays a significant role. For example, banks that struggle to meet Basel III capital 

requirements are perceived as risky, driving negative sentiment and reducing access to capital (BCBS 2017; 

Oyetade, Obalade and Muzindutsi 2023). At the same time, well-capitalised banks may attract investors 

seeking lower bankruptcy risks and stable returns, highlighting the complex relationship between regulatory 

compliance, sentiment, and performance (Bandt et al. 2018). 

This study addresses the gap in understanding how investor sentiment influences the performance 

of African banks, where market inefficiencies and weak regulatory compliance increase the importance of 

behavioural factors. Existing studies have mainly focused on developed markets, leaving the relationship 

between sentiment and bank performance in emerging markets underexplored. Furthermore, given that 

factors like bank size, non-performing loans, and economic stability influence the sentiment-performance 

relationship (Caglayan and Xu 2016), this study will also consider these elements within the African context. 

Regulatory changes, such as the transition from Basel II to Basel III, create additional sentiment dynamics 

that shape bank performance, making it essential to investigate how capital adequacy interacts with 

sentiment to affect profitability. By filling these gaps, this research provides insights into how African banks 

can manage sentiment-related risks, attract investment, and enhance stability, contributing to the broader 

literature on investor sentiment in under-researched markets.  

 

METHODS 

 

Data 

This study employed a quantitative approach to investigate the impact of global investors' sentiment 

on the performance of African banks. Panel data of commercial banks listed on African stock exchanges 

from 2000 to 2022 were collected from multiple online databases. Their financial information and sentiment 



D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

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proxies’ data were sourced from the Bloomberg database, while macroeconomic variables data was sourced 

from the World Bank database. The initial dataset comprised 137 commercial banks listed on stock 

exchanges in Africa. Two sample selection criteria were used. Firstly, only those that have adopted either 

Basel II or Basel III capital ratios were selected, resulting in the exclusion of eighty banks due to a lack of 

additional information disclosure of regulatory capital. Furthermore, twenty-two banks were omitted due to 

insufficient data availability across the sample period. Consequently, the final sample consists of 35 
commercial banks with sufficient data over the sample period, providing a focused dataset for analysis. 

Global investor sentiment 
This study constructed a global investor sentiment index using principal component analysis (PCA), 

as no universal investor sentiment measure exists. Different measures have been proposed and employed in 

the literature. This includes Google Trend data and lexicons analysing text drawing inferences about 

investors' prevailing sentiment (Agoraki, Aslanidis and Kouretas 2022, de Bandt et al. 2018, Shah and 

Albaity 2022). However, the most popular approach for panel data analysis is to use investor sentiment 

proxies (Cai, Pagano and Sedunov 2023; Shen, Yu and Zhao 2017). Baker and Wurgler (2006) and Rupande, 

Muguto and Muzindutsi (2019) found that proxies combined into indices measure sentiment better than 

individual, potentially imperfect proxies.  

PCA helps combine these proxies into a single sentiment index, reducing data dimensionality, 

capturing maximum variance and improving measurement accuracy, especially for banking (Muguto et al. 

2022). Accordingly, seven proxies were employed to construct a global sentiment index, namely: business 

confidence index, consumer confidence index, global price of gold and oil, US dollar index, Bloomberg 

commodity index and volatility index. These proxies were standardised to mitigate scale effects, 

orthogonalised against a set of global macroeconomic variables, extracting residuals for sentiment index 

construction. This approach removed macroeconomic effects from proxies, ensuring that only behavioural 

components are left in the proxies (Muguto et al. 2019). Table 1 below reports the results of the procedure.  

 

Table 1. Principal component analysis output 

Eigenvalues: (Sum = 7, Average = 1) 

Number Value Difference Proportion Cum. Value Cum. Prop. 

1 3.0594 1.2462 0.4371 3.0594 0.4371 

2 1.8132 1.1312 0.2590 4.8727 0.6961 

3 0.6820 0.1471 0.0974 5.5547 0.7935 

4 0.5349 0.1316 0.0764 6.0897 0.8700 

5 0.4032 0.0821 0.0576 6.4930 0.9276 

6 0.3211 0.1353 0.0459 6.8141 0.9735 

7 0.1858 --- 0.0265 7.0000 1.0000 

Eigenvectors (loadings) 

Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 

Bci -0.2768 0.4861 0.1189 0.7255 0.2882 0.0569 -0.2450 

Cci -0.3360 0.3934 0.5219 -0.3055 -0.4985 0.3433 0.0020 

Com 0.3879 0.4592 0.1799 -0.0684 0.3078 -0.0383 0.7108 

Gol 0.4568 -0.1253 -0.0952 0.5235 -0.5604 0.3866 0.1696 

Oil 0.4770 0.2069 -0.0710 -0.2819 0.3095 0.5393 -0.5081 

Usd -0.4668 -0.1899 -0.3428 -0.0192 0.2614 0.6421 0.3838 

Vix 0.0753 -0.5507 0.7411 0.1467 0.3086 0.1568 0.0159 

Ordinary correlations  
Bci Cci Com Gol Oil Usd Vix 

Bci 1.0000       

Cci 0.5033 1.0000 
     

Com 0.0670 -0.0617 1.0000 
    

Gol -0.3677 -0.5232 0.3551 1.0000 
   

Oil -0.2678 -0.3249 0.7047 0.5264 1.0000 
  

Usd 0.2173 0.2438 -0.6783 -0.5597 -0.6255 1.0000 
 

Vix -0.3942 -0.2753 -0.2450 0.1736 -0.0905 -0.0267 1.0000 

Source: Authors' estimations (2024) 

GlobSent was defined using PCA, where the first component accounts for 43.71 percent of the total 

variance. This is slightly lower than the 46 percent reported by Reis and Pinho (2020) on their European 



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index and the 53 percent reported by Baker and Wurgler (2006) on the US market. However, the figure is 

robust. Key variables correlating with this first principal component, denoted PC 1, are Oil (0.4770), USD 

(0.4668), and Gol (0.4568). However, Com (0.3879), Cci (0.3360) and Bci (0.2768) also have some 

significant correlation with the first principal component. However, Vix (0.0753) showed an exceptionally 

low correlation, likely influenced by macroeconomic factors against which it was orthorgonalised. Using 

these values, GlobSent was defined as:  

 

GlobSent = -0.276Bci - 0.336Cci + 0.388Com + 0.457Gol + 0.477Oil - 0.467Usd + 0.075Vix (1) 

 

Estimation model 

Sentiment and bank performance 
Following similar studies such as Cubillas, Ferrer and Suárez (2021) and Cai, Pagano and Sedunov 

(2023), this study examines the impact of Globsent on bank performance using equation 1:  

 

ROAit = β0 + β1Globsentit + β2Controlit + ∅′Yeari + εit (2) 

 

where 𝑖 is the individual bank in year 𝑡. Equation 2 controls for year effects by introducing year 

dummies (𝑌𝑒𝑎𝑟𝑖) in line with studies such as those of Cubillas, Ferrer, and Suárez (2021). Year effects are 

included to control for time-fixed effects, unobserved heterogeneity, and business cycles across the country 

over time (Bond and Eberhardt 2013). 𝛽 and  ∅ are coefficients of the model that capture the effects on the 

dependent variable, and 𝜀 is the error term.  

 

Table 2. Definition of key variables 

Variable Abbreviation Definition Sources  Expected 

sign 

Return on asset ROA ROA (%)=Net profit 

after tax/average total 

assets 

de Bandt et al. (2018) Dependent 

variable 

Global investor 

sentiment 
Globsent A sentiment index 

constructed with seven 

indices 

Author’s own 

construct using PCA 

Positive 

Size  Isize A natural logarithm of  

total asset. Divided into 

five quintiles 

Di, Shaiban, and 

Hasanov (2021) 

Positive or 

negative  

Deposit ratio  Dep_growth Total deposit/total asset 

as a measure of  total 

liabilities 

Cai, Pagano, and 

Sedunov (2023) 

Negative  

Loan growth Loan_growth The growth rate of  total 

loans divided by total 

assets 

Shah and Albaity 

(2022) 

Positive 

Non-performing 

loans 

Npl_ta Non-performing 

loans/Total assets 

Shah and Albaity 

(2022) 

Negative 

Gross domestic 

product 

GDP_growth Real GDP which have 

been adjusted for 

inflation 

Shah and Albaity 

(2022) 

Positive 

Inflation Inflation Proxy by consumer 

price index 

Di, Shaiban, and 

Hasanov (2021) 

Negative 

Financial 

development 

Findev Domestic private credit 

by banks as a % of  GDP 

 Positive 

 

The dependent variable - 𝑅𝑂𝐴 is the proxy for bank performance measure. ROA is a significant 

indicator of the quality of a bank's earnings (Di, Shaiban, and Hasanov 2021). It is also a widely used 

performance ratio to gauge bank profitability because it shows how efficiently a bank transforms available 

assets into earnings (Yuan et al. 2022). Furthermore, ROA is superior to other performance ratios, such as 

return on equity (ROE), because it allows investors to understand banks' capability to invest and use financial 

resources to generate profit (Yuan et al. 2022). Therefore, ROA was used as the performance measure for 

the dependent variable. For robustness checks, the study also uses net interest margin (NIM), return on 



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equity (ROE), and Zscore as alternative performance measures for equation 2. 

The explanatory variables - The primary variable of interest for this study is the global investor 

sentiment proxy by 𝐺𝑙𝑜𝑏𝑠𝑒𝑛𝑡. 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 represents two control variables, namely macroeconomic variables 

(GDP growth, inflation, and financial development proxy by domestic private credit by banks as a % of 

GDP) and bank-specific variables (deposit ratio, size, loan growth, and non-performing loans). Bank-specific 

variables, including deposit ratio, bank size, loan growth, and non-performing loans, may influence bank 

performance considerably (Shah and Albaity 2022). This study expects control variables to affect the 

Globsent-bank performance relationship in Africa. 

 

Capital regulations and Globsent effect on bank performance 
To examine whether the regulatory capital impacts the Globsent-performance relationship for African 

banks, 𝐶𝐴𝑃 was introduced. It represents banks that are either Basel II or Basel III compliant. BII_cap 

represents banks that have implemented the Basel II capital ratio, and BIII_cap for banks that have 

implemented the Basel III capital ratio. A bank with low capital is likely to influence the effects of Globsent 

on profits. To test this hypothesis, capital ratios were introduced for regulatory capital to test the effect of 

capital adequacy on the relationship between Globsent and bank performance using Equation 3:  

 

ROAit = β0 + β1Globsentit + β2CAPit + β3Controlit + ∅′Yeari + εit         (3) 

 

Equations (2) and (3) are estimated using fixed effects (FEM) and random effects (REM) models. 

Specification tests were carried out to test the validity of both estimation models.   F-test was used to check 

the fit of the model to the appropriate estimator between Pooled ordinary least square (Pooled OLS), FEM, 

and REM.  

Furthermore, diagnostic tests were performed to account for issues with auto-correlation and 

heteroskedasticity in the panel data. Thus, a fixed effect model with robust standard error was reported to 

correct for these issues. Also, this choice was considered appropriate for this panel data analysis as it 

accounts for time-invariant, unobserved factors at the bank level that might influence performance. These 

unobserved factors could be bank-specific management styles, business strategies, or inherent risk profiles. 

By controlling for these fixed effects, the effects of the independent variables on bank performance within 

each bank over time were isolated, allowing their analysis. 

 

RESULTS AND DISCUSSION 

 

Descriptive statistics 

Figure 1 presents the global investor sentiment index (GlobSent) from 2000 to 2022. The index effectively 

captured key global events corresponding to significant sentiment fluctuations, such as the 2008 financial 

crisis and the COVID-19 pandemic, affirming its reliability. These events triggered sharp declines in 

sentiment, driven by heightened investor pessimism and expectations of severe economic fallout, especially 

in the African banking sector. Investors anticipated adverse outcomes and feared their funds might become 

inaccessible amid the uncertainty, leading to a rapid downturn in sentiment. However, the index shows that 

sentiment rebounded swiftly after these crises, reflecting renewed investor confidence despite the initial 

pessimism.  

This behaviour contrasts with trends observed in developed markets, where sentiment typically stabilises 

more gradually following major disruptions (Shen, Yu and Zhao 2017; Stambaugh, Yu and Yuan 2012). 

For example, investor sentiment did not decline drastically during the 2008 crisis in developed economies, 

suggesting that investors were less pessimistic and more resilient. These behavioural differences may stem 

from legal frameworks, regulatory institutions, and market infrastructure disparities between developed and 

emerging markets. More robust institutional frameworks in developed markets likely mitigate excessive 

pessimism, promoting more stable recovery patterns. 



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Source: Author's calculation based on data obtained from databases 

Figure 1. Investor sentiment index 

 

Table 3 presents the summary statistics for key variables, including the dependent variable, return on 

assets (ROA), and the independent variables - GlobSent, Basel capital ratios, bank-specific ratios, and 

macroeconomic indicators. Though modest, the average ROA of 2.3 percent surpasses that of banks from 

other emerging economies. For example, Yuan et al. (2022) reported an average ROA of just 0.11 percent 

for Indian and Bangladeshi banks. Notably, some African banks achieved ROA figures as high as 13 percent, 

indicating strong profitability and making these banks attractive to investors seeking higher returns. 

However, the concern lies in the average non-performing loan (NPL) ratio of 3.5 percent, with a standard 

deviation of 6 percent, which signals potential risks to future profits. 

 

Table 3. Key variables 

Variable Obs Mean Std. dev. Min Max 

ROA 783 2.302 1.832 -8.992 13.795 

Globsent 805 0.011 2.7 -6.517 2.654 

Dep_growth 664 10.50 27.179 -199.627 182.457 

loan_growth 764 5.297 42.096 -199.064 164.813 

Npl_ta 770 3.565 6.081 0 63.398 

GDP_growth 796 4.144 3.178 -14.144 15.329 

Inflation 790 9.247 5.688 -0.692 41.51 

Findev 737 32.185 18.788 3.11 70.38 

BII_capratio 554 16.352 8.804 4 147 

BIII_capratio 523 17.947 7.026 2.901 73.807 

Source: Author's calculation based on data obtained from databases 

 
The deposit growth ratio averages 10.5 percent, which appears reasonable, but the high standard 

deviation of 27 percent indicates significant volatility, suggesting liquidity challenges across the sector. Some 

banks may struggle to gather adequate deposits, limiting their capacity to lend and expand operations. This 

liquidity constraint is reflected in the modest loan growth average of just 5 percent, highlighting a cautious 

lending environment. Such conservative lending practices, while potentially reducing risk, may also limit 

profitability and hinder the sector's growth. These findings emphasise the importance of balancing 

profitability with sound risk management to ensure sustainable performance in the African banking sector. 

 

Empirical results 
Table 4 presents the results of the impact of investor sentiment, captured by GlobSent, on bank 

performance in Africa, measured through return on assets (ROA). A fixed effects model with robust standard 

errors and year effects was employed across all models. Four performance measures were estimated: ROA, 

net interest margin (NIM), return on equity (ROE), and Z-score. Among these, the ROA model exhibited 

the highest adjusted R-squared, making it the most appropriate for explaining bank performance. The results 

align with theoretical expectations, showing a positive and significant relationship between GlobSent and 

ROA at the 5 percent significance level. This indicates that investor sentiment is crucial in influencing bank 



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153 

 

performance, conditioned by bank-specific and macroeconomic factors. The findings confirm that investors 

in Africa consider both firm-level characteristics and broader economic conditions when making investment 

decisions, consistent with studies by Agoraki, Aslanidis and Kouretas (2022) and Cai, Pagano and Sedunov 

(2023). 

 

Table 4. Globsent effect on bank performance 
 ROA NIM ROE Zscore 

Globsent 0.586** -0.054 4.334 -0.040** 
 (0.288) (0.136) (2.612) (0.015) 

_Isize_2 -0.754 -0.093 0.482 -0.003 
 (0.504) (0.394) (1.933) (0.062) 

_Isize_3 -0.852 -0.289 0.315 -0.005 
 (0.656) (0.389) (2.140) (0.084) 

_Isize_4 -1.129* -0.810* -3.839 0.007 
 (0.652) (0.457) (2.952) (0.090) 

_Isize_5 -2.058*** -1.049 -11.344*** -0.026 
 (0.724) (0.736) (3.450) (0.110) 

Dep_growth -0.000 -0.003 -0.028 0.001 
 (0.003) (0.003) (0.021) (0.001) 

Loan_growth -0.115 0.106 0.937 0.033 
 (0.175) (0.187) (1.064) (0.029) 

Npl_ta -0.075** -0.001 -0.261* -0.002* 
 (0.035) (0.023) (0.145) (0.001) 

GDP_growth 0.045 0.088* 0.471** 0.003 
 (0.032) (0.048) (0.227) (0.003) 

Inflation 0.005 0.040** 0.287*** -0.003** 
 (0.009) (0.016) (0.095) (0.001) 

Findev -0.031*** -0.025 -0.140 -0.003* 
 (0.011) (0.016) (0.119) (0.001) 

_cons 4.814*** 4.848*** 27.625*** 1.500*** 
 (0.757) (0.683) (6.657) (0.106) 

N 598 580 591 596 

R-squared 0.2601 0.2181 0.1922 0.2647 

Adjusted R-squared 0.168 0.062 0.090 0.116 

Robust standard errors are in parentheses * p<0.1, ** p<0.05, *** p<0.001  

Source: Author's calculation based on data obtained from databases 

 

Control variables provide further insights into factors shaping ROA. Bank size exhibited a negative 

and significant impact on ROA, suggesting that larger banks may face challenges in efficiently utilising their 

assets and capital to generate higher profits. This outcome aligns with Agoraki, Aslanidis and Kouretas 

(2022), who found that larger US banks managed smaller loan portfolios, possibly explaining the negative 

size-performance relationship. Non-performing loans also significantly negatively affected ROA, 

underscoring the detrimental impact of poor loan quality on profitability. Additionally, financial 

development showed an unexpected negative and significant effect on ROA, implying that the relationship 

between financial development and bank performance in Africa may be more complex than anticipated, 

warranting further investigation. The findings related to macroeconomic variables, such as GDP growth and 

inflation, were statistically insignificant, indicating that broader economic conditions may have a limited 

direct influence on bank profitability over the observed period.  

Table 5 examines how Basel capital adequacy requirements—specifically Basel II and Basel III—

moderate the relationship between investor sentiment and bank performance. In Model 1, a positive and 

significant relationship was found between investor sentiment and bank performance for Basel II-compliant 

banks, suggesting that sentiment is crucial in driving profitability for these banks. Similarly, Model 2 showed 

a positive and significant impact of investor sentiment on the performance of Basel III-compliant banks. 

While investors typically view strict capital regulations as a potential drag on profitability due to reduced 

shareholder returns (Bandt et al. 2018), the positive impact of sentiment across both Basel II and Basel III-

compliant banks indicates that investors may still favour these banks. This suggests that investors, driven by 



D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

154 

 

sentiment, may irrationally prioritise short-term performance over strict capital buffers, perceiving these 

banks as stable and profitable despite regulatory constraints. 

 

Table 5. Capital regulations and Globsent effect on bank performance   
Basel II Basel III  
ROA ROA 

Globsent 0.965*** 0.911***  
(0.139) (0.121) 

BII_cap 0.019 
 

 
(0.017) 

 

BIII_cap 
 

0.072***   
(0.014) 

_Isize_2 -0.341 -0.639  
(0.580) (0.447) 

_Isize_3 -0.458 -0.791  
(0.725) (0.534) 

_Isize_4 -1.169 -1.188  
(0.870) (0.834) 

_Isize_5 -1.746** -1.931**  
(0.745) (0.838) 

Dep_growth -0.002 -0.002  
(0.004) (0.003) 

Loan_growth -0.135 0.097  
(0.217) (0.241) 

Npl_ta -0.082* -0.078*  
(0.042) (0.045) 

GDP_growth 0.019 -0.001  
(0.042) (0.038) 

Inflation 0.027 0.024  
(0.017) (0.019) 

Findev -0.010 -0.001  
(0.021) (0.019) 

_cons 4.711*** 3.480***  
(0.678) (0.784) 

N 482 461 

R-squared 0.3203 0.4937 

Adjusted R-squared 0.1702 0.301 

Robust standard errors are in parentheses * p<0.1, ** p<0.05, *** p<0.001  

Source: Author's calculation based on data obtained from databases 

 

Interestingly, both models reveal that investor sentiment positively influences the return on assets 

(ROA) for banks compliant with either Basel II or Basel III standards. This indicates that a baseline capital 

adequacy level promotes investor confidence beyond the specific capital framework, reinforcing the positive 

relationship between sentiment and performance. Investors appear to be drawn to banks meeting regulatory 

standards, as these banks are perceived as less risky and capable of maintaining profitability even during 

downturns. The findings imply that while capital adequacy regulations shape investor perceptions, sentiment 

remains a powerful force influencing bank performance across regulatory frameworks. However, the direct 

impact of Basel II compliance on performance is insignificant, suggesting that stricter Basel III standards 

may substantially influence investor behaviour. 

The results also confirm several patterns observed in previous models regarding control variables. 

Bank size continues to exhibit a negative and significant effect on performance, indicating that larger banks 

may struggle with efficiently utilising assets to generate profits. Similarly, non-performing loans retain their 

negative and significant impact, emphasising the critical need for effective loan management to protect 

profitability. The mixed but generally insignificant effects of inflation, GDP growth, and financial 

development on ROA suggest that macroeconomic conditions play a limited role in directly driving bank 

performance, warranting further exploration. These findings highlight the nuanced interplay between capital 

regulations, investor sentiment, and performance, underscoring the importance of regulatory compliance 



D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

155 

 

and sentiment-driven behaviour in shaping the outcomes for African banks. 

Specification tests were carried out to test the validity of both estimation models. As a result, the 

Hausman test was carried out (see Table 6: Appendix) to select the best estimation technique. Although the 

Hausman test chose RE, in the F-test used to check the model's fit, we rejected the null hypothesis that fixed 

effects are non-zero, implying that the cross-sectional or time-specific effects (year dummies) are significant. 

Therefore, OLS and random effect will be biased; thus, FE is considered an appropriate and efficient 

estimator.  

Diagnostic tests were carried out to test the presence of autocorrelation and heteroscedasticity for 

the validity of estimation models and the validity of the findings. The diagnostic tests conducted, namely 

the Modified Wald and Woolridge tests, were used to detect and correct heteroskedasticity and 
autocorrelation, respectively. The hypotheses for these tests were H0 of homoskedasticity and no 

autocorrelation. The results presented in Table 8 indicate the presence of both heteroskedasticity and 

autocorrelation, as H0 was rejected for both tests. This implies that the OLS assumptions are violated, 

leading to biased coefficient estimates. Also, t-statistics and confidence intervals would be invalid for 

inference problems. Thus, robust standard errors were used in the regression to address these issues. 

 

Discussion of findings 
Our findings reveal that investors become extremely pessimistic toward African banks during major 

crises, with sharp declines in sentiment posing significant risks. This is dangerous for African banks, as 

investors may exploit the situation to undervalue these institutions, especially given the less developed 

regulatory environments in African markets. This contrasts with the neutral investor behaviour observed by 

Shen, Yu and Zhao (2017) in developed markets, where investors remained measured during the 2008 

financial crisis. These findings suggest a distinct dynamic in African markets, where investor sentiment may 

be more responsive to growth opportunities and less influenced by past crises. Our study highlights the 

vulnerability of African banks to pessimism during crises. It highlights the need for African policymakers to 

develop robust legal and institutional frameworks to protect banks from harmful investment behaviour. 

Our findings further demonstrate that bank-specific and macroeconomic factors and the level of 

financial development play crucial roles in the positive and significant impact of investor sentiment on bank 

performance in Africa. Interestingly, this result diverges from behavioural finance theory, as the positive 

sentiment reflects an optimistic outlook on key controlling factors, contributing to improved bank 

performance. These results align with Cai, Pagano and Sedunov (2023) for liquidity creation in banks and 

with Agoraki, Aslanidis and Kouretas (2022) for US bank lending. Optimistic sentiment may encourage 

banks to expand operations and take advantage of favourable market conditions, thus enhancing 

profitability. This finding highlights the importance of understanding how sentiment influences bank 

performance, particularly in regions where financial markets are still developing. 

Control variables provide additional insights into the challenges faced by African banks. Non-

performing loans negatively and significantly impact bank performance, emphasising the importance of 

managing loan quality. Similarly, bank size has a negative and significant effect, suggesting that larger banks 

may struggle with operational inefficiencies, diminishing profitability and asset quality. This result aligns 

with Shah and Albaity (2022) for banks in MENA and Gulf countries and Agoraki, Aslanidis and Kouretas 

(2022) for US banks. Larger African banks may also face stricter regulatory constraints, including 

compliance costs for "too-big-to-fail" institutions, further impacting profitability. Addressing these 

operational challenges is essential for improving the performance of larger banks and enhancing their 

competitiveness in the region. 

The impact of financial development on bank performance is also negative and significant, 

highlighting the general and widespread underdevelopment of Africa's financial markets, institutions, and 

regulatory frameworks. In developed financial systems, lower transaction costs, better access to liquidity, 

and more efficient capital mobilisation foster bank growth and economic development (World Bank Group 

2012). While positive sentiment can create opportunities for expansion, negative sentiment may prompt 

more conservative strategies. Over the long term, sustaining positive sentiment requires African banks to 

focus on operational efficiency, building customer trust, and ensuring regulatory compliance. These 

strategies will improve profitability and performance, helping African banks navigate volatile market 

conditions better. 

Our results also reveal that compliance with Basel II and Basel III capital standards positively 

influences the relationship between investor sentiment and bank performance. This suggests that investors 

view banks adhering to Basel standards as less risky, regardless of whether they comply with Basel II or the 

more recent and encompassing Basel III. However, only Basel III compliance directly influences bank 

performance, reflecting its stricter regulatory framework. Although this finding contrasts with Faia (2017), 

who observed that investors favoured banks with strong capital bases during sovereign risk periods, it 

indicates that investor confidence in Basel-compliant African banks remains high. This optimism may be 



D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

156 

 

driven by the strong financial positions and growth potential of African economies, which enhance the 

appeal of these banks to investors. 

The findings offer valuable insights. Policymakers should encourage African banks to adopt higher 

Basel standards to attract global investors seeking low-risk opportunities. Tailored business models and 

innovative financial products are essential for larger banks to overcome the negative impact of size on 

performance. Strengthening risk management practices and improving operational efficiency are crucial to 

enhancing profitability. Robust regulatory frameworks, including enhanced investor protection, will attract 

more capital inflows to African banks. Finally, recognising investor sentiment as a powerful performance 

driver underlines the importance of fostering stable policies to build investor confidence. Adequate creditor 

protection, sound governance, and robust risk management practices will help mitigate the adverse effects 

of investor pessimism during crises, supporting sustainable growth in Africa's banking sector. 

 

CONCLUSION 

 
This study explored the intricate relationship between investor sentiment and bank performance in 

Africa, shaped by individual bank characteristics and broader macroeconomic conditions. While the 

findings confirmed a generally positive impact of investor sentiment on bank performance, variability based 

on bank-specific attributes and market contexts indicated that investors weigh risks and opportunities 

rationally. Notably, compliance with Basel II and Basel III capital requirements strengthened the 

relationship between sentiment and performance, though only the higher Basel III standards significantly 

enhanced bank performance. This highlights the importance of stricter capital regulations in improving 

profitability and stability within the African banking sector. 

Tailored policy responses are necessary to address African banks' unique challenges, including the 

regulatory disparities across different regions. Policymakers can use these insights to enhance legal 

frameworks and investor protection policies, creating an environment that attracts global capital, fosters 

investor confidence, and mitigates the risks of sentiment-driven capital flows, particularly during financial 

turbulence. Strengthening central bank oversight and encouraging robust risk management practices are also 

critical to increasing the resilience of African banks to fluctuating investor sentiment. 

Promoting sound governance and risk management frameworks will help banks mitigate the potential 

adverse effects of sentiment on performance. These measures will also enable banks to capitalise on positive 

sentiment, driving growth and profitability. By aligning investor perceptions with banks' true risk profiles, 

policymakers can ensure a more stable financial environment, better preparing African banks to withstand 

market volatility. This study provides a foundation for future research further to investigate the mechanisms 

behind sentiment-driven behaviour in African banking and explore strategies for sustainable growth in the 

sector. 

 

REFERENCES 

 
Aboluwodi, D., P. F. Muzindutsi, and B. Nomlala. 2024. Investor sentiments and performance of  selected 

ESG indices in BRICS markets during bull and bear conditions. Investment Analysts Journal, 1-18. 

doi: https://doi.org/10.1080/10293523.2024.2397889.  

Agoraki, M-E., A. Nektarios, and P. Georgios. 2022. US banks' lending, financial stability, and text-based 
sentiment analysis. Journal of  Economic Behavior & Organization 197:73-90. doi: 

https://doi.org/10.1016/j.jebo.2022.02.025. 
Ali, A., and U. Gurun. 2009. Investor Sentiment, Accruals Anomaly, and Accruals Management. Journal of  

Accounting, Auditing & Finance 24 (3):415-431. doi: 10.1177/0148558X0902400305. 

Archibong, B., C. Brahima, and N. Okonjo-Iweala. 2021. Washington Consensus Reforms and Lessons for 
Economic Performance in Sub-Saharan Africa. Journal of  Economic Perspectives 35 (3):133-56. doi: 

10.1257/jep.35.3.133. 
Baker, M., and J. Wurgler. 2006. Investor Sentiment and the Cross-Section of  Stock Returns. The Journal of  

Finance 61 (4):1645-1680. doi: https://doi.org/10.1111/j.1540-6261.2006.00885.x. 

BCBS. 2017. Basel III: Finalising post-crisis reforms. Basel Committee on Banking Supervision:1-162. 
https://www.bis.org/bcbs/publ/d424.htm.  

Bond, S., and M. Eberhardt. 2013. Accounting for unobserved heterogeneity in panel time series models. 
University of  Oxford:1-11. https://ora.ox.ac.uk/.  

Caglayan, M., and B. Xu. 2016. Sentiment volatility and bank lending behaviour. International Review of  

Financial Analysis 45:107-120. doi: https://doi.org/10.1016/j.irfa.2016.03.009. 

Cai, J., M. Pagano, and J. Sedunov. 2023. The role of  investor sentiment in bank liquidity creation. Finance 

Research Letters 58:104663. doi: https://doi.org/10.1016/j.frl.2023.104663. 

https://doi.org/10.1080/10293523.2024.2397889
https://doi.org/10.1016/j.jebo.2022.02.025
https://doi.org/10.1111/j.1540-6261.2006.00885.x
https://www.bis.org/bcbs/publ/d424.htm
https://ora.ox.ac.uk/
https://doi.org/10.1016/j.irfa.2016.03.009
https://doi.org/10.1016/j.frl.2023.104663


D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

157 

 

Chen, W. 2021. Equity investor sentiment and bond market reaction: Test of  overinvestment and capital flow 
hypotheses. Journal of  Financial Markets 55:100589. doi: 

https://doi.org/10.1016/j.finmar.2020.100589. 

Cordes, H., S. Nolte, and J. Schneider. 2023. Dynamics of  stock market developments, financial behaviour, 
and emotions. Journal of  Banking & Finance 154:106711. doi: 

https://doi.org/10.1016/j.jbankfin.2022.106711. 

Cubillas, E., E. Ferrer, and N. Suárez. 2021. Does investor sentiment affect bank stability? International 
evidence from lending behaviour. Journal of  International Money and Finance 113:102351. doi: 

https://doi.org/10.1016/j.jimonfin.2020.102351. 

Dayi, O., F. Girancourt, A. Fjer, and Z. Makgatho. 2022. African banking: The productivity opportunity. 
McKinsey & Company. https://www.mckinsey.com/industries/financial-services/our-

insights/african-banking-the-productivity-opportunity.  

Bandt, O., B. Camara, A. Maitre, and  P. Pessarossi. 2018. Optimal capital, regulatory requirements and 
bank performance in times of  crisis: Evidence from France. Journal of  Financial Stability 39:175-186. 

doi: https://doi.org/10.1016/j.jfs.2017.03.002. 

Di, L, M. Sharaf, and A. Hasanov. 2021. The power of  investor sentiment in explaining bank stock 

performance: Listed conventional vs. Islamic banks. Pacific-Basin Finance Journal 66:101509. doi: 

https://doi.org/10.1016/j.pacfin.2021.101509. 
Faia, E. 2017. Sovereign risk, bank funding and investors' pessimism. Journal of  Economic Dynamics and 

Control 79:79-96. doi: https://doi.org/10.1016/j.jedc.2017.03.010. 

Irresberger, F., J. Mühlnickel, and G. Weiß. 2015. Explaining bank stock performance with crisis sentiment. 
Journal of  Banking & Finance 59:311-329. doi: https://doi.org/10.1016/j.jbankfin.2015.06.001. 

Kamoune, A., and N. Ibenrissoul. 2022. Traditional versus Behavioral Finance Theory. International Journal 

of  Accounting, Finance, Auditing, Management and Economics 3 (2-1):282-294. doi: 

10.5281/zenodo.6392167. 

Muguto, H., L. Muguto, A. Bhayat, H. Ncalane, J. Jack, S. Abdullah, T. Nkosi, and P-F. Muzindutsi. 2022. 

The impact of  investor sentiment on sectoral returns and volatility: Evidence from the Johannesburg 
stock exchange. Cogent Economics & Finance 10 (1):2158007. doi: 10.1080/23322039.2022.2158007. 

Muguto, H., L. Rupande, and P-F. Muzindutsi. 2019. Investor sentiment and foreign financial flows: 
Evidence from South Africa.  Zbornik Radova Ekonomski Fakultet u Rijeka 37 (2):473-498. doi: 

https://doi.org/10.18045/zbefri.2019.2.473.  

Muzindutsi, P-F., R. Apau, L. Muguto, H. T. Muguto. 2023. The impact of  investor sentiment on housing 

prices and the property stock index volatility in South Africa. Real Estate Management and 

Valuation, 31(2):1-17. doi:10.2478/remav-2023-0009. 

Nartea, G., H. Bai, and J. Wu. 2020. Investor sentiment and the economic policy uncertainty premium. 
Pacific-Basin Finance Journal 64:101438. doi: https://doi.org/10.1016/j.pacfin.2020.101438. 

Oyetade, D., A. Obalade, and P-F. Muzindutsi. 2021. Basel capital requirements, portfolio shift and bank 
lending in Africa. ACRN Journal of  Finance and Risk Perspectives 10:296-319. https://www.acrn-

journals.eu/jofrpvol1001p296.html. 

Oyetade, D., A. Obalade, and P-F. Muzindutsi. 2023. Basel IV capital requirements and the performance of  
commercial banks in Africa. Journal of  Banking Regulation. doi: 10.1057/s41261-021-00181-1. 

Reis, P., M. Nogueira, and C. Pinho. 2020. A new European investor sentiment index (EURsent) and its 
return and volatility predictability. Journal of  Behavioral and Experimental Finance 27:100373. doi: 

https://doi.org/10.1016/j.jbef.2020.100373. 

Rupande, L., H. Muguto, P-F. Muzindutsi. 2019. Investor sentiment and stock return volatility: Evidence 

from the Johannesburg Stock Exchange. Cogent Economics & Finance 7 (1):1600233. doi: 

10.1080/23322039.2019.1600233. 

Shah, S., and M. Albaity. 2022. The role of  trust, investor sentiment, and uncertainty on bank stock return 
performance: Evidence from the MENA region. The Journal of  Economic Asymmetries 26:e00260. doi: 

https://doi.org/10.1016/j.jeca.2022.e00260. 
Shen, J., J. Yu, and S. Zhao. 2017. Investor sentiment and economic forces. Journal of  Monetary Economics 

86:1-21. doi: https://doi.org/10.1016/j.jmoneco.2017.01.001. 
Stambaugh, R., J.Yu, Y. Yuan. 2012. The short of  it: Investor sentiment and anomalies. Journal of  Financial 

Economics 104 (2):288-302. doi: https://doi.org/10.1016/j.jfineco.2011.12.001. 

Statista. 2023. Largest banks in the United States 2022, by return on equity. Online: Statista Research 

Department. https://www.statista.com/.  

World Bank Group. 2012. Global Financial Development Report 2013: Rethinking the Role of  the State in 

Finance. Online: World Bank, Wahington DC. https://openknowledge.worldbank.org/entities/.  

https://doi.org/10.1016/j.finmar.2020.100589
https://doi.org/10.1016/j.jbankfin.2022.106711
https://doi.org/10.1016/j.jimonfin.2020.102351
https://www.mckinsey.com/industries/financial-services/our-insights/african-banking-the-productivity-opportunity
https://www.mckinsey.com/industries/financial-services/our-insights/african-banking-the-productivity-opportunity
https://doi.org/10.1016/j.jfs.2017.03.002
https://doi.org/10.1016/j.pacfin.2021.101509
https://doi.org/10.1016/j.jedc.2017.03.010
https://doi.org/10.1016/j.jbankfin.2015.06.001
https://doi.org/10.18045/zbefri.2019.2.473
https://doi.org/10.1016/j.pacfin.2020.101438
https://www.acrn-journals.eu/jofrpvol1001p296.html
https://www.acrn-journals.eu/jofrpvol1001p296.html
https://doi.org/10.1016/j.jbef.2020.100373
https://doi.org/10.1016/j.jeca.2022.e00260
https://doi.org/10.1016/j.jmoneco.2017.01.001
https://doi.org/10.1016/j.jfineco.2011.12.001
https://www.statista.com/
https://openknowledge.worldbank.org/entities/


D.T. Oyetade, H.T. Muguto, P.-F. Muzindutsi/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

158 

 

Yuan, D., I. Harymawan, B. Dhar, and A. Hossain. 2022. Profitability determining factors of  the banking 
sector: Panel data analysis of  commercial banks in South Asian countries. Frontiers in Psychology 13. 

doi: 10.3389/fpsyg.2022.1000412. 

 

 

APPENDIX 

 

Table 6. Hausman test  

Hausman ROA ROE NIM Capital 

 P>10 percent P<1 percent P>10 percent P<1 percent 

 Fail to reject Ho Reject Ho Fail to reject Ho Reject Ho 

Decision 
Random effect is 

preferred 

Fixed effect is 

preferred 

Random effect is 

preferred 

Fixed effect is 

preferred 

Note: Hausman hypothesis-H0: Random effects is preferred. 𝐻1: Fixed effects is preferred 

 

 

Table 7. F-test for OLS vs Fixed effect model 

 Statistics P-value 

F-test (34, 531) 15.97 0.000 

 

 

Table 8. Modified Wald test 

Modified Wald test  Woolridge test for auto-correlation 

Chibar2 (26) 283.31 F (1, 531) 20.32 

Prob > Chi2  0.000 Prob > F  0.000 

Note: Modified Wald test for heteroskedasticity in FEM and Woolridge test for autocorrelation in panel 

data 

Source: Author's Compilation 


