1. Introduction
After the most recent financial-economic crisis’s outbreak, the governments of various countries carried out financial reforms through legislation in order to strengthen banks’ solvency rates. According to Baker [
1], this growing trend, as seen from a political economy perspective, constitutes an effort to avoid the onset of a new crisis in the financial system. However, the reforms have translated into a crisis of trust in the banking sector, causing a liquidity crunch and disruption in the stock markets.
The bankruptcy of various global systemically important banks (G-SIBs) in 2007 affected the entire financial system worldwide, which in turn had an impact on the sustainability of global economic activity in most industries. The present study did not examine the origins of—or the factors that caused—the financial crisis or seek to identify all the measures taken to combat the crisis’s effects. Instead, the objective was to create a model that would facilitate predictions of G-SIBs’ profitability and that could take into account the different reforms affecting interim measures and solvency that were instituted as a result of the financial crisis. These predictions can facilitate decision making for corporate governing bodies of financial entities.
An analysis of prior studies that sought to determine which factors affect banks’ profitability revealed that some researchers have focused on the relationship between profitability and economic activity [
2]. Other studies have been done on profitability and banks’ corporate governing bodies [
3] or performance as a function of a bank’s organizational structure [
4]. Another common focus has been performance and capital in the financial markets [
5]. However, no prior study has attempted to combine all these variables together into a single model while taking into account the direct and indirect relationships between them.
The literature also contains much research analyzing banks from the same country or geographical area. The following studies can be highlighted as examples—for the United States (US), Serrano-Cinca and Gutiérrez-Nieto [
6], for Spain, Crespí et al. [
3], for Venezuela, Ayala et al. [
7], for Switzerland, Dietrich, and Wanzenried [
8], for Europe, Choudhry and Jayasekera [
9], and for Asia, Soedarmono, Machrouh, and Tarazi [
10].
The present study sought to contribute to the existing literature in three distinct ways. First, this research applied a new methodology as investigations have rarely used partial least squares (PLS) to predict banks’ profitability. Second, the variables included were combined together into one model that used financial institutions’ accounting and financial ratios, as well as the ratios of the countries in which these banks were headquartered, in order to predict banks’ profitability. Last, the approach applied was expected to contribute to the current literature because of the quite recent period chosen (i.e., 2011–2015) with the objective of analyzing the impact of measures taken in December 2010 after the implementation of the Basel III legislation. This legislation established a set of banking regulations meant to reinforce banks’ regulation, supervision, and risk management in response to the financial crisis.
The rest of this paper is structured as follows. The next section reviews the relevant literature and discusses the proposed hypotheses. The third section describes the methodology, while the fourth and fifth sections cover the results, discussion, and conclusions.
4. Results and Discussion
The analyses were carried out in two stages. Confirmatory factor analysis (CFA) was first conducted to assess the measurement model’s suitability, and then the constructs’ validity was evaluated by means of reliability analysis, followed by convergent and discriminant validity assessments. CFA was used to verify the measurement scale’s reliability and validity. The results revealed that a series of indicators needed to be eliminated, namely, AQ3, AQ6, CA5, CA6, SZ1, SZ2, SZ3, OP4, OP5, CP2, CP4, CP5, CP6, PF1, and PF2. Once these indicators were removed, the model showed an adequate specification for the proposed factorial structure.
Subsequently, an assessment was conducted of the reliability and validity of the instrument used to measure the reflective constructs (i.e., asset quality, capital adequacy, operations, and profitability). This evaluation was necessary to check that the results are shown in
Table 2 comply with simple reliability criteria, including Cronbach’s alpha (CA), which must be superior to 0.70, according to Nunnally and Bernstein [
30]. In addition, the assessment checked composite reliability (CR), which must be higher than 0.6, according to Bagozzi [
31], and convergent validity based on the average variance extracted (AVE), which must be over 0.5, according to Fornell and Larcker [
16]. To evaluate the size of the factor loadings, Hair et al.’s criteria [
32] were used, thus ensuring that all loadings in
Table 2 are greater than 0.70.
To evaluate the model’s discriminant validity, Fornell and Larcker’s criteria [
16] were applied in order to estimate the correlation matrix between latent variables and the heterotrait-monotrait (HT/MT) ratio of correlations [
33]. Inadequate discriminant validity exists if the HT/MT ratio is over 0.85, according to Clark and Watson and Kline [
34,
35], or 0.90, according to Gold et al. and Teo et al. [
36,
37]. The present analysis found an HT/MT ratio below these values, as shown in
Table 3, so the model’s discriminant validity was confirmed.
Finally, the bootstrapping technique was used to estimate these parameters’ significance, and the Student’s t and p-value were determined. A series of random samples were obtained from the original sample in order to replace it. The new samples’ average values were estimated and compared with those of the original sample to assess whether the estimates of the original parameters were statistically significant. The analysis was carried out based on the following premises:
Individual sign change is permissible, according to Hair et al. and Henseler et al.’s [
23,
32] criteria.
A total of 5000 subsamples must be used, that is, a larger quantity than the original sample of 4500, to comply with Hair et al.’s [
32,
38] criteria.
Each subsample’s size is always that of the original sample, in accordance with Hair et al.’s [
32,
38] criteria.
As can be seen from
Table 2 above, in all cases, a
p-value under 0.01 was obtained, so the results support the conclusion that the parameter estimates are statistically significant.
Collinearity analysis was conducted to assess the validity and reliability of the measurement instrument for the formative constructs (i.e., size and country profile). As
Table 4 shows, all the variance inflation factor (VIF) indicators are inferior to 5, so, based on Hair et al.’s criteria [
38], no collinearity problem was found.
Similarly, an analysis was carried out of the relationship between the indicators’ weight and load and their significance using bootstrapping. If indicators have a significant weight higher than 0.50, the indicators receive empirical support and need to be kept in the model, according to Hair et al.’s criteria [
32] (see
Table 2 above).
Once the measurement instrument had been refined and the structural model’s evaluation was complete, the model was estimated once again, and the significance of the relationships between structures was evaluated a second time using bootstrapping. In this way, the latent dependent variables’ variance explained by their predicting construct (i.e.,
R-squared [
R2]) could be examined. The results in
Table 5 reveal that in no case is
R2 inferior to 0.1. In other words, the constructs’ variance explained by the model is always above 0.1, thereby complying with Falk and Miller’s criteria [
39].
Given that the size of
R2 has been acknowledged to be relevant predictive criteria on numerous occasion, this study applied the blindfolding technique developed by Geisser and Stone [
40,
41] to evaluate the model’s predictive validity. The technique required the omission of some data during estimations in the present research. This included profitability, operations, and asset quality from the latent dependent variables, as well as capital adequacy, size, and country profile from the latent independent variables. Thus, attempts were made to adjust these data using the information obtained previously. In this study, the omission distance was fixed at 7, which is a prime number between 5 and 10 that is not an exact divisor of the sample size, as required by Wold’s criteria [
42]. The results support the conclusion that the proposed model has predictive validity since, in all cases,
Q2 is superior to 0 (see
Table 6), which fits the criteria described by Geisser and Stone [
40,
41].
Once the model had been checked for predictive validity, the structural relationships’ significance was evaluated to determine which hypotheses were confirmed, by applying the previously mentioned bootstrapping technique. Based on the results (see
Table 7), all the hypotheses put forward were confirmed, as no risk of rejecting a null hypothesis exists. The final adjusted model is shown in
Figure 3, which includes the weight and load of each formative and reflective indicator, as well as the dependent constructs’ variance explained by the variables that predict them.
5. Conclusions
5.1. Implications
This research sought to use SEM to analyze factors affecting banks’ profitability. The resulting model can be used to determine which constructs affect profitability in direct or indirect and positive or negative ways. Five main findings were obtained. First, operational efficiency improves bank profitability. Second, the credit risk associated with banks’ assets negatively influences these entities’ profitability. Third, major capital acquisitions positively affect operational efficiency and improve bank profitability. Fourth, banking entities’ size contributes to improving their exposure to existing credit risk, thereby enhancing banks’ profitability. Last, improved macroeconomic indicators reduce credit risk and contribute to increasing bank profitability.
The present study’s results have important theoretical implications. The first is the methodology applied since previously published research has rarely used PLS to predict banks’ profitability. The second implication is the variables employed, as they were combined into a single model that links financial entities’ accounting and financial ratios as macroeconomic indicators. The last implication is the period covered in order to examine the impact of Basel III legislation put into effect during December 2010, which means a quite recent period was analyzed (2011–2015).
In addition, the present research’s findings have important practical implications because the new model proposed permits banks’ corporate governing bodies to make strategic decisions in terms of operations, solvency, capital, size, and location and to gain more control over their banks’ profitability. Variations in profitability due to changes in any of the indicators included can also be quantified.
5.2. Limitations and Future Lines of Investigation
The present study’s sample was limited to the 100 most important banks worldwide in order to achieve the objectives of minimizing lost values and working with data on the largest financial entities in terms of volume of assets around the globe. Although the sample’s size was confirmed as adequate by the potency test conducted, the number of banks could be expanded in future research or duplicated in studies of specific countries or regions to check if the results are consistent.
A second limitation is the period of time examined. The sustainability of banks’ profitability needs to be evaluated over a longer time horizon so that crises and normal periods are also represented. The 2011–2015 period was selected because this study sought to analyze the immediate consequences of specific reforms beginning with December 2010, namely, after Basel III legislation came into force.
Future lines of investigation could include studies of specific regions. Further research can also extend the present study’s approach to incorporate other variables that more closely reflect, for example, each bank’s credit policy or new trends such as advances in information technology (e.g., digitalization).