Does behavioral biases matter in SMEs' borrowing decisions? Insights from Morocco

  • 94 Views
  • 26 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Bank financing decisions by small and medium-sized enterprises (SMEs) are crucial to their growth and survival, particularly in emerging economies such as Morocco. This study aims to assess the impact of behavioral biases on these decisions, an area little explored in the existing financial literature. The main objective is to analyze how behavioral biases such as overconfidence, risk aversion, confirmation bias, anchoring, and managerial myopia biases influence bank financing decisions of Moroccan SMEs. The approach adopted is quantitative and uses robust least squares regression to analyze data collected from 167 Moroccan SMEs. The results reveal that overconfidence and anchoring have a significant positive impact on the propensity to take out bank loans, while risk aversion and confirmation bias have a negative effect. Managerial myopia had no significant influence. Control variables such as past financial performance, the length of the banking relationship, and lower risk also positively influence the financing decision.

view full abstract hide full abstract
    • Figure 1. Normality test for OLS regression residuals
    • Figure 2. Normality test for RLS regression residuals
    • Figure 3. Cross-sectional analysis
    • Figure 4. Recursive residuals
    • Figure 5. Influence statistics (COVRATIO)
    • Figure 6. CUSUM test and the CUSUMSQ tests for RLS regression
    • Table 1. Research hypotheses and variables that represent them
    • Table 2. Descriptive statistics
    • Table 3. Correlation matrix
    • Table 4. Variance inflation factors for OLS
    • Table 5. Heteroscedasticity test (Breusch-Pagan-Godfrey) for OLS regression
    • Table 6. Ramsey RESET test for RLS regression
    • Table 7. Variance inflation factors for RLS
    • Table 8. Heteroskedasticity test: Breusch-Pagan-Godfrey for RLS
    • Table 9. RLS regression results
    • Conceptualization
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Data curation
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Formal Analysis
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Funding acquisition
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Investigation
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Methodology
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Project administration
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Resources
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Software
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Supervision
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Validation
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Visualization
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Writing – original draft
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani
    • Writing – review & editing
      Khalid Ayad, Anass Touil, Nabil El Hamidi, Khaoula Dobli Bennani