Abstract
This chapter aims at analysing banking litigations according to a behavioural perspective. The purpose is to uncover the intensity of affection for distortions or cognitive biases suffered by customers and to do an impact assessment. In fact, perfect rationality during the decision-making process might not be applicable due to several interferences. We focused on mortgage litigations, which, according to a pre-sampling procedure, resulted to be one of the most prone categories of case law to show behavioural distortion. We exploited the dataset managed by the Italian alternative dispute resolution mechanism, that is, the Arbitro Bancario Finanziario (hereinafter, ABF), and focused on three well-known cognitive biases in a financial environment: narrow framing, overconfidence and regret. In our empirical analysis, firstly, we built up the sample by randomly selecting 75 decisions from the ABF archive through an extraction algorithm. This procedure guarantees a homogeneous distribution and subsequently allowed to apply a statistical hypothesis testing of proportions to the sample. Secondly, we studied each litigation singly to state its degree of distortion. Guidelines used for this goal have been stated ex ante in the form of fundamental parameters for each kind of bias to ensure an objective and impartial statement. Thirdly, we run a statistical hypothesis testing to infer the sample to the whole population. Finally, but not marginally, we run a multivariate analysis to assess the impact of the presence of such biases. Our results indicate that if a litigation arises with the presence of a cognitive bias, the probability that the appeal is successful is low after controlling for time and the variability of the judge committee composition.
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Notes
- 1.
Ackert Lucy and Richard Deaves. “Behavioural Finance: Psychology, Decision-making and Markets”, 2010, Mason, OH: South-Western Cengage Learning.
- 2.
Michael D. Grubb (2015). “Overconfident Consumers in the Marketplace,” Boston College Working Papers in Economics 877, Boston College Department of Economics. He actually applies those concepts to the consumers’ choices in the marketplace.
- 3.
According to Game Theory, it is the outcome of non-cooperative strategies adopted by the players. It does not always coincide with the Pareto efficient equilibrium. See: Osborne, M. J., and Rubinstein, A., “A Course in Game Theory”, Cambridge, MA: MIT, 1994.
- 4.
Precisely: Credit line, Banking and postal cheques, Debit cards and ATMs, Credit transfer, Credit cards, Central credit register, Central financial risks register, Interbank alarm office, Discontinuance of the matter, Banking and postal current accounts, Banking contracts, Consumer credit, Damage suffered, Savings accounts, Security deposit, Credit backed by one-fifth, Lack of jurisdiction, Leasing, Mortgage, Evidence, Credit reference agencies. See Mazzocchini F.J., “Framing, Overconfidence and Regret in Italian Banking Litigations”, LAP Lambert Academic Publishing, 2018.
- 5.
It is examined in depth in: Druckman, J.N. (2001). “The Implications of Framing Effects for Citizen Competence”. Political Behavior. 23 (3): 225–256.
- 6.
For examples, see the following paper: Alemanni B. and Lucarelli C., (2015): “Individual behaviour and long-range planning attitude”, The European Journal of Finance. https://doi.org/10.1080/1351847X.2014.1003313.
- 7.
- 8.
This argument can be examined in depth in Mood A. M., Graybill F. A., Boes D. C., “Introduction to the Theory of Statistics”, McGraw-Hill, 1989.
- 9.
\( \frac{\overline{x}-E\left(\overline{x}\right)}{\sqrt{V\left(\overline{x}\right)}}\ \overset{D}{\to}\ N\left(0,1\right) \), where \( E\left(\overline{x}\right) \) is the sample mean, \( V\left(\overline{x}\right) \) is the sample variance, N(0, 1) stands for normal distribution, P is the probability that an event occurs, E(X) is the population mean, V(X) is the population variance and n is the sample numerousness. Moreover, considering that a Bernoulli distribution has its mean equal to the probability of success and its variance equal to the product between the probability itself and its counter-probability, we have:
$$ \left\{{\displaystyle \begin{array}{c}E(X)=P\\ {}V(X)=P\cdot \left(1-P\right)\end{array}}\ \overset{\mathrm{yields}}{\to}\ \right\{{\displaystyle \begin{array}{c}E\left(\overline{x}\right)=P\\ {}V\left(\overline{x}\right)=\frac{P\cdot \left(1-P\right)}{n}\end{array}} $$ - 10.
\( {\mathrm{CI}}_{1-\alpha}=\left[\overline{x}-{z}_{1-\frac{\alpha}{2}}\cdot \sqrt{\frac{\overline{x}\cdot \left(1-\overline{x}\right)}{n}};\kern0.375em \overline{x}+{z}_{1-\frac{\alpha}{2}}\cdot \sqrt{\frac{\overline{x}\cdot \left(1-\overline{x}\right)}{n}}\right] \), where \( {z}_{1-\frac{\alpha}{2}} \) represents the quantile of the normal distribution and is a constant.
- 11.
\( \Big\{{\displaystyle \begin{array}{c}{H}_0:P\ge 60\%\\ {}{H}_1:P<60\%\end{array}} \)
- 12.
If we assume that the null hypothesis asserts that the proportion of biased litigations among the population is larger or equal to 60%, it must be accepted since the quantile Z is larger than the lower boundary of the normal distribution.
$$ Z=\frac{\overline{x}-0.6}{\sqrt{\frac{0.6\cdot \left(1-0.6\right)}{75}}}=0.94,\kern1.375em Z>-1.96 $$
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Lucarelli, C., Mazzocchini, F.J. (2019). Framing, Overconfidence and Regret in Italian Mortgage Banking Litigations. In: Gualandri, E., Venturelli, V., Sclip, A. (eds) Frontier Topics in Banking. Palgrave Macmillan Studies in Banking and Financial Institutions. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16295-5_6
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