###### Abstract  
This paper innovatively analyses the joint occurrence of cognitive biases in groups of stock exchange investors. It considers jointly a number of common fallacies: confirmation bias, loss aversion, gambler's fallacy, availability cascade, hot-hand fallacy, bandwagon effect, and Dunning-Kruger effect, which have hitherto been studied separately. The paper aims to highlight the diverse range of investor's profiles which are characterised by such fallacies, and the considerable differences observed based on their age, stock market experience and perception of market trends. The analysis is based on k-means and hierarchical clustering, feature importance and Principal Component Analysis, which were applied to data from the Tehran Stock Exchange. There are a few essential findings which contribute to the existing literature. Firstly, the results show that gender does not have a role to play in diversifying the investors' profiles. Secondly, cognitive biases are bundled, and we distinguish four investors' profiles; thus, they should be analysed jointly, not separately. Thirdly, the exposure to cognitive biases differs significantly due to the individual features of investors. The group most vulnerable to almost all analysed biases are inexperienced investors, who are pessimistic about market developments and have invested a large amount. Fourthly, the ages of investors are essential only in connection with other factors such as experience, market perception and investment exposure. Young (20-40 years), experienced investors with huge investments (+1000 mh rials/+24,000 USD) are mostly less exposed to all biases and much less risk-averse. Additionally, older (50+) and experienced investors (5-10 years) who are more optimistic about trends (hot hand bias) were affected much less by cognitive biases, only showing vulnerability to the Dunning-Kruger effect. Fifthly, more than 40% of investors apply consultation and technical analysis approaches to succeed in trading. Finally, from a methodological perspective, this study shows that unsupervised learning methods are effective in profiling investors and bundling similar behaviours. cognitive bias; stock market; behavioural finance; investor's profile; Teheran Stock Exchange; unsupervised learning; clustering +
Footnote †: journal: Applied Sciences  
doi: 10.3390/00000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-0000-00000-0000-0  
traditional theoretical models have failed to justify investors' cognitive biases. This phenomenon in financial markets which causes stock prices to deviate from their fundamental values consequently attracts arbitrageurs and creates stock market bubbles and causes capital flight. Economists have underlined that these fallacies have a considerable impact when they result in collective actions, that is, when they are replicated and enforced by the actions of other investors [1; 2]. In the case of financial markets, this may bring about instability or even crises. Economic and psychological literature (e.g., [3]) lists almost 100 different cognitive biases; however, not all of them refer to investors' behaviour in financial markets. We selected seven of them: confirmation bias, loss aversion, gambler's fallacy, availability cascade, hot-hand fallacy, bandwagon effect, and Dunning-Kruger effect. These are often listed as the most critical fallacies occurring in financial markets. However, the literature thus far has primarily analysed them individually, which has created an important research gap. This paper considers the joint cognitive biases affecting stock exchange investors; this is the main area of innovation in the research. We propose the hypothesis that these bundles of cognitive biases are heterogeneous among different groups of investors, and that this may form the foundations of some specific market distortions. As many researchers have shown (e.g., [4; 5]), inter-cultural differences affect the economic and investment decision-making process.