PurposeThe main purpose of this study is to investigate the investment choices' relationship with cognitive abilities, risk aversion, risky investment intentions, subjective financial literacy and objective financial literacy.Design/methodology/approachTo examine the relationship, two investment choices were given to 256 subjects from Pakistan. Questionnaire had total 20 questions for measuring five variables. To review this nexus, discriminant analysis was used as to explore the depth of the nexus that is the ability of the variables to predict the investment choices.FindingsThis study establishes the findings that Investment choices are guided by risk aversion, risky investment intentions, financial literacy (subjective and objective) and cognitive abilities. The risk aversion has negative relation to investment choices and other variables depict positive relationship to with investment choices.Practical implicationsThis study provides a new and useful understanding into the existing literature on investment choices. The results are significant as the cognitive abilities show a positive contribution to the investment choices. This is point of significance as the portfolio managers and advisors would get help in regards of advising investments as they are aware what factors impact the investment choices.Originality/valueThis study is novel in its nature to evaluate investment choices using the cognitive ability alongside risk attitudes and financial literacy.

AI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in turn will lead to the automation of corporate risk management. So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest, decision tree, naïve Bayes, and KNN. A secondary data collection technique will be used. For this purpose, we collected annual data of nonfinancial companies in Pakistan for the period ranging from 2006 to 2020, and the data are analyzed and tested through Python software. Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques.