In the psychology and economics a number of methods to elicit risk preferences have been developed using hypothetical scenarios and economic experiments. These methods of eliciting individual risk preferences are usually applied to small samples because they are expensive and the implementation are organizationally complex to perform on large representative samples. However, for practical application often the risk preferences of large cohorts need to be measured. Data science methods exploiting existing register data on individual characteristics could be used to predict risk preferences of large cohorts and in that way mitigate the risk preferences elicitation challenge.
A large number of supervised learning models ranging from linear regression to support vector machines are used to predict risk preference measures using socio-economic register data such as age, gender, migration background and other demographic variables in combination with data on income, wealth, pension fund contributions, and other financial data. The employed machine learning models cover a range of assumptions and properties as well as a diverse set of regression metrics. The optimum model is selected using the metrics and interpretabilty of the model. The optimal models are lasso regression and gradient boosting machines with mean average percentage error of about 30\%. This can be loosely interpreted as approximately 70\% accuracy for prediction of the individual risk preference.
The results are informative regarding which of the above factors (features) are most strongly correlated with individuals’ risk preferences. This is important as it helps to estimate risk attitudes without actually measuring them. It should be noted that with the current accuracy the tested models are not ready for deployment for applications that require high accuracy. However, the results do indicate which models should be used in situations that do not require the most accurate predictions such as augmentation data for pensions’ recommendation.