In today’s scenario, investors’ preferences towards different investment avenues depend upon their risk tolerance level and return associated with investment plan. The tolerance level of investors for risk is influenced by many demographic and psychological factors. Personality traits (PTs) are one of the important factors that impact the tolerance levels of investors for risk. Thus, the existing study focuses on whether (a) the direct effect of Big Five PTs on financial risk tolerance (FRT) or (b) PTs as a second-order (higher-order) factor leads to FRT. Data are cross-sectional in nature, which were collected from 599 investors who invested through Angel Broking Co. (Securities co.) in Delhi and the National Capital Region (NCR) by using online structured questionnaire. To examine the strength of the relationship between variables’ correlation and regression tests were applied using the structural equation modelling approach. The study found that among Big Five personality dimensions, only agreeableness, conscientiousness and openness are significantly associated with FRT, whereas PTs as a second-order (higher-order) factor have a strong association with FRT of investors. Thus, the PT as a second order is the preferred model. JEL Code: G02

Endeavors to realistically model physical processes responsible for earthquake occurrence and sustained large uncertainties in the results have lead to the application of techniques like artificial neural network for estimation of rate/probability of earthquake occurrence in future. The earthquake occurrence in India has been re-visited and artificial neural networks have been applied to learn the cyclic behavior of seismicity in the independent seismogenic sources to predict their future trends. As a prerequisite, the whole country has been divided into 24 seismogenic sources for which the seismicity cycles were studied. Their cyclic behavior has been captured in form of four stages of earthquake occurrence and the future trends have been predicted using ANN. To validate the trained ANN model, testing has been carried out in two ways: first, by giving the samples that are not used in training (NT) and second, by giving the total samples (T). As a method of testing, standard errors and correlation coefficients between the network output patterns and observed patterns of the testing sample given were considered. The outcome of the ANN is used to interpret the future seismicity of each of the 24 seismogenic zones in terms of various stages of the future seismicity cycles.