Search Results (3)

Investment Risk Appetite (IRA) is a pivotal concept in risk management, reflecting an investor’s willingness to tolerate financial risks within acceptable thresholds. As empirical investigations into this construct gain momentum, there is a growing need for a scientifically validated tool to facilitate in-depth examinations of risk appetite. There are no scales to measure risk appetite. The present study addresses this gap by developing and validating a scale on risk appetite. Leveraging data collected from 405 respondents and employing established methodologies, the study introduces the Investment Risk Appetite (IRA) Scale. The questionnaire had a five-point scale. The scale consists of two factors: risk tolerance (α = 0.837, composite reliability = 0.836) and risk aversion (α = 0.905, composite reliability = 0.906). The validation was done by exploratory and confirmatory factor analysis (EFA and CFA). The loadings for EFA and CFA exceeded the threshold limit of 0.40. The scale demonstrates robust internal consistency, content, and construct validity. Hence, this scale has all the required validity. Overall, this scale demonstrates robust validity and reliability. In addition, this study examined the differences based on the demographics of the respondents. The scale, poised to make a significant contribution to the literature on risk appetite, will provide a theoretical foundation for future in-depth investigations. This study is expected to inspire future empirical examinations of this compelling construct. Full article

It is eminent to understand, be aware of and encourage domestic retail investors towards investment in the capital market in a developing economy such as India for tackling the situation of capital insufficiency and financial instability. Therefore, the study was purposed to find out the different dimensions of cognition that affect investment attitude and the different characteristics of risk absorption affecting the investment decision making. The study also intended to find the direct and the mediating impact of investors’ cognition directly and through risk-absorption scenarios on the level of interest on investment. The study used the causative research design and by using stratified random sampling, received 392 responses from investors with risk-absorption characteristics from four strata of Odisha (a state of India) through a self-constructed questionnaire. Factor analysis was used to find out the factor of cognition and risk absorption. Multiple linear regression was used to find out the effect of both factors of cognition and risk absorption on the intensity of purchase financial product or level of interest in investment. Mediation analysis was used to find the mediating impact showing the direct and indirect impact of cognition on interest in investment and through the factors risk absorption. The study found that the dimensions of cognition (hot, cold, social and meta) have a significant impact on the level of interest towards investment, so financial product sellers must use these dimensions and sources of cognition to bring up interest from the domestic investor to invest in the domestic capital market. It has also been found that the risk-absorption characteristics play a mediating and vital role in the relation between investors’ cognition and level of interest in investment. Therefore, it is imperative to uplift the risk-absorption capacity through different dimensions of cognition and sources of information, which can reflect in a better understanding of the market and investment scenarios. Full article

In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor’s guidance, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients covering the period from January 1st to August 12th 2019. We use a modified behavioral finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information—such as gender, residence region, and marital status—does not explain client behaviours, whereas eight variables for trade and transaction frequency and volume are most informative. Hence, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours. Full article