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This study analyzes the differences between salaried employees and self-employed people, in terms of risk tolerance, time preference, and choice of financial investment channels. Both groups completed research questionnaires designed to elicit responses relevant to these questions. Analysis of the results show that self-employed people have less risk aversion and a stronger future preference. These preferences are consistent with independent work, where the fruits of success can sometimes be harvested only after a long period of time, the risks are greater, and the level of uncertainty is higher. The subjects were also asked to choose investment channels for different amounts of money. Both self-employed people ‎and employees chose less risky investment channels as the amounts increased. However, the self-employed people ‎chose riskier investment channels than the employees. This decision-making tendency is clearly reflected in their choice of occupation, risk tolerance and time preference. This study can increase the self-employed people's awareness ‎of the characteristics that influence their financial decision-making. In addition, there is now a trend for investment advisors to focus on the psychological and demographic characteristics of clients when advising them regarding the composition of their investment portfolios. This study shows that the client's occupation can be an important factor that ought to influence composition of the portfolio, potentially improving client satisfaction.
This content provides financial analysts, investment professionals, and financial planners with a review of how financial risk-tolerance tests can and should be evaluated. It begins by clarifying terms related to risk taking and is followed by a broad overview of two important measurement terms: validity and reliability. It concludes with examples for practice.
If risk aversion and willingness to take on risk are driven by emotions and we as humans are bad at correctly identifying them, the finance profession has a serious challenge at hand—how to reliably identify the individual risk profile of a retail investor or high-net-worth individual. In this series of CFA Institute Research Foundation briefs, we have asked academics and practitioners to summarize the current state of knowledge about risk profiling in different key areas.
An investor’s risk attitude is a stable characteristic, like a personality trait, but risk-taking behavior can change based on the investor’s age, recent market events, and life experiences. These factors change investors’ perceptions of the risks. Differences in risk tolerance between men and women or in different circumstances trace back to emotional as much as rational considerations. Financial advisers should consider all of these factors when advising clients and can use four simple steps to incorporate best practices: be aware, educate, nudge, and hand hold.
This book sheds light on the emotional side of risk taking behaviour using an innovative cross-disciplinary approach, mixing financial competences with psychology and affective neuroscience. In doing so, it shows the implications for market participants and regulators in terms of transparency and communication between intermediaries and customers.
Updates and advances the theory of expected utility as applied to risk analysis and financial decision making.
This piece examines risk profiling through a behavioral finance lens. Behavioral finance attempts to understand and explain actual investor behavior, in contrast to theorizing about investor behavior. It differs from traditional (or standard) finance, which is based on assumptions of how investors and markets should behave. Much has been written about the tension that exists between the willingness to take risk and the ability to take risk. Risk appetite is the willingness to take risk and risk capacity is the ability to take risk. In the behavioral context, risk appetite and risk capacity are defined in terms of known risks and unknown risks. Irrational client behavior often occurs when a client experiences unknown risks. To aid in the advisory process, advisors can use Behavioral Investor Types to help make rapid yet insightful assessments of what type of investor they are dealing with before recommending an investment plan. With a better understanding of behavioral finance vis-à-vis risk taking, practitioners can enhance their understanding of client preferences and better inform their recommendations of investment strategies and products.
This handbook in two parts covers key topics of the theory of financial decision making. Some of the papers discuss real applications or case studies as well. There are a number of new papers that have never been published before especially in Part II.Part I is concerned with Decision Making Under Uncertainty. This includes subsections on Arbitrage, Utility Theory, Risk Aversion and Static Portfolio Theory, and Stochastic Dominance. Part II is concerned with Dynamic Modeling that is the transition for static decision making to multiperiod decision making. The analysis starts with Risk Measures and then discusses Dynamic Portfolio Theory, Tactical Asset Allocation and Asset-Liability Management Using Utility and Goal Based Consumption-Investment Decision Models.A comprehensive set of problems both computational and review and mind expanding with many unsolved problems are in an accompanying problems book. The handbook plus the book of problems form a very strong set of materials for PhD and Masters courses both as the main or as supplementary text in finance theory, financial decision making and portfolio theory. For researchers, it is a valuable resource being an up to date treatment of topics in the classic books on these topics by Johnathan Ingersoll in 1988, and William Ziemba and Raymond Vickson in 1975 (updated 2 nd edition published in 2006).
Financial decision making under time pressure, though ubiquitous, is poorly understood; classical and behavioral finance are silent about the time required for a decision to be made. In an experiment, calibrating allowable decision times to 1, 3, and 5 s, we find that classical moment-based preferences reflect time-invariant sensitivity to expected reward, purchase impulsiveness under extreme time pressure, and decreased aversion to variance and increased aversion to skewness with decision time. These time varying sensitivities translate into increased probability distortions and decreased risk aversion for gains under prospect theory (PT). Strikingly, moment-based theory provides a better fit than PT.