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US-based firm utilizes game theory in risk profiling

The application of game theory to financial services isn’t a novel idea, but the man behind US-based firm Capital Preferences believes he knows a way to make game theory a much more useful and economically rewarding concept for his clients.  The big breakthrough for the risk profiling and suitability tool, developed by UC Berkeley economics professor Shachar Kariv, is a literal game presented to clients that is designed to find their “revealed” instead of their “stated” preferences, based on their reactions to various risk-and-reward scenarios.  From this introductory read, my first reaction was that of surprise – surprise that it is not more common practice to base risk profiling on reactions to scenarios rather than on subjective and often-misleading questionnaires.

This is a direct application of the game theory we have been learning about in Networks, albeit at a higher level.  The extent and mode of the use of game theory in Kariv’s risk profiling tool isn’t explicitly stated, but the implications are clear.

The game he presents to clients is used to gauge their reactions to certain scenarios involving risk and reward trade-offs, and it is easy to envision some type of payoff matrix with multiple players (client vs. client, client vs. market, etc.).  Then, it is then up to the client to make decisions via his or her specific strategy to collect the corresponding payoff – all of which Kariv and the Capital Preferences team would observe and use to predict the client’s risk tolerance in the real-life financial world.  In fact, the game may be, at its very simplest core, a Hawk-Dove Game as we talked about in class – the client can either play passively or aggressively (risk-averse or risk-seeking), with no single dominant strategy (which makes sense in practice, as the market is always fluctuating and difficult to predict).  Furthermore, it is very possible that the analysts then take these observations and utilize game theory yet again to optimize asset allocation for the client, again pitting the payoffs and strategy of the client’s portfolio vs. that of other clients, vs. the market, etc.



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