Author(s)

Michael Cohen¹, Munirul Nabin¹, Sukanto Bhattacharya¹, Kuldeep Kumar²

¹Deakin Business School, Deakin University, Geelong, Australia.
²Bond Business School, Bond University, Queensland, Australia.

This paper contributes to the limited-information literature on savings in a stochastic environment. In particular, it contributes techniques and concepts to the question of state verification (or filtering), by including learning about aggregate income shocks, based on signals. As a seminal contribution to the extant literature, a “conviction function” is introduced, which takes into account histories of past prediction errors in determining how rational agents internalize such information in taking personal investment decisions. For purpose of a more transparent illustration, a numerical rendition of the posited model is provided for five consecutive time periods. We also perform a series of Monte Carlo simulations to demonstrate how the posited approach could potentially outperform traditional forward-looking models in the presence of sudden large extraneous shocks reminiscent of the recent Global Financial Crisis.

Financial Risk, Stochastic Optimization, Conviction Function, Bayesian Learning, Large Shocks

Cohen, M. , Nabin, M. , Bhattacharya, S. and Kumar, K. (2018) Conviction Function? A New Decision Paradigm for Personal Financial Risk Management in the Face of Large Exogenous Shocks. Theoretical Economics Letters, 8, 918-934. doi: 10.4236/tel.2018.85065.