Abstract and Keywords
This article provides an overview on the Bayesian approach to investment decisions, emphasizing its foundations, its most practical uses, and the computational techniques that are essential to its effective implementation. The Bayesian approach provides a convenient framework for incorporating subjective information and views into an investment decision, through prior distribution. The Bayesian approach to investment decisions begins with a statistical model that relates historical data, such as past returns, to important parameters, such as expected future returns. Bayes' theorem is a simple relationship between the probability of an event A conditional on another event B and the probability of B conditional on A. The posterior distribution encapsulates the information content of both the data and the prior and is often the central focus of a Bayesian statistical analysis. The predictive distribution contains all information about the future that is of interest to the investor, combining the information content of the prior distribution with that of the historical data. Estimation risk means the investment risk is associated with not knowing the true values of parameters and is a focus of a Bayesian investment analysis. Utility theory and subjective probability are two of the main concepts in the Bayesian approach to investment decisions.
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