![]() However, it can emerge from an intertemporal mean-variance model with negative serial correlation of returns. As indicated above, it is not confirmed by models with log utility. This procedure becomes complex very quickly if there are more than a few time periods or more than a few assets.ĭollar cost averaging is gradual entry into risky assets it is frequently advocated by investment advisors. In dynamic programming, the last period decision rule, contingent on available wealth and the realizations of all previous periods' asset returns, is devised in advance then the next-to-last period's decision rule is devised, taking into account how the results of this period will influence the final period's decisions and so forth backward in time. The mathematical method of dealing with this need for current decision-making to take into account future decision-making is dynamic programming. If the investor's utility function is the risk averse log utility function of final wealth W T, W_), However, under certain circumstances the optimal portfolio decisions can be arrived at in a way that is separated in time, so that the shares of wealth placed in particular assets depend only on the stochastic asset return distributions of that particular period. ![]() In a general context the optimal portfolio allocation in any time period after the first will depend on the amount of wealth that results from the previous period's portfolio, which depends on the asset returns that occurred in the previous period as well as that period's portfolio size and allocation, the latter having depended in turn on the amount of wealth resulting from the portfolio of the period before that, etc. ![]() ![]() See also: Stochastic programming § Multistage portfolio optimization Time-independent decisions ![]()
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