  We study the problem of optimizing the betting frequency in a dynamic game
setting using Kelly's celebrated expected logarithmic growth criterion as the
performance metric. The game is defined by a sequence of bets with independent
and identically distributed returns X(k). The bettor selects the fraction of
wealth K wagered at k = 0 and waits n steps before updating the bet size.
Between updates, the proceeds from the previous bets remain at risk in the
spirit of "buy and hold." Within this context, the main questions we consider
are as follows: How does the optimal performance, we call it gn*, change with
n? Does the high-frequency case, n = 1, always lead to the best performance?
What are the effects of accrued interest and transaction costs? First, we
provide rather complete answers to these questions for the important special
case when X(k) in {-1,1} is a Bernoulli random variable with probability p that
X(k) = 1. This serves as an entry point for future research using a binomial
lattice model for stock trading. The latter sections focus on more general
probability distributions for X(k) and two conjectures. The first conjecture is
simple to state: Absent transaction costs, gn* is non-increasing in n. The
second conjecture involves the technical condition which we call the sufficient
attractiveness inequality. We first prove that satisfaction of this inequality
is sufficient to guarantee that the low-frequency bettor using large n can
match the performance of the high-frequency bettor using n = 1. Subsequently,
we conjecture, and provide supporting evidence that this condition is also
necessary.
