A portfolio which has a maximum expected growth rate is often referred to in the literature as a logoptimal portfolio or a growth-optimal portfolio. The origin of the log-optimal portfolio is arguably due to Kelly [27] when he observed that logarithmic wealth is additive in sequential investments and invented a betting strategy for gambling that relies on results from information theory. As a result of the law of large numbers, if investment returns are serially independent and identically distributed, the growth rate of any constant rebalanced portfolio (the log-optimal portfolio included) converges to its expectation. Moreover, under such conditions, one of the strongest advantages of the logoptimal portfolio is that, when implemented repeatedly, the log-optimal portfolio outperforms any other causal portfolio in the long run with probability 1. In other words, if all of these conditions are met, there is no sequence of portfolios that has a higher growth rate than that of the log-optimal portfolio. Stock markets however are different from casinos in the sense that investment returns are not serially independent and identically distributed. Also, since trading incurs transaction costs, investors are discouraged from making frequent trades. Plus, the probability distribution of stock returns is never precisely known, which impedes the calculation of the log-optimal portfolio. In this project, we generalize the results for the log-optimal portfolio. In particular, we establish similar guarantees for finite investment horizons where the distribution of stock returns is ambiguous.By focusing on constant rebalanced portfolios, we exploit temporal symmetries to formulate the emerging distributionally robust optimization problems as tractable conic programs whose sizes are independent of the investment horizon.