Research Article  Open Access
Guoyi Zhang, "Optimal Geometric Mean Returns of Stocks and Their Options", International Journal of Stochastic Analysis, vol. 2012, Article ID 498050, 8 pages, 2012. https://doi.org/10.1155/2012/498050
Optimal Geometric Mean Returns of Stocks and Their Options
Abstract
The optimal geometric mean return is an important property of an asset. As a derivative of the underlying asset, the option also has this property. In this paper, we show that the optimal geometric mean returns of a stock and its option are the same from Kelly criterion. It is proved by using binomial option pricing model and continuous stochastic models with selffinancing assumption. A simulation study reveals the same result for the continuous option pricing model.
1. Introduction
The original question of Kelly criterion [1] is how to bet the fraction of your total wealth to maximize your longterm wealth when the odds and probabilities of a gambling game are known. Latane [2] first introduced the geometric mean investment strategy into finance and economics. As an application of generalized Kelly criterion, Latane and Tuttle [3] proposed a wealth maximizing model for building portfolios using geometric mean return. Bickel [4] discovered the relationship between optimal long run growth rate and the efficient portfolios based on the minimum variance criterion. Weide et al. [5] and Maier et al. [6] developed a strategy which maximizes the geometric mean return on portfolio investment. Similar research can be found by Ziemba [7], Elton and Gruber [8], and Bernstein and Wilkinson [9]. How to optimize the geometric mean return by the Kelly criterion becomes an important question faced by many portfolio managers and researchers.
In the literature, Kelly criterion is also known as growth optimal portfolio, capital growth theory of investment, geometric mean strategy, investment for the long run, and maximum expected log. Estrada [10] used it as geometric mean maximization (GMM) and compared the popular mean variance analysis and Kelly criterion from an empirical perspective. Merton [11] was the first one to address the dynamic portfolio choice problem using the idea from Kelly criterion, which becomes a wellknown topic in finance. McEnally [12] provided an overview of Kelly criterion, and MacLean et al. [13] summarized desirable and undesirable properties of Kelly criterion.
Stock options are popular in many financial markets. An option is a contract between a buyer and a seller that gives the buyer right to buy or to sell a particular stock at a later day with a fixed price. A call option gives buyers right to buy stock and a put option gives buyers right to sell stock. The theoretical value of an option can be evaluated according to several models. Most of the theorems and models assume that market is free of arbitrage. Arbitrage is to make a guaranteed profit with no invested capital. Arbitrage can be considered as a sure win betting scheme such that investors can apply some certain strategy at the beginning and collect the guaranteed profit at the end. Cox et al. [14] proposed a simple discrete time binomial option pricing model for evaluating options. The celebrated BlackScholes model [15] is a special limiting case of the binomial tree model, which assumes that there is no arbitrage opportunities and stock price is a geometric Brownian motion process.
The goal of this paper is to investigate the relationship between the optimal geometric mean returns of a stock and its option from Kelly criterion, assuming there is no betting strategy that leads to a sure win. This paper is organized as follows. In Section 2, we introduce Kelly criterion. In Section 3, we prove that the optimal geometric mean returns of a stock and its option are the same for a binomial option pricing model. Section 4 extends the study to continuous stochastic model with selffinancing assumption and performs a simulation study. Section 5 gives the summary.
2. Kelly Criterion
Suppose the odds and the probabilities of a gambling game are known; that is, we could double our bet with probability and lose the bet with probability , where . The question is how to maximize our total wealth in the long run, assuming we can play the game again and again. Kelly [1] proposed to bet a fraction of the total capital each time. After bets, the total wealth is where is the starting capital and and are the number of wins and losses among the bets, respectively. The geometric mean return over periods is . The optimal geometric mean return is .
More generally, let be the riskfree interest rate, and let and be two possible returns with probabilities and , respectively. The corresponding excess returns are and . In this case, the total wealth after periods is . The optimal fraction is and the optimal geometric mean return is
3. Binomial Option Pricing Model
In this section, we use a call option to illustrate the onestep binomial option model. We state the arbitrage free call option price in Lemma 3.1 and prove that the optimal geometric mean returns are the same for a stock and its option in Theorem 3.2, assuming the market is free of arbitrage opportunities and the onestep binomial option model is appropriate. An example is given to illustrate Theorem 3.2.
Lemma 3.1. Assume the market is free of arbitrage opportunities. The current price of a stock is , and after one time period the stock either goes up to with probability or down to with probability . The riskfree interest rate is and the exercise price is between and . The arbitrage free call option price is
Lemma 3.1 states that given the no arbitrage assumption, the price of the option is unique and is not related to the probability distribution of .
Theorem 3.2. Assume the market is free of arbitrage opportunities and the onestep binomial option model is appropriate. The optimal geometric mean returns are the same for a stock and its option. The optimal geometric mean returns and optimal fractions of the stock and option depend on but the ratio of optimal fractions of stock and option is not related to .
Proof. By (2.2) and (2.3), the optimal fraction and geometric mean return for the stock are
where and are the corresponding excess returns for stock.
Similarly, the optimal fraction and geometric mean return for the option are
where and are the corresponding excess returns for option.
To prove the equivalence of (3.3) and (3.5), it is sufficient to show that
By applying (3.2) and (3.4), (3.6) reduces to , which can be derived by Lemma 3.1. Theorem 3.2 is proved.
Equation (3.7) gives an interesting result derived from (3.6): that is, the ratio of optimal fractions of stock and options is not related to . The following gives an example to illustrate Theorem 3.2.
Example 3.3. Suppose , , , , and . From Lemma 3.1, the price of a call option is 11.36, which is not related to . Table 1 gives the optimal fractions and optimal geometric mean returns corresponding to different values using (2.2). We can see from Table 1 that the optimal fractions and geometric mean return increase as the probability increases. The ratio of the optimal fractions of stock and options is 2.2 from (3.7), which can also be derived using the data from Table 1. In order to have a higher return, we should bet a larger proportion of the capital if the probability of return is also higher.

4. Continuous Stochastic Models
In this section, we prove that the optimal geometric mean returns of a stock and its option are the same, when stock price is a geometric Brownian motion process using a selffinancing strategy over time. A small simulation study is conducted to study the optimal geometric mean returns for a geometric Brownian motion process.
4.1. Continuous Stochastic Models with SelfFinancing Assumption
Let stock price follow a geometric Brownian motion. Let the price of a call option be given by BlackScholes formula, hence no arbitrage opportunity with maturity and strike price . Consider a financial market containing a riskfree money market account whose value is given by . For all , the values of these processes can be modeled as solutions of the following stochastic differential equations , and , where is a standard Brownian motion and the drift rate and the volatility are both positive constants. It follows that for all , , where is the Black and Scholes [7] formula for the price of a call option with , and . An application of Itô’s formula yields for all .
Now we have and . Hence
Let denote the fraction invested in a stock using selffinancing strategy over time. For all , the wealth can be expressed as The selffinancing property of the portfolio then yields Next, we use Itô’s formula to obtain for all .
Now, the optimal geometric mean return can be obtained by the trading strategy that maximizes the growth rate that is equivalent to , for all . Set to get the optimal fraction . Putting this expression into (4.5), we obtain the following stochastic differential equation for the optimal wealth:
Let denote the fraction invested in a call option of the stock and use selffinancing strategy over time. The wealth can be expressed as for all . Repeat the previous process to discover that the optimal fraction for all , and the stochastic differential equation for the optimal wealth is as follows: for all by virtue of (4.3). Hence, the optimal geometric mean returns of a stock and its option are exactly the same.
4.2. Simulation Studies
A popular model used for option pricing is as follows: where is the price of a stock at time , is the initial price of the stock, and is a Brownian motion process with drift coefficient and variance parameter . is called a geometric Brownian motion process. In this case, the price of the call option can be calculated by BlackScholes formula as follows: where is the standard normal distribution function, , and . The option price is related to variance parameter but not related to drift coefficient . However, is important in calculating the optimal fractions and geometric mean returns.
Suppose the excess return has a continuous probability distribution . The generalized Kelly criterion is to bet an optimal fraction of the total capital such that geometric mean return reaches its maximum. This is equivalent to select by maximizing , with . There is no close form for optimal fractions and geometric mean returns in this case but we can use Monte Carlo simulations to estimate and . The following are listed steps for simulation study:(1)Calculate price of option using BlackScholes formula (4.11).(2)Generate independent samples from normal distribution with mean and variance .(3)Calculate stock price at time using (4.10).(4)Calculate excess returns for the stock .(5)Calculate excess returns for the option where if and if .(6)Setting a grid of , for each , the corresponding . Find the maximum . The corresponding is the optimal fraction and the optimal geometric mean return is .
Table 2 gives a simulation study under , , , , and . We can see that the optimal fractions and geometric mean return increase as increases. The optimal geometric mean returns of a stock and its option are approximately the same.

5. Concluding Remarks
In this research, we show that the optimal geometric mean returns of a stock and its option are the same from Kelly criterion. It is proved by using binomial option pricing model and continuous stochastic models with selffinancing assumption. It is shown to be approximately true for the continuous option pricing model by simulation studies. For the discrete case, we also show that the ratio of the optimal fractions of a stock and its option is not related to the probability distribution of the return. This means that we can use a small amount of options to replace the underlying asset without changing the optimal geometric mean return and knowing the probability distribution of the return. Hence, in practice, either there are sure win chances or the prices of options are more expensive than their theoretical values. Otherwise, one should always hold more uncorrelated options instead of stocks.
Acknowledgment
The author wants to thank the referees for their careful reading of the paper and helpful comments.
References
 J. L. Kelly Jr, “A new interpretation of information rate,” The Bell System Technical Journal, vol. 35, pp. 917–926, 1956. View at: Google Scholar  MathSciNet
 H. Latane, “Criteria for choice among risky ventures,” Journal of Political Economy, vol. 67, no. 2, pp. 144–155, 1959. View at: Publisher Site  Google Scholar
 H. Latane and D. Tuttle, “Criteria for portfolio building,” Journal of Finance, vol. 22, no. 3, pp. 359–373, 1967. View at: Google Scholar
 S. Bickel, “Minimum variance and optimal asymptotic portfolios,” Management Science, vol. 16, no. 3, pp. 221–226, 1969. View at: Google Scholar
 J. H. V. Weide, D. W. Peterson, and S. F. Maier, “A strategy which maximizes the geometric mean return on portfolio investments,” Management Science, vol. 23, no. 10, pp. 1117–1123, 1977. View at: Google Scholar  MathSciNet
 S. F. Maier, D. W. Peterson, and J. H. V. Weide, “A monte carlo investigation of characteristics of optimal geometric mean portfolios,” The Journal of Financial and Quantitative Analysis, vol. 12, no. 2, pp. 215–233, 1977. View at: Google Scholar
 W. Ziemba, “Note on optimal growth portfolios when yields are serially correlated,” Journal of Financial and Quantitative Analysis, vol. 7, no. 4, pp. 1995–2000, 1972. View at: Google Scholar
 E. Elton and M. Gruber, “On the maximization of the geometric mean with lognormal return distribution,” Management Science, vol. 21, no. 4, pp. 483–488, 1974. View at: Google Scholar
 W. Bernstein and D. Wilkinson, “Diversification, rebalancing, and the geometric mean frontier,” Social Science Research Network, Working Paper Series, 1997, http://ssrn.com/abstract=53503. View at: Google Scholar
 J. Estrada, “Geometric mean maximization: an overlooked portfolio approach?” Journal of Investing, vol. 19, no. 4, pp. 134–147, 20102010. View at: Google Scholar
 R. C. Merton, “Optimum consumption and portfolio rules in a continuoustime model,” Journal of Economic Theory, vol. 3, no. 4, pp. 373–413, 1971. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 R. McEnally, “Latanes bequest: the best of portfolio strategies,” Journal of Portfolio Management, vol. 12, pp. 21–30, 1986. View at: Google Scholar
 L. MacLean, W. Ziemba, and G. Blazenko, “Growth versus security in dynamic investment analysis,” Management Science, vol. 38, no. 11, pp. 1562–1585, 1992. View at: Google Scholar
 J. C. Cox, S. A. Ross, and M. Rubinstein, “Option pricing: a simplified approach,” Journal of Financial Economics, vol. 7, no. 3, pp. 229–263, 1979. View at: Google Scholar
 F. Black and M. Scholes, “The pricing of options and corporate liabilities,” Journal of Political Economy, vol. 81, no. 3, pp. 637–654, 1973. View at: Google Scholar
Copyright
Copyright © 2012 Guoyi Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.