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In this paper, we considered the different strategies that generate the optimal wealth on investment. The strategy examine depends on the utility function an investor is willing to adopt, say H* at time N in every 2n possible states; in an N period setting. Negative exponential, logarithm, square root and power utility functions were established, as the market structures changed according to a Markov chain through a martingale approach. The problem of maximization is solved via Lagrange method. The performance of the investment from day-to-day is driven by the ratio of the risk neutral probability and the probability of rising to falling.

Portfolio management is a fundamental aspect in economics and finance. It is an all natural and important activity in our society for households, pension fund managers, as well as for government debt managers. The principle covers numerous and various situations of daily life. In a financial terminology, the problem of portfolio optimization of an investor trading in different assets is to choose an optimal strategy for an investment. This involves how many shares of which asset he should hold at any trading time, in order to maximize some subjective (depending on his preferences) criterion relying on his total wealth and/or consumption which in turn depends on the prices. Portfolio management is all about strengths, weaknesses, opportunities and threats in the choice of debt versus equity, domestic versus international, growth versus safety, and other trade-offs en- countered at the attempt to maximize return at a given appetite for risk. Investors need to balance the objective of maximizing the return of their investment with the constraint of minimizing the risk involved. It is generally accepted that the greater the expected return is, the greater the risk involved is.

Portfolio management (PM) guides the investor in a method of selecting the best available securities that will provide the expected rate of return for any given degree of risk and also to mitigate (reduce) the risks. Prices of assets depend crucially on their risk as investors typically demand more profit for bearing more uncertainty. Therefore, today’s price of a claim on a risky amount realized tomorrow will generally differ from its expected value. Most commonly, investors are risk-averse and today’s price is below the expectation, remunerating those who bear the risk.

Due to the high liquidity, leverage effects and their non-linear pay-off profile, together with options and other derivatives are now widely used as an investment opportunity. However, a straight forward generalization of the stochastic control approach leads to a much more complicated form of the Hamilton Jacobi Bellman equations (HJB-equations). Thus, the martingale approach of portfolio optimization deals with this problem. Hence, for any investment we have,

where;

With payment

The traditional technique for solving dynamic programming [DP] problem stated suffers from the so-called curse of dimensionality. That is, the computational requirements of a dynamic programming problem [that needs to be solved numerically] grow exponentially in the dimensionality of the problem. One technique that applies to solving portfolio optimization problems in complete markets, is the Martingale method.

The idea behind this method is very simple: since markets can be assumed to be stable and complete, in the sense that there is no fluctuation, every contingent claim is attainable and therefore the initial price of any random variable representing a contingent claim can be computed.

In financial market where investors are facing uncertainty, the strategy that optimizes the return of an invest- ment in assets is in general not known. Suppose that the investor compares random returns whom he knowns the

probability distributions on some probability space

expected utility of his terminal wealth in a complete market with a stochastic interest rate [

The mean-variance criterion of Markowitz [

More precisely, by denoting

The increasing property of the utility functions means that the investor prefers more wealth. Thus, the choice of the utility function allows investor the notions of risk aversion and risk premium related uncertainty, hence the strategy that delivers the optimal terminal wealth.

In a financial market where investors are facing uncertainty, the return of an investment in assets is in general not known. For example, a stock yield depends on the resale price and the dividends. How to choose between several possible investments? In order to determine desirable strategies in an uncertain context, the preferences of the investor should be made explicit, and this is usually done in terms of expected utility criterion.

Negative exponential, Logarithmic, square root, power utility functions returns on investment were es- tablished [

Thus for negative exponential, we have

Now, suppose an investor wishes a Logarithmic rate of return, then

A market structure with a square root utility function with even step structure could also be,

The power utility function is a generalization of the square root utility function. Thus, for an investor with power utility function return we have,

The strategy generating the optimal wealth resulted in the terminal wealth

Thus, the wealth value at time t is given by

On and on to

This is some sort of multiplicative effect of the wealth

with,

But, terminal wealth must be equivalent to optimal strategy,

Now, suppose an investor wishes a Logarithmic return on investment, its optimal strategy will be

An investor with intension of a less risk investment such as the square root will have

Morealso, for the power model is a generalization of the square root model, thus we have

This study concludes that the optimal strategy is determined by the ratio

different utility models showed that the Negative exponential utility model gave the best strategy of wealth which of-course, more risky.

Various strategies with different Utility functions were established. The utility functions considered are negative exponential, logarithm, square root and power utility functions. The N-step utility model results show the ratio of the utility functions at time point i in comparison with the initial starting time. The strategy with different utility models depends on the amount of risk an investor is willing to bear at each trading period. The results showed that the Negative exponential utility model gave the best strategy.

In the subsequent paper, the model will be used to predict the performances of some selected companies in the Nigeria Capital Market.

J. T. Eghwerido,E. Ekuma-Okereke,E. Ekuma-Okereke,E. Efe-Eyefia,Edwin Iguodala,T. O. Obilade, (2016) Measure of Investment Optimal Strategy. Journal of Mathematical Finance,06,269-274. doi: 10.4236/jmf.2016.62023