The Main Motive of Investing in the Contemporary Investment Environment - Essay Example

The main motive of investing in the contemporary investment environment is to earn maximum return on their investments. Existing investment portfolio theories provides investors and financial planners with a methodology for allocating funds within a given investing portfolio for maximum returns and minimal risks for a given amount of allocated resources (Omisore et al, 2012). Investing in a single financial instrument is quite risky and therefore, having an investment portfolio is advised. An investment portfolio refers to a grouping of various financial assets like bonds, stocks as well as their cash equivalents (mutual, closed and exchange traded funds). In this paper, we discuss how created an investment portfolio comprising of Australian shares, bonds and cash. Having been presented with three assets by the investor, the objective is to come up with an efficient portfolio having maximum return with minimum risk. The investor requires a detailed investment portfolio to ensure his new fortune does not depreciate but rather appreciate. According to his wishes and expectations, the investor is not too knowledgeable when it comes to the investment sector, but even so he still wishes to increase his fortune by 10% before the end of the financial year. To easily achieve this goal the first move was to make an investment portfolio which ensures that the finances are not only well utilized but also directed to ventures that will eventually yield the highest income as well as those that do not expose the investor to too high risk levels. In this section, we describe how to optimize the portfolio expected return using the Markowitz portfolio optimization method. The construction of an efficient portfolio can be achieved by using the Markowitz Portfolio Theory (Becker, 2015). The first step in the construction of an efficient portfolio is the construction of a covariance matrix using the individual shares and their expected returns. Portfolio return matrix Year Australian Shares % Return Australian Bonds % Returns Cash Rate % Average Returns 1990 -20.1 12.1 14.8 1991 22.2 9.4 10.3 1992 -6 8.9 6.5 1993 39.1 6.7 5.1 1994 -9.2 10 5.4 1995 16.5 8.2 7.5 1996 7.2 7.4 7 1997 7.9 6.1 5.4 1998 7.7 5 5 1999 13.5 7.8 4.8 2000 2.9 5.5 6 2001 2.6 6 4.9 2002 -9.6 5.2 4.6 2003 8.7 5.6 4.8 2004 22.8 5.3 5.3 2005 15.7 5.2 5.5 2006 19.8 5.9 5.8 2007 17.9 6.3 6.4 2008 -45.8 4 6.6 2009 34.1 5.7 3.2 2010 2.2 5.5 4.4 E(r) 7.147 6.753 6.157 The obtained covariance matrix is as follows; We take the return matrix and subtract expected return matrix to find the Excess returns Excess return matrix Australian Shares % Return Australian Bonds % Returns Cash Rate % Average Returns -27.247 5.347 8.643 22.2 9.4 10.3 -6 8.9 6.5 39.1 6.7 5.1 -9.2 10 5.4 16.5 8.2 7.5 7.2 7.4 7 7.9 6.1 5.4 7.7 5 5 13.5 7.8 4.8 2.9 5.5 6 2.6 6 4.9 -9.6 5.2 4.6 8.7 5.6 4.8 22.8 5.3 5.3 15.7 5.2 5.5 19.8 5.9 5.8 17.9 6.3 6.4 -45.8 4 6.6 34.1 5.7 3.2 2.2 5.5 4.4 The main objective of this process is to optimize the Sharpe ratio. Sharpe ratio is the average return earned in excess of the risk-free rate per unit of volatility. The outcome of the Markowitz optimization were as follows;   Shares Bonds Cash       Total Weights 49.40% 23.90% 26.70% 0.00% 0.00% 100% Expected Return 7.15% 6.75% 6.16%         Variance/Covariance Matrix           Shares Bonds Cash   Shares 0.063473 0.000320 -0.000202     Bonds 0.000320 0.045185 -0.000164     Cash -0.000202 -0.000164 0.037164                             Output from solver for plotting the portfolio frontier     X axis = SD Y axis = E[r] Weights St.Dev E[r] w[1] w[2] w[3] w[4] w[5] 11.73% 14.16% 0.00% 0.00% 0.00% 0.00% 100.00% 19.79% 18.77% 0.00% 0.00% 0.00% 100.00% 0.00% 9.13% 15.00% 5.27% 10.77% 19.42% 0.00% 64.54% 8.86% 16.00% 12.56% 14.56% 19.26% 12.62% 41.00% 10.18% 17.00% 19.09% 17.59% 18.73% 26.54% 18.04% Portfolio frontier plot The optimum or efficient portfolio would be made up of 49.4% in Shares, 23.90% in bonds and 26.70% in Cash assets. Advantages of a diversified investment portfolio are all related to risk management issues. A diversified investment portfolio such as the one we built for this particular investor helps in reducing risks and market shocks associated with certain financial instruments. International portfolio diversification is even better and this it is for this reason that we bought German Treasury bills, bonds and notes and combined them with the US ones at a 50-50 ratio (Bouslama & Ouda, 2014).The ideal portfolio idea falls under the cutting edge portfolio hypothesis. The hypothesis accepts (in addition to everything else) that speculators fanatically attempt to minimize danger while striving for the most elevated return conceivable. The hypothesis expresses that speculators will act reasonably, continually settling on choices went for expanding their return for their adequate level of danger. The ideal portfolio was utilized as a part of 1952 by Harry Markowitz, and it demonstrates to us that it is workable for distinctive portfolios to have differing levels of danger and return. Every speculator must choose the amount of danger they can deal with and then apportion (or expand) their portfolio as per this choice. The outline beneath delineates how the ideal portfolio functions. The ideal danger portfolio is typically dead set to be some place amidst the bend in light of the fact that as you go higher up the bend, you assume proportionately more hazard for a lower incremental return. On the flip side, okay/low return portfolios are pointless in light of the fact that you can attain a comparable return by putting resources into danger free resources, in the same way as government securities. This exercise reveals to us the role of software and neural networks in the prediction of stock market behavior as well as portfolio prediction. Fok, Tam and Ng’s (2008, p.1) comparison of learning algorithms to predict stock price behaviour shows that the neural network back propagation algorithm provides higher prediction accuracies than linear regression techniques. Tsang et al. (2007, p.453) concur that the back propagation is a useful technique for simulating neural network models in financial forecasting. Their evaluation of the technique’s use in the Hong Kong stock exchange show a high success rate (above 70 percent), which demonstrates its reliability in providing accurate predictions on stock prices. Vanstone & Finnie (2009, p.1) provide a methodology for designing ANNs for stock markets. The methodology separates the process for generating training samples into distinct steps. This allows developers to test each step for correctness and accuracy before proceeding to the next. This testing may be carried out in the context of the stock trading system or out of its context. The methodology aims to address the three core roles of the stock market training system, namely: entry and exit rules, risk control and financial management (p.7). It uses ratios and indexes, instead of actual prices and volumes, to predict the trend of stocks. To test the architecture of the neural network in the context of the stock system, the methodology requires input of ratios as the filter ratio, timeframe (in years), number of securities or stocks to be screened or deduced, probability of a win, probability of a trade loss, average amount that can be won or lost and expectancy ratio. The following formula can be used to determine the expectancy ratio of a security or investment: Expectancy ratio = ((AW× PW) + (AL ×PL)) |AL| Where AW is the average amount gained, PW is the likelihood of a win, AL is average amount lost and PL is likelihood of a loss (Vanstone & Finnie 2009, p.11). The expectancy ratio can then be used to determine when to buy or sell stocks in the exchange market. Since the primary aim of stock market trading is to generate profit, the neural network should be subjected to external benchmarking using ratios such as number of trades, payoff index, net profit, annual profit, portfolio stability, Ulcer index, Luck coefficient and Sharpe ratio (Vanstone & Finnie 2009, p.12). While the stock forecasting methods discussed previously focus on the use of artificial neural networks for predicting prices, few address the influence of environmental factors on stock patterns. Assaleh, El-Baz and Al-Salkhadi’s (2011, p.82) study evaluates the role of political, social and psychological factors in forecasting stock prices. They propose an advanced ANN approach using the Polynomial Classifiers theory to forecast stock prices in the Dubai foreign exchange. In comparison to the ANN approach, the polynomial classifier approach produces better results than neural networks. In the context of economic factors, Weckman et al. (2008, p.36) propose that ANN developers take into consideration the different types of investors in the securities industry including consumers, private businesses, healthcare, financial, energy, manufacturing and telecommunications industries. Baker and Wurgler (2008, p.10) recommend that ANN techniques should also incorporate social factors (such as investor reaction) in predicting stock trends. The construction of an efficient portfolio must be conducted carefully in order to avoid any errors associated with choosing an inappropriate iterative value. The use of Markowitz Portfolio Theory in the determination of the most efficient portfolio should therefore be encouraged among all investment fund managers. References Read More