A boom economy cycle refers to the period within which consumers have a high demand for products and those who provide the products make large sales that have high profit margins (Goldstein and Lardy 2004). A bust economy on the other hand refers to duration during which there is reduced levels of demand and supply and slow economic growth (Goldstein and Lardy 2004). A normal economy is one in which there is no marked acute market spirals. There is stability and economic activities are slower than they are in a boom economy.
According to Gibson (2008), asset allocation policy refers to manner in which investors place value on assets in long-term mix so as to show balance between returns and risk taken. This is of great importance to investors who seek to increase potential for returns while reducing chances of loses.
Markowitz portfolio optimization theory indicates a method of allocating wealth in a way that incorporates the expectations on returns as well as preferences of investors for the amount of risk they want to take (Bai et al. 2007).
Through the use of different alternatives in investment, investors can attain higher levels of profits while at the same time, distributing their investment risk and thus maintaining minimum risk for anticipated returns. The concepts for this are expressed in mathematical formulations. Markowitz modern theory has been in practice for over half a century guiding the principles of asset investment (Bai et al. 2007).
The boom and bust economies are similar in some aspects. One of the effects of boom economy is that this type of economy does not necessarily stay stagnant and is liable to changes that might not be explained by normal market movement (Alpanda 2007). Booms increase the returns on investment because of a flourishing economy. By using Markowitz’s optimization theory an investor may increase the return and yet take a short minimal risk.
However, in long-term investment, the risk would increase as the boom period turns to be bust. According Bai et al. (2007), investors do not necessarily invest according to the logic of mathematical calculations. Some times investors trust their feelings about stock or bonds and may choose to ignore the use of tools to indicate safer investments or investment that may yield greater returns.
A bust economy like a boom economy does not necessarily stay stagnant and is liable to changes that might also not be explained by normal market movement. In a bust economy therefore investors would be led to make investment that may yield very little margin of returns. The prices of stock reduce during this period which is also marked by unemployment and less market confidence. As a result, less investment is made and investors often try to avoid losses by withdrawing their investment.
As a consequence, this worsens the economic bust. Still, even during this time investment can be made. With greater risk, returns are usually higher. Markowitz’s optimization theory treats risk in upward volatility as negative. However, that may not be viewed the same by investors since it is only a marker of risk taken and not loss which investors try to avoid. Greater risk in any case may yet yield greater profits.
According to Bai et al. (2007), Markowitz’s optimization theory model in most cases is not intuitive. As a result, its estimates do not reflect the reality in the market. This may both lead to unexpected returns or to higher risk taken than had been desired. Although this can turn out to be favorable (greater returns with the increased risk), it can also lead to less returns with less risk taken that had been anticipated. For this reason, Michaud (1989) cites markowitz’s optimization theory as having potential to mislead.
According to Michaud (1989), Markowitz’s optimization theory in addition does not factor in asset valuation. This hampers its ability to effectively predict market reactions and the real value of an investment. For that reason, it is hard to predict the movements in the market which are not always effective.
During a bust or boom economy, this is important as it leads to better choices of the investments to be included in the portfolio. According to Gibson (2008), an efficient portfolio is the one that maximizes the returns paired with regards to a certain volatility degree.
During a boom economy, returns are high as stock prices rise and the confidence is also high. It is during this time that asset investment should be increased in order to maximize returns. There is less risk during a boom period when it is at its peak or early stages. However, as the boom economy progresses, it is marked by inflation, reduction in stock prices and greater risk in investment. Unemployment and lower confidence marks this period as it slowly unwinds.
Another important part of investment is the ability to use macroeconomics factors in order to anticipate returns. There is more to a market than just the assets and their investment potential. People’s reactions can greatly affect the outcome of investment over time. Markowitz’s optimization theory additionally takes the duration of an investment singularly whereas in reality, returns tend to be correlated.
This can make it a weak determinant of market movement and as such as an indicator of returns and risk. Nonetheless, as a consequence, risk is often minimized than it should be if correlation is depended upon. It is for this reason that Markowitz’s optimization theory can be helpful in measuring risk irrespective of correlation between past investment periods and new investment period.
During a boom economy, economists encourage investment and asset allocation to be reviewed. According to Goldstein and Lardy (2004), during a boom economy there is a forward momentum that propels people to invest as they feel certain that the current situation will prevail, at least in time for them to receive returns. As a result, the economy is accelerated which in turn brings about an end to the boom after some time (Goldstein and Lardy 2004).
In an examination of China economy that has been moving towards a boom period in late 1990s and early 2000, Goldstein and lardy (2004), state that the economists call for a slowing down of the economy to avoid the sharp turn in economy. The slowing is particularly targeted at banks and investment institutions (Goldstein and lardy 2004). The result of a boom economy is often the case as has been seen in China according to Goldstein and Lardy (2004), where investment becomes unstable.
In order to avoid a collapse of the investment, measures have to be taken through macroeconomics to ensure that investment and economy pace is well maintained and adequately balanced. An examination of China’s asset allocation indicate that every time the boom in China rises above 30% it falls again in about four years to lower than 30% (Goldstein and lardy 2004).
Despite theories used, these trends persist and have come to be associated with China’s economic history. Michaud (1989) states that in cases like these, Markowitz’s optimization theory does not offer optimum portfolios but is instead inadequate to offer meaningful portfolios that will survive the bust economy following a boom economy.
The failure of Markowitz’s optimization theory to correspond to its theory according to Bau, Liu and Wong (2007) has been largely attributed to indicate that optimum returns are as a result of large returns from a numerous stocks. This is contrary to what Markowitz’s optimum theory proposes.
It is this stocks that provide optimum returns and not smaller portfolios. According to Michaud (1989), the bases for Markowitz’s optimization theory which is returns and risk are prone to errors in estimation. This lead to Markowitz’s optimization theory to favor investment that presents bigger returns while indicating little variance (Michaud 1989). In essence, however, the covariance matrix is complex and changes may exceed expectation due to margin of error.
In a study by Weiwei (2007), Chinese investment by firms was examined between 1993 and 2004. The results found were that during the bust economy, companies reduced their capital so as to secure assets investment. Still there was considerable similarity between the sensitivity to sales and to asset investment. A somehow similar study was conducted by Alpanda (2007) on Japanese market and the effect of macroeconomics on asset prices.
Alpanda (2007) noted that there were crashes in stock and land prices for the period of 1980-1990. Alpanda (2007), study revealed how assets prices were determined by government policy and productivity growth. The government policies led to cycles that maintained the fall in asset prices due to taxation. However as the economy continued to slow down, the prices stabilized and people were once more able to invest despite the macroeconomics variables witnessed.
A normal economy is regarded as falling between a boom and bust economy. This economy is marked by moderate prices in stock without surges as witnessed during a boom economy or drops as witnessed during a bust economy. A normal economy can change at any time with economic growth and give way to a boom economy. Asset allocation at this time correlates more with Markowitz’s optimization theory and it is during this time that Markowitz’s optimization theory calculations could be most dependable.
This is because according to Michaud (1989), at its peak, this period presents stability in market without sharp changes in market confidence. Growth indicated by a growing gross domestic product (GDP) is generally steady and as a result, it is easier to calculate the expected returns and risks. However, as the normal economy progresses to a boom economy, there is marked growth and asset allocation can be adjusted to reflect the market changes in investment potential.
According to Gibson (2008), asset allocation is determined by client’s individual tolerance of risk. The efficient frontier although ideal may still not present levels of volatility than all clients can handle despite the maximization of returns (Gibson 2007). As a result, Markowitz’s optimization theory can be used to calculate the returns particular to a client.
According to Gibson (2008), however, historical data does not always help to make proper estimations for making returns and reducing risk. This is because variations occur and become part of the input that is computed in the calculations during asset allocation.
Past returns can be adequately used to make sound expectation of returns in the future for an investment (Gibson 2008). Gibson (2008) states that the returns from stock though unexpected may sometimes exceed those of safer options like government treasury bills. This is what happened in the period 1978-1987 in America.
The stocks which had showed limited returns out performed the treasury bills that had been expected to lead in returns (Gibson 2008). Thus, it is advocated that for Markowitz’s optimization theory to really make better estimates, longer historical relationships should be drawn upon (Gibson 2008).
According to Choi (2006), it is important in asset allocation to invest in non-related assets. This offers a much better opportunity to subsidize risk while maintaining returns. Investment that readily show their performance like mutual funds are also ideal as investors can gauge them and make better investment that will satisfy their level of desired returns and risk tolerance (Choi 2006).
Choi (2006), states that diversification remains important in asset investment as in every quarter, asset allocation speaks to around 93% total variations. In addition, between 75% to 98% total variation can be explained by it (Choi 2006). It is because of this impact that diversification of portfolio is important in ensuring that asset allocation is well primed.
According to Goldstein and Lardy, as has been noticed in China, a boom unwinding can last longer in large economies. As a result, there is an extended period in the last stages of a boom. This increases the time period within which returns can still be high. However, since investment is already on a downward spiral it increase risk in new investments. According to Markowitz’s optimization theory, this time may be harder to predict as the market movements are unpredictable.
In this kind of economic situation, Markowitz’s optimization theory model has received criticism as it does not make use of factors which could increase its accuracy in predicting returns and risks that will be actualized in the market. According to Michaud (1989), Markowitz’s optimization theory needs to be modernized to minimize its limitations.
There has been a push towards improving Markowitz’s optimization theory by the use of what is now called modern Markowitz’s optimization theory. According to Goldstein and Lardy (2004), this can give better estimates that will relate to the market trends witnessed in the global business.
Markowitz’s optimization theory model has been in use and has guided the way asset allocation was conducted since 1950s. However, there have been increased efforts to make it more reliable in the current economy which have been less predictable.
Global business and investment further complicates the use of Markowitz’ optimization theory as there are factors which make the market different in different markets. The tools used in one market may prove unreliable in another foreign market. In addition, emerging markets may not behave as might be expected. It is for this reason that more diligence has to be used in accurately determining the level of risks and expected returns.
Alpanda, S., 2007. The boom-bust cycle in Japanese asset prices. (Online). Available At:
http://mpra.ub.uni-muenchen.de/5895/1/MPRA_paper_5895.pdf (Accessed 21st December, 2010).
Bai, Z. Liu, H and Wong, W., 2007. Making Markowitz’s portfolio optimization theory practically useful. (Online). Available at:
http://www.rmi.nus.edu.sg/documents/research/rmiworkingpp/wp0710.pdf (Accessed 21st December, 2010).
Choi, D., 2006. Cases for asset allocation. Journal of Business and Economics Research, 4 (9), pp. 17-24.
Gibson, R. C., 2008. Asset allocation: balancing financial risk. New York, NY: McGraw-Hill, Inc.
Goldstein, M and Lardy, N. R., 2004. What kind of landing for Chinese economy? Institute for International Economics, 4 (7), pp. 1-10.
Michaud, R. O. 1989. The Markowitz optimization enigma: is ‘optimized’ optional? Financial Analysts Journal, 45 (1), pp. 31-42.
Weiwei, Y., 2007. Does macro economy have any effect on firm investment cash flow sensitivity? Frontiers of Economics in China, 2 (3), pp. 388-403.