Investors should study and understand the behavior of the stock market because the overall stock market influences significantly an individual stock. Using optimum market timing can help to maximize the return on investment. A good timing is enabled by prediction based on appropriate methods.
Optimum Investing Timing
To earn money in the stock market, it is important for investors to study
the overall stock market because the market exerts a significant influence
on the behavior of individual stocks. Since the market risk is a major
contributing factor in a stock performance, the ability to understand the
market reduces the number of surprises for investors. Generally, the stock
market depends on many components, and often it is hard to predict its
next move. However, it is not a chaotic thing but rather a complicated
system with a certain degree of volatility and uncertainly.
Neither bear nor bull market is bad for knowledgeable investors because
both can be used to their benefits - the most important thing is stock
market predictability. Basically, the stock market prediction can be built
on the following approaches: Efficient Market Hypothesis (it states that
the prices captures all known information), Fundamental analysis (it
considers companies performance), or Technical analysis (it uses
historical prices and volumes statistics to detect trend). Using the
combination of these methods may improve the accuracy of prediction.
However, even a prediction based on many techniques can fail. Fortunately,
there are some principles that work in worst-case scenarios, like these:
"everything is subject to change", "it is always darkest
before sunrise", or "the good times come back when you least
expect them".
While prices are low and the stock market is down, there is a good
argument to buy the solid stocks that are likely to make a good recovery.
When the market failed and started bottoming the question is if there is
only way up left or still some room to go lower. How to identify correctly
the current situation? To solve a complicated problem, such science as
mathematics uses different kinds of transformation. If a problem exists in
time domain, one can transform data into another space (for example,
frequency domain), relatively easy get a solution there, and then make an
inverse transformation. Since frequencies and periods are just inversely
proportional quantities, either of them can be used in a transformed
space.
S&P 500 Index (^GSPC) can be considered as a model of the overall
market. By looking at the historical stock price chart of ^GSPC, one can
notice that the market does not follow just a linear trend - it has some
deviations from a linear function. Some cycles are well-known, such as,
four-year presidential cycle or annual and quarterly fiscal reporting
cycles. In addition, some cycles are defined by intrinsic characteristic
properties of the system. The stock market performance curve can be
considered as a sum of the cyclical functions with different periods and
amplitudes. It would be easy to analyze the repetition of typical patterns
in stock market performance if they did not mask themselves. In other
words, sometimes cycles overlap to form an abnormal extremum or offset to
form a flat period. It is clear that a simple chart analysis has a certain
limit in identifying and predicting the trend.
Fortunately, mathematics is able to extract basic cycles so that
historical quote curve can be decomposed into a set of sinus (or cosines)
functions with different periods, amplitudes, and phases - that is
something similar to a spectral analysis or a time series analysis. By
selecting data with different historical periods, such spectral analysis
can identify the major cycles, which have a dominant effect in a
particular time frame.
The recent research by Addaptron Software using SMAP-2
tool helped to detect for 40, 20, 10, and 5-year periods of ^GSPC the
following major cycles (lines): 10, 8, 5, and 1.6 year. Since each line
has own amplitude it is easy to estimate the significance of timing
analysis for overall investment performance. For example, an average
annual return for the last 5 years approximated by linear function equals
around 13% and the amplitude of cycle with 1.6-year period is equal 4.8%.
Therefore, in case of 1.6-year cycle only, the return can be diminished to
13-4.8*2= 3.4% with the worst timing or it can be maximized to 13+4.8*2=
22.6% with the best timing.
One of the techniques to build an extrapolation (forecasted curve) is to use the following two steps: (1) applying spectral (or time series) analysis to decompose the curve into basic functions, (2) composing these functions beyond the historical data. As a practical example by Addaptron software, the stock market prediction for the next five years on the basis of spectrum analysis: the stock market will suffer some volatility within the next several months but eventually it will go up until 2010-2011. Then it will crash in 2012-2013. Note, these years are approximate because the phase of cycles is fluctuating and, of course, something extraordinary can change the prediction picture. This prediction can be strengthened or weakened by comparing with other forecasting techniques.
To summarize, the direction of the overall market influences significantly an individual stock. There is no method that has been successfully enough to consistently beat the market. The same is applied to a simple combination of different methods. If all predictions fail, everybody scared, and nothing seems to work, one of the approaches to try is a time series prediction because it analyzes historical data and then builds prediction on the basis of changing performance "as is", objectively, without any pressure of emotion or external information.
Copyright © 2008 Alex Shmatov
