Investment Theories:An investment theory suggests what parameters one should take into account before placing his (or her) capital on the market. Traditionally the investment community accepts two major theories: the Firm Foundation and the Castles in the Air. Reference to these theories allows us to understand how the market is shaped, or in other words how the investors think and react. It is this sequence of 'thought and reaction' by the investors that defines the capital allocation and thus the level of the market.
There is no doubt that the majority of the people related to stock markets is trying to achieve profit. Profit comes by investing in stocks that have a good future (short or long term future). Thus what they are trying to accomplish one way or the other is to predict 16 the future of the market. But what determines this future? The way that people invest their money is the answer; and people invest money based on the information they hold. Therefore we have the following schema:
The factors that are under discussion on this schema are: the content of the 'Information' component and the way that the 'Investor' reacts when having this info.
On the other hand, according to the Castles in the Air theory the investors are triggered by information that is related to other investors' behavior. So for this theory the only concern that the investor should have is to buy today with the price of 20 and sell tomorrow with the price of 30, no matter what the intrinsic value of the firm he (or she) invests on is.
Prediction Methods:The prediction of the market is without doubt an interesting task. In the literature there are a number of methods applied to accomplish this task. These methods use various approaches, ranging from highly informal ways (e.g. the study of a chart with the fluctuation of the market) to more formal ways (e.g. linear or non-linear regressions). We have categorized these techniques as follows:
- Technical Analysis Methods
- Fundamental Analysis Methods
- Traditional Time Series Prediction Methods
- Machine Learning Methods
The criterion to this categorization is the type of tools and the type of data that each method is using in order to predict the market. What is common to these techniques is that they are used to predict and thus benefit from the market's future behavior.
Technical Analysis
"Technical analysis is the method of predicting the appropriate time to buy or sell a stock used by those believing in the castles-in-the-air view of stock pricing". The idea behind technical analysis is that share prices move in trends dictated by the constantly changing attributes of investors in response to different forces. Using technical data such as price, volume, highest and lowest prices per trading period the technical analyst uses charts to predict future stock movements. Price charts are used to detect trends, these trends are assumed to be based on supply and demand issues which often have cyclical or noticeable patterns.
From the study of these charts trading rules are extracted and used in the market environment. The technical analysts are known and as 'chartists'. Most chartists believe that the market is only 10 percent logical and 90 percent psychological. The chartist's belief is that a careful study of what the other investors are doing will shed light on what the crowed is likely to do in the future.
This is a very popular approach used to predict the market, which has been heavily criticized. The major point of criticism is that the extraction of trading rules from the study of charts is highly subjective therefore different analysts might extract different trading rules by studying the same charts. Although it is possible to use this methodology to predict the market on daily basis we will not follow this approach on this study due to its subjective character.
Fundamental Analysis
"Fundamental analysis is the technique of applying the tenets of the firm foundation theory to the selection of individual stocks". The analysts that use this method of prediction use fundamental data in order to have a clear picture of the firm (industry or market) they will choose to invest on. They are aiming to compute the 'real' value of the asset that they will invest in and they determine this value by studying variables such as the growth, the dividend payout, the interest rates, the risk of investment, the sales level, the tax rates an so on. Their objective is to calculate the intrinsic value of an asset (e.g. of a stock). Since they do so they apply a simple trading rule. If the intrinsic 21 value of the asset is higher than the value it holds in the market, invest in it. If not, consider it a bad investment and avoid it. The fundamental analysts believe that the market is defined 90 percent by logical and 10 percent by physiological factors. This type of analysis is not possible to fit in the objectives of our study. The reason for this is that the data it uses in order to determine the intrinsic value of an asset does not change on daily basis. Therefore fundamental analysis is helpful for predicting the market only in a long-term basis.
Traditional Time Series Prediction:The Traditional Time Series Prediction analyzes historic data and attempts to approximate future values of a time series as a linear combination of these historic data. In econometrics there are two basic types of time series forecasting: univariate (simple regression) and multivariate (multivariate regression). These types of regression models are the most common tools used in econometrics to predict time series. The way they are applied in practice is that firstly a set of factors that influence (or more specific is assumed that influence) the series under prediction is formed. These factors are the explanatory variables xi of the prediction model.
Then a mapping between their values xit and the values of the time series yt (y is the to-be explained variable) is done, so that pairs {xit , yt} are formed. These pairs are used to define the importance of each explanatory variable in the formulation of the to-be explained variable. In other words the linear combination of xi that approximates in an optimum way y is defined. Univariate models are based on one explanatory variable (I=1) while multivariate models use more than one variable (I>1). Regression models have been used to predict stock market time series. A good example of the use of multivariate regression is the work of Pesaran and Timmermann (1994) .
They attempted prediction of the excess returns time series of S&P 500 and the Dow Jones on monthly, quarterly and annually basis. The data they used was from Jan 1954 until Dec 1990. Initially they used the subset from Jan 1954 until Dec 1959 to adjust the coefficients of the explanatory variables of their models, and then applied the models to predict the returns for the next year, quarter and month respectively.
Machine Learning Methods:Several methods for inductive learning have been developed under the common label "Machine Learning". All these methods use a set of samples to generate an approximation of the underling function that generated the data. The aim is to draw conclusions from these samples in such way that when unseen data are presented to a model it is possible to infer the to-be explained variable from these data. The methods we discuss here are: The Nearest Neighbor and the Neural Networks Techniques. Both of these methods have been applied to market prediction; particularly for Neural Networks there is a rich literature related to the forecast of the market on daily basis.
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