Fundamental analysis studies the effect of supply and demand on price. All relevant factors that affect the price of a security are analyzed to determine the intrinsic value of the security. If the market price is below its intrinsic value then the market is viewed as undervalued and the security should be bought. If the market price is above its intrinsic value, then it should be sold. Examples of relevant factors that are analyzed are financial ratios; e.g. Price to Earnings, Debt to Equity, Industrial Production Indices, GNP, and CPI.
Fundamental analysis studies the causes of market movements, in contrast to technical analysis, which studies the effect of market movements. Interest Rate Parity Theory and Purchasing Power Parity Theory are examples of the theories used in forecasting price movements using fundamental analysis. The problem with fundamental analysis theories is that they are generally relevant only in predicting longer trends.
Fundamental factors themselves tend to lag market prices, which explains why sometimes market prices move without apparent causal factors, and the fundamental reasons only becoming apparent later on. Another factor to consider in fundamental analysis is the reliability of the economic data. Due to the complexity of today’s global economy, economic data are often revised in subsequent periods therefore posing a threat to the accuracy of a fundamental economic forecast that bases its model on the data. The frequency of the data also pose a limitation to the predictive horizon of the model.
ANNs and Trading Systems
Today there are many trading systems being used in the financial trading arena with a single objective in mind; that is; to make money. Many of the trading systems currently in use are entirely rule-based, utilizing buy/sell rules incorporating trading signals that are generated from technical/statistical indicators such as moving averages, momentum, stochastic, and relative strength index or from chart patterns formation such as head and shoulders, trend lines, triangles, wedge, and double top/bottom.
The two major pitfalls of conventional rule-based trading systems are the need for an expert to provide the trading rules and the difficulty of adapting the rules to changing market conditions. The need for an expert to provide the rules is a major disadvantage in designing a trading system as it is hard to find an expert willing to impart his/her knowledge willingly due to the fiercely competitive nature of trading. Furthermore, many successful traders are unable to explain the decision-making process that they undergo in making a trade. Indeed, many of them just put it down to ’gut feel’2. This makes it very difficult for the knowledge engineer3 to derive the necessary rules for the inference engine4 of an expert system to function properly. The inability to adapt many rule-based systems to changing market conditions means that these systems may fail when market conditions change; for example, from a trending market to a non-trending one.
Different sets of rules may be needed for the different market conditions and, since market are dynamic, the continuous monitoring of market conditions is required. Many rule-based systems require frequent optimization of the parameters of the technical indicators. This may result in curve fitting of the system.5 ANNs can be used as a replacement of the human knowledge engineer in defining and finding the rules for the inference engine. An expert’s trading record can be used to train an ANN to generate the trading rules [Fishman 1991]. ANNs can also be taught profitable trading styles using historical data and then used to generate the required rules. In addition, they can learn to identify chart patterns, thereby providing valuable insight for profitable trading opportunities.
This was demonstrated by Kamijo and Kanigawa  who successfully trained a neural network to identify triangular patterns of Japanese candlestick charts. Finally, ANNs which are presented with fundamental data can find the rules that relate these fundamental data (such as GNP, interest rates, inflation rates, unemployment rates, etc.,) to price movements. Freisleben  incorporated both technical and fundamental analysis in his stock market prediction model while Kimoto and Asakawa  used fundamental/economic data such as interest rate and foreign exchange rate in their forecasting model. The research reported in this thesis incorporates technical analysis into an ANN, to the extent that it incorporates historical price data and a statistical value (from the AR model).
2 It is interesting that some recent studies have linked the neurons in the brain to activities in the stomach. Therefore, the term ‘gut feel’ may be more than just a metaphor!
3 A knowledge engineer is a term used to describe expert system computer programmers. Their job function is to translate the knowledge they gather from a human expert into computer programs in an expert system.
4 The inference engine is a computer module where the rules of an expert system are stored and used.
5 A system is said to be curve fitting if excellent results are obtained for only a set of data where the parameters have been optimized but is unable to repeat good results for other sets of data.
Prof. Clarence N W Tan