Technical analysis covers two areas of the stock market, its sentiment (sometimes termed its “psych”) and the analysis of supply and demand. The sentiment of the market is the general feeling of investors. This is rather intangible, but does show itself in overall share movements. For instance, if investors are feeling generally optimistic (as in a bull market), share prices will tend to rise. The supply and demand aspect of the stock market is a measure of how much money there is available to invest in shares: If an investor has little spare money, for example, this restricts his or her ability to invest in shares and drive the market higher.

Any stock market produces vast amounts of data on a daily basis, far too much for anyone to cope with in its raw form. You only have to look at the share price pages in the Financial Times to get some idea of the quantities of figures that make up just one day’s trading. To make any sense of this deluge, we must apply various statistical measures that turn the raw data into meaningful information. There are several measures in common use, and different technical analysts tend to prefer different measures. These measures all have one thing in common – they reduce the amount of noise present on the data and help to reveal any underlying pattern.

The Purpose of Technical Analysis

Inevitably, the ultimate aim of Technical Analysis is to make money. The secret of successful share trading, boiled down to one sentence, is to buy when the share price is low and to sell when it is high. As the share prices rise and fall, there will be times when one makes the correct judgement and makes a profit, and times when one gets it wrong and makes a loss. With successful technical analysis, the accumulated profits will outweigh the total losses.

What is Technical Analysis?

Technical analysis is the science (some might say art) of predicting the prices of share commodities based on their current price and past performance. Share prices wander up and down, changing many times every day. If you plotted the price of a share during the course of a particular time period (day, month or year), you would get a line that moves up and down sharply, like some demented saw blade. You might, however, be able to spot a general pattern underneath all that variation. The random variation is termed “noise” and the purpose of technical analysis is to remove that noise as far as possible.

Why doesn’t everybody use them?

No-one knows exactly how many organisations and individuals use ANNs to predict stock market prices as people tend to be secretive about their methods. One of the postulates of the Efficient Market Hypothesis is that information flows efficiently round stock markets and so any ANN that had a high success rate would, if made public knowledge, be quickly adopted by others. Essentially, this begs the question: Why spoil a good thing by making it generally available?

So perhaps the perfect neural network, that can predict every twist and turn in the market, is out there. If so, the chances are that you and I will never get to hear about it!

Are they any good? The simple answer is yes! Several scientific studies by leading universities in the United States such as Stanford, have shown that ANNs can give a distinct advantage over both experienced human stock traders and Expert Systems. In these studies, the neural networks are trained on past price changes in the stock market or currency market, and then their predictions are compared to subsequent prices to see if the advice they give is valid. It is not generally recorded whether the researchers have made any real money using them, possibly due to the ethical considerations that might arise from such speculation.

It won’t surprise you to learn that stock price prediction was one of the first uses of Artificial Intelligence, but in the early days, programmers tried to encode the sort of rules of thumb that human predictors used, such as “If the price of a share goes above a 7-point moving average, issue a buy signal.” Systems based on series of rules like this are called Expert Systems, and they have been used with some success. The main disadvantage that Expert Systems have compared to ANNs is that they require the rules to be explicitly programmed into them. These rules are often difficult to derive from stock predictors (many of whom seem to work on little more than instinct) and are subject to error and over-generalisation. ANNs, on the other hand, if given sufficient, typical training data, can derive the most suitable rules for themselves. On the other hand, ANNs are bad at describing those rules as they are stored in the form of numbers (weighted connection strengths). Expert systems can easily “explain” how they reached their conclusions.

One thorny issue with neural networks is that the inputs need to be coded properly in order that the network can extract useful information from them. For instance, training the network simply on the share prices themselves would be unsatisfactory, as that particular pattern of prices would depend on the absolute value of the stock market. In many cases, the obvious strategy is the correct one, and in the case of stock market prediction an obvious strategy suggests itself. It is not the absolute value of the stock prices that is of interest, but the changes in prices from one time period to the next. This is where the money is to be made, regardless of whether the general value of shares is hovering around the 4000 mark, the 5000 mark or the 6000 mark. For this reason, it would make more sense for the inputs to the ANN to be the changes in share price

ANNs essentially associate input patterns with output patterns. The inputs could be the raw stock market data, since this is the material that technical analysts use to predict movements in the market. The outputs could be any one of several things. For instance, given inputs representing the share prices on day 1, day 2 and day 3, the output might be a prediction of the share prices on day 4 (or possibly even on days 4 and 5). Alternatively, the outputs might be simple “buy” and “sell” signals for shares in particular companies.

The advantage of ANNs is that they are flexible. They will, if correctly trained, learn to classify any pattern in the training library correctly: if they are given a pattern on which they have been previously trained, they will produce the correct output. However, they will also produce the correct output if they are given a pattern similar to one they have seen during training. They will therefore classify patterns that they have never seen before, based on the closest matching training pattern.