Darren Winters

What do P&F charts look like?

A point and figure chart is always plotted as a series of columns, ideally on squared or graph paper. Each column contains either letter Xs , representing rising prices, or letter Os representing falling ones (never both!) The vertical axis of the chart represents share price, the horizontal one, time – but not in a linear sense! A new column is only added to the chart when the share price substantially changes direction, so one column on the chart can represent a single day, several days, or even several weeks!

What are Point and Figure Charts?

Point and Figure charts are a way of plotting the movement of share prices in such a way as to remove the effect of the noisy variations and just leave the important information. They were developed in the years before computers made chart plotting and interpreting easier as a quick method of tracking the stock market. Although their popularity declined when computers became common, there has recently been a renewal of interest in them.

A bull market, also known as a bull run, is one in which shares are generally rising in price. The investors are generally optimistic and keen to buy, thereby driving prices up further. A good example is the U.S. stock market in the early nineties, when all the stock market indices such as the Dow Jones rose almost continually. A bear market, on the other hand, is one where share prices are falling due to investor pessimism. The most famous bear market was the Wall Street Crash that started in October 1929.

This is another one of those “Looks a bit like what it means” terms. In this case, the price in shares dips in a gradual U shape and then picks up, followed by a slight drop marking the handle of the cup, and then a further climb. The peak between the cup and the handle occurs when the stock approaches the peak price on the initial side of the cup. Investors aren’t keen to test the stock price beyond that value, so the price holds steady (we say it “trades sideways”) with a slight downward trend – the handle of the cup.

The bounces that happen when shares reach their support or resistance levels tend to be repeated, that is, when a share falls to a support level, it may well bounce repeatedly off that level, and similarly for rising to a resistance level. However, shares can break through their support or resistance levels, referred to as a price breakout. Once a share has broken out, its tendency is to keep on moving.

Resistance is a mirror image to support for a share price. In this case, a share price is rising and reaches a level at which investors won’t sustain it any further and the price falls back. It is as though the share price has bounced off a glass ceiling. Having fallen back slightly, it may climb again and even bounce back at the same price. We say that investors are providing resistance at that price. Resistance levels indicate the level at which sellers generally outnumber buyers.

Share prices rise and fall depending on, amongst other things, investor confidence. When shares are falling, they may reach a level where investors start to buy, perhaps feeling that the shares are now a “bargain”. The price would then rally slightly. Effectively, the share price has “bounced” off a minimum value. The rally in the share price may be temporary – it may start to fall again, and even bounce when it reaches roughly the same price. We say that investors are providing support for the shares at that price. Support levels indicate the price at which most investors believe the price will rise.

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.