Stock market prediction

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Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price could yield significant profit. Some believe that stock price movements are governed by the random walk hypothesis and thus are unpredictable. Others disagree and those with this viewpoint possess a myriad of methods and technologies which purpotedly allow them to gain future price information.

Contents 1 The random walk hypothesis 2 Prediction methods 2.1 Fundamental analysis 2.2 Technical analysis 2.3 Technological methods 3 Validity 4 References

[edit] The random walk hypothesis

When applied to a particular financial instrument, the random walk hypothesis states that the price of this instrument is governed by a random walk and hence is unpredictable. If the random walk hypothesis is false then there will exist some (potentially non-linear) correlation between the instrument price and some other indicator(s) such as trading volume or the previous day's instrument closing price. If this correlation can be determined then a potential profit can be made.

[edit] Prediction methods

Prediction methodologies fall into three broad categories which can (and often do) overlap. They are fundamental analysis, technical analysis (charting) and technological methods.

[edit] Fundamental analysis

Fundamental Analysts are concerned with the company that underlies the stock itself. They evaluate a company's past performance as well as the credibility of it's accounts. Many performance ratios are created that aid the fundamental analyst with assessing the validity of a stock, such as the P/E ratio. Warren Buffett is perhaps the most famous of all Fundamental Analysts.

[edit] Technical analysis

Technical analysts or chartists are not concerned with any of the company's fundamentals. They seek to determine the future price of a stock based solely on the (potential) trends of the past price (a form of time series analysis). Numerous patterns are employed such as the head and shoulders or cup and saucer. Alongside the patterns, statistical techniques are utilised such as the exponential moving average (EMA).

[edit] Technological methods

With the advent of the digital computer, stock market prediction has since moved into the technological realm. The most prominent technique involves the use of artificial neural networks (ANNs). ANNs can be thought of as mathematical function approximators. Their value in stock market prediction is that if a (potentially non-linear) relationship exists then it is possible that it could be found with enough indicators, the correct network structure and a large enough dataset.

The most common form of ANN in use for stock market prediction is the feed forward network utilising the backward propagation of errors algorithm to update the network weights. These networks are commonly referred to as back propagation networks. Since NNs require training and have a large parameter space, it is useful to modify the network structure for optimal predictive ability. Recently this has involved pairing NNs with genetic algorithms, a method of finding optima in multi-dimension parameter spaces utilising the biological concepts of evolution and natural selection. Moreover, some researchers has tried to extract meaningful indicators from the news flash and discussion rooms about a certain stock using Data Mining techniques. But the people can have different opinion about the same stock at the same time.

[edit] Validity

There have been numerous academic studies on the validity of fundamental analysis, technical analysis and Artificial Neural Networks as stock market prediction methods. Some studies report success in all camps, on particular markets and with particular datasets. Others dispute the ability to even predict the stock market at all and adhere to the random walk hypothesis. In fact the theory of pricing financial derivatives relies on the random walk hypothesis (stock prices are usually modelled as random walks in derivative pricing models).

[edit] References

• Graham, B. The Intelligent Investor HarperCollins; Rev Ed edition, 2003.
• Lo, A.W. and Mackinlay, A.C. A Non-Random Walk Down Wall Street 5th Ed. Princeton University Press, 2002.
• Azoff, E.M. Neural Network Time Series Forecasting of Financial Markets John Wiley and Sons Ltd, 1994.
• StockMarketPrediction Blog at blogger