Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price information.
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The Efficient Markets Hypothesis and the random walk
The efficient market hypothesis posits that stock prices are a function of information and rational expectations, and that newly revealed information about a company's prospects is almost immediately reflected in the current stock price. This would imply that all publicly known information about a company, which obviously includes its price history, would already be reflected in the current price of the stock. Accordingly, changes in the stock price reflect release of new information, changes in the market generally, or random movements around the value that reflects the existing information set. Burton Malkiel, in his influential 1973 work A Random Walk Down Wall Street, claimed that stock prices could therefore not be accurately predicted by looking at price history. As a result, Malkiel argued, stock prices are best described by a statistical process called a "random walk" meaning each day's deviations from the central value are random and unpredictable. This led Malkiel to conclude that paying financial services persons to predict the market actually hurt, rather than helped, net portfolio return. A number of empirical tests support the notion that the theory applies generally, as most portfolios managed by professional stock predictors do not outperform the market average return after accounting for the managers' fees.
While the efficient market hypothesis finds favor among financial academics, its critics point to instances in which actual market experience differs from the prediction-of-unpredictability the hypothesis implies. A large industry has grown up around the implication proposition that some analysts can predict stocks better than others; ironically that would be impossible under the Efficient Markets Hypothesis if the stock prediction industry did not offer something its customers believed to be of value.
Maps Stock market prediction
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.
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 its 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.
Fundamental analysis is built on the belief that human society needs capital to make progress and if a company operates well, it should be rewarded with additional capital and result in a surge in stock price. Fundamental analysis is widely used by fund managers as it is the most reasonable, objective and made from publicly available information like financial statement analysis.
Another meaning of fundamental analysis is beyond bottom-up company analysis, it refers to top-down analysis from first analyzing the global economy, followed by country analysis and then sector analysis, and finally the company level analysis.
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 used such as the exponential moving average (EMA). Candle stick patterns, believed to have been first developed by Japanese rice merchants, are nowadays widely used by technical analysts.
Data Mining Technologies (e.g. ANN)
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) and Genetic Algorithms. Scholars found bacterial chemotaxis optimization method may perform better than GA. ANNs can be thought of as mathematical function approximators. The most common form of ANN in use for stock market prediction is the feed forward network utilizing the backward propagation of errors algorithm to update the network weights. These networks are commonly referred to as Backpropagation networks. Another form of ANN that is more appropriate for stock prediction is the time recurrent neural network (RNN) or time delay neural network (TDNN). Examples of RNN and TDNN are the Elman, Jordan, and Elman-Jordan networks. (See the Elman And Jordan Networks)..
For stock prediction with ANNs, there are usually two approaches taken for forecasting different time horizons: independent and joint. The independent approach employs a single ANN for each time horizon, for example, 1-day, 2-day, or 5-day. The advantage of this approach is that network forecasting error for one horizon won't impact the error for another horizon--since each time horizon is typically a unique problem. The joint approach, however, incorporates multiple time horizons together so that they are determined simultaneously. In this approach, forecasting error for one time horizon may share its error with that of another horizon, which can decrease performance. There are also more parameters required for a joint model, which increases the risk of overfitting.
Of late, the majority of academic research groups studying ANNs for stock forecasting seem to be using an ensemble of independent ANNs methods more frequently, with greater success. An ensemble of ANNs would use low price and time lags to predict future lows, while another network would use lagged highs to predict future highs. The predicted low and high predictions are then used to form stop prices for buying or selling. Outputs from the individual "low" and "high" networks can also be input into a final network that would also incorporate volume, intermarket data or statistical summaries of prices, leading to a final ensemble output that would trigger buying, selling, or market directional change. A major finding with ANNs and stock prediction is that a classification approach (vs. function approximation) using outputs in the form of buy(y=+1) and sell(y=-1) results in better predictive reliability than a quantitative output such as low or high price. This is explained by the fact that an ANN can predict class better than a quantitative value as in function approximation--since ANNs occasionally learn more about the noise in the input data.
Since NNs require training and can have a large parameter space, it is useful to modify the network structure for optimal predictive ability.
Internet-based data sources for stock market prediction
Tobias Preis et al. introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends. Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports, suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets. Out of these terms, three were significant at the 5% level (|z| > 1.96). The best term in the negative direction was "debt", followed by "color".
In a study published in Scientific Reports in 2013, Helen Susannah Moat, Tobias Preis and colleagues demonstrated a link between changes in the number of views of English Wikipedia articles relating to financial topics and subsequent large stock market moves.
The use of Text Mining together with Machine Learning algorithms received more attention in the last years, with the use of textual content from Internet as input to predict price changes in Stocks and other financial markets.
The collective mood of Twitter messages has been linked to stock market performance. The study, however, has been criticized for its methodology.
The activity in stock message boards has been mined in order to predict asset returns. The enterprise headlines from Yahoo! Finance and Google Finance were used as news feeding in a Text mining process, to forecast the Stocks price movements from Dow Jones Industrial Average.
Applications of Complexity Science for stock market prediction
Using new statistical analysis tools of complexity theory, researchers at the New England Complex Systems Institute (NECSI) performed research on predicting stock market crashes. It has long been thought that market crashes are triggered by panics that may or may not be justified by external news. This research indicates that it is the internal structure of the market, not external crises, which is primarily responsible for crashes. The number of different stocks that move up or down together were shown to be an indicator of the mimicry within the market, how much investors look to one another for cues. When the mimicry is high, many stocks follow each other's movements - a prime reason for panic to take hold. It was shown that a dramatic increase in market mimicry occurred during the entire year before each market crash of the past 25 years, including the financial crisis of 2007-08.
Notes
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.
- Christoffersen, P.F. and F.X. Diebold. Financial asset returns, direction-of-change forecasting, and volatility dynamics. Management Science, 2006. 52(8): p. 1273-1287
Source of article : Wikipedia
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