Stock market predictions are actions to try to determine the future value of a company's stock or other exchange-traded financial instrument. A successful prediction of the future price of a stock can yield significant profits. An efficient market hypothesis shows that stock prices reflect all the information available today and any price changes that are not based on newly revealed information are thus essentially unpredictable. Others disagree and those with this point of view have various methods and technologies that purportedly allow them to get price information for the future.
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Efficient Market Hypothesis and Random Path
An efficient market hypothesis states that stock prices are a function of information and rational expectations, and newly revealed information about the prospects of a company is almost immediately reflected in the current stock price. This means that all publicly known information about the company, which clearly includes its historical price, will be reflected in the current stock price. Thus, stock price changes reflect the release of new information, changes in the market in general, or random movements around values ​​that reflect the existing set of information. Burton Malkiel, in his influential research in 1973, A Random Walk Down Wall Street, claims that stock prices can not be accurately predicted by looking at price history. As a result, Malkiel argues, stock prices are best illustrated by a statistical process called "random road" which means daily deviations from the central value are random and unpredictable. This led Malkiel to conclude that paying people's financial services to predict the market actually hurt, rather than help, net portfolio returns. A number of empirical tests support the notion that theory applies in general, since most portfolios managed by professional stock predictors do not outperform market returns after taking into account manager costs.
While efficient market hypotheses find an advantage among financial academics, the critics point to examples where actual market experience differs from the prediction-of-hypotesis uncertainty implies. Large industries have grown around the implication proposition that some analysts can predict stocks better than others; ironically it is not possible under the Efficient Market Hypothesis if the stock prediction industry does not offer something that its customers believe to be valuable.
Maps Stock market prediction
Prediction method
The prediction methodology is divided into three broad categories that can (and often) overlap. They are fundamental analysis, technical analysis (charting) and technological methods.
Fundamental analysis
Fundamental analysts are concerned about the company underlying the stock itself. They evaluate the company's past performance as well as its account credibility. Many performance ratios are made that help fundamental analysts by assessing stock validity, such as P/E ratio. Warren Buffett is probably the most famous of all Fundamental Analysts.
Fundamental analysis is built on the belief that human society needs capital to make progress and if the company is operating well, it must be given additional capital and generate a surge in stock prices. Fundamental analysis is widely used by fund managers because it is the most plausible, objective and made from publicly available information such as financial statement analysis.
Another meaning of fundamental analysis is beyond the bottom-up company analysis, this refers to a top-down analysis of the first analysis of the global economy, followed by state analysis and then sector analysis, and finally enterprise-level analysis.
Technical analysis
Analysts or technical mapmakers do not care about the fundamentals of the company. They are looking to determine the future price of a stock based solely on the (potential) trend of the past price (a form of time series analysis). Many patterns are used such as head and shoulders or cups and saucers. Along with patterns, techniques are used such as the exponential moving average (EMA). The wax stick pattern, believed to be first developed by Japanese rice traders, is currently widely used by technical analysts.
Data Mining Technology (e.g. ANN)
With the advent of digital computers, stock market predictions have since moved into the tech world. The most prominent technique involves the use of artificial neural networks (ANN) and Genetic Algorithms. Scholars find methods of chemotaxis optimization of bacteria can perform better than GA. ANN can be considered as a mathematical function estimator. The most common form of ANN used for stock market prediction is the advanced feed network utilizing inverse propagation of the error algorithm to update network weights. These networks are usually referred to as Backpropagation networks. Another form of ANN that is more appropriate for stock prediction is the repeated neural network (RNN) or time delay neural network (TDNN). Examples of RNN and TDNN are the Elman, Jordan, and Elman-Jordan networks. (See Elman and Jordan Networks)..
For stock prediction with ANN, there are usually two approaches taken to forecast different time horizons: independent and shared. An independent approach uses a single ANN for each time horizon, for example, 1 day, 2 days, or 5 days. The advantage of this approach is that network forecasting errors for one horizon will not impact errors for other horizons - because each time horizon is usually a unique issue. The shared approach, however, combines several time horizons together so that they are determined simultaneously. In this approach, forecasting errors for a single time horizon can share its faults with other horizons, which can degrade performance. There are also more parameters required for the combined model, which increases the risk of overfitting.
More recently, the majority of academic research groups studying ANN for inventory forecasting seem to use an independent ANN independent assemble method more often, with greater success. An ANN ensemble will use a low price and time slot to predict future lows, while other networks will use the highest value left to predict the highest value in the future. The predicted low and high predictions are then used to form a stop price to buy or sell. The output of each "low" and "high" network can also be incorporated into the final network which will also combine volumes, inter-market data or price statistics summaries, leading to the output of the final ensemble that will trigger purchases, sales, or market direction. change. The main finding with ANN and stock prediction is that the classification approach (vs functional approach) using output in form of buy (y = 1) and selling (y = -1) yields better predictive reliability than quantitative output such as low or high prices. This is explained by the fact that ANN can predict classes better than quantitative values ​​as in the function approach - because ANN occasionally learns more about noise in the input data.
Because NN requires training and can have a large parameter space, it is useful to modify the network structure for optimal predictive capabilities.
Internet-based data source for stock market predictions
Tobias Preis et al. introduced a method for identifying online precursors for stock market movements, using trading strategies based on search volume data provided by Google Trends. Their analysis of Google's search volume for 98 terms of various financial relevance, published in Scientific Report , shows that an increase in search volume for financially relevant search terms tends to precede major losses in financial markets. Of these provisions, three are significant at the 5% level (| z | & gt; 1.96). The best term in the negative direction is "debt", followed by "color".
In a study published in the Scientific Report in 2013, Helen Susannah Moat, Tobias Preis and colleagues pointed out the relationship between the change in the number of views of the English Wikipedia article relating to financial topics and the subsequent major stock market movements.
The use of Text Mining along with the Machine Learning algorithm has received more attention in recent years, with the use of textual content from the Internet as input to predict price changes in Shares and other financial markets.
The collective mood of Twitter messages has been attributed to the performance of the stock market. The study, however, has been criticized for its methodology.
Activities on stock message boards have been mined to predict asset returns. Corporate headlines from Yahoo! Finance and Google Finance are used as coverage in the text mining process, to forecast the stock price movement of the Dow Jones Industrial Average.
Complexity Science Application for stock market predictions
Using a new statistical analysis tool from complexity theory, researchers at the New England Complex Systems Institute (NECSI) conducted a study to predict the fall of the stock market. It has long been thought that market crashes are triggered by panic that may or may not be justified by external news. This study shows that it is the internal structure of the market, not an external crisis, which is mainly responsible for crashes. The number of different stocks moving up or down together is shown as an indicator of mimicry in the market, how many investors are looking at each other for clues. When mimicry is high, many stocks follow each other's movements - the main reason for panic to hold. It shows that a dramatic increase in market mimicry occurs for a full year before every market crash in the last 25 years, including the 2007-08 financial crisis.
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References
- Graham, B. Intelligent Investor HarperCollins; Rev Ed Edition, 2003.
- Lo, A.W. and Mackinlay, A.C. Non-Random Walker Wall Street 5th Ed. Princeton University Press, 2002.
- Azoff, E.M. Neural Network Time Series Forecasting Financial Markets John Wiley and Sons Ltd, 1994.
- Christoffersen, P.F. and F.X. Diebold. Return of financial assets, forecasting of change direction, and dynamics of volatility . Science Management, 2006. 52 (8): p.Ã, 1273-1287
Source of the article : Wikipedia
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