Stock market prediction
Stock market prediction is the act of trying to determine the future value of a company
The Efficient Markets Hypothesis and the random walk
The
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.[1]
Intrinsic value
Intrinsic value (true value) is the perceived or calculated value of a company, including tangible and intangible factors, using fundamental analysis. It's also frequently called fundamental value. It is used for comparison with the company's market value and finding out whether the company is undervalued on the stock market or not. When calculating it, the investor looks at both the qualitative and quantitative aspects of the business. It is ordinarily calculated by summing the discounted future income generated by the asset to obtain the present value.
Prediction methods
Prediction methodologies fall into three broad categories which can (and often do) overlap. They are fundamental analysis, technical analysis (charting) and machine learning.
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
What fundamental analysis in the stock market is trying to achieve, is finding out the true value of a stock, which then can be compared with the value it is being traded with on stock markets and therefore finding out whether the stock on the market is undervalued or not. Finding out the true value can be done by various methods with basically the same principle. The principle is that a company is worth all of its future profits added together. These future profits also have to be discounted to their present value. This principle goes along well with the theory that a business is all about profits and nothing else.
Contrary to technical analysis, fundamental analysis is thought of more as a long-term strategy.
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 usually less concerned with any of a company's fundamentals. They seek to determine possibilities of future stock price movement largely based on trends of the past price (a form of
Machine learning
With the advent of the
A 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.
Deep learning methods have been used to some extent. The Gated Three-Tower Transformer (GT3) is a transformer-based model designed to integrate numerical market data with textual information from social sources to enhance the accuracy of stock market predictions.[12]
Since NNs require training and can have a large parameter space; it is useful to optimize the network for optimal predictive ability. 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.[13]
Implementations using random forests and supervised statistical classification follow the same approach of predicting stock movement as a binary classification problem. Under this formulation, the sign of a future return is the label of the data, with forecasted returns being split between negative and non-negative, and the observable features used to feed the classification model can be lagged returns, the lagged sign of returns or any other lagged explanatory economic data.
The loss function used to evaluate the quality of the classification model can be either the accuracy of the prediction (defined as the number of times that the classifier predicted the correct sign divided by the total number of predictions made)[10] or the total return of a trading strategy that bought when the classifier predicted a positive sign and sold when the classifier predicted a negative return.[11] As standard in all statistical classification problems, it is important to split the data available into training and test samples and only evaluate the model based on the test sample results as it is generally considered more trustworthy than evidence based on in-sample performance, which can be more sensitive to outliers and data mining.[14] Out-of-sample forecasts also better reflect the information available to the forecaster in "real time".
Data sources for 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.[15] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports,[16] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets.[17][18][15][19][20][21][22][23] 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,[24] 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.[25]
The use of Text Mining together with Machine Learning algorithms received more attention in the last years,[26] 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.[27] The study, however, has been criticized for its methodology.
The activity in stock message boards has been mined in order to predict asset returns.[28] 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.[29]
References
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- ^ Mislinski, Jill (3 March 2020). "Market Cap to GDP: An Updated Look at the Buffett Valuation Indicator". www.advisorperspectives.com. Archived from the original on 14 March 2020.
it is probably the best single measure of where valuations stand at any given moment
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- ^ Osler, Karen (July 2000). "Support for Resistance: Technical Analysis and Intraday Exchange Rates," FRBNY Economic Policy Review (abstract and paper here).
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- ^ Thawornwong, S.; Enke, D. Forecasting Stock Returns with Artificial Neural Networks, Chap. 3. In: Neural Networks in Business Forecasting, Editor: Zhang, G.P. IRM Press, 2004.
- ^ "Glossary:In-sample vs. out-of-sample forecasts". ec.europa.eu. Retrieved 2024-03-22.
- ^ S2CID 167357427. Retrieved August 10, 2013.
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- ^ Bernhard Warner (April 25, 2013). "'Big Data' Researchers Turn to Google to Beat the Markets". Bloomberg Businessweek. Archived from the original on April 26, 2013. Retrieved August 10, 2013.
- ^ Hamish McRae (April 28, 2013). "Hamish McRae: Need a valuable handle on investor sentiment? Google it". The Independent. Archived from the original on 2022-05-25. Retrieved August 10, 2013.
- ^ Richard Waters (April 25, 2013). "Google search proves to be new word in stock market prediction". Financial Times. Retrieved August 10, 2013.
- ^ David Leinweber (April 26, 2013). "Big Data Gets Bigger: Now Google Trends Can Predict The Market". Forbes. Retrieved August 10, 2013.
- ^ Jason Palmer (April 25, 2013). "Google searches predict market moves". BBC. Retrieved August 9, 2013.
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: CS1 maint: multiple names: authors list (link - ^ "Wikipedia's crystal ball". Financial Times. May 10, 2013. Retrieved August 10, 2013.
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- ^ Bollen, Johan; Huina, Mao; Zeng, Xiao-Jun. "Twitter mood predicts the stock market". Cornell University. October 14, 2010. Retrieved November 7, 2013
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- ^ Beckmann, M. (January 24, 2017). Doctoral Thesis: Stock Price Change Prediction Using News Text Mining. COPPE/Federal University of Rio de Janeiro