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 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.

Contents

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. [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 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. He uses the overall market capitalization-to-GDP ratio to indicate the relative value of the stock market in general, hence this ratio has become known as the "Buffett indicator". [2] [3] [4]

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 analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, [5] and research on whether technical analysis offers any benefit has produced mixed results. [6] [7] [8]

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 time series analysis). Numerous patterns are employed such as the head and shoulders or cup and saucer. Alongside the patterns, techniques are used such as the exponential moving average (EMA), oscillators, support and resistance levels or momentum and volume indicators. Candle stick patterns, believed to have been first developed by Japanese rice merchants, are nowadays widely used by technical analysts. Technical analysis is rather used for short-term strategies, than the long-term ones. And therefore, it is far more prevalent in commodities and forex markets where traders focus on short-term price movements. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends and lastly that history (of prices) tends to repeat itself which is mainly because of the market psychology.

Machine learning

With the advent of the digital computer, stock market prediction has since moved into the technological realm. Several research papers have been published with implementations of machine learning techniques to predict stock markets including, but not limited to, artificial neural networks [9] (ANNs), random forests [10] and supervised statistical classification. [11]

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.

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]

See also

Related Research Articles

Fundamental analysis, in accounting and finance, is the analysis of a business's financial statements ; health; competitors and markets. It also considers the overall state of the economy and factors including interest rates, production, earnings, employment, GDP, housing, manufacturing and management. There are two basic approaches that can be used: bottom up analysis and top down analysis. These terms are used to distinguish such analysis from other types of investment analysis, such as quantitative and technical.

In finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. As a type of active management, it stands in contradiction to much of modern portfolio theory. The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results. It is distinguished from fundamental analysis, which considers a company's financial statements, health, and the overall state of the market and economy.

<span class="mw-page-title-main">Efficient-market hypothesis</span> Economic theory that asset prices fully reflect all available information

The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.

<span class="mw-page-title-main">Prediction</span> Statement about a future event

A prediction or forecast is a statement about a future event or about future data. Predictions are often, but not always, based upon experience or knowledge of forecasters. There is no universal agreement about the exact difference between "prediction" and "estimation"; different authors and disciplines ascribe different connotations.

Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and assessment of its accuracy. Usage can vary between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.

Prediction markets, also known as betting markets, information markets, decision markets, idea futures or event derivatives, are open markets that enable the prediction of specific outcomes using financial incentives. They are exchange-traded markets established for trading bets in the outcome of various events. The market prices can indicate what the crowd thinks the probability of the event is. A typical prediction market contract is set up to trade between 0 and 100%. The most common form of a prediction market is a binary option market, which will expire at the price of 0 or 100%. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.

Stock valuation is the method of calculating theoretical values of companies and their stocks. The main use of these methods is to predict future market prices, or more generally, potential market prices, and thus to profit from price movement – stocks that are judged undervalued are bought, while stocks that are judged overvalued are sold, in the expectation that undervalued stocks will overall rise in value, while overvalued stocks will generally decrease in value. A target price is a price at which an analyst believes a stock to be fairly valued relative to its projected and historical earnings.

Economic forecasting is the process of making predictions about the economy. Forecasts can be carried out at a high level of aggregation—for example for GDP, inflation, unemployment or the fiscal deficit—or at a more disaggregated level, for specific sectors of the economy or even specific firms. Economic forecasting is a measure to find out the future prosperity of a pattern of investment and is the key activity in economic analysis. Many institutions engage in economic forecasting: national governments, banks and central banks, consultants and private sector entities such as think-tanks, companies and international organizations such as the International Monetary Fund, World Bank and the OECD. A broad range of forecasts are collected and compiled by "Consensus Economics". Some forecasts are produced annually, but many are updated more frequently.

Market timing is the strategy of making buying or selling decisions of financial assets by attempting to predict future market price movements. The prediction may be based on an outlook of market or economic conditions resulting from technical or fundamental analysis. This is an investment strategy based on the outlook for an aggregate market rather than for a particular financial asset.

<span class="mw-page-title-main">Granger causality</span> Statistical hypothesis test for forecasting

The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969. Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. Since the question of "true causality" is deeply philosophical, and because of the post hoc ergo propter hoc fallacy of assuming that one thing preceding another can be used as a proof of causation, econometricians assert that the Granger test finds only "predictive causality". Using the term "causality" alone is a misnomer, as Granger-causality is better described as "precedence", or, as Granger himself later claimed in 1977, "temporally related". Rather than testing whether Xcauses Y, the Granger causality tests whether X forecastsY.

<span class="mw-page-title-main">Stock trader</span> Person or company involved in trading equity securities

A stock trader or equity trader or share trader, also called a stock investor, is a person or company involved in trading equity securities and attempting to profit from the purchase and sale of those securities. Stock traders may be an investor, agent, hedger, arbitrageur, speculator, or stockbroker. Such equity trading in large publicly traded companies may be through a stock exchange. Stock shares in smaller public companies may be bought and sold in over-the-counter (OTC) markets or in some instances in equity crowdfunding platforms.

<span class="mw-page-title-main">Market sentiment</span> General attitude of investors to market price development

Market sentiment, also known as investor attention, is the general prevailing attitude of investors as to anticipated price development in a market. This attitude is the accumulation of a variety of fundamental and technical factors, including price history, economic reports, seasonal factors, and national and world events. If investors expect upward price movement in the stock market, the sentiment is said to be bullish. On the contrary, if the market sentiment is bearish, most investors expect downward price movement. Market participants who maintain a static sentiment, regardless of market conditions, are described as permabulls and permabears respectively. Market sentiment is usually considered as a contrarian indicator: what most people expect is a good thing to bet against. Market sentiment is used because it is believed to be a good predictor of market moves, especially when it is more extreme. Very bearish sentiment is usually followed by the market going up more than normal, and vice versa. A bull market refers to a sustained period of either realized or expected price rises, whereas a bear market is used to describe when an index or stock has fallen 20% or more from a recent high for a sustained length of time.

Google Trends is a website by Google that analyzes the popularity of top search queries in Google Search across various regions and languages. The website uses graphs to compare the search volume of different queries over time.

There are several concepts of efficiency for a financial market. The most widely discussed is informational or price efficiency, which is a measure of how quickly and completely the price of a single asset reflects available information about the asset's value. Other concepts include functional/operational efficiency, which is inversely related to the costs that investors bear for making transactions, and allocative efficiency, which is a measure of how far a market channels funds from ultimate lenders to ultimate borrowers in such a way that the funds are used in the most productive manner.

A financial forecast is an estimate of future financial outcomes for a company or project, usually applied in budgeting, capital budgeting and / or valuation. Depending on context, the term may also refer to listed company (quarterly) earnings guidance. For a country or economy, see Economic forecast.

Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioral biases in conjunction with valuation.

Norgate Data, based in Australia, was founded in 1992. Norgate provides end-of-day price data for stock markets in Australia, Canada and USA, worldwide futures price data, cash commodifty and foreign currency data. Such data can be charted through Technical Analysis charting packages such as MetaStock and AmiBroker, and accessed in programming languages such as Python.

Demand forecasting, also known as demand planning and sales forecasting (DP&SF), involves the prediction of the quantity of goods and services that will be demanded by consumers or business customers at a future point in time. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods:

Energy forecasting includes forecasting demand (load) and price of electricity, fossil fuels and renewable energy sources. Forecasting can be both expected price value and probabilistic forecasting.

Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on using mathematical, statistical and machine learning models to predict electricity prices in the future. Over the last 30 years electricity price forecasts have become a fundamental input to energy companies’ decision-making mechanisms at the corporate level.

References

  1. ↑ Martin J. Gruber (1996). "Another Puzzle: The Growth in Actively Managed Mutual Funds". The Journal of Finance. 51 (3): 783–810. doi:10.1111/j.1540-6261.1996.tb02707.x.
  2. ↑ "Buffett Indicator: Where Are We with Market Valuations?".
  3. ↑ 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
  4. ↑ "Warren Buffett On The Stock Market What's in the future for investors--another roaring bull market or more upset stomach? Amazingly, the answer may come down to three simple factors. Here, the world's most celebrated investor talks about what really makes the market tick--and whether that ticking should make you nervous. - December 10, 2001". archive.fortune.com. Fortune Magazine. 2001. Archived from the original on 8 March 2020.
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  12. ↑ Chen, Jia; Chen, Tao; Shen, Mengqi; Shi, Yunhai; Wang, Dongjing; Zhang, Xin (2022-09-01). "Gated three-tower transformer for text-driven stock market prediction". Multimedia Tools and Applications. 81 (21): 30093–30119. doi:10.1007/s11042-022-11908-1. ISSN   1573-7721.
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  14. ↑ "Glossary:In-sample vs. out-of-sample forecasts". ec.europa.eu. Retrieved 2024-03-22.
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  24. ↑ Helen Susannah Moat, Chester Curme, Adam Avakian, Dror Y. Kenett, H. Eugene Stanley and Tobias Preis (2013). "Quantifying Wikipedia Usage Patterns Before Stock Market Moves". Scientific Reports . 3: 1801. Bibcode:2013NatSR...3.1801M. doi:10.1038/srep01801. PMC   3647164 .{{cite journal}}: CS1 maint: multiple names: authors list (link)
  25. ↑ "Wikipedia's crystal ball". Financial Times . May 10, 2013. Retrieved August 10, 2013.
  26. ↑ Khadjeh Nassirtoussi, Arman; Aghabozorgi, Saeed; Ying Wah, Teh; Ngo, David Chek Ling (2014-11-15). "Text mining for market prediction: A systematic review". Expert Systems with Applications. 41 (16): 7653–7670. doi:10.1016/j.eswa.2014.06.009.
  27. ↑ Bollen, Johan; Huina, Mao; Zeng, Xiao-Jun. "Twitter mood predicts the stock market". Cornell University. October 14, 2010. Retrieved November 7, 2013
  28. ↑ Ramiro H. Gálvez; Agustín Gravano (2017). "Assessing the usefulness of online message board mining in automatic stock prediction systems". Journal of Computational Science. 19: 1877–7503. doi:10.1016/j.jocs.2017.01.001. hdl: 11336/60065 .
  29. ↑ Beckmann, M. (January 24, 2017). Doctoral Thesis: Stock Price Change Prediction Using News Text Mining. COPPE/Federal University of Rio de Janeiro
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