Stock Market Prediction Using Elliot Wave Theory and Classification

Stock Market Prediction Using Elliot Wave Theory and Classification

Saeed Tabar, Sushil Sharma, David Volkman
Copyright: © 2021 |Pages: 20
DOI: 10.4018/IJBAN.2021010101
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Abstract

The area of stock market prediction has attracted a great deal of attention during the past decade especially after multiple market crashes. By analyzing market price fluctuations, we can achieve valuable insight regarding future trends. This research proposes a novel method for prediction using pattern analysis and classification. For the first part of the research, a trend analysis algorithm, Elliot wave theory, is used to classify price patterns for DJIA, S&P500, and NASDAQ into three categories: LONG, SHORT, and HOLD. After labeling patterns, classification learning algorithms including decision tree, naïve Bayes, and support vector machine (SVM) are used to learn from the patterns and make a prediction for the future. The algorithm is implemented during the market crashes of May 2010 and August 2015, and the obtained results show that it correctly identifies the market volatility by issuing HOLD and SHORT signals during those crashes.
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Introduction

The fundamental question about the stock market is whether the market is predictable, unpredictable, both, or none of them. One of the famous theories of the stock market is the Efficient Market Hypothesis (EMH) which was first proposed by E.F. FAMA in 1970 (Fama E., 1970). It states that securities are fairly priced and current market prices fully reflect the information about the value of the stocks. Price fluctuations are the result of the tendency of the market to adjust prices to their fair values. EMH also states that changes in stock prices are indicative of the release of new information in the market. Large investors immediately react to the fresh news by adjusting prices to their fair values. In fact, investors identify securities that are undervalued but predicted to be profitable. These securities are typically priced below the actual value of the security. Although securities are priced fairly, the price fluctuations are random and unpredictable. Therefore, the stock price is said to follow a random walk (Fama E. F., 1965). One conclusion about EMH is that market prices are completely random and unpredictable, and nobody can beat the market (Mc Donald, 2002). Since stocks are traded at their fair value, it is impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. Supporters of the EMH believe that it is pointless to search for undervalued stocks or to try to predict trends in the market through either fundamental or technical analysis. The only way that an investor can obtain higher returns is by chance or by making riskier investments (Bergen, 2004). This camp believes in the rationality of the stock market.

There is another party in the stock market which does not believe in EMH. They argue that the financial crises of the 1930s and the early 2000s prove that the stock market acts irrationally (Fox, 2009) (Nocera, 2009) (Lowenstein, 2009). This group believes that the stock market is predictable only if most effective psychological factors on human behavior such as news, economic and political changes, as well as cognitive factors and biases are identified and analyzed carefully. They state that the assumption that any price fluctuations will ultimately return to its fair value is not correct all the time. Sometimes short deviations from the fair value may become bigger and make large waves that will change the direction of the entire market. To study these inefficiencies in the market, researchers turned their attention to individuals’ cognitive biases and began to study human behaviors. This branch of finance where finance and psychology cohere is referred to as behavioral finance. It was first introduced by Amos Tversky and Daniel Kahneman (Kahneman D. T. A., 1979) (Kahneman D. T. A., 1992). By analyzing human behavior and also monitoring human actions and reactions in groups some of the causes of inefficiencies in the market can be explained. The underlying assumption of behavioral finance is that the information structure and the characteristics of market participants influence individuals’ investment decisions. The human brain often processes information using shortcuts and emotional filters. These emotional filters make investors act irrationally which leads leading to predictable errors in their forecasts (Kolb, 2010). According to behavioral finance, most people do not act rationally when they are under stress. People prefer to avoid pain than to achieve gain (Rebell B., 2016). That is one of the important reasons why people tend to sell winning stocks too early to avoid any probable loss, and at the same time, people would like to hold losing stocks too long with the hope to reduce their losses. Prospect theory (Kahneman D. T. A., 1979) (Kahneman D. T. A., 1992) suggests that investors are more risk-averse when dealing with profitable investment and more risk-seeking with regards to losses. Also, due to some cognitive biases such as herding, investors try to adjust their investment strategies with popular trends without regard to the causes behind the trend (Ramsey M.Raafat, 2009).

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