Technical Analysis Applications in the Financial Markets - Essay Example

Technical Analysis Applications in the Financial Markets Teacher Affiliated Technical Analysis Applications in the Financial Markets Behavioral Finance People make various financial decisions in their daily lives. One of them could be an investment in stock market. The purpose is to generate profit from the investment. There are theories and formulas to help investors in buying and selling stocks so they can generate profit. The formulas and theories claim that the market is rational and logical, and the investor can buy or sell stocks at fair market value. However, if this were the case, one would not see massive fluctuations of stock prices or crash of a stock market. It raises question: perhaps the conventional financial theories, such as Capital Asset Pricing Model (CAPM), Efficient Market Hypothesis (EMH) cannot adequately capture all aspects related to stock market trading (Ricciardi & Simon, 2000). There must be other causes and effect relationships those can explain the erratic behavior of investors and market. Behavioral finance combines human psychology and economic theories to explain the various erratic behavior of the market. Anchoring. People use irrelevant information as a reference point in order to estimate or evaluate some unknown value and information. For example, anchoring recent high price that a stock has achieved but fallen considerably in a very short period, investors consider as an opportunity to buy that stock at a discount price. Investors do not believe that the decline in the value of the asset has taken place due to changes in some underlying fundamentals (Shefrin, 2000). Mental accounting An individual allocates different levels of utility (expectation) for each asset group. It affects the individual’s behavior and the consumption decision. For example, one divides investment in safe and risky portfolios but does not consider that net wealth will be the same if the individual had held one larger portfolio (Shefrin, 2000). Confirmation bias People pay more attention to information that backs their opinion without rationalizing rest. After mentally accepting a floating rumor, one tends to find information to support it without going deep into the story. It may be a rumor about a hot stock, or about a company is on the verge of declaring bankruptcy. As a result, the investor will sustain losses either buying a bad stock at a high price or selling a good stock at a low price. The above is a good example why markets do not always behave rationally (Park et al., 2011). Hindsight Bias Individuals often believe that past event was predictable whereas, in fact, it could not have been predicted. This attitude may become a cause for potentially dangerous mindsets, which is defined as overconfidence. It causes investors or traders making a wrong judgment. Gambler Fallacy A gambler believes that if something had happened in the past would happen again. In stock investment, investors decide to sell a security that has fallen multiple sessions or hold on to a stock that has gone up several consecutive trading sessions. Herd Behavior It is the tendency of people to mimic the actions of a larger group while most of them would not have made the same choice individually. One of the infamous events of herd behavior is bursting of the internet bubble. Overreaction and Availability Bias Participants in the stock market often overreact to new information causing the stock price go high or low. The price change is not a permanent trend, usually sudden and sizable that erodes over time. If one can avoid this bias, will understand the real significance of information and act accordingly. Conventional financial models perhaps can determine the actual value of an asset; however, it is also cannot be ignored that market sentiment causes the real value of an asset to diverge. It is the market sentiment that creates financial bubbles and crises. Market sentiment is described as emotions and feelings of its participants. The discussion in the previous paragraphs illustrates some of the attributes of the market sentiment. It is obvious that we cannot build up successful financial theories without considering the sentiment of the market participants. Therefore, economic theories need to be combined with the behavioral and cognitive psychological theories. Behavioral finance combines these theories. That is why; use of behavioral finance is essential in making a financial decision. Recent Development in the area of Behavioral Finance The discussion in the previous section reviewed the discovery of Amos Tversky and Daniel Kahneman. In 1974, they acknowledged people used three heuristics when making a decision under uncertainty. They are representativeness, availability, anchoring and adjustment (Tversky & Kahneman, 1974). Thaler (1980) added that consumers act in a manner that is inconsistent with economic theory. The combination of the above discovery and observations created a new field of study that is called behavioral finance. Since its birth, the new field started becoming popular and researchers kept continuing the study on how phycology affects finance. Human desires, goals, and motivation brings progress; however, overconfidence, perceptual illusions, over-reliance on the rule of thumb, emotion, and greed create bias and errors (Shefrin). Nevertheless, all of the attributes mentioned above are parts of human psychology. Therefore, psychology started becoming a constituent part of financial theories. We discussed that herd behavior was a part psychology (cause) and infamous dot com, and many other issues (effect) were the result of herd behavior. Banerjee (1992) developed a simple model of herd behavior to study similar cause and effect relationships. It is claimed that over the past century, stock returns are a few percent higher than that of a government bond. Benarjee and Tahler (1995) explained this claim from the viewpoint of investors’ loss aversion behavior. Grinblatt, Titman and Wermers (1995) categorized the equity premium phenomenon to momentum strategies and herding. Studies show people are manipulated by informational cascade; they abandon their information signals in favor of an interpretation of earlier people’s action (Easley, 2010). It is called informational cascade cause and effect relationship. In 1998, Bikchandani, Hirshleifer and Welch (1998) explained that informational cascade is the result of observational learning, and it can explain the phenomenon like stock market crashes. Daniel (2002) explains that investors often rely on masses and more experienced players’ decisions; thus become prone to systematic errors. Ritter (2002), based on the study of US and Japanese markets, concluded that systematic errors are caused by investors’ excessive confidence that makes them blind over market rationality and efficiency. He asserted that such behavior created bubbles in stock prices (Ritter, 2002). Hoje and Man (2008) conducted research and established that sometimes investors’ preference for stocks depended not on rationality but the protest against socially irresponsible companies. The above brief review refers to different theoretical works of last two decades showing salient aspects of human behavior in asset pricing model. Trend Analysis of S&P 500 and FTSE 100 Indices The assignment has selected S&P 500 and FTSE 100 indices. Figure 1 and 2 respectively illustrate closing prices of S&P 500 and FTSE 100 for the monthly interval from Jan 2004 to Dec 2014. On Jan 2, 2004, the S&P 500 index started with the value of 1131 and until Oct 01, 2007 made a steady linear rise to the value of 1549. Then, by Feb 02, 2009, it sharply dropped to the value of 735, which equals to 53% decline in 16 months. Until Apr 01, 2010, the index rose sharply to the value of 1187 contributing to 61% growth in 14 months. During the period, from Apr 01, 2010 to Dec 1, 2014, the index steadily rose with some bums to the value of 2059 contributing to 73% increase in 57 months. The overall trend is attributed to linear that contributed to 82% increase in 132 months. Figure 1. Trend of S&P 500 index The FTSE 100 started with the value of 4391 and by Oct 01, 2007 steadily rose to the value of 6722 contributing to 35% increase in 45 months. Then by Feb 02, 2009, it sharply dropped to the value of 3830 contributing to 43% decline in 16 months. Until Apr 01, 2010, it sharply rose to the value of 5553 contributing to 31% increase in 14 months. During the period, from Apr 01, 2010 to Dec 1, 2014, the index rose slowly with some fluctuations in the value of 6679 contributing to 17% increase in 57 months. The overall trend is attributed to linear that contributed to 52% increase in 132 months. Figure 2. Trend of FTSE 100 index Table 1 illustrates the trend comparison between these indices. Even though, the values are different but it demonstrate the same qualitative character of two indexes. Both markets over a particular period reacted in the same way. Table 1. Trend comparison between S&P 500 and FTSE 100 Study of the Crossover Strategy in Stock Trading Over a period, stock prices go up and down. The concept that was generated from this observation implies that one can make a higher profit by selling stock when it is going down and buying it when it is going up. The concept can be verified using the moving average technique along with the historical data of stock prices. This process is called crossover strategy in stock trading. Figures 3 illustrates a typical crossover diagram. It has three elements: prices of the asset over a period, two moving average lines; the red line is created using smaller time interval and the black line using bigger time interval. Figure 3 also shows that the two lines cross each other at various points and creates two domains: “Buy” domain when the prices are going up, and “Sell” domain when the prices are going down. This phenomenon is defined as crossover, and it gives option when to buy and sell stocks so to maximize profit from an investment. Figure 3. Typical crossover diagram (“Moving Average”, n.d.) This assignment verified the above concept using the following options: daily values of S&P 500 index and exponential moving average lines constructed using 10 and 150-day time intervals (Option 1); daily values of S&P 500 index and exponential moving average lines created using 10 and 100-day time intervals (Option 2); daily values of S&P 500 index and exponential moving average lines created using 10 and 50-day time intervals (Option 3); daily values of FTSE 100 index and exponential moving average lines constructed using 10 and 150-day time intervals (Option 4); monthly values of S&P 500 and FTSE 100 indices and exponential moving average lines created using 2 and 6 month time intervals (Options 5 and 6). The “buy” and “sell” decisions in crossover strategy depend on how the slow moving average line crosses the fast line. When slow line crosses the fast from the top, it creates the “Sell” domain giving signal to sell the stock; similarly, when the slow line crosses the fast from the bottom, it creates the “Buy” domain giving signal to buy the stock. In the first case, the slow line acts as a resistance to the stock price so it cannot go up. In the second case, it serves as a foundation so the stock price cannot go down. This assignment uses this concept for making “Buy” and “Sell” decisions. The assignment is using values of two market indices: S&P 500 and FTSE 100 for years from 2006 to 2014. We also use the following assumptions to conduct the authenticity of the crossover strategy: if one buys the index at the beginning of 2006 and holds it until the end of 2014, it will generate daily return that can be calculated using the formula Ri =(Index value i-1 – Index value i ) /; where i is the current day and (i-1) is the previous day. At the end of the period, all returns can be summed to find cumulative and average yearly return; the cumulative return will be the sum of all positive and negative returns caused by market fluctuation; however, if one can get rid of the index when it goes downward then the average daily and yearly returns must be higher than that of the value of return obtained by holding the index for the entire period. The results of calculations of above-described options are presented in the attached Excel worksheet. Presented below is calculation method used for these options. Calculation Method The option 1 used 2,265 daily values of S&P index and two EMA lines calculated using 10-day and 150-day intervals. Daily returns were calculated using the formula described in the previous section. The buy and sell signals were generated using the crossover principles described in the previous sections. It is assumed that during the “Sell” signal, we do not have the index and during the “Buy” signal, we have the index. Therefore, for “Sell” option, we used the return of the concurrent day, and for “Buy” option of the next day. The returns are presented in the Daily return column of the Excel sheet. For all options, the cumulative returns of indices were calculated starting from the day of lagging indicator of slow EMA. The options 1, 2 and 3 use different time intervals for slow EMA. The option 4 uses 2,353 values of FTSE 100 index derived using daily interval. The two EMAs are calculated using 10 and 150-day time intervals. The options 5 and 6 respectively use monthly values of S&P 500 and FTSE 100 indices. In both cases, EMAs were calculated using 2-month and 6-month time intervals. Return Analysis From an investor viewpoint, the crossover strategy should provide an option so the investor can make a higher profit by withdrawing investment when the market is on a downturn. It implies that in all options we should see that the average yearly returns generated using crossover strategy when the index is in the “Buy” domain are higher than that of returns generated without the use of crossover strategy. Because everything else held constant the total return without the crossover strategy uses returns generated when the stocks go up as well as own. Table 2 presents the results of all returns obtained from the analysis. In option 1, average yearly return without crossover strategy is higher than the return generated using crossover strategy when the index was in the “Buy” domain. Table 2. Analysis of returns However, it changes (Option 2 and Option 3) with the reduction in a time interval of slow EMA. The results imply that different lag indicators provide different number of crossover points. The smaller value of slow EMA keeping the value of the fast EMA fixed provides more crossover points giving investor opportunity for generating higher returns. Observations using daily interval values of S&P index demonstrated the authenticity of crossover strategy; however, observation (Option 4) using daily interval values of FTSE 100 index did not satisfy authenticity of crossover strategy. The average yearly return generated without the use of crossover strategy is 3.31%; however, with the use of crossover strategy when the index is in the “Buy” domain the average yearly return is negative 2.15% and when it is in the “Sell” domain, the return is 3.58%. When we used values of FTSE 100 from monthly interval, returns became more ridiculous; the average yearly return without crossover became negative 5.58%; with crossover strategy when the index was in the “Buy” domain, the average yearly return became negative 3.45%, and negative 41.40% when the index was in the “Sell” domain. The character of returns of S&P index with monthly interval mimicked the character of the daily interval. However, returns were more realistic with the daily interval data. The analysis shows that the crossover strategy worked for the US market but did not work for the UK market. Conclusion The study of crossover strategy in stock trading shows that it is an excellent tool for investors and traders; however, it is not perfect. The crossover strategy clearly distinguishes “Buy” and “Sell” domains on the plot of historical prices, but it does not consider the fluctuation of stock prices inside the domain. For example, in option 4 (FTSE 100, 10, 150) the average yearly return without the use of crossover strategy is 3.31% but the same is negative 2.15% when the index is in the “Buy domain, and 3.58% when the index is in the “Sell” domain. One can conclude that the negative returns generated by the “Buy” domain and the positive return generated by the “Sell” domain were caused by the fluctuation of values inside the domain. The crossover strategy considers a linear increase and decrease in prices when the index respectively is inside “Buy” or “Sell” domain; however, in reality it is not linear. Further studies are needed to develop method on how to consider the nonlinear behavior of stock prices within the “Buy” and “Sell” domains. References Banerjee, A. (1992). A Simple Model of Herd Behavior. The Quarterly Journal of Economics, 107(3), 797-817. Benarjee, A. & Thaler, R.J. (1995). Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics, 110(1), 73-92. Bikas, E & Dubinskas, P. (2013). Behavioral finance: The emergence and development trend. Social and Behavioral Science, Volume 82, 870-876. Bikhnchndani, S., Hirshleifer, D. & Welch, I. (1998). Learning from the behavior of others: Conformity, fads, and informational cascades. The Journal of Economic Perspectives, 123(3), 151-170. Daniel, K, Hirshleifer, D & Teoh, S. (2002). Investor psychology in the capital market: Evidence and Policy Implications. Journal of Monetary Economics, 49, 139-209. Easley, D & Kleinberg, J. (2010). Network, crowds and Market Retrieved from http://www.cs.cornell.edu/home/kleinber/networks-book/ Grinblatt, M., Titman, S. & Wermes, R. (1995). Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior. The American Review, 85 (5), 1088-1105. Hoje, J., & Man., D.K. (2008). Recent development of behavioral finance. Retrieved from http://www.freepatentsonline.com/article/International-Journal-Business-Research/190463116.html Kuo, J. (2013). Real world application of behavioral science. Retrieved from http://www.nerdwallet.com/blog/investing/2013/realworld-applications-behavioral-finance/ Moving Average Crossover Studies. (n.d). Retrieved from http://www.gettingtechnical.com/wordpress/?p=1375 Park, J.H., Konana, P., Gu Bin, Kumar, A., Ragunathan, R. (2011). Confirmation bias, overconfidence, and investment performance: Evidence from stock message boards. Retrieved from https://www.misrc.umn.edu/wise/papers/p1-3.pdf Ricciardi, V & Simon, H. K. (2000). What is Behavioral Finance?. Business, Education & Technology Journal, Vol. 2, No. 2, pp. 1-9. Ritter, R.J. (2002). Behavioural finance. Retrieved from http://bear.warrington.ufl.edu/ritter/publ_papers/Behavioral%20Finance.pdf Shefrin, H. (2002). Beyond greed and fear. London: Oxford University Press Sewell, M. (2010). Behavioural finance. Retrieved from http://www.behaviouralfinance.net/behavioural-finance.pdf Thaler, R. (1980). Toward a Positive Theory of Consumer Choice. Journal of Economic Behavior & Organization, 1(1), 39-60. Tahler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39-60. Tversky A., & Kahneman D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124 -1131. Read More