Theoretical background
This section gives a summary of the concepts that will be used during this paper. Firstly, the general concept of trading strategy analysis is discussed. Afterwards, more in-depth information on the concept of technical strategy analysis is given. Since this is an empirical research, several technical trading strategies will be used for analysis. The most common groups of technical analysis are discussed on both theoretical background and earlier findings. Lastly, different types of pricing models are elaborated upon.
2.1 General concept of trading strategies
Trading strategies that appear to “beat the market” go back to the beginning of trading in financial assets. It was believed that returning pattern in stock returns could lead to “abnormal” profits to trading strategies. Ever since, there has been an increasing interest in the predictability of asset returns based on their past history or fundamental values. Many of the techniques used today have been utilized for over 60 years. These techniques for discovering hidden relations in stock returns can range from extremely simple to quite elaborate (Brock, Lakonishok, & LeBaron, 1992). Most of these trading strategies are based on the concept of an anomaly. This is a term describing the phenomenon when the there is a structural, replicable pattern, that cannot be explained in the framework of existing financial theory, but can be economically. An anomaly provides evidence that a given assumption or model does not hold in practice. In reality it is often the case that trading restrictions or trading fees eliminate the possibility of making money from possible anomalies. If no money can be made from it, the anomaly is not anomalous after all. Overall, anomalies often occur with respect to asset pricing models, in particular the capital asset pricing model (CAPM). Although the CAPM was derived by using innovative assumptions and theories, it often does a poor job in predicting stock returns. The numerous market anomalies that were observed after the formation of the CAPM helped form the basis for those wishing to disprove the model. Although the model may not hold up in empirical and practical tests, that is not to say that the model does not hold some utility.
Based on the possibility of making abnormal returns, investors have developed trading strategies over time, which are supposed to profit from this mispricing in the markets. In general, there are five major styles of equity trading: scalping, momentum trading, technical trading, fundamental trading and swing trading (Van Bergen, 2016). For a detailed breakdown of these types of traders and their description, have a look at appendix A. When looking at academic literature, most articles focused on strategy analysis on either fundamental or technical analyses. The focus of this research lies within technical analysis, but in order to give a complete view of trading strategies, fundamental analysis is shortly discussed below.
Investors who focus on fundamental strategies, trade stocks based on fundamental analysis, which examines things like corporate events such as actual or expected earnings reports, stock splits, reorganizations or acquisitions. Some of the most common financial data used in this type of analysis is earnings per share, revenue and cash flow. Two famous anomalies found on a fundamental basis, are the SMB (small minus big) and HML (high minus low) factors, found by Fama & French in 1996. These anomalies focus on size and book-to-market (BM) ratios respectively, in which it is anticipated that smaller companies are more profitable relative to bigger companies and that companies with high BM ratios are more profitable compared to low ratios. These anomalies have become so well known, that they are now incorporated in the Fama & French three-factor model, which is an addition to the CAPM. More on this is discussed in section 2.4. pricing models.
2.2 Technical strategy analysis
Technical analysis is considered by many to be the original form of investment analysis, dating back to the 1800s (Brock, Lakonishok, & LeBaron, 1992). In general, technical analysis studies the historical price patterns or trends or any other clues that are indicative of future price movements (Chong & Ng, 2008). One of the greatest gulfs between academic finance and industry practice is the separation that exists between technical analysts and their academic critics. In contrast to fundamental analysis, which was quick to be adopted by the scholars of modern quantitative finance, technical analysis has been an orphan from the very start (Lo, Mamaysky, & Wang, 2000).
However, several academic studies suggest that despite its jargon and methods, technical analysis may well be an effective means for extracting useful information from market prices. The attitude of academics towards technical analysis, until recently, is well described by Malkiel (1981): “Obviously, I am biased against the chartist. This is not only a personal predilection, but a professional one as well. Technical analysis is anathema to the academic world. We love to pick on it. Our bullying tactics are prompted by two considerations: (1) the method is patently false; and (2) it’s easy to pick on. And while it may seem a bit unfair to pick on such a sorry target, just remember: it is your money we are trying to save”. Nonetheless, technical analysis has been enjoying a renaissance on Wall Street. All major brokerage firms publish technical commentary on the market and individual securities, and many of the newsletters published by various “experts” are based on technical analysis.
An import difference between technical analysis and quantitative finance is that technical analysis is primarily visual, whereas quantitative finance is primarily algebraic and numerical.
Therefore, technical analysis employs the tools of geometry and pattern recognition, and quantitative finance employs the tools of mathematical analysis and probability and statistics. In the wake of recent breakthroughs in financial engineering, computer technology, and numerical algorithms, it is no wonder that quantitative finance has overtaken technical analysis in popularity-the principles of portfolio optimization are far easier to program into a computer than the basic tenets of technical analysis. Nevertheless, technical analysis has survived through the years, perhaps because its visual mode of analysis is more conducive to human cognition, and because pattern recognition is one of the few repetitive activities for which computers do not have an absolute advantage (Lo, Mamaysky, & Wang, 2000).
The main challenge of technical analysis is that there are literally hundreds of technical indicators available – enough to make even the most advanced statistician’s or mathematician’s eyes bug out. Next to that, there is also no single indicator that can be considered as the best, since each indicator might be applicable only to specific circumstances. Because of the unique patterns that highly traded stocks might exhibit throughout history, some indicators may be relevant only to certain individual stocks. Technical indicators are not to be used as a silver bullet solution for when to buy or sell. They are poor predictors of exact timing, but they are good at indicating which stocks are candidates for further analysis. As such, technical analysis can be viewed as a starting point – the historical patterns do not necessarily translate into an exact picture of future performance.
2.3 Different types of technical strategy analysis
As mentioned earlier, there are several hundreds of indicators used in technical analysis. In this section some of the most common groupings are discussed. To be noted, these groupings are limited to indicators applicable to individual stocks – there are many indicators that might be useful to predict an index or industry group, but that is not what this paper is concerned about.
2.3.1. Relative Strength Index – RSI
The relative strength index (RSI) is a momentum indicator developed by noted technical analyst Welles Wilder, that compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. It is primarily used to attempt to identify overbought or oversold conditions in the trading of an asset. The RSI provides a relative evaluation of the strength of a security’s recent price performance, thus making it a momentum indicator. RSI values range from 0 to 100. The default time frame for comparing up periods to down periods is 14, as in 14 trading days.
Traditional interpretation and usage of the RSI is that RSI values of 70 or above indicate that a security is becoming overbought or overvalued, and therefore may be primed for a trend reversal or corrective pullback in price. On the other side of RSI values, an RSI reading of 30 or below is commonly interpreted as indicating an oversold of undervalued condition that may signal a trend change or corrective price reversal to the upside.
In a paper by Chong & Ng (2008), the authors performed a technical analysis on the London stock exchange, using the RSI rules and the FT30 (similar to the Dow Jones Industrial Average). This is the longest UK index and has a sample period from July 1935 to January 1994. They divided the sample in three sub-periods. They found that in the third sub-period, the RSI rule generates the highest number of significant returns. In the first sub-period it was less significant and in the second sub-period it was not significant at all. Overall, they concluded that the RSI rule outperformed the buy-and-hold strategy.
2.3.2. Trading ranges
A trading range occurs when a stock or average moves up and down between a consistent high and low for an extended period of time (days, to weeks, to months). The bottom of the range becomes fairly solid support as the top becomes fairly solid resistance the more times either holds. We play stocks within the trading ranges if they are loose enough to give us some room to manoeuvre, e.g., a 5-point range or more. A tight trading range is one that is significantly narrower than a particular stock’s usual trading fluctuations. A tight trading range on low volume is usually a very good indicator that a move up is coming.
A series of high, low and closing prices are plotted on a graph for a certain period of time, and support and resistance lines are drawn across the bottom and top of the range. A breakout occurs when the price sustains a movement, even for a period or two, above or below the range.
A buy signal is generated when the price penetrates the resistance level. The resistance level is defined as the local maximum. Technical analysts believe that many investors are willing to sell at the peak. This selling pressure will cause resistance to a price rise above the previous peak. However, if the price rises above the previous peak, it has broken through the resistance area. Such a breakout is considered to be a buy signal. Under this rule, a sell signal is generated when the price penetrates the support level which is the local minimum price. The underlying rationale is that the price has difficulties penetrating the support level because many investors are willing to buy at the minimum price. However, if the price goes below the support level, the price is expected to drift downward. In essence, technical analysts recommend buying when the price rises above its last peak and selling when the price sinks below its last trough (Brock, Lakonishok, & LeBaron, 1992).
2.3.3. Pattern analysis
This may be the form of technical analysis that is easiest to understand. The same price charts discussed above are analysed for specific patterns that have historically appeared in the same stock or for common patterns that have been seen in many stocks over time. The most commonly observed patterns are head-and-shoulders patterns, triangle-up or triangle-down patterns, rounded tops or rounded bottoms, cup-and-handle formation, and so on.
A chart pattern is a distinct formation on a stock chart that creates a trading signal, or a sign of future price movements. Chartists use these patterns to identify current trends and trend reversals and to trigger buy and sell signals.
Head and shoulder pattern – The formalization of the geometry of a head-and-shoulders pattern is as follows: three peaks, with the middle peak higher than the other two (Lo, Mamaysky, & Wang, 2000). The head-and-shoulders pattern is considered by practitioners to be one of the most, if not the most, reliable of all chart pattern. If trading based on this pattern generates excess profits, investigating other patterns may prove interesting. Conversely, if profits are insignificant, then this entire branch of visually based technical analysis may be called into question (Osler & Chang, 1995). These authors found that in order to summarize the predictive power or their trading strategy, they investigate profits from speculating in all six currencies simultaneously over the same time horizon. Their findings show that these aggregate profits would have been both statistically and economically meaningful regardless of transaction costs, interest differentials or risk. They conclude that head-and-shoulders signals have some predictive power for the mark and yen during the twenty years since the advent of floating exchange rates. Basically, this would mean that as an investor, you would have to locate the neckline, wait till the pattern is complete and when the neckline brakes, you would invest.
In the paper of Lo, Mamaysky & Wang (2000), the authors use a different type of technical analysis. The authors propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression. They find that several technical indicators indeed provide incremental information and may have some practical value. They mainly focus on three different technical indicators: head-and-shoulders (and inverted), rectangle tops (and bottoms) and double tops (and bottoms). They chose these indicators specifically, to illustrate the power of smoothing techniques in automating technical analysis. They claim that these patterns are most difficult to quantify analytically. The added value of their paper to this research, would definitely be the methodology part, where they describe how to quantify these techniques and so how to apply these rather difficult techniques to the dataset.
2.3.4. Trend analysis
Highly complex and mathematical, trend analysis looks at short and long-term trends and tries to identify crossovers, where prices cross over their long-term averages. The long-term averages are referred to as moving averages, where a price range is smoothed for a period of time by averaging a series of data points and plotting the smoothed line against the actual price line of the stock. The moving average convergence divergence (MACD) is used to identify crossovers, divergence and convergence, and overbought and oversold conditions.
Moving Average (crossover, MACD, ribbon) – Yule (1909), Wold (1938), Zoicas-Lenciu (2016) = Buy/sell signal when short-term crosses long-term (double or triple), Many moving averages are placed onto the same chart and are used to judge the strength of the current trend.
According to the moving average rule, buy and sell signals are generated by two moving averages of the level of the index – a long-period average and a short-period average. In its simplest form this strategy is expressed as buying (or selling) when the short-period moving average rises above (or falls below) the long-period moving average. The idea behind computing moving averages is to smooth out an otherwise volatile series. When the short-period moving average penetrates the long-period moving average, a trend is considered to be initiated (Brock, Lakonishok, & LeBaron, 1992).
Unlike existing studies that apply technical analysis to either market indices or individual stocks, Han, Yang & Zhou (2013) apply it to portfolios sorted by volatility or other characteristics of the stocks that reflect information uncertainty. Their use of technical analysis focuses on applying the popular technical tool, moving averages, to time investments. This is a trend-following strategy (TFS), and hence the profitability of the strategy relies on whether there are detectable trends in the cross section of the stock market. They find that the application of a moving average timing strategy generates investment timing portfolios, that substantially outperform the buy-and-hold strategy. They find that especially the combination with the volatility anomaly is of great economic significance. Their paper shows that the moving average technique could definitely enhance an investment strategy.
Han, Huang & Zhou (2015) argue that most anomalies are based on low frequency attributes and therefore ignore higher frequency information. In their research, they implement higher frequency data in order to test the consistency of the anomalies. They find that by doing so, significant economic value will be added. They find that for the eight major anomalies used in their research, the enhanced anomalies can double the average returns while having similar or lower risks.
The main part of their paper that is of interest to this research, is that they use both technical and traditional analysis. They use the traditional anomalies, but they use the moving average technique to enhance these strategies. The moving average is used to rebalance their portfolios.
In the paper of Brock, Lakonishok & LeBaron (1992), the authors test two of the most popular trading strategies: the moving average and the trading range break. Their results provide strong support for the technical strategies. The returns obtained from these strategies are inconsistent with the four popular null models: the random walk, the AR(1), the GARCH-M and the exponential GARCH. Next to that, they find that buy signals consistently generate higher returns than sell signals and these signals are also less volatile. Their paper shows again the importance of the moving average method, but in this case also the trading range break strategy, so in overall, a support for the use of technical analysis.
2.3.5. Gap analysis
A gap occurs when the opening price of a stock is significantly higher or lower than its closing price the previous day, possibly because of company news released overnight or some other factor. The gap trader is concerned with the performance of the stock above or below its open, which may indicate further movement in either direction. In this sense, the trader’s decisions may be closer in style to that of the momentum trader than the technical analyst.
Gap analysis refers to the process through which a company compares its actual performance to its expected performance to determine whether it is meeting expectations and using its resources effectively. Gap analysis seeks to define the current state of a company or organization and the target state of the same company or organization. By defining and analysing these gaps, a business management team can create an action plan to move the organization forward and will the gaps in performance.
Conducting a gap analysis can help a company re-examine its goals to determine whether it is on the right path for accomplishing them. Gap analysis consists of four steps, ending in a compilation report that identifies areas of improvement and outlines an action plan to achieve increased company performance. The steps are: construct organizational goals, benchmark the current state, analyse the gap data and compile a gap report.
2.4 Pricing models
When comparing the different strategies, it is usually tested whether a certain investment strategy generates a significant high risk-adjusted return. The returns of the strategy are compared to different version of a pricing model. Up until several decades ago, the CAPM functioned as a benchmark. This model was introduced by Treynor (1961), Sharpe (1964), Lintner (1965) and Mossin (1966) independently, building on earlier work of Markowitz on diversification and modern portfolio theory. CAPM is defined as:
E(R_i )=R_f+β_i (E(R_m )-R_f)
Where E(R_i)is the expected return on the capital asset, R_fis the risk-free rate of interest, such as interest arising from government bonds, β_i is the sensitivity of the expected excess asset returns to the expected excess market returns, E(R_m) is the expected market return and E(R_m )-R_f is known as the market premium. Nowadays, three different models are often used, which are all adding-based on the CAPM: the three, four- and five factor models.
2.4.1. Three factor model
The three factor model is introduced by Fama and French in 1993. It is an addition to the CAPM, which contains the risk premium, but also a size factor and a value factor. As was mentioned earlier, it is assumed that smaller firms generate higher excess returns than bigger firms (small minus big, SMB) and companies with high book-to-market ratios generate higher excess returns than low ratios (high minus low, HML). These two factors will now shortly be discussed.
E(R_i )=R_f+β_1i (E(R_m )-R_f )+β_2i SMB+β_3i HML
The magnitude of the size anomaly is around 2-4% per annum, mostly in the 1960s and 1970s. it is quite possible that after the discovery of the anomaly, investors traded on it and hence the magnitude decreased to insignificant levels. There are two economic motivations begin the size anomaly. First, small firms receive less analyst attention. This means that their prices are updated less often, meaning that this would carry a risk for which compensation would be required. Second, small firms are not traded much. This means that their prices update less often and that they are less liquid (and that they carry higher transaction costs). Again, this carries a risk for which compensation would be required.
According to the value anomaly, value stocks (stocks with a high B/M-ratio) earn higher returns than growth stocks (stocks with a low B/M-ratio), even after corrections have been made for their market risk. The magnitude of this anomaly is around 4%-6% per annum. The economic motivation behind the value anomaly starts from the fact that ultimately asset prices are determined by (expected) pay-outs. If market value is close to book value (high B/M), the firm appears to be in dire shape (no growth opportunities that have any value), and is therefore riskier. This risk raises the required return. (However, it must be said that this explanation is just one of the possible explanations behind the value anomaly.) The problem with the financial distress hypothesis is that the factor associated with the value anomaly has low correlations with measures of distress. However, (Lettau & Ludvigson, 2001) suggest the value factor may work primarily in times that are already bad.
2.4.2. Four factor model
Carhart build further on the three factor model, by adding a fourth factor in 1997. He found a momentum factor in which companies that could be considered as “winners”, would outperform the “losers” (winners minus losers, WML).
E(R_i )=R_f+β_1i (E(R_m )-R_f )+β_2i SMB+β_3i HML+β_4i WML
The momentum anomaly states that, based on middle-long term autocorrelation, assets (stocks) that have performed well in the recent past (say 3-12 months) will outperform ‘losers’ for another year. The magnitude of this anomaly is mostly 4%-6% per annum but can be as high as 2% per month. This outperformance can be obtained by sorting past ‘winners’ and ‘losers’ and then (for example) buy the 20% best performing stocks and finance this by short selling the 20% worst performing stocks. Obviously this requires careful selection and rebalancing because at some time winners stop winning and losers stop losing. The momentum anomaly is not constructed into the SML and SDF because from an economical point of view it has nothing to do with risk (especially the when do we get returns part, because it is based on the past which is irrelevant). There is no economic argument to support the idea that returns are less desirable just because the stock value has increased the past period.
Trading on the momentum anomaly seems easy, why doesn’t it disappear rapidly? 2 possible explanations:
- Trading costs. The short positions are costly to obtain and maintain. There are also transaction costs that can make it expensive the replicate the strategy. Taking this into account there might not be an anomaly after all.
- Illiquidity effects. Especially in smaller stocks, shortselling might be encountered by illiquidity. Illiquid stocks may fall a lot further if you try to sell them in a decreasing market. Both explanations are based on market imperfections (violation of our assumptions). The momentum anomaly is rather robust (if you correct of risk with the SDF for the other anomalies or macroeconomic factors it is still there). Furthermore, illiquidity effects are hard to measure so testing on this is also hard to do.
2.4.3. Five factor model
The five-factor model does not include the WML factor from Carhart. This model builds on the three factor model by adding two extra factors, the profitability factor (robust minus weak, RMW) and the investment factor (conservative minus aggressive, CMA).
The profitability factor states that companies with higher future earnings will have higher stock market returns. RMW is the return spread of the most profitable firms minus the least profitable ones. The problem here has always been finding a proxy today that predicts earnings tomorrow.
The investment factor states that companies with a conservative investment strategy will generate higher future returns than companies with an aggressive strategy. CMA is the return spread of firms that invest conservatively minus aggressively.
E(R_i )=R_f+β_1i (E(R_m )-R_f )+β_2i SMB+β_3i HML+β_4i RMW+β_5i CMA
The results also show that the Fama-French five factor model explains between 71% and 94% of the cross-section variance of expected returns for the size, value, profitability and investment portfolios. It has been proven that a five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns performs better than the three-factor model in that it lessens the anomaly average returns left unexplained (ValueWalk, 2015).
The new model shows that the highest expected returns are attained by companies that are small, profitable and value companies with no major growth prospects. The five-factor model’s main setback however is its failure to capture the low average returns on small stocks whose returns perform like those of firms that invest a lot in spite of low profitability as well as the model’s performance being indifferent to the way its factors are defined (Fama & French, 2016).
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