40 Chapter Three point in the asset’s price. Traders may enter long positions at these low points especially when they believe the price will rebound. Likewise, at peaks traders may believe that the asset’s price may eventually fall, causing them to lose interest in buying and liquidating their assets. This activity causes the asset price to decline. Trend lines can use used to indicate support levels and resistance levels. The support is the price level in which the stock has difficulty in falling below. Resistance is the price level in which the stock has difficulty in rising above. Figure 3.6 provides a display of support and resistance levels. Figure 3.06: Resistance and Support Source: Adapted from Stock Charts (2016a) Trend lines can be used to construct several patterns. One of the most basic patterns is the rectangle pattern. In a rectangle pattern, the asset price fluctuates between support and resistance. In order for a pattern to be established as a rectangle, both support and resistance must be at least touched twice. Figure 3.06 also displays a rectangle pattern. To confirm support and resistance levels, traders may search for bounces. When a stock price reaches the resistance level, it should ‘bounce’ off resistance and decline. Likewise, when the price reaches support, it should bounce off support and increase. Sometimes prices do not bounce off support or resistance. Instead, they break support or resistance levels. When prices exceed resistance or support levels, it referred to as a breakthrough or a breakout. An upside
Technical Tools and Technical Analysis 41 break through or upside breakout is where the asset’s price has exceeded the resistance. This may occur if there is an increase in buying pressure for the asset. A downside breakthrough or downside breakout is where the asset’s price falls below support. This occurs when there is an increase in the selling pressure for the asset. Figure 3.07 illustrates an upside breakout and a downside breakout. Where an upside breakout occurs, a previous resistance level may become a support level. Likewise, where a downside breakout occurs, a previous support level may become a resistance level. See Figure 3.07. Figure 3.07: Upside Breakout and Downside Breakout Source: Adapted from Stock Charts (2016a)
42 Chapter Three Rectangles can be used to identify an uptrend. An uptrend is a series of higher peak prices and higher trough prices. In other words, it is an upward sloping rectangle due to both support and resistance rising over time. Uptrends and bullish indicators. Rectangles can also be used to identify downtrends. Downtrends are the inverse of uptrends. It is a downward sloping rectangle that results from support and resistance declining over time. Downtrends are bearish indicators. See Figure 3.09. Figure 3.08: Uptrend Source: Adapted from Identifying Trends (2016)
Technical Tools and Technical Analysis 43 Figure 3.09: Downtrend Source: Adapted from Identifying Trends (2016) A sideways trend is a sequence of equal peak and low prices. The rectangle pattern displayed in Figure 3.06 also displays a sideways trend. Apart from rectangles, trend lines can also form triangles. A triangle is where there is a convergence in resistance and support levels over time. A descending triangle is where resistance is declining to converge to support over time. A rising triangle is where support is rising to converge to support. A symmetric triangle is where both resistance and support are converging. Descending triangles are bearish patterns, rising triangles are bullish patterns, while symmetric triangles are uncertain patterns.
44 Chapter Three Figure 3.10: Types of Triangles Source: Adapted from Stock Charts (2016a) In the evaluation of trends, investors can consider the time horizon for trends. Trends occurring within 0 to 3 months are referred to as short-term trends. Trends occurring within 3 to 12 months are referred to as intermediate trends. Trends lasting periods in excess of 1 year is referred to as long-term trends. Rectangles and triangles are continuation patterns. They are called continuation patterns since the price continues to follow such pattern once they have been established. However, trends can change over time. In fact, a stock that was previously experiencing an uptrend can then experience a
Technical Tools and Technical Analysis 45 lower peak price and lower trough price. Alternatively, a stock that was previously experiencing a downtrend can then experience a higher peak price and a high trough price. In both cases, then the trend has changed and resulted in a reversal. A reversal is a change in the price trend of an asset to the opposite direction. An uptrend can be reversed to a downtrend. Likewise, a downtrend can be reversed to an uptrend. See Figure 3.11. Figure 3.11: Basic Reversal Source: Adapted from Investopedia (2006)
46 Chapter Three There are many complex reversal patterns. Some of the main patterns include: x Head and Shoulder; x Double Top Reversal; x Double Bottom Reversal; x Falling Wedge; and x Rising Wedge. The Head and Shoulder is a reversal pattern that is comprised of 1 head and two shoulders. This pattern is comprised of 3 consecutive peaks (troughs) in the asset price. However, the middle peak (trough) is higher (lower) than the other peaks (troughs). The head is the middle peak (trough) while the shoulders are the 1st and 3rd peaks (troughs). Figure 3.12 provides an illustration of a head and shoulder reversal. Figure 3.12: Head and Shoulder Reversal Source: Investopedia (2006) A Double Top Reversal is a bearish pattern where the asset’s price made 2 consecutive peaks before making an overall decline. Likewise, a Double Bottom is a bullish pattern where the asset’s price made 2 consecutive troughs before making an overall increase. See Figure 3.13.
Technical Tools and Technical Analysis 47 Figure 3.13: Double Top and Double Bottom Reversal Source: Janssen et al. (2006) Wedges are patterns formed by combining trends with triangles. A Rising Wedge is a bullish pattern resulting from a combination of a downtrend and a rising triangle. A Falling Wedge is a bearish pattern resulting from a combination of an uptrend and a declining triangle. See Figure 3.14. Figure 3.14: Rising and Falling Wedges Source: Adapted from Janssen et al. (2006) Apart from reversals, another important chart pattern encountered by traders are flags. A bull flag is a combination of a strong uptrend and a rectangle. The strong uptrend of often referred as a pole. A Bear Flag is a combination of a strong downtrend and a rectangle. Some flags form pennants. A pennant is a combination of a pole and a triangle. A bull pennant is a combination of a strong uptrend and a triangle, while a Bear
48 Chapter Three Pennant is a combination of a strong downtrend and a triangle. See Figure 3.15. Figure 3.15: Flags and Pennants Source: 4exanalysis (2016) Another relevant chart pattern that traders encounter is a gap. A gap is a jump in the price between marketing closing and the next open. Consider the following example. Assume that the price of an asset closed at $20. Then the next trading day, the price opened at $50. This space between the close and the open price is a gap. It is important to note, gaps can be identified with candlesticks and bar charts, but not line charts as line charts illustrate continuous movement in the asset’s price. Gaps usually occur due to a significant event or announcement regarding an asset. The main types of gas include breakaway, runaway, and exhaustion. Breakaway Gaps are gaps that mark the occurrence of a new price trend. Runaway Gaps occur within a price trend. Exhaustion gaps occur at the end of a price trend. It is important to note when identifying patterns, it is essential that a trader tries to confirm these patterns to volume. For instance, if an upside or downside breakout occurs, there should be an increase in volume as
Technical Tools and Technical Analysis 49 traders may believe a new trend is being established. Reversal patterns are often accompanied by increases in trade volume as swing traders may try to capitalize on changing trends. Figure 3.16: Downside Breakout and an Increase in Trading Volume Source: Adapted from Stock Charts (2016b) Charting techniques and pattern recognition can also be used to take into consideration the psychology of the people in the market. One such technique is based on the Elliott Wave Theory. The following subsection discusses the theory in greater detail. 3.4.1 The Elliott Wave Theory After analyzing 75 years’ worth of stock data, Ralph Nelson Elliott realized that financial markets are not as chaotic as people thought. In fact, he found that the market traded in repetitive cycles. He explained that such cycles were due to the emotions of investors, which in turn was influenced by news or the predominant psychology of the masses at the time. Elliott (1938) asserted that the upward and downward swings in the price of financial assets are caused by the collective psychology of people, and it always shows up in the same repetitive patterns. He referred to these upward and downward swings in asset price as ‘waves’. He argued that if
50 Chapter Three a trader can correctly identify the repeating patterns, they can accurately predict where the asset prices will go next. An important aspect of the of the Elliott waves, is that they are fractals. Fractals are structures which can be deconstructed into part, with each part being very similar to the whole. In nature, there are many examples of fractals. For example, a sea-shell, snow-flake, and a cloud are all fractals. Elliott demonstrated that a trending market can move in a 5-3 wave pattern. He referred to the first 5-wave pattern as impulse waves, while the remaining 3-wave pattern is referred as corrective waves. Waves 1, 3, and 5 in the impulse wave pattern are motive, which suggests that they go along with the overall trend. However, waves 2 and 4 are corrective. Figure 3.17 provides an illustration. As seen in Figure 3.17, wave 1 reflects an upward movement in the price of the currency pair. In this example with real empirical data, there was relatively strong movement in the price of the EUR-USD currency pair over the February 2002 to October 2004 period. In wave 2, there is a contraction in the price of the currency pair, perhaps due to traders finding the currency pair is overvalued at that point, and closing their long positions to capture profits. In wave 3, there is another rebound in the price of the currency pair, its peak is higher than the previous peak in wave 1 perhaps due to strong speculation by traders in the price of the currency pair. Eventually, there will be a pullback when traders decide to close long positions and pull out their profits. Finally, the impulse pattern ends with an upward sloping wave 5. The previous example considered a bullish scenario. However, patterns can also emerge in bear markets. Consider Figure 3.18 which illustrates both a 5-Wave Impulse Pattern and a 3-Wave Countertrend.
Technical Tools and Technical Analysis 51 Figure 3.17: 5-Wave Impulse Pattern Source: FX Choice (2018)
52 Chapter Three Figure 3.18: 5-Wave Impulse Pattern and 3-Wave Countertrends Source: FX Choice (2018) In Figure 3.18, the 5-Wave Impulse Pattern can be seen from wave 1 to wave 5. In contrast to the example in Figure 3.17 which was bullish, this case is a bearish pattern. After the Impulse Pattern, a 3-Wave Countertrend emerges as a corrective wave pattern. As previously indicated, Elliott Waves are fractals. As clearly seen in Figures 3.17 and 3.18, each wave is comprised of sub-waves. Consider a more detailed illustration in Figure 3.19. In Figure 3.19, there is a large Elliott Wave which displays an uptrend over the October 2017 to April 2018 period for the EUR-USD currency pair. However, the large Elliott Wave is comprised of a series of smaller Elliott Waves. The shorter waves occur over a shorter period of time, but the large waves occur over a longer period of time. Given that it is possible to distinguish the main waves from the subwaves based on time, the Elliott Wave Theory has proposed a series of categories for the waves. They are: x Grand Super-cycle (multi-century); x Super-cycle (approximately 40–70 years); x Cycle (one year to several years);
Technical Tools and Technical Analysis 53 x Primary (a few months to a few of years); x Intermediate (weeks to months); x Minor (weeks); x Minute (days); x Minuette (hours); and x Sub-Minuette (minutes). Figure 3.19: Fractals in the Elliott Waves Source: FX Choice (2018) The basic principles identified in Elliott Wave Theory can be used to identify a series of more complex chart patterns. In fact, they can be used to identify rectangles, triangles, Rising and Falling Wedges, and more complex patterns. However, it may be difficult to an uninformed trader to recognize the correct patterns. When trading based on the basis of the Elliott Wave Theory, the trader should remember some basic rules to help identify chart patterns. They are: 1. Wave 3 should not be the shortest impulse wave; 2. Wave 2 should not go beyond the start of Wave 1; 3. Wave 4 should not cross the same price area as Wave 1; and
54 Chapter Three 4. Waves 2 and 4 may frequently bounce off Fibonacci Retracement Levels. If the chart pattern does not conform to the aforementioned rules, then the trader’s Elliott Wave count may be wrong. Charts should not be analyzed in isolation. Traders often use technical indicators to verify patterns observed in charts. Technical indicators are quantitative tools which utilize data on prices of assets to provide information about their patterns. Technical indicators can be categorized into leading and lagging indicators. Leading indicators are those indicators which are designed to precede price movement. Lagging indicators are those which follow price movements. The most of the main leading indicators are oscillators.50 Indicators such as Moving Averages, and Bollinger Bands are lagging indicators. The aforementioned technical indicators are explored in the next section. 3.5 Oscillators An oscillator is a Technical Analysis indicator that fluctuates between set levels or about a central point.51 Oscillators are useful in identifying patterns, especially when a trend cannot be clearly seen. Oscillators are used to determine if assets are overbought or over-sold. Some popular oscillators used by traders include the Momentum Oscillator; the OnBalance-Volume (OBV); the Stochastic Oscillator; the Relative Strength Index (RSI); and the Money Flow Index (MFI), and Fibonacci Retracement Levels (FRL).52 50 Oscillators are indicators which are plotted within a bounded range. 51 Centered oscillators fluctuate around a center point or line. Banded oscillators fluctuate between upper and lower bands. When the banded oscillator exceeds the upper band it suggest the asset is overbought. Likewise, the banded falls below the lower band, it suggests the asset is over-sold. 52 The aforementioned oscillators are the main types encountered by traders and investors. However, there are multiple modifications to the main oscillators.
Technical Tools and Technical Analysis 55 3.5.1 Momentum Oscillator The Momentum or the Rate of Change (ROC) Oscillator computes percentage change in the price of an asset.53 It is derived by the following equation ܴܱܥ ൌ ିష ష כ ͳͲͲ (3.01) where ௧ is the closing price of an asset; ௧ି is the closing price of an asset at period n. If the rate of change in the asset’s price is increasing, the momentum oscillator will be increasing. Likewise, if the rate of change is decreasing, the momentum oscillator will decrease. 3.5.2 On-Balance-Volume On-Balance-Volume (OBV) is an indicator which that uses the traded volume of an asset to predict changes in stock price. Granville (1960s) believed that when the volume of an asset increases suddenly without any change in price, a change in the asset’s price would soon follow. Likewise, a sudden decrease in the trading volume would be accompanied by a decline in the asset’s price.54 3.5.3 Relative Strength Index The Relative Strength Index (RSI) is an oscillator, introduced by Wilder (1978), which measures an asset’s price movements.55 The RSI can be used to determine if an asset is overbought or over-sold. It was computed via the following equation: ܴܵܫ ൌ ͳͲͲ െ ଵ ሺଵାோௌሻ (3.02) 53 The ROC is a centered oscillator. 54 The OBV is based on the concept of demand and supply. Increase in demand, as evidenced by the increase in trading volume would cause the asset’s price to increase. Likewise, a decrease in demand, as evidenced by a decline in trading volume would cause the asset’s price to decline. 55 The RSI is a banded oscillator.
56 Chapter Three where ݏ݀݅ݎ݁ͳͶݎ݁ݒ݊݅ܽ݃݁݃ܽݎ݁ݒܽ ൌܴܵ ൗ ݏ݀݅ݎ݁ͳͶݎ݁ݒݏݏ݈݁݃ܽݎ݁ݒܽ The theoretical range of the RSI is from 0 to 100. If the RSI of an asset increases to 80 and above, it suggests that the asset is overbought. This indicates that there may be a pullback56, sending a sell signal to investors. If the RSI of an asset decreases to 20 or below, it suggests that the asset is over-sold. This sends a buy signal to investors. It is important to note, the RSI should be used in conjunction with charts and other tools to accurately determine what position an investor should take in the market. 3.5.4 Relative Volume Although by definition it is not an oscillator, the relative volume is a popular index used in Technical Analysis. The relative volume is a ratio that compares the current trading volume of a stock to its normal trading volume for the same time of day. The theoretical range of the relative volume is 0 to +. If the relative volume of a stock is 1, it means that the stock is currently trading at its normal level. If the relative volume is less than 1, it means that the stock is trading below its normal level. If the relative volume is above 1, it means the stock is trading more than its normal level. A relative volume of 2 or higher indicates that a stock is trading 100% more than it normally trades.57 A relative volume of 2 or higher can be considered as high. 3.5.5 Money Flow Index The MFI is a volume-weighted RSI. It is computed via the following steps. Step 1: Compute the Typical Price. ܶܲ ൌ ሺାାሻ ଷ (3.03) where ܶܲ is the typical price; 56 A pullback is a reversal. 57 A relative volume of 2 indicates that the stock is trading at twice as much it normally trades.
Technical Tools and Technical Analysis 57 is the highest price; is the lowest price; is the closing price. Step 2: Compute the Raw Money Flow ܴܨܯ ൌ ܶܲ כ) ܳ 3.04) where ܴܨܯ is the raw money flow; ܳ is the volume traded. Step 3: Compute the Money Flow Ratio ܨܯ ܴൌ ሺଵସௗ௦௧௩ோெிሻ ሺଵସௗ௧௩ோெிሻ (3.05) Step 4: Compute the MFI ܫܨܯ ൌ ͳͲͲ െ ଵ ሺଵାெிோሻ (3.06) The MFI is a better measure to identify overbought and over-sold conditions than the RSI as it takes into consideration both price action and volume traded. 3.5.6 Stochastic Oscillator The Stochastic Oscillator compares an assets’ closing price to a specified its price range over a period of time.58 Like other oscillators, it is used to determine the best time to long or short an asset. The Stochastic Oscillator is determined by calculating two values. The first number is computed via the following equation Ψܭ ൌ ͳͲͲሾሺܥ െ ܮሻȀሺܪ െܮሻሿ (3.07) where C is the most recent closing price of the asset; 58 The stochastic oscillator is a banded oscillator.
58 Chapter Three ܮ is the lowest price of the asset in the period n; ܪ is the highest price of the asset in the period n; By default, n is set to 14 periods. The second number is calculated via the following equation: (08.3 (ܭΨ݂݁݃ܽݎ݁ݒܽ݃݊݅ݒ݉݀݅ݎ݁͵ ൌ ܦΨ The theoretical range of the Stochastic Oscillator is between 0 and 100. If the %D or %K is above 80 the asset is considered to be overbought. If the %D or %K is below 20, the asset is considered to be over-sold. Buy signals are triggered when both %K and %D are below 20, as it signals a pending reversal. A sell signal is sent to traders if both %K and %D are above 80, as it signals a downtrend reversal. 3.5.7 Fibonacci Retracement Levels Leonardo Fibonacci identified a sequence of numbers that share a mathematical relationship. The Fibonacci Sequence of numbers is as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610 etc. Each number in the sequence is the sum of the two preceding terms. For example, 377 = 233 + 144. One of the characteristics of the Fibonacci Sequence is that each number is approximately 1.618 times greater than the preceding number. Thus, the Fibonacci sequence can produce the ratio of 61.8% between a number and its predecessor. In other words, any number divided by its successor in the Fibonacci Sequence should produce a ration of approximately 61.8%. The key Fibonacci Ratios are 23.6%, 38.2%, 50%, 61.8% and 100%. The 38.2% ratio is derived by dividing a number by its 2nd successor. For example, 55/144 = 38.19%. The 23.6% ratio is found by dividing one number by its 3rd successor. For example, 21/89 = 23.59%. The Fibonacci Ratios can be used to identify critical points in the movement of financial asset prices on financial markets. In fact, they can be used to determine points when there may be a reversal in the asset price (Mitchell 2001). Fibonacci Retracement Levels are the Fibonacci Ratios (23.6%, 38.2%, 50%, 61.8% and 100%) which are related to the asset price. Most modern financial trading platforms contain a tool that which can draw in the lines to identify the Fibonacci Retracement Levels.
Technical Tools and Technical Analysis 59 In order to accurately identify the Fibonacci Retracement Levels (FRL), a trader should identify the recent significant highs and lows. On modern trading platforms, the trader can identify the FRLs by clicking the FRL tool, then click from the most recent highest high to the lowest low to identify a downtrend, or clicking from the most recent lowest low to the highest high to identify an uptrend. Figure 3.20: Fibonacci Retracement Levels Source: FX Choice (2018) As can be seen from Figure 3.20, the FRL for XAU/USD were 1242.45 (23.6%), 1244.77 (38.2%), 1246.64 (50.0%), and 1249 (61.8%) over the June to July 2018 period. The expectation is that if XAU/USD currency pair retraces from the recent high of 1256.85, it will find support at one of the FRL as traders may place buy orders at these levels as they anticipate a price pulls back.
60 Chapter Three 3.5.8 Force Index The Force Index is an indicator that sends signals about the market based on the direction and size of the asset’s price movement, as well as the trading volume. The Force Index may be derived by the following formula ܫܨଵ ൌ ሾሺ ሻെ ሺሻሿ כ (09.3݉݁ (ݑ݈ݒ݃݊݅݀ܽݎݐݐ݊݁ݎݎݑܿ (10.3 (ଵܫܨ݂ܣܯܧ݀݅ݎ݁ െ͵ ͳ ൌ ଵଷܫܨ where ܫܨଵ is the Force Index in period 1; and ܫܨଵଷ is the 13 point Exponentially Weighted Moving Average of the Force Index. The theoretical range of the Force Index is from - to +. Negative values of the Force Index suggest that the asset price is closing lower than previous prices. A large negative Force Index indicates that there was a decline in the asset price, as well as strong trading volume. This also indicates strong selling power of an asset which is declining in price. Thus, it may be interpreted as a sell signal. A small negative Force Index shows that there was a decline in the asset’s price, but weak trading volume. Thus, it highlights that the downward price movement is a false signal. A large positive Force Index indicates that the asset price is rising, and there is strong buying power. Thus, it is a buy signal on a market. A small positive force index shows the weak buying power associated with the positive price movement. Therefore, a small positive force index reveals the positive price movement to be a false signal. It can be noted that the Force Index is influenced by three (3) majour variables: the asset price, the magnitude of the price change, and the amount of trading volume. Large price movements, and large trading volumes result in large values for the Force Index, and vicey versa. The Force Index can be used to identify trends and divergences. A Bullish Divergence occurs where the Force Index is rising from below, and the asset price was previously decreasing. It suggests that a Bottom Reversal may occur. Likewise, a Bearish Divergence occurs where the Force Index is decreasing from above, and the asset price was previously decreasing. This suggests a Top Reversal.
Technical Tools and Technical Analysis 61 Figure 3.21: The Force Index Source: FX Choice (2018) In Figure 3.20, the Bullish Divergence can be seen with the Force Index moving up from below. Arrow A shows the direction of the Force Index. The Bearish Divergence can be seen with the Force Index moving down from above. Arrow B illustrates the direction of the Force Index’s movement. It is noteworthy that the Force Index may be used in conjunction with the RSI to confirm reversals, or in conjunction with Bollinger Bands to confirm patterns.
62 Chapter Three 3.6 Moving Averages Patterns can also be established via the use of moving averages. Here, a Moving Average refers to the average price for an asset over a specified period. Moving Averages are a simple method to filter out noise59 from the trend in prices. Moving Averages can have different lengths. For example, a 10-day Moving Average is the average price of an asset over the past 10- day period. 3.6.1 Simple Moving Average A Simple Moving Average60 is computing by adding up the value of the asset for a number of periods, then dividing the sum the number of periods. As the Moving Average moves forward, the oldest value of the asset is dropped, and the newest value is included. For example Assume that the closing price of an asset the past 5 days took the following values: $21, $23, $27, $22, $21 Then the simple moving average for that period was ̈́ʹͳ ̈́ʹ͵ ̈́ʹ ̈́ʹʹ ̈́ʹͳ ͷ ൌ ̈́ʹʹǤͺ Assume, on the 6th day, the closing price of the asset was $23. Then if the Simple Moving Average moves forward it will be: ̈́ʹ͵ ̈́ʹ ̈́ʹʹ ̈́ʹͳ ̈́ʹ͵ ͷ ൌ ̈́ʹ͵Ǥʹ Moving Averages lag asset prices since they are computed from past prices. In other words, moving averages follow a trend in asset prices. The longer the time period used to compute the moving average, the greater the lag in following the asset’s price. Subsequently, moving averages with longer periods are smoother than moving averages with shorter periods. 59 Noise here refers to random fluctuations in price. 60 A Simple Moving Average is also referred to as an Equally Weighted Moving Average.
Technical Tools and Technical Analysis 63 For example, consider the following table of prices for an asset over a period. The information is used to compute a 5-day Moving Average, and a 10-day Moving Average. Table 3.01: Prices, 5 day and 10-day Moving Averages Date Close 5-day Moving Average 10-day Moving Average 12-Feb-16 3.6 16-Feb-16 3.72 17-Feb-16 3.82 18-Feb-16 3.5 19-Feb-16 3.25 3.58 22-Feb-16 3.11 3.48 23-Feb-16 3.25 3.39 24-Feb-16 3.23 3.27 25-Feb-16 2.69 3.11 26-Feb-16 2.69 2.99 3.29 29-Feb-16 2.98 2.97 3.22 01-Mar-16 3.44 3.01 3.20 02-Mar-16 3.47 3.05 3.16 03-Mar-16 3.38 3.19 3.15 04-Mar-16 3.3 3.31 3.15 07-Mar-16 3.2 3.36 3.16 08-Mar-16 3.19 3.31 3.16 09-Mar-16 3.04 3.22 3.14 10-Mar-16 2.9 3.13 3.16 11-Mar-16 2.9 3.05 3.18 14-Mar-16 2.84 2.97 3.17 Source: Yahoo Finance online database (2016) Observe in Figure 3.21 how the 5-day Moving Average is smoother than the closing price of Sky Solar Holdings (SKYS) stocks over the February 12, 2016, to March 14, 2016, period. Also, observe how the 10- day Moving Average is smoother than the 5-day Moving Average.
64 Chapter Three Figure 3.21: SKYS Stocks and Moving Averages Source: Yahoo Finance online database (2016) The duration of Moving Averages frequently used in trading include 1- minute, 5-minutes, 15-minutes, 30-minutes, 1-hour, 4-hours, 1-day, 5- days, 10-days, 50-days, 100-days, and 200-days. Apart from the Simple Moving Average, there are other moving averages. Namely: x the Exponentially Weighted Moving Average (EWMA); and x the Volume Weighted Moving Average (VWAP). 0 1 2 3 4 5 6 7 Price Axis Title Close 5 day moving average 10 day moving average
Technical Tools and Technical Analysis 65 3.6.2 Exponentially Weighted Moving Average The EWMA computes the average by placing a higher weight on more recent data, and a lower weight on older data. For example, in the previous Table 3.01, the simple 5-day moving average placed an equal weight upon each asset price. In that case, the weight applied to each asset price was 1/5 (20%). If an EWMA is used, it will apply higher weights to the most recent prices. For instance, the most recent data point can be given a 30% weight, the 2nd recent a 25% weight, the 3rd recent a 20% weight, the 4th recent a 15% weight, and the 5th recent a 10% weight. Table 3.02 provides the data with the EWMA.61 Table 3.02: 5-day EMWA Date Close 5-day simple moving average 5-day EWMA 12-Feb-16 3.6 16-Feb-16 3.72 17-Feb-16 3.82 18-Feb-16 3.5 19-Feb-16 3.25 3.58 3.53 22-Feb-16 3.11 3.48 3.39 23-Feb-16 3.25 3.39 3.31 24-Feb-16 3.23 3.27 3.24 25-Feb-16 2.69 3.11 3.06 26-Feb-16 2.69 2.99 2.92 29-Feb-16 2.98 2.97 2.91 01-Mar-16 3.44 3.01 3.04 02-Mar-16 3.47 3.05 3.17 03-Mar-16 3.38 3.19 3.29 04-Mar-16 3.3 3.31 3.34 07-Mar-16 3.2 3.36 3.33 08-Mar-16 3.19 3.31 3.27 09-Mar-16 3.04 3.22 3.18 10-Mar-16 2.9 3.13 3.08 11-Mar-16 2.9 3.05 3.00 14-Mar-16 2.84 2.97 2.93 61 Note, the EWMA is often used to compute volatility. In such case, variance or standard deviation is measure of volatility, and the EWMA is used place higher weights on the more recent data used to compute the standard deviation or variance.
66 Chapter Three 3.6.3 Volume Weighted Moving Average The VWMA is a price average that takes into consideration the number of assets traded on a given day. The VWAP is computed via the following steps: 1. Choose the time period 2. Calculate the typical price for each period. The typical price (T) is given by ܶܲ ൌ ሺ݄݈ ሻܿ ͵ 3. Multiply the typical price by the total volume of assets traded in that period. This will produce TP*Q.62 4. Compute the cumulative TP*Q. 5. Compute the cumulative volume. 6. Divide the cumulative TP*Q by the cumulative volume. The VWAP is used in conjunction with the MVWAP. The VWAP is computed daily, but the MVWAP is computed as an average of VWAPs over a number of days. In other words, the MVWAP is a moving average of the VWAP. If an investor purchases an asset at a price lower than the VWAP, then it suggests that they purchased the asset at a better price than the volume weighted average price. Likewise, if they purchased the asset at a price higher than the VWAP, it suggests they paid too much for that asset that day. 3.6.4 Moving Average Convergence Divergence The Moving Average Convergence Divergence (MACD) oscillator is a centered oscillator that is computed from two different period moving averages. The MACD is derived by subtracting the longer period moving average from the shorter period moving average. It is given by the following equation: 62 Recall ܴܨܯ ൌ ܶܲ כ .ܳ
Technical Tools and Technical Analysis 67 (11.3 (ܣܯܧݕܽ݀ʹ െ ܣܯܧݕܽ݀ʹͳ ൌ ܦܥܣܯ Additionally, a 9 day EMA of the MACD is computed and plotted against the MACD. This 9 day EMA is called the signal line and is used by traders and investor to determine buy and sell signals. The MACD can be used to determine crossover trading strategies. Crossovers will be discussed in greater detail in Chapter 4. The MACD indicates whether there is convergence or divergence in the moving averages. Convergence occurs where the two moving averages are converging towards the same value. Divergence occurs when the two moving averages are moving apart from each other. 3.7 Bollinger Bands: Another Technical Indicator A Bollinger Band is a confidence interval that is plotted one standard deviation above and below a moving average. It can be used to identify extreme short-term fluctuations in an asset. Standard deviation is used to create Bollinger bands since it is a commonly used indicator of volatility. Under normal market conditions, the price of an asset will lie within the Bollinger bands. The size of the Bollinger Bands will adjust to the level of volatility in the market. The Bollinger bands experience expansion when there is an increase in volatility, and contraction when there is a decrease in volatility. Bollinger Bands can also be used to identify patterns and changes in volatility. For instance, periods of low volatility and narrow Bollinger Bands are often followed by periods of high volatility and wide Bollinger Bands. Subsequently, a trader observing narrow Bollinger Bands may anticipate a significant increase in volatility in the near future. Traders can also inspect the price data to see if they exceed the Bollinger Bands. If prices exceed the upper Bollinger Band, it suggests that the asset is overbought, and a reversal may occur in the near future. Conversely, if the prices exceed the lower Bollinger band, it suggests that the asset is over-sold, and rapid rise in prices may occur in the near future. Figure 3.22 provides an illustration of Bollinger Bands. The Bollinger Bands are around the candlesticks. Observe how the Bollinger Bands contract and become narrow during periods of low volatility, but expand and become wide during periods of high volatility. In can also be noted
68 Chapter Three from the example in Figure 3.22 that most of the price movement occurred within the Bollinger Bands. Figure 3.22: Bollinger Bands Source: FX Choice (2018) Technical indicators are tools used by traders and investors to create trading strategies. Chapter 4 will consider various trading strategies in greater detail. 3.8 Linear Regression Models Technical Analysis involves the analysis of price data to provide sight about the direction of the market, which in turn can be used inform the decisions of retail traders in their trading activities. Linear regression models can be very useful for trading as they can be used for the forecasting. Regression is a statistic tool that is used to evaluate the relationship between a given variable and one or more other variables. A linear regression is a statistical technique which takes a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (Brooks 2008; Freedman 2009). More simply expressed, a linear regression is a statistical technique
Technical Tools and Technical Analysis 69 which tries to determine a relationship between one variable and other variables by plotting a straight line exactly through the middle-dispersion of the data points of all the variables. There are many different regression models. The first linear regression which is introduced to students of econometrics is the Classical Linear Regression Model (CLRM). The CLRM, also referred to as the Ordinary Least Squares (OLS) model is determined by regressing a variable upon other variables. During the process, a straight line is used to fit or match the general pattern of the data. However, there is highly unlikely there will be a perfect fit or match between the actual data and the line, especially if financial data is used. Thus, there will be positive errors, where the line is above the actual data, as well as negative errors, where the line is below the actual data. What the OLS method seeks to do is plot a line through the middle-dispersion of the data of the variables while minimizing the sum of the squared errors. Consider Figure 3.23. Figure 3.23: Scatterplot of Two Variables Source: Brooks (2008) Figure 3.23 Part A displays a scatterplot of two variables x and y. In Figure 3.23 Part B, the OLS method is used to plot a line exactly through the middle-dispersion of the data. It minimizes the sum of the positive errors as well as the negative errors. In fact, it is highly desirable for the
70 Chapter Three OLS model to be applied in such a way that the positive errors cancel out the negative errors, resulting in the value of zero for the average or expected error term. Economist and econometricians take regression analysis even further. Each line can be equation. A straight line can be expressed in the form of ܻ ൌ ܣ ܤ)ܺ3.12) where A is the point where the line intercepts the Y axis, B is the slope or gradient of the line, and Y and X are two variables. This same concept can be applied to the OLS model. In fact, in equation (3.12), Y is the dependent variable whose value is influenced by the values of variable X. Parameters A and B are estimated via the OLS method. The parameter B is the import parameter of interest in linear regressions since it is the coefficient that indicates the marginal effect. In other words, in an OLS model, the B coefficient indicates the magnitude to which the variable Y will change when there is a change in variable X. Econometrics students are required to note that OLS models are based on the following assumptions: 1. the model is linear, ܻ ൌ ܣ ܤ ;ܺ 2. the expected value of the errors is zero, ܧሺݑ௧ሻ ൌ Ͳ; 3. the variance of the errors is constant, ݒܽݎሺݑ௧ሻ ൌߪଶ; 4. the errors are linearly independent of one another, ܿݒሺݑ௧ǡ ݑ௧ିଵሻ ൌ Ͳ; and 5. the estimated parameters are unbiased, ܧ൫ߚመ൯ൌߚ . Properties 3-5 are the properties for white noise. In summary, white noise is a standard used to verify that a linear regression model is robust. It requires that i) the estimated parameters must be unbiased, and be true representations of the actual parameters; ii) there must be a presence of homoscedasticity and an absence of heteroscedasticity, as evidenced by the constant variance of the error term; and iii) the absence of serial correlation, so that errors of the past must not affect errors of the future. Apart from the OLS model, another popular model that is introduced to students of financial econometrics is the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is a univariate
Technical Tools and Technical Analysis 71 model. In other words, it is a regression that can model the outcome of a variable as a function of past values of itself, and past errors in estimation. Mathematically, this may be expressed as ܻ௧ ൌߙߚଵܻ௧ିଵ ڮߚܻ௧ି ߠଵݑ௧ିଵ ڮߠݑ௧ି ߝ௧ (3.13) where the variable Y in period t, is the dependent variable, variables ܻ௧ିଵ and ܻ௧ି denote time lags in variable Y to previous periods, ݑ௧ିଵ and ݑ௧ି denote lags in the error term, ߙǡ ߚǡand ߠ are estimated parameters, and ߝ௧ is the current error term. The ARIMA (p,d,q) model is attractive, especially in the case of financial data, because it allows a researcher to model stocks, forex, and other asset prices as a function of their own past values, without taking into consideration the values of other variables. The ‘p’ refers to the order of the autoregressive component in the ARIMA model. In other words, it indicates how many lags of the dependent variable will be included in the model. The ‘q’ refers to the order of the moving average component. Alternatively expressed, it indicates how many lags of the error term is included in the model. The ARIMA model incorporates the concept of stationarity. In simplistic terms, stationarity refers to the extent to which the statistical properties of a time series is constant over time. There is strong sense and weak sense versions of stationarity. In the strong sense, stationarity requires all the moment conditions of a time series to be independent of time. Whereas weak sense stationarity is where the mean and variance of a time series is independent of time. Stationarity is a very important concept in financial econometrics. If a time series mean and variance changes with every observation, then a linear regression model will not be able to make accurate predictions about future values of the dependent variable. For this reason, it is highly desirable for a time series to be stationary, at least in the weak sense. In the ARIMA (p,d,q) model, the ‘d’ refers to the order of integration or the extent to which a time series used in the regression is stationary. Usually, before applying the ARIMA (p,d,q) model, the researcher/ analyst63 would be required to perform a series of tests for stationarity. 63 Note, here the person performing the test is referred to as a researcher or analyst as it typically requires some training in econometrics to undertake such
72 Chapter Three Tests such as the Augmented Dicky Fuller (ADF), the Phillips Peron (PP), and the Kwaitkowski-Phillips-Schmidt-Shin (KPSS) tests can be performed for stationarity. A more technical researcher could consider tests such as the Perron (1997), the Zivot and Andrews (1992) tests for stationarity in the presence of structural breaks. However, such tests are not explained in detail as they are beyond the scope of this book. If a series is found to be stationary, also denoted as I(0), then the researcher may use the raw data in the ARIMA model. If the data is found to be non-stationary, and containing 1 unit root, also denoted as I(1), 64 then the researcher would be required to apply a first difference65 to the time series to make it stationary before specifying the ARIMA (p,d,q) model. The correct order of the ‘p’ and ‘q’ are determined by applying the Box-Jenkins Iterative Process. Box and Jenkins (1976) were the first to use a systematic approach to specify ARIMA (p,d,q) models. Their approach involved three steps: Step 1: Identification; Step 2: Estimation; and Step 3: Diagnostic Testing. In the Identification Step, the graphs of the Autocorrelation Function (ACF) and Partial Autocorrelation Functions (PACF) are used to suggest the order of ‘p’ and ‘q’. For an Autoregressive (AR) process the ACF does not vanish but the PACF number of significant spikes will determine the AR (p) process. For a MA (q) process, the PACF does not vanish, and the number of significant spikes for the ACF will suggest the order of the MA (q) process. Consider the example in Table 3.04. assignment. In other words, it is typically a more advanced trader with an understanding of financial econometrics that would perform such task. 64 It is possible for a non-stationary series to contain 2 unit roots. However, the basic stationary tests (ADF, PP, and KPSS) usually indicate that a time series contain 1 unit root. 65 A first difference is a basic transformation that involves the subtracting 1 lag of a variable from itself. It may be denoted by ݀ሺݕ௧ሻ ൌ ݕ௧ െ ݕ௧ିଵ. Such transformation allows and I(1) series to become weak sense stationary, I(0).
Technical Tools and Technical Analysis 73 Table 3.04: ACF and PACF Included observations: 261 Autocorrelation Partial Correlation AC PAC Q-Stat Prob .|**** | .|**** | 1 0.530 0.530 74.065 0.000 .|**** | .|** | 2 0.479 0.276 134.86 0.000 .|*** | .|. | 3 0.347 0.025 166.83 0.000 .|*** | .|* | 4 0.345 0.111 198.58 0.000 .|** | .|. | 5 0.268 0.009 217.78 0.000 .|*** | .|* | 6 0.336 0.160 248.15 0.000 .|** | .|. | 7 0.302 0.062 272.82 0.000 .|** | .|. | 8 0.258 -0.033 290.87 0.000 .|** | .|. | 9 0.226 0.009 304.73 0.000 .|* | *|. | 10 0.159 -0.069 311.65 0.000 In Table 3.04, the ACF does not vanish, indicating an AR process. Since it appears to have 2 significant spikes in the PACF it suggests that the process could be an AR (2). With regards to the MA, the PACF seems to vanish after 2 significant spikes. This may suggest that there is no MA process. Thus, the output in Table 3.04 seems to suggest an ARMA (2,0) model. In the Estimation Stage, the researcher would have to estimate the parameters of the model using econometric software. Some popular econometrics software used by researchers include Eviews, Stata, SPSS, R, and MatLab. Eviews, Stata, SPSS are generally Graphical User Interface (GUI) type software. Thus, a researcher has a menu of options which they can graphically navigate through in order to specify a model. R and MatLab are programming type software, which relies primarily on codes to perform all operations. Modeling performed in R and MatLab are undertaken by researchers with a good understanding of the programming language as well as econometrics and mathematics. This book does not go into detail about explaining how to use econometric software. In the Diagnostic Testing Stage, the researcher is required to perform a series of tests to verify model authenticity. The first diagnostic test a researcher must specify is a test for white noise. Recall, white noise is the absence of serial correlation, the absence of heteroscedasticity in the error term, and the unbiasedness of the estimated parameters. Although 3 principles are used to establish white noise, in practice, white noise is investigated by testing for serial correlation. The Ljung-Box test is
74 Chapter Three frequently used to test for serial correlation in ARIMA (p,d,q) models. Models that fail the white noise test must be abandoned and re-estimated. Models that pass the white noise test may proceed to further tests to verify robustness. Additional tests which should be applied include heteroscedasticity tests, normality tests, tests for the statistical significance of each parameter, and tests for the joint statistical significance of all the estimated parameters. While the aforementioned econometric analysis may sound very complex for a new trader, it can be very easily applied by the new trader. In fact, traders with absolutely no knowledge about econometrics can apply a linear regression model to real life financial data while on a broker’s platform. This is attributed to many brokers offering a tool to undertake linear regression on their platform. Consider Figure 3.24. Figure 3.24 displays the information for the USD/JPY currency pair over the October 2017 to June 2018 period. A linear regression model was applied to the USD/JPY currency pair data for the 2 January 2018 to the 2 April 2018 period. While a ߚ coefficient was not produced by the model, it managed to display a downward sloping pattern for the USD/JPY currency pair over the corresponding time period. Thus, anyone can easily identify that the USD/JPY currency pair was displaying a bearish pattern during that period. While such linear regression model offered by a broker is very easy to use, it contains a significant limitation. Observe over the 2 October 2017 to the 2 January 2018 period that the USD/JPY currency pair was moving in a horizontal direction, over the 2 January 2018 to the 2 April 2018 period the USD/JPY currency pair was moving in a downward direction, but over the 2 April 2018 to the 29 June 2018 period the USD/JPY currency pair became bullish. Any linear forecast based solely on the data from the 2 January 2018 to the 2 April 2018 period would forecast bearish and declining prices for the USD/JPY currency pair. The linear regression models would not be able to identify that a turning point would occur around 2 April 2018, and the market would reverse to bullish conditions. Thus, linear regression models can be misleading.
Technical Tools and Technical Analysis 75 Figure 3.24: Linear Regression Model Source: FX Choice (2018) A number of approaches have been developed to address such limitation of linear regression models. One approach is to apply the concept of structural breaks66 and specify a regime switching model67. The model would have piecewise linearity68, but as a whole, it would be considered as non-linear. An alternative approach is to use a more complex non-linear model, such as an Artificial Neural Network (ANN) model, or a Machine Learning model. ANNs and Machine Learning models are increasingly used to help inform the trade of stocks, forex, and other financial assets. However, ANNs and Machine Learning models are relatively complex, and are executed by advanced traders. Both ANNs and Machine Learning models are outside the scope of this book. 66 A structural break is a specific point in a line where there could be a change in its gradient, its y-axis intercept, or both. In financial markets, structural breaks tend to occur very frequently. They are often a response of asset prices to significant events. 67 A regime switching model is used to model non-linearity in data by assuming different behavior (structural break) in one subsample (or regime) to another. 68 Piecewise linearity is where parts of a regression model are linear. In other words, the regression model is made up of different parts, but each part is linear.
76 Chapter Three 3.9 Summary Insight Technical Analysis is a very important and useful analytical framework that is used by retail traders to help inform their decisions. Unlike Fundamental Analysis which places greater emphasis on the evaluation of an asset’s intrinsic value, Technical Analysis focuses on the price movements in charts, and various indices to evaluate an asset’s performance, and the potential direction in which the price of the asset may go. This chapter examined the basics principles of Technical Analysis that a retail trader should know before entering the market. A retail trader should be fully aware of what are candlesticks, how to interpret them, how to analyze candlestick charts, and how to recognize chart patterns. Furthermore, the retail trader should know what indices, and oscillators would be able to adequately complement their analysis of charts. A wide range of Technical Analysis tools are available. Heikin-Ashi charts react slower than regular candlesticks, thus they are good indicators of medium-term to long-term patterns as their delayed signals eliminate a lot of noise. Moving Averages are a popular tool used by retail traders to identify trends, crossovers, and long-term changes in the market direction. Regular candlestick charts, Heikin-Ashi charts, and Moving Averages can be complemented by indices such as the RSI, the ROC Oscillator, and the OBV. Likewise, Fibonacci Retracement Levels can be used to identify potential resistance and support levels, which in turn could highlight possible turning points. Retail traders can also use linear regressions to plot the general direction of the market. More advanced traders can input asset price data in econometric software to run specific econometric models and determine the direction of the market. In fact, the most advanced traders would be able to use advanced models such as ANNs and Machine Learning models to forecast future prices of assets. The Technical Analysis tools explored in this chapter can all be used to inform a trading strategy of a retail trader. Trading strategies are discussed in greater detail in Chapter Four.
CHAPTER FOUR TRADING STRATEGIES 4.0 Introduction Trading is more than just randomly selecting stocks to long or short. Successful economic agents typically rely upon a trading strategy to profit from trading. In fact, it is difficult for any trader to consistently generate gains on a long run basis without a systematic approach. There are multiple types of trading strategies.69 Some trading strategies are simplistic and can be implemented by the average trader. Other strategies are more sophisticated and rely on computerized software and machines. This chapter considers some simple trading strategies which can be implemented by the average economic agent. 4.1 Trading Strategies A trading strategy is a set of rules a trader uses to decide when to enter and close a trade. Trading strategies utilize both trade filters and triggers. A trade filter is the set of conditions that must be met in order for an asset to enter the watch-list for a trade. A trade trigger identifies the exact point where a trade will be entered. All trading strategies should have rules for entry, rules for exit, rules for risk management, and rules for position sizing. Entries are the points the trader has identified to enter trades. They can be filtered by a number of conditions. For instance, the trader can specify an entry position at the 69 It is important to note, investing also has strategies. For instance, an investor may adopt a value investing strategy in which they first attempt to identify stocks whose price is undervalued relative to their long run fundamentals, and then take a long position on the stock. They may select stocks with lower than average priceto-book ratios, lower than average price-to-earnings ratios, or higher than average dividend yields.
78 Chapter Four open price at the market open, or the close price for a market close. Or, after confirming a chart pattern, the trader can set an entry position as the first or second candlestick that is consistent with the identified pattern. Exits can specify positions that would minimize a loss, or close a winning position after a target profit has been achieved. All trading strategies will carry some risk, as there will always be a possibility that the market participant can incur some loss. The most successful trading strategies are those which minimize loss whenever they occur. This does not mean the total elimination risk. Rather, it cuts the losses early and allows the trader to move on.70 Position sizing refers to the number of shares or contracts a market participant risks with each trade. It is dependent upon size the trading capital of the market participant. Obviously, traders with larger trading capital would be able to take larger positions than traders with small trading capital.71 Apart from the main rules, trading strategies can also be arranged in different categories. Some main types include crossovers, momentum, volatility breakouts, reversals, event trading, and Heikin-Ashi. 4.1.1 Crossovers A crossover is a basic trading strategy that is based on the price or moving average of an asset moves from one side of a longer moving average to the other side. Crossover trading strategies can be generalized into two types: a price crossover, and a moving average crossover. A Price Crossover occurs when the price of an asset increases above (or decreases below) a moving average of that asset. For example, assume that the price of an asset was initially below its 5-day moving average. If the price of the asset suddenly increases and exceeds the 5-day moving average, then a price crossover strategy has occurred. A Moving Average Crossover occurs when a moving average of an asset crosses over another moving average of a longer length. For example, assume that the 5-day moving average of an asset was initially below the 10-day moving average of the asset. Then, assume the price of 70 Risk management is discussed in greater detail in Chapter Five. 71 There are advanced position sizing techniques such as adjusting to volatility,
Trading Strategies 79 the asset significantly increases causing the 5-day moving average to increase. If the 5-day moving average exceeds the 10-day moving average, then a moving average crossover has occurred. Crossovers are used by traders to identify changes in trends. They can be used to determine if an asset’s price is breaking resistance or support, signaling a new uptrend or downtrend. Price Crossovers will occur more frequently than moving average crossover. However, they may send false signals to traders. Traders searching for breakouts on the basis of Price Crossover strategies may identify inaccurate trends as support or resistance may not be broken. Indeed, assets whose prices are highly volatile may crossover short moving averages on a frequent basis, but this does not necessarily mean a new uptrend or downtrend has occurred. Figure 4.01: Price Crossover of TROVW Stocks, Jan 4 – Jan 26, 2016 Source: Yahoo Finance (2016) As can be seen by Figure 4.01, the Trova Gene, Inc (TROVW) appeared to experience a Price Crossover on Thursday, January 21, 2016, as its stock price (US $3.5 exceeded its 5-day moving average (US$1.87). The closing price of Trova Gene’s stock also crosses the 10-day moving average on the same day. Traders and investors may want to confirm a 0 0.5 1 1.5 2 2.5 3 3.5 4 Price US $ Trovw Close 5-day MA 10-day MA
80 Chapter Four new trend and may wait for a moving average crossover before they decide to ride a momentum. Since a moving average crossover does not occur in the displayed period, the price crossover may be a false indication of an uptrend. Consider Figure 4.02 which illustrates the price of Trova stocks over a longer time period. A Moving Average Crossover occurs initially on Wednesday, January 27, 2016, as the 5-day moving average exceeds the 10-day moving average. However, this does not last long as the 5-day moving average falls below the 10-day moving average a few days later. Another Moving Average Crossover occurs on Thursday, February 11, 2016. The data clearly shows this Moving Average Crossover last 6 days. Thus, the second moving average crossover may be a better indicator of a new trend than the Price Crossover. Figure 4.02: Price Crossover of TROVW Stocks, Jan 4 - Feb 22, 2016 Source: Yahoo Finance (2016) Note, in Figure 4.02, the Price Crossover always occurred before the Moving Average Crossover. Thus, a Moving Average Crossover would be a better indication of a new trend than the price crossover as a moving 0 0.5 1 1.5 2 2.5 3 3.5 4 Price US $ Trovw Close 5-day MA 10-day MA
Trading Strategies 81 average crossover would only occur after an uptrend or downtrend has been established. Crossovers may be bullish or bearish. A Bullish Crossover occurs when the price (or short Moving Average) increases above the Moving Average (or longer Moving Average). It signals an uptrend. Traders or investors may take a long position. A golden cross is a bullish crossover. It occurs when the 50-day Moving Average moves above the long-term, 200-day average. A Bearish Crossover occurs when the price (or short moving average) decreases below the Moving Average (or longer Moving Average). Bearish Crossover sends signals of downtrends. Traders or investors may subsequently take short positions or exit previously long positions. A death cross is a bearish crossover. It occurs when the short-term, 50-day moving average, decreased below the long-term, 200-day moving average. Many traders and investors may use multiple moving averages to establish changes in trends. For example, an investor may use a 50-day moving average crossing a 100-day moving average in addition to a 50- day moving average crossing a 200-day moving average. Note, the latter Moving Average Crossover Strategy would be an indicator of a trend since a trend must be established before the moving average crossover occurs. Furthermore, longer Moving Average Crossovers are better indicators of long-term trends while shorter Moving Average Crossovers are better indicators of the short-term trend. An investor would be interested in utilizing long length moving average crossover strategies as they are interested in the long-term direction of the market. They would prefer a moving average crossover strategy that is slow to react to short-term price fluctuations in the market. Thus, a 50-day Moving Average crossing a 200-day Moving Average, and a 100-day Moving Average Crossing a 200-day moving average would be of interest to an investor. A day trader would be interested in short horizon moving averages. For instance, a day trader may utilize a 5-minute moving average crossing over a 10-minute moving average and a 10-Minute Moving Average Crossing over a 15-Minute Moving Average. This sends short-term signals to traders when to enter or exit positions. There is no perfect moving average length. The type of moving average selected and used by a trader or investor would depend upon their
82 Chapter Four trading strategy, their aversion to risk, and the duration of time in which they intend to hold their asset. In addition to crossovers, traders and investors may utilize filters to confirm patterns and determine when to trade. For example, an investor trading on a 10-day Moving Average crossing over a 50-day Moving Average may wait until the 10-day moving average is at least 10% above the 50-day Moving Average before entering a trade. The filter is used to validate the crossover and decrease false signals. The downside to relying on filters is that trends are identified after they occur, thus the investor may lose out on some of their potential gains. Although in the previous examples in this section, the Simple Moving Average Crossover was used, a trader can opt to use Exponentially Weighted Moving Averages for their crossover strategy. The type of Moving Average used will depend upon the trader’s tolerance for false signals. 4.1.2 Moving Average Envelopes and Bollinger Bands Moving Average Envelopes are another type of trading strategy that utilizes moving averages. It involves constructing a confidence interval (perhaps a 10% confidence interval) about a medium-term moving average (perhaps a 25-day moving average) to identify support and resistance levels. If the price of the asset moves beyond this 5% confidence level, it sends signals to the investor/ trader. For example, assume the price of an asset moved below 10% of the 25-day moving average. This suggests to the investor that the price of the asset has broken support and may be experiencing a downtrend. Alternatively, a Bollinger Band can be used instead of the moving average envelope. If the price of the asset moves above 1 standard deviation from the moving average, it suggests to the investor that the asset’s price has broken resistance and an uptrend is occurring. Subsequently, the investor/ trader may take a long position on the asset. 4.1.3 Momentum Momentum trading is where traders trade stocks that are moving significantly in one direction on high volume. The trader uses technical analysis to determine the overall direction of the market, and then enters a position which would allow them to earn a profit. It the trader identifies a
Trading Strategies 83 bullish trend, they will take a long position to ride the momentum. Likewise, it the trader establishes a bearish trend, they will short sell the stock with the intention to cover at a later point and earn a profit. To satisfy the trading condition, the stock price should break resistance or support. Secondly, the stock should be trading at relatively high volume. The upside breakout can be identified by the stock price trading at a new high. Likewise, the downside breakout can be identified by the stock price trading at a new low. More sophisticated traders may use econometric techniques, or computerized software to established support and resistance levels, and breakouts. However, those advanced methodologies are outside the scope of this book. This book is geared towards the novice trader than is aspiring to increase their knowledge base. Alternatively, a trader can establish their own rules to facilitate momentum trading. For instance, as a condition to long a stock, a trader may require the most recent candlestick to breakout and set a new high over the last ‘N’ candlesticks. If ‘N’ is set at 5, then, once the last candlestick has broken the high of the previous 5 candlesticks, it would send a buy signal to the trader. Another example of rules to establish a buy signal, a trader may require the second candlestick to step outside of the Bollinger Bands. Since the majority of the stock price movement occurs within the Bollinger Bands, movement outside the bands can be interpreted as a breakout. A third example of trading rules, a trader could require that the last candlestick rise by at least ‘X’ percent of the previous ‘N’ candlesticks, and the high of the last candlestick to be greater than the high of the previous 2N bars. For example, the trader can enter the long position if the last candlestick to rise by more than 0.5% over the previous 3 candlesticks, and the high of the last closed candlestick to be greater than the highs achieved over the previous 6 bars. The trader can experiment with different values for ‘X’ and ‘N’, and choose the options that generate the most profitable trades. The aforementioned trading rules can also be supported by increases in trading volume. For instance, the trader may also require the trading volume to increase by at least ‘X’ percent, in addition to the stock price changes. Or the trader can require a relative volume of at least 2 in order to confirm the new momentum. Such a strategy is sensible since the trading volume should increase when new trends emerge.
84 Chapter Four Day traders also search for parabolic moves. A parabolic move is an exponential change (increase or decrease) in the stock’s price. Parabolic moves can occur as a consequence of a stock responding to news. Good news regarding a company’s sales and its profitability should cause upward price movement. Bad news regarding a company’s profitability or public relations tends to cause a negative stock price movement. 4.1.4 Volatility Breakouts A Volatility Breakout is a trading strategy based on trading upside and downside breakouts. It based on the premise that if the market moves a certain percentage in excess of resistance or support a breakout will occur. To capitalize on a breakout, a trader’s strategy should have conditions that must be made before marking an entry. For instance, the trader may require at least three (3) 5-minute candles to break resistance, as well as the relative volume to be greater than 2, in order to go long. The strategy may also include a rule for the position size (perhaps 5% of the total equity), and a rule for exit (perhaps closing the order after on the first 1- minute candle to make a pullback after achieving at least a 15% gain). Like most trading strategies, Volatility Breakouts have the potential for profits or losses. If a trader interprets false signals they may incur losses. For example, if a trader wrong mistake a 1-minute candle rising above resistance by 10% for a breakout, they may go long. However, a reversal may occur instead with the asset’s price. Thus, by going long, the trade took the wrong position and may incur a large loss. Due to the occurrence of false signals, traders may use delayed indicators to identify breakouts in order to avoid false signals. For instance, the trader may require an Exponentially Weighted Moving Average to also break resistance or support to confirm the pattern. The downside of using delayed signals is that by the time they confirm a pattern, the pattern may soon end, and the trader may have lost the opportunity to earn a profit. 4.1.5 Reversals A Reversal Trading Strategy is based upon trading reversals. The trader performs Technical Analysis to identify reversals, and then make the appropriate trade.
Trading Strategies 85 The RSI is a useful indicator to identify reversals. As previously mentioned, if the RSI is greater than 80, it suggests that a stock is overbought. This indicates a possible reversal for the trader. Thus, the trader using the reversal strategy may short sell the asset. Likewise, an RSI less than 20 suggests that a stock is oversold. A trader using the reversal strategy would go long on the asset. Cautious traders may set their own more stringent conditions when trading on a reversal. For instance, they may require the RSI to drop below 10 to go long, or the RSI to rise above 90 to go short. This can be supplemented by the trader looking for at least 1 candlestick to reverse, after about 3 consecutive 5-minute candlesticks of the same color has hit resistance or support. Ideally, the trader should try to catch the stock as close to resistance or support as possible in order to earn larger profit margins when trading reversals. 4.1.6 Events Trading In the case of stocks, news on a company’s financial health, profitability, operational challenges, as well as scandals can all affect the price of stocks. Macroeconomic news which can affect the financial health of the company can also affect stock prices. In the case of forex markets, currency pairs tend to react to major economic news. While the major currency pairs react to most economic news from developed and influential countries, the biggest movers and most watched news come from the US (Bauwens et al. 2005; Roache et al. 2010; Lahaye et al. 2011). The reason is that the US has the largest economy in the world and the US Dollar is the world’s reserve currency (Reinbold and Wen 2018). This means that the US Dollar is a participant in the majority of all forex transactions (Blinder 1996; Forest et al. 2018). Economic news on the US economy such as GDP growth, inflation rate, and the Federal Reserve’s (Central Bank) repo rate all can influence the market speculation and the extent to which the US moves against other countries. Geopolitical news on events such as war, natural disasters, political unrest, and elections can also affect speculation on the US dollar. For example, in May 2007, the seasonally adjusted unemployment rate in the US was 4.4%. However, as the financial crisis and economic recession took root in the US, the unemployment rate quickly rose to 10% by October 2009 (US BLS 2018). Such rising unemployment reflected a
86 Chapter Four weakening of the US dollar. Thus, there was no surprise when the US dollar depreciated against major currencies during the associated period (Fratzscher 2009). A retail trader basing their trading mainly on news can do so by looking for a period of consolidation ahead of the release of routine economic news, then trading on the breakout. Positions taken in news based trading can be held for a short moment (as in intraday trading) or for a few days depending on the news. In short, good news cause financial assets (stock prices and currency pairs prices) to increase, while bad news causes the financial assets prices to decline. In the case of stocks, this arises due to multiple traders going long on a stock after reports of good news, increasing demand and rising price. Whereas bad news is accompanied by traders closing long positions, or short selling, causing a decline in demand, and the decline in the stock price. The practice of trading based upon news is known as event trading. Consider an example, on Tuesday 25 April, 2017, Nord Anglia Education Inc., a Hong Kong-based operator of international schools, announced that it would be bought by the Canada Pension Plan Investment Board and Baring Private Equity Asia for US$4.3 billion. The positive news of the acquisition caused the price Nord Anglia Education Inc. (NORD) stocks to increase by 17.38% by 10:00 am on the same day. If a trader takes a correct position based on news and catches the momentum early, windfall profits are the result. Conversely, if the trader took the wrong position, or continued to hold the wrong position in the aftermath of bad news, large drawdowns can occur. Likewise, traders that trade based on black swan events72 can earn huge profits or losses depending on whether they took the correct position or not. Consider a hypothetical example. Assume that a publicly traded stateowned oil company was poorly managed and it was one the verge of bankruptcy. Assume this information was announced on the news. The obvious reaction of people hearing such news would be to sell off their 72 Black swan events are extremely rare events that can have huge effects on financial markets. They are random and highly unpredictable. Some examples include the Wall Street Crash of 1929 and its associated Great Depression, the dotcom bubble of 2001, the 2008 US housing market crisis and associated financial crisis.
Trading Strategies 87 stocks of that state-owned oil company. Assume that a person who had stocks of the state-owned oil company heard about the company’s problem the day before it was announced on the news. In such a case they would sell out all of their stocks and would be able to make more profit than if they had waited on the news. Hence trading based on news would be more beneficial to the retail trader than trading without consideration of news. Note, when stakeholders of a company trade based on information before it is released in the news it is called insider trading. Such activity is considered unethical and is illegal in many countries. Significant news tends to increase the volume of trade of the affected stocks, currency pairs, or financial assets. In the forex market, since the volatility tends to increase when significant news are released, many forex brokers tend to widen the spread between bid and ask. For example, the spread between a currency pair may be comprised of a bid price of US$1.258 but an ask price of US$1.260. The difference between US$1.258 and US$1.260 is 0.0002 or 2 pips. When trading with market orders during periods of news related volatility, market orders can be filled at a significantly different price to what the retail trader intended. For example, the ask price of a currency pair may be US$1.260. The trader may go long and purchase the currency pair because they believe that the bid price may rise to a value significantly higher than US$1.260. However, assume the trader place a long market order during a moment of extremely high volatility. It is possible for the order to be filled at an ask price significantly higher than US$1.260. If that occurs, it would now take more upward price movement to cover the bid-ask spread, as well as the cost of commission in order for the trader to generate a profit. The same problem can occur on the downside, causing the retail trader to experience slippage. Consider another example. Assume the trader anticipating a decline in the market placed a short market order for the currency pair. Assume the order was placed at a moment of high volatility. It would be possible for the order to be filled at a price significantly lower than what the trader expected, and thus causing the trader to experience slippage. It is noteworthy that after the announcement of big market news, financial markets often do not move in only one direction. There can be jumps and long candlesticks for price movement in both directions
88 Chapter Four (Lahaye et al. 2011). It is possible during such news related volatility for delays to occur in the filling of orders. Even if a trader places an order at the right time, delays in filling the order can result in the trader incurring losses. For example, assume that a retail trader placed a long order for a currency pair at US$1.260, hoping to go short at US$1.270 to make a profit. Assume there was a delay in the order being filled, then the currency pair price jumped to US$1.501 where it was filled. Then assume there was another jump back to US$1.265. Since the long order was filled at a significantly higher price, the trade was not profitable for the retail trader. A retail trader should be mindful of the aforementioned volatility related risks that are associated with news trading. As a precaution, the retail trader can opt to use limit orders with target profits to help manage this volatility risk during periods of high news related volatility. 4.1.7 Heikin-Ashi Some day traders use Heikin-Ashi charts rather than normal candlestick charts to identify their patterns. In fact, Heikin-Ashi charts can be used for crossovers, momentum, and reversals trading strategies. The choice of the trader to use Heikin-Ashi Charts depends on their tolerance for false signals. Most brokers offering online trading platforms will have the option to display price movements as Heikin-Ashi candlesticks. 4.1.8 Elliott Wave Based Trading As mentioned in Chapter Three, the Elliott Wave Theory can be used to identify chart patterns. Such patterns can be used to inform decisions to go long or go short. Consider an example in Figure 4.01. A retail trader may look at the charts of the XAU/ USD currency pair in July 2018 and wonder if to buy or sell gold (XAU). Recall, the rules regarding the Elliott Wave Theory: Wave 2 should not go beyond the start of Wave 1, and Waves 2 and 4 may frequently bounce off FRLs. Assume that a trader applied such rules to Figure 4.03. This application may be reflected in Figure 4.03b.
Trading Strategies 89 Figure 4.03a: XAU/ USD Currency Pair July 3 – July 5 2018 Source: FX Choice (2018) Figure 4.03b: XAU/ USD Currency Pair July 3 – July 5 2018 Source: FX Choice (2018)