Graph Showing Attributes Of Uptrend Finance Essay
A literature review is a specific type of research paper that focuses on published literature related to the topic. In this chapter the review of literature has been made which is divided into three sections. Conceptual Review - synthesizes areas of conceptual knowledge that contribute to a better understanding of the issues. Empirical review is the research based study that relates or argues positively with the study’s hypothesis and variables. Contextual review
2.1 Conceptual Review
One of the most important concepts in technical analysis of stocks is that of trend. A trend is the general direction in which a security or market is headed.
Fig # 2.1: Figure showing unidirectional trend
It is not hard to see that the trend in Figure Fig # 2.1 is up. However, it is not always this easy to see a trend:
Fig # 2.2: Graph showing zigzag trend
There are lots of ups and downs in the Fig # 2.2, but there is not a clear indication of which direction this security is headed. In technical analysis, it is the movement of the highs and lows that constitutes a trend. For example, an uptrend is classified as a series of higher highs and higher lows, while a downtrend is one of lower lows and lower highs.
Fig # 2.3: Graph showing attributes of Uptrend
Fig # 2.3 is an example of an uptrend. Point 2 in the chart is the first high, which is determined after the price falls from this point. Point 3 is the low that is established as the price falls from the high. For this to remain an uptrend each successive low must not fall below the previous lowest point or the trend is deemed a reversal.
Types of Trend
There are three types of trend:
Uptrend - each successive peak and trough is higher
Downtrend - each successive peaks and troughs are getting lower
Sideways/Horizontal Trend - little movement up or down in the peaks and troughs
Support and Resistance
Fig # 2.4: Graph showing Support and Resistance
As it can be seen in the Fig # 2.4, support is the price level through which market or a stock rarely falls (demonstrated by the upward arrows). Resistance, on the other hand, is the price level that market or a stock rarely surpasses (demonstrated by the downward arrows).
Once a resistance or support level is broken, its role is reversed. If the price drops below a support level, that level is called as resistance. If the price increases above a resistance level, it will be called as support. As the price moves past a level of support or resistance, it is thought that there has been a shift in supply and demand, causing the breached level to reverse its role. For a true reversal to occur, however, it is very important that the price makes a strong move through either the support level or resistance level.
Fig # 2.5: Graph showing attributes of zigzag trend
As an example from the Fig # 2.5, the dotted line is shown as a level of resistance that has prevented the price from heading higher on two previous occasions (Points 1 and 2). However, once the resistance is broken, it becomes a level of support (shown by Points 3 and 4) by propping up the price and preventing it from heading lower again.
Head and Shoulders
Fig # 2.6 Graph showing Head and Shoulders trend
In the Fig # 2.6 Head and shoulders top is shown on the left. Head and shoulders bottom, or inverse head and shoulders, is on the right. Head and shoulders is one of the reversal chart patterns that when formed, indicates that the share price is likely to move against the preceding trend. As it can be seen in Fig # 2.6, there are two versions of the head and shoulders chart pattern. Head and shoulders top (shown on the left) is a chart pattern that is formed at the high of an upward movement and signals that the upward trend is about to end. Head and shoulders bottom, also known as inverse head and shoulders (shown on the right) is the lesser known of the two, but is used to signal a reversal in a downtrend.
This theory has been developed over several years after the early proponent Henri Poincare proposed it in the 1880s. Chaos Theory is defined as a mathematical concept which explains that, it is possible to get random results from normal equations. The main principle of this theory is that the underlying notion of small occurrences significantly affecting the outcomes of seemingly unrelated events. Also referred to as "non-linear dynamics".
Chaos theory has been used and applied to many different spheres, from predicting weather patterns to the stock market. Basically, chaos theory is an attempt to see and understand the underlying order of multifaceted or complex systems that may appear to be without order at the first glance. Related to financial markets, proponents of this theory believe that price is the last thing to that would change for a stock, bond, or some other kind of security. The following factors can be used to determine the price change using some of stringent mathematical equations.
1) A trader's own personal motives, needs, desires, hopes, fears and beliefs are complex and
2) Volume changes.
3) Acceleration of the changes
4) Momentum behind the changes
Chaos theory is highly controversial and extremely complicated.
Efficient Market Hypothesis (EMH)
In finance, the Efficient Market hypothesis (EMH) claims that financial markets are "informationally efficient". As a result of this, a trader/Investor cannot consistently achieve returns in excess of average market returns on a risk-adjusted basis, given that the information is made available at the time of investment.
There are three versions of the hypothesis:
The weak-form EMH claims that prices on traded assets (e.g. bonds, stocks or property) already reflect all the information that is publicly available in the past. The Semi-Strong form of EMH claims that both, the prices reflect all publicly available information and that price instantly changes to reflect newly available public information. The strong-form EMH additionally claims that prices instantly reflect even hidden or "insider" information. Critics have blamed this belief in rational markets for much of the late-2000s financial crisis. In response, supporters of the hypothesis have stated that market efficiency does not mean having no uncertainty about the future, that market efficiency is a simplification of the world which may not always hold true, and that the market is practically efficient for investment purposes for most individuals.
2.1.1 - Using Technical Indicators to Develop Trading Strategies
Jean Folger (2011) in her article states that indicators such as moving averages and Bollinger Bands are technical tools that are mathematically based and analyse the past prices to predict the future prices, trends and patterns. The goal of using the indicators is to identify the trading opportunities.
Strategies, on the other hand, mostly employ indicators in an objective manner to determine entry, exit timings. A strategy is a definitive set of rules that specifies the exact conditions under which trades will be established, managed and closed. Strategies typically include the detailed use of indicators or, more frequently, multiple indicators, to establish instances where trading activity will occur.
The author gives a set of questions that needs to be answered by the trader in order to distinguish a strategy from a decision taken merely based on the indicators.
The author explaining on the use of technical indicators says that an indicator can help traders identify market conditions; a strategy is a trader's rulebook: How the indicators are interpreted and applied in order to make educated guesses about future market activity. There are many different categories of technical trading tools, including trend, volume, volatility and momentum indicators. The trader must take care while selecting the indicators, so that the issue of multicollinearity will not arise.
Regardless of how many and which indicators are used, a strategy must identify exactly how the indicators will be interpreted and precisely what action will be taken. Indicators are tools that traders use to develop strategies; they do not create trading signals on their own.
The author further gives guidelines and checkpoints in choosing the indicators for a strategy. What type of indicator a trader needs to use for developing a strategy depends on what type of strategy the trader intends build. This relates to trading style and risk tolerance. If a trader looks at long term moves and large profits, then the trader could consider trend based indicator such as moving average. On the other hand, if the trader is looking at short time and frequent gains then he could go for one of the volatility based indicators. Again, different types of indicators may be used for confirmation. The following figure shows the four basic categories of technical indicators with examples of each.
Types of Indicators
Moving Averages, MACD, Parabolic SAR
Stochastic Indicator, CCI, Relative Strength Index
Bollinger Bands, Average true Range, Standard Deviation
Chaikin Oscillator, OBV, Rate of Change(ROCV)
The author concludes by saying that Indicators alone do not make trading signals. The author stresses on the fact that a trader must be very clear on the indicators that will be used to signal trading opportunities and to develop strategies. Identifying clear and complete set of rules, as with a strategy, will allow the trader to back test in order to determine the viability of a particular strategy. This is very critical to technical analysts/traders as it helps them to continually monitor and evaluate the performance of the strategy. The author leaves the option at the trader to learn about the variety of technical analysis tools that are available, research how they perform according to their individual needs and develop relevant strategies.
2.1.2 - Behavioural Finance and Technical Analysis
Kosrow Dehnad (2011) in his article states that Behavioural finance has challenged many claims of EMH i.e. efficient market hypothesis. Unfortunately many of these challenges are in the form of anecdotal evidence therefore lack quantification. The study uses market data together with some simple statistics to show that in practice certain assertions of EMH and mathematical finance can be rejected with a high degree of confidence. The working of the FX market is used to demonstrate certain shortcomings of refined results in mathematical finance that render them inappropriate in practice. An approach based on Markov chains is developed to model certain heuristic notions such as “fast market”, “support” and “resistance” that are widely used by technical analysts and practitioners. Using market observation, it is shown that this model fits better with historical data than that implied by the assumption that daily returns are independent and normally distributed. The author develops 2 models i.e. Brownian motion and Markov Chain model and conducts a simple test based on the market observation to determine which model better represents the daily US Dollar / Japanese Yen (USD/JPY) markets.
The author found that, in daily trading of very liquid assets, such as JPY, one has to be aware of notions such as “market sentiments,” “support,” “resistance,” etc. Many of the mathematical models applied in finance ignore these notions and dismiss them despite the fact that all practitioners are very much aware of them. Continuous time finance also makes certain simplifying assumptions that greatly cut the applicability of its results to daily trading. The author demonstrates some of these issues and proposes a modified version of the Markov chain that will enable practitioners to model, in a reliable and consistent manner, concepts that are used in “technical analysis.” It also remedies one of the major shortcomings of technical analysis which is its inability to assign probabilities to various outcomes – a fundamental ingredient of successful trading.
2.1.3 – The best of traders classroom
Jeffrey Kennedy (2009) in his paper citing the work of Dr. Deming says that majority of the traders fail in the stock market not because they don’t try hard enough or they don’t want to succeed, but because they follow faulty trading system or they don’t follow any system at all.
The author says that method, essentials to follow the method, experience, the mental fortitude to accept that loses are part of the game and the mental fortitude to accept huge are the essentials to be a good trader.
220.127.116.11 - Ready Aim... Fire: Knowing when to place a trade:
The author gives certain guidelines to the traders in case the traders are using the Wave principle in trading. The guidelines are as follows.
Fig # 2.7: Graph showing ready position
As a trader the author applies a 3 step approach to decide when to place a trade. The above figure shows a schematic diagram of a five wave advance, followed by a three wave decline. This type of wave, which is zigzag in nature, is called as the ready stage.
Fig # 2.8: Graph showing aim position
In the above figure, the prices are moving upward as indicated by the arrow. At this stage the author AIMS as he watches the price action to confirm the wave count by moving in the direction determined by the previous labelling
Once the prices confirm the wave count, the trader then has to determine the price level where the trade has to be placed.
Pull the Trigger
Fig # 2.9: Graph showing triggering position
The trader has to wait for the extreme of wave B of a zigzag before initiating the position. In this way the traders allows the market to prove or disprove the wave count. Moreover, once the extreme of wave B is exceeded, it leaves behind a three wave decline from the previous extreme.
The author concludes saying that all the markets have a wave count, however all the wave count does not offer a trading opportunity. The author warns the trader to carefully watch and confirm the wave count before pulling the trigger to fire i.e. to place an order.
18.104.22.168 - How the wave principal can improve your trading
Jeffrey Kennedy highlights the limitations of some of the technical indicators in his paper and states that technical indicators such as moving average convergence divergence (MACD), directional moving Index (ADX), Rate of change (ROC) and commodity channel index (CCI) do a good job of illuminating the way for traders, yet each fall short for one major reason. They limit the scope of traders understanding of current price action and how it relates to the overall picture of the market. For example, MACD reading ABC stock is positive, indicates that the trend is up. This information is useful but it would better if it could also answer the following questions
Is this a new trend or an old trend?
If the trend is up, how far will it go?
The author continues by saying that, most technical studies don’t reveal relevant information regarding maturity of a trend and a definable price target – but the wave principle does.
The author highlights five ways in which the wave principle will improve trading and they are as follows.
Identifies trend: a wave principle identifies the direction of the dominant trend. A five wave advance identifies the overall trade is up and vice versa.
Identifies countertrend: the tree wave pattern in a corrective response to the preceding impulse wave.
Determines the maturity of the trend: the wave patterns form larger and smaller versions of themselves. This repetition in form means that the price activity is fractal. As illustrated in the figure below. Wave (1) subdivides into five smaller waves and yet is a part of larger five wave pattern and this will help the trader in recognising the maturity trend.
Fig # 2.10: Graph showing multiple Elliot Waves
Provides prices targets: the author citing the work of R.N. Elliott who wrote about wave principle in nature’s law, states that Fibonacci sequence was the basis for wave principle. Elliot waves, both impulsive and corrective adhere to Fibonacci proportions.
Provides specific points of Ruin: the author provides insights on the points of Ruin in the form of Elliott wave rules.
Fig # 2.11: Graph explaining the points of ruin
Rule 1 – Wave 2 can never retrace 100% of wave 1
Rule 2 – wave 4 may never end in the price territory of wave 1.
Rule 3 – Out of the three impulsive waves – 1, 3 and 5 – wave 3 can never be the shortest.
The author concludes by warning that a violation of one or more of these rules is the outcome of the incorrect wave count.
2.1.4 - Elliott Waves Vary Depending on the Time Frame and Direction of the Pattern
Rich Swannell (2003) in the article states that liquid markets do not move at random. Understanding this can facilitate more accurate market forecasts. Followers of Elliot have always known that consequences of the emotions as they play out in the market are far more random. in fact, the consequences of price movement are patterned. These patterns are based on the Wave Principle. The Wave Principle defines about common patterns found in the price data of liquid markets. By identifying the beginning of common Elliott patterns, it is possible to calculate the probability of those patterns completing, and thus, where and when the market is likely to change direction. However the author claims that, until now, no follower of Elliott has known the exact probability of a market turning at any given price and time.
Liquid markets are, by definition, traded by a large number of people. Although it is nearly impossible to determine what a single trader will do, it is possible to determine the statistical probability of what a large crowd of traders will do. This so-called mass psychology swings back and forth like a pendulum, as mass human emotion oscillates between optimism and pessimism. When the Elliott Wave Principle is applied to highly liquid markets, it has been proven to be accurate in identifying the changes in mass human emotion, and it thereby reveals the key turning points in the market.
Liquidity is not only essential for consistent Elliott behaviour; it is a prerequisite. Stocks such as those on the S&P and NASDAQ, for example, often exhibit strong and reliable Elliott Wave patterns. These markets are driven by mass psychology. No individual trader, institution, or Government can manipulate these markets. They are truly liquid, driven by supply and demand - the result of the “state of mind” of the crowd, as it moves from fear to hope and back again. Conversely, thinly traded markets, such as speculative stocks, do not generally show consistent Elliott Wave behaviour
2.1.5 - Swing Trading
Larry swing (2008) in his book “A practical guide to swing trading” gives insights on what is swing trading and how swing trading is done. He also explains certain concepts associated with trends, when and how to enter into a trade creating a master plan and so on.
As per the author, swing trading is based on the wave movement. As an example, when the share prices move up, it takes rest or pulls back in between. When the trader swing trades an uptrend, he buys on the pull back. In swing trading the trader capitalises on the predictability of the pattern. The trader buys during the pull back to increase the chances of making a profit. Similarly in a downtrend, the trader short sells on the pull ups.
The author while explaining the steps for swing trading has given certain primary guidelines which are applicable to the US stock Market. i.e.
The minimum share price should be $7
The average daily volume traded should be at least 500,000 shares
The additional general guidelines are as follows
1 – Identify a stock that follows a particular trend, it can be uptrend or a downtrend.
2 – Identify those stocks, which experience a pull back in case of an uptrend. In case of downtrend, identify those stocks that are experiencing a pull-up.
3 – On identifying appropriate stocks, in order to buy (uptrend) or sell short (downtrend), the trader needs to place a limit order on the stock based on the Master Plan.
4 – based on the risk taking ability (if price drops) and the amount of profit the trader wants to make (if price rises) the trader has to place a stop loss order and limit order. (Ideally, these two orders are placed together as an OCO (One Cancels Other) order.
5 – The traded has to adjust the stop loss prices based on the Master plan at the end of the day.
The author explains a Master Plan as a set of rules that determines when to enter and exit a trade. The rules are mechanical hence it will help the trader to overcome two main obstacles to successful trading, which are human emotions of fear and greed.
The author on explaining how and when to enter a trade gives suggestions based on the whether the share prices gap up or gap down in terms of US Dollars.
The author further explains on how to make money when the share prices are falling. When the share prices are predicted to drop, a short swing is used to make money. The trader sells short the stock and buys back at a lower price. Short swing and long swing are the mirror images of one another. In a downtrend of a stock, the share price tends to have a periodic, short-term rallies which is also known as pull-ups as the price moves lower. This short rally is the set up for the short swing. The master plan which has the decision rules help to enter the trade when the stock is resuming its downward path.
2.1.6 – Essential technical analysis
Leigh Stevens (2002) in book writes about concepts such as rationale to the technical approach, trading and investing game plan, basics about price and volume, trend and retracements, constructing trend lines etc
The author in his book highlights certain trend reversal setups. They are as follows
double and triple tops or bottoms
W bottoms or M tops
V tops or bottoms
rounding tops and rounding bottoms
the head and shoulders pattern
broadening tops or bottoms, which are traced out over an extended period
breakaway and exhaustion gaps
The author also explains some of the popular technical indicators such as relative strength index, Bollinger bands, net A/D oscillator, the McClennan oscillator, call/put ratio and sentiment etc. at a deeper level.
The author concludes by advising the traders to further study the moving averages along with other indicators as moving averages helps the trader to understand where does the stock stand at any given point. The author also advises to study those indicators which give addition information about the trend such as direction, strength or momentum and overbought and oversold information.
2.1.7 Introducing the Volume Price Confirmation Indicator (VPCI): Price & Volume Reconciled
Buff Dormeier, CMT (2004) in his article introduces a new volume price measurement tool that the author claims could provide the clearest picture of the volume – Price relationship of any other indicator devised. VPCI identifies the inherent relationship between price and volume as harmonious or inharmonious. This study shows that investors who use the VPCI properly may increase their profits and the reliability of their trades, while simultaneously reducing risk.
The author uses the following components in building up of VPCI.
SMA - simple moving average
VWMA - volume-weighted moving average
The VWMA is calculated by multiplying Day Ones’s price with Day One’s volume of the total range expressed as a fraction
VPC (+/-) - volume-price confirmation/contradiction
The VPC is calculated by subtracting a long-term SMA from the same timeframe’s VWMA.
VPR - volume-price ratio
VPR makes the VPC+/- more noticeable relative to the short-term price-volume relationship. The VPR is calculated by dividing the short-term VWMA by the short-term SMA
VM - volume multiplier
The objective of VM is to overweight the VPCI when the volumes are rising and underweight the VPCI when the volumes are dropping. This is achieved by dividing the short-term volume average by the long-term volume average.
The VPCI is not a complete or a standalone indicator. Most volume-price indicators do not consider price trend while giving signals. The VPCI does not give any indications outside of its relationship to price; it only confirms or contradicts the price trend. There are numerous ways to use VPCI in combination with price indicators and price trends. These include a VPCI greater than zero, the rising or falling of VPCI, a smoothed (moving average) rising or falling VPCI, or VPCI as a multiplier.
The author concludes the paper by stating that based on the proportional weights; the VPCI reconciles price and volume. The likelihood of the current trend being continued can be confirmed using this information. The conclusion also highlights the importance of using VPCI indicator along with the trend following as this combination has consistently improved performance across all major areas measured by the study. The author claims VPCI to be maestro’s baton in the hands of investor. A tool capable increasing profits, minimising risk and empowering the investor in making more reliable investment decisions.
2.1.8 - Scientific Frontiers and Technical Analysis.
Kevin P. Hanley, CMT (2006) in his paper questions if there are any scientific foundations to Technical Analysis or if it is a pseudo-science? Academia, embracing the Random Walk Theory, the Efficient Market Hypothesis (EMH) and Modern Portfolio Theory (MPT) has argued the latter for some 20 years or more. The author states that, In fact, according to current orthodoxy, both TA and Fundamental Analysis are fruitless distractions and cannot add value. The advent of Behavioural Science has illuminated some of the flaws in the standard model. Andrew W. Lo’s Adaptive Markets Hypothesis reconciles efficient markets with human behaviour by taking an evolutionary perspective. According to Lo, markets are driven by competition, adaptation, and natural selection. What is missing is a more accurate and comprehensive model of the market itself. Complex and Chaos system theories provide a more comprehensive understanding of market behaviour. The markets can be seen as chaotic, complex, self-organizing, evolving and adaptive, driven by human behaviour and psychology. Patterns in the market are emergent properties. Identifying these patterns has predictive value, but certainties must be left behind; only probabilities remain. TA, shown to be the inductive science of financial markets, is an important tool for identifying these emergent properties and analyzing their probabilities. Lastly, so that the science of TA may advance, the field must distinguish between scientific, empirically based, market analysis theory and the categories of interpretation and practical trading strategies.
2.1.9 Does the Wave Principle Subsume all Valid Technical Chart Patterns?
Robert R. Prechter Jr., CMT (2009) in his article investigates whether the Wave Principle subsumes forms asserted in other types of pattern analysis. If the Wave Principle constitutes the primary market pattern, as supporters assert, then all other proposed patterns must either be spurious or fall within the structure of the Wave Principle. Based on the study the author found that technicians may reduce the large and varied catalogue of proposed market patterns down to five essential forms
2.2 Empirical Review
2.2.1 - Dimitris N. Politis, Ph.D (2006) in his paper Volatility Bands With Predictive Validity revisits the issue of volatility bands, It is shown how the rolling geometric mean of a price series can serve as the centreline of a novel set of bands that enjoy a number of favourable properties including predictive validity. The author provides a method to construct volatility bands that are at the same time predictive bands having a pre-specified level of predictive coverage. The bands are easy to construct with basic spread-sheet calculations, and can be used wherever Bollinger bands are used. It was noticed that the latter lack any predictive validity. Finally, a discussion was given on choosing the band parameters q and Q, Namely, the window sizes for the Geometric Moving Average and the rolling estimate of variance
2.2.2 - Manuel Amunategui, CMT (2006) In the paper Global Squawk Box - Evaluating Internet Financial Message Board Traffic as a Technical Indicator attempts to tackle the mass of financial message board postings on the Internet and turn it into a simple and profitable technical indicator. The Global Squawk Box Indicator tries to offer a similar source of emotional measurement that floor traders have enjoyed for decades by simply watching and listening to surrounding trading pits. This study also aims at introducing the process and value of seeking alternative data. It was found that, the GSB proves to be a viable and versatile indicator. The study looked at three ways of applying GSB data to different trading situations. As an alert, the GSB indicator easily confirmed major news and financial events and, in some situations, before being factored into the stock’s price. In the trend-following system, the GSB offered a useful gauge on the crowd’s interest level on a stock thus alerting the trader to stay out of the game on those potentially volatile days. In the bottom-picking system, just as in the trend-following system but the other way around, the GSB pointed towards unusually high interest levels and the potential for a larger trading range to profit from. The data might also be a useful addition in testing trading systems historically. It can help uncover unrealistic profits by avoiding price shocks and other unpredictable events. Overall, the GSB indicator, along with ‘Pre-Market’ and ‘After Hours’ indicators, offers a great way to gauge the intensity of an upcoming trading day for a particular stock. Some might choose to lay off on such days while others might devise trading systems to those situations. With a little effort, basic programming skills and imagination, GSB and GSB derivatives are bound to improve a trading system.
2.2.3 - Blake LeBaron (1999) in his article Technical Trading Rule Profitability and Foreign Exchange Intervention states that there is reliable evidence that simple rules used by traders have the ability to predict the future movement of foreign exchange prices to some extent. This paper discusses the economic magnitude of the predicting ability after few research papers were discussed. The intervention data from the Federal Reserve is used to analyse the profitability of the trading rules in connection with central bank activity. The objective was to find out the extent to which foreign exchange predictability can be limited to periods of activity of central bank in the foreign exchange market and it was found that after removing periods in which the Federal Reserve is active, exchange rate predictability is dramatically reduced.
2.2.4 - LO, A.W., et al., 2000. In their paper Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation state that Technical Analysis which is also known as charting has been used in the financial sector for many decades now, but has failed to receive the same level of academic acceptance and assessment as that of the traditional approaches, such as fundamental analysis. The author claims that the highly subjective nature of technical analysis to the main barrier and that, one who is looking at a chart may interpret the chart contents in his own way. In this paper the authors proposes a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and they apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution conditioned on specific technical indicators such as head-and-shoulders or double bottoms and it was found that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value."
2.2.5 - Fern´andez-Rodr´ıguez, Gonz´alez-Martel and Sosvilla-Rivero (2000) in the paper on the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market attempts to examine the profitability of a simple trading strategy based on Artificial Neural Networks (ANNs). The investment strategy was examined on the General Index of the Madrid Stock Market and the strategy was found to be always superior to a buy-and-hold strategy in both bear market and stable market condition in the absence of trading cost. The study also found that buy-and-hold strategy generates higher returns than the trading rule based on artificial neural network only in the case of bull market.
2.2.6 - Neely and Weller (2001) in their paper Technical analysis and central bank intervention, uses genetic programming technique to find out if the Unites States foreign exchange intervention information results in incremental profitability for about two of four exchange rates over part of the out of sample period. The study revealed that rules trade contrary to intervention and was found to generate unusual profits on days prior to intervention. This signifies the trend blocking nature of the intervention. Intervention seems to be more successful in checking such trends in the out-of-sample (1981-98) period than in the in-sample (1975-80) period. Precise estimation of the relationship between past and current foreign exchange rates, rather than from information about contemporaneous intervention would result in improvement in performance.
2.2.7 - CESARI, R. and D. CREMONINI, (2003). In their article Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation, does an extensive simulation comparison of popular dynamic strategies of asset allocation. For each strategy, alternative measures have been calculated for return, risk and risk-adjusted performance (Sharpe ratio, return at risk, Sortino ratio). Moreover, the strategies are evaluated in different market situations (bull market, bear market, no-trend market) and with diverse market volatility, taking into consideration discrete rebalancing of portfolios and transaction costs. The model exhibits a superior role of constant proportion strategies no trend and bear market and a prefers benchmarking strategies in bull markets. The authors conclude by saying that these results are independent of the volatility level and the risk-adjusted measure adopted.
2.2.8 - F.E James Junior (2001) in his article Monthly moving averages, an effective investment tool examines the effectiveness of the Moving Averages over different ranges on time period. The article describes a series of experiments that were performed upon actual market data, using moving averages of different lengths and weights. This investigation was carried out with as a part of a larger number of experiments which were designed to test the random walk hypothesis in security prices. The data for the experiment was the month end closing prices of common stocks for the year 1926 – 1960. The author found that the decision taken based on the moving averages was much superior to the decision rule i.e. buy and hold. The author says that the moving average technique has been said to passes enough predictive power to enable the user to sell somewhere near the price peak (after it has passed) and repurchase near the bottom of the ensuing decline. Finally the author warns that the results may vary if share prices of different time period ranges such as daily, weekly. Fortnightly were used
2.2.9 - Kim Man Lui (2010) in his paper Discovering Pattern Associations in Hang Seng Index Constituent Stocks states that the problem of finding patterns in financial time series data has been dealt by systematic observations of trends, statistical analysis or the use of artificial intelligence techniques in trend analysis. These techniques are more suitable for the discovering of patterns in data rather than the understanding of association relationships between the discovered patterns. As time series patterns more often overlap with each other, identifying and discovering association relationships among them can be very tough and challenging. To tackle these problems, the author proposes a method to determine if there exists any association relationship between two sequential patterns in a financial time series data. The method is based on the use of machine learning techniques and has been tested with data from Hang Seng Index (HSI) constituent stocks. The data was collected from Hang Seng Index (HSI) which is a market value weighted index, as well as its 43 constituent stocks which covered 11 industrial sectors and represent about 65% of capitalisation of Hong Kong Stock Exchange. The author has used real time stock data from December 17th, 2007 to July 4th, 2008 for this experiment. Based on the author’s assumption artificial intelligence technique such as stock ranking, price prediction, market trends and turning point analysis were used. The statistics analysis showed how some patterns may be associated with the others and whether the results are statistically significant. It was found that there is statistical evidence of association relationships between some of the stocks whereas there is no evidence for such a relationship between some others. The author concluded by saying that the price behaviour of these HSI stocks is easier to understand than that of the HSI index.
2.2.10 - David R. Aronson, CMT and John R. Wolberg (2009) in their paper Purified Sentiment Indicators for the Stock Market attempts to improve the predictive power and stationarity of the stock market sentiment indicators (SI) by ignoring the recent price dynamics such as acceleration, velocity and volatility. The authors call the result a purified sentiment indicator (PSI). PSI is derived with an adaptive regression model using price dynamics indicators as variables to predict sentiment indicator. PSI is the difference between noticed SI and expected SI standardized by model error. The Research produces PSI for the following SI:, Hulbert’s Stock Newsletter Sentiment Index (HUL), Investors Intelligence Bulls minus and Bears (INV), CBOE Implied Volatility Index (VIX), CBOE Equity Put to Call Ratio (PCR), and American Association of Individual Investors Bulls minus Bears (AAII). All SI sequences are foreseen from price characteristics (r-squares ranging from 0.25 to 0.70). the authors obtain the signalling guidelines using cross-validation for each SI, Price dynamics indicator and PSI evaluate them with a random signal in terms of their out-of-sample profit factor (PF) trading the SP500. The stationarity of SI is improved by purification by stabilising variability and reducing drift. However, it generally reduces PF for AAII, HUL, INV and PCR indicating that at least a few of their predictive power arises from price characteristics. In contrast, PF of VIX is considerably improved by purification, indicating it contains predictive information above and beyond price characteristics but which is hidden by price dynamics. The study found among all the indicators that were test Purified VIX was far superior.
2.3 Contextual Review
2.3.1 - Jerome F. Hartl, CMT (2006) in his article Window of Opportunity? Evidence for the existence of a predictable and exploitable influence upon chart patterns arising from disparities in capital gain taxation tries to study whether the Anniversary effect is common with traders and investors. Anniversary effect (AE) is nothing but the investor is aware of the short term and long term capital gains due to the price appreciation of the security and tries to realize the profits after 366 days to benefit from the reduced taxes on the long term capital gains. The author found that given the sizable disparity between the control and experimental data, it appears likely that the AE phenomenon is both real and potentially exploitable by technical traders and/or investors. However, while the data obtained through his comparative experiment appear strongly suggestive of the anniversary effect theory, it is prudent to examine other possible explanations for these results. One such alternative view relates to quarterly earnings reporting. Since the companies of the Russell 2000 all release earnings reports on a quarterly basis in compliance with U.S. law, technical weakness that recurs on an annual basis could be related to such reports. For instance, if the “Widget Corporation” had a history of inconsistent or disappointing earnings reports, a chart analysis of “Widget Corp.” might well display repeated instances of weakness around these earnings release dates. However, it should be noted that in such a case, the technical patterns observed and statistically examined in this study would be just as valid and exploitable, even if arising from a different phenomenon.
2.3.2 - LEE, C.M.C. and B. Swaminathan (2000) in their paper Price Momentum and Trading Volume” show that past trading volume provides an important connection between “momentum” and “value” strategies. The study revealed that the firms which had high(low) turnover ratios in the past showed many surprising characteristics. These firms earned lower(higher) return in the future and for about 8 quarters. The magnitude and persistence of the price momentum is known from the past trading volumes. Over the next five years, price momentum effect is said to reverse and those winners (losers) with high (low) volume would experience faster reversal. Overall the findings revealed that the past volume helps to reconcile long horizon “over reaction” and intermediate horizon “under reaction” effects.
2.3.3 - Kavajecz, K.A., E.R. Odders-White and O. Journals, (2003), in their paper Technical Analysis and Liquidity Provision says that the obvious issue between educational theories of market efficiency and the level of resources dedicated to specialized research by experts is a long-standing challenge. Therefore the author discovers an unexamined feature of technical analysis with regards to liquidity provisions. The author illustrate that the level of resistance and support match with the limit order books crest and troughs and moving average predictions reveal information about the comparative position of troughs on the book. Furthermore, the author shows that these relationships originate from technical rules finding troughs already in place to limit order book.
2.3.4 - Ivona Hrušová (2011) in her paper how Rewarding Is Technical Analysis? Evidence from Central and Eastern European Stock Markets assess whether technical analysis can generate substantial profits in Central and Eastern European stock markets with a special focus on the Prague Stock Exchange. The author investigates a well established trend follower Moving Average Convergence MACD as well as a counter trend indicator stochastic oscillator and relative strength index and introduces test statistics and bootstrap methodology in order to explore the profitability of these technical trading rules. The empirical results suggest that rewards of technical analysis differ according to individual stock markets. Whereas both indicators considered are found to yield significantly positive returns especially in the Bucharest and Prague Stock Exchanges, but have no predictive power on the Frankfurt Stock Exchange. The author concludes that by saying that the findings raise a question about the efficiency of the less developed stock markets.
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