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This research deals with determining which method among the technical tools and open interest method has more accurate predictability power of trends. We have used the technical analysis indicators such as MACD, RSI, Bollinger band, Stochastic oscillator and Money flow. We have used the data of 7 years, from 2012 to 2019. The data used is of NIFTY forwards to study the effectiveness of the indicator. We then further carried out strategy for each indicator and calculated its profitability as well as its predictability to give result.
The procedure will be explained further in the report.
This research was taken for determining an effective method that can be used by investors for trading in the stock market. If an superior method among the above mentioned methods can be determined, then it will not only make trading simpler but also profitable. Earlier, there has been research done in the area. However the earlier comparative research has been among the technical indicators. Our research has taken a new factor of open interest in the comparison.
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Thus, our study will first determine the various tools to be used, then we will take the necessary data of the indices. Further we will form the necessary buying selling strategies for each of the tools and finally compare the profits generated.
The synthesis of past studies reveals a multifaceted view of market prediction techniques. The informational content of open interest in derivatives markets, as discussed in the 2005 study on US equity options, highlights its potential predictive capacity.
Contrastingly, technical analysis, especially when combined with trading volume, has shown promise in emerging markets for its return predictability, as per a 2010 study. The debate extends to the efficiency of Put–Call Ratios, options markets, and innovative combinations of technical analysis with machine learning, all suggesting varied degrees of success in forecasting market returns.
Gerald Appel, publisher of Systems and Forecasts, developed the Moving Average Convergence/Divergence (MACD) oscillator. A variation of the moving average crossover, the MACD is calculated using the difference between two exponential moving averages. Traditionally, a 26-period EMA is subtracted from a 12-period EMA, but these times are adjustable for shorter and longer period analysis. This calculation results in a value that oscillates above and below zero.
A positive MACD indicates that the average price during the past 12 periods exceeds the average price over the past 26 periods. The MACD line is plotted at the bottom of a price chart along with another line—the signal line. The signal line is an exponential moving average of the MACD; a nine-period EMA is the most common. A histogram of the difference between the MACD and the signal line often appears at the bottom of the chart. You can see this type of plot in chart. The chart displays the MACD (thin black line), the signal line (thick gray line), and the histogram of the difference between the MACD and its signal line for Apple Computer over the same period.
Steps for MACD:
The importance of MACD lies in the fact that it takes into account the aspects of both momentum and trend in one indicator. As a trend-following indicator, it will not be wrong for very long. The use of moving averages ensures that the indicator will eventually follow the movements of the underlying security. By using exponential moving averages, as opposed to simple moving averages, some of the lag has been taken out. As a momentum indicator, MACD has the ability to foreshadow moves in the underlying security. MACD divergences can be key factors in predicting a trend change.The MACD is useful in a trending market because it is unbounded
In June 1978, J. Welles Wilder introduced the relative strength index (RSI) in an article in Commodities (now known as Futures) magazine. The RSI measures the strength of an issue against its history of price change by comparing “up” days to “down” days. The RSI is part of a class of indicators called momentum oscillators. There are a number of indicators that fall in this category, the most common being Relative Strength Index, Stochastic, Rate of Change. Although these indicators are all calculated differently. An oscillator is an indicator that moves back and forth across a reference line or between prescribed upper and lower limits. When an oscillator reaches a new high, it shows that an uptrend is gaining speed and is likely to continue.
The RSI can range from a low of 0 (indicating no up days) to a high of 100. In his original calculations, Wilder used 14 days as the relevant period. Although some analysts have attempted to use a time-weighted period, these methods have not been well accepted, and the 14-day period remains the most commonly used. After calculating the RSI for the first 14 days, Wilder used a smoothing method to calculate RSI for future days. This process dampens the oscillations. The RSI has many characteristics that can generate signals. For example, when the RSI is above 50, the midpoint of the bounded range, the underlying trend in prices is usually upward. Conversely, it is downward when the RSI is below 50. RSI divergences with price often give warning of trend reversal.
RSI is a momentum oscillator generally used in sideways or ranging markets where the price moves between support and resistance levels. It is one of the most useful technical tool employed by many traders to measure the velocity of directional price movement. The RSI is a price-following oscillator that ranges between 0 and 100. Generally, technical analysts use 30% oversold and 70% overbought lines to generate the buy and sell signals. Go long when the indicator moves from below to above the oversold line. Go short when the indicator moves from above to below the overbought line. Note here that the direction of crossing is important; the indicator needs to first go past the overbought/oversold lines and then cross back through them.
Bollinger Bands use the standard deviation calculation. To construct Bollinger Bands, first calculate a simple moving average of prices. Bollinger uses the SMA because most calculations using standard deviation use an SMA. Next, draw bands a certain number of standard deviations above and below the moving average. For example, Bollinger's standard calculation, and the one most often seen in the public chart services, begins with a 20-period simple moving average. Two standard deviations are added to the SMA to plot an upper band.
The lower band is constructed by subtracting two standard deviations from the SMA. The bands are self-adjusting, automatically becoming wider during periods of extreme price changes. Chart shows the standard Bollinger Band around the 20-period moving average with bands at two standard deviations. Of course, both the length of the moving average and the number of standard deviations can be adjusted. Theoretically, the plus or minus two standard deviations should account for approximately 95% of all the price action about the moving average. In fact, this is not quite true because price action is non-stationary and non - random and, thus, does not follow the statistical properties of the standard deviation calculation precisely. However, it is a good estimate of the majority of price action. Indeed, as the chart shows, the price action seems to oscillate between the bands quite regularly.
Bollinger bands are trading bands developed by John Bollinger. It consists of a 20 period simple moving average with upper and lower bands. The upper band is 2 standard deviation above the moving average and similarly lower band is 2 standard deviation below the moving average. This makes these bands more dynamic and adaptive to volatility
Interpretation of Bollinger Bands:
A stochastic oscillator is a momentum indicator comparing a particular closing price of a security to a range of its prices over a certain period of time. The sensitivity of the oscillator to market movements is reducible by adjusting that time period or by taking a moving average of the result. It is used to generate overbought and oversold trading signals, utilizing a 0-100 bounded range of values.
C = The most recent closing price;
L14 = The lowest price traded of the 14 previous trading sessions;
H14 = The highest price traded during the same14-day period;
%K = The current value of the stochastic indicator
For example : The stochastic oscillator is included in most charting tools and can be easily employed in practice. The standard time period used is 14 days, though this can be adjusted to meet specific analytical needs. The stochastic oscillator is calculated by subtracting the low for the period from the current closing price, dividing by the total range for the period and multiplying by 100. As a hypothetical example, if the 14-day high is $150, the low is $125 and the current close is $145, then the reading for the current session would be: (145-125)/(150-125)*100, or 80.
By comparing current price to the range over time, the stochastic oscillator reflects the consistency with which price closes near its recent high or low. A reading of 80 would indicate that the asset is on the verge of being overbought.
The Money Flow Index (MFI) is a technical oscillator that uses price and volume for identifying overbought or oversold conditions in an asset. It can also be used to spot divergences which warn of a trend change in price. The oscillator moves between 0 and 100. Unlike conventional oscillators such as the Relative Strength Index (RSI), the Money Flow Index incorporates both price and volume data, as opposed to just price. For this reason, some analysts call MFI the volume-weighted RSI.
Steps for calculating the MFI :
For example, a very high Money Flow Index that begins to fall below a reading of 80 while the underlying security continues to climb is a price reversal signal to the downside. Conversely, a very low MFI reading that climbs above a reading of 20 while the underlying security continues to sell off is a price reversal signal to the upside. The overbought and oversold levels are also used to signal possible trading opportunities. Moves below 10 and above 90 are rare. Traders watch for the MFI to move back above 10 to signal a long trade, and to drop below 90 to signal a short trade.
Open interest is the total number of outstanding derivative contracts, such as options or futures that have not been settled for an asset. The total open interest does not count, and total every buy and sell contract. Instead, open interest provides a more accurate picture of the options trading activity, and whether money flows into the futures and options market are increasing or decreasing. The relationship between the buyer and seller creates one contract, and a single contract equates to 100 shares of the underlying asset. The contract is considered 'open' until the counterparty closes it. Adding up the open contracts, where there are a buyer and seller for each, results in the open interest.
If a buyer and seller come together and initiate a new position of one contract, then open interest will increase by one contract. However, if a buyer or seller passes off their current position to a new buyer or seller, then open interest remains unchanged. Open interest is a measure of market activity. Little or no open interest means there are no opening positions, or nearly all the positions have been closed. High open interest means there are many contracts still open, which means market participants will be watching that market closely. Open interest is a measure of the flow of money into a futures or options market. Increasing open interest represents new or additional money coming into the market while decreasing open interest indicates money flowing out of the market.
Consider there are 7 Individual trader using different tools such RSI, MACD, Bollinger Band, MFI, Stochastic oscillator, Open interest and simple buy and close strategy in Forward market. Each individual takes buying and selling calls depending on the tool’s indication. These investors invest for the given time period in Nifty 50 forward with maturity of 1 months. The methodology of research is that when any indicator indicates BUY/SELL signal, trader will respond accordingly and square it off at when there is reverse signal or at the expiry date. In case of Simple Buy and Close Strategy, Nifty 50 forwards are bought at the beginning of the month and sold at the end of the month.
For calculation point of view, standard deviation is taken for the period between BUY and SELL transaction only and not the entire period. 10 year treasury bond rate is taken as risk free rate. However, this rate keeps on changing so in order to tackle this uncertainty, an average of rate is taken for a period and then multiply by the days during which transaction takes place.
The time period taken for the research is from 2012-2019. In order to further evaluate the efficiency of each indicator, we have divided the years into three phase – growth phase (2012-2013), boom phase (2014-2016) and decline phase (2017-2019). This division of phase is based on the GDP Growth rate. We then determine which of the following makes the maximum profits, Sharpe ratio and minimum standard deviation at the end of the time period. We can thus conclude by this methodology of research, which of the tools are better at predicting the trends of the index movements.
The comparative analysis underscores the complexity of market prediction and the nuanced efficacy of different analytical tools across varying market conditions. While technical analysis tools offer substantial predictive power, especially in growth and boom phases, open interest provides crucial insights during market downturns. This heterogeneity in predictive accuracy suggests that a composite approach, leveraging both technical indicators and open interest, might offer a more holistic strategy for traders aiming to navigate the convoluted terrains of the stock market.
In essence, our study not only sheds light on the individual strengths of various market prediction tools but also opens avenues for future research to explore hybrid models that amalgamate the predictive powers of technical analysis and open interest for enhanced market foresight.
Predictive Power of Technical Tools versus Open Interest in Stock Market Trends. (2024, Feb 17). Retrieved from https://studymoose.com/document/predictive-power-of-technical-tools-versus-open-interest-in-stock-market-trends
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