Technical Analysis for Financial Market - oscillators

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seekers_
Join date: 2016.05.01
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#1

This research paper aim to examine the profitability of various kinds of oscillator used in technical analysis on market index of NSE (National Stock Exchange) S&P CNX NIFTY 50 during 2004-2014. We have selected the most commonly used three oscillators i.e., Stochastic oscillator, RSI Oscillator and Commodity Channel Index (CCI). The results clearly express that CCI outperform the remaining two oscillators in terms of profitability for S&P CNX NIFTY 50 Index.

nbtrading
Join date: 2013.11.30
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#2
seekers_:

This research paper aim to examine the profitability of various kinds of oscillator used in technical analysis on market index of NSE (National Stock Exchange) S&P CNX NIFTY 50 during 2004-2014. We have selected the most commonly used three oscillators i.e., Stochastic oscillator, RSI Oscillator and Commodity Channel Index (CCI). The results clearly express that CCI outperform the remaining two oscillators in terms of profitability for S&P CNX NIFTY 50 Index.

Is this going to be a series of posts?
seekers_
Join date: 2016.05.01
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#3
nbtrading:
Is this going to be a series of posts?

Yes

Here is this

One highly documented method to test a capital market for weak form efficiency is to identify the return predictability of technical trading rules in that market. Studies on these tests are fewer in number in emerging markets than that of in developed markets and most of the tests have drawn conclusion by including only trend indicators in their trading rules. But it has already been recognized in some previous developed markets studies that trend indicators generally fail to identify sufficient information content in the past prices; hence practitioners very often use these trend indicators combined with confirming indicator (Loh 2007). The current study has investigated Dhaka Stock Exchange, an emerging market of South Asia, for weak form market efficiency by approaching the tests of technical trading rules and has confirmed the profitability of these rules up to 2.15 percent costs per transaction. Here it has used stochastic oscillator as a confirming indicator combined with moving averages (trend indicators) which is the first study of its kind in this market, and has found that it can improve the return predictability only for the short length moving averages.

seekers_
Join date: 2016.05.01
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#4

Trading Systems

In this paper we examine four different approaches in trading rules for stock returns. More specifically we examine the popular procedures in technical analysis, which are the moving average and the Moving Average Convergence-Divergence (MACD) oscillator. The third approach is the simple random walk autoregressive model and the fourth model we propose is a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) regression with wavelets decomposition and Monte- Carlo simulations algorithm developed in MATLAB. We examine five major stock market index returns for a testing forecasting period of 10 days ahead. We conclude that moving average and MACD might lead to net profits, but not in all cases, therefore are not consistent procedures. Furthermore, moving average 1-30 provides the best results. On the other hand random walk autoregressive model leads in all cases to net losses. Finally, the model we propose not only leads always to net profits, but also to significant higher profits in three stock indices than the respective conventional technical analysis tools.


seekers_
Join date: 2016.05.01
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#5

Trading Systems

Motivated by the pricing of first touch digital options in exponential Lévy models and corresponding credit risk applications, we study numerical methods for solving related partial integro-differential equations. The goal of the paper is to consider advantages of the Laplace transform-based approach in this context. In particular, we show that the computational efficiency of the numerical methods which start with the time discretization can be significantly enhanced (often, in several dozen of times) by means of the Laplace transform technique. As an additional result we provide a new Wiener-Hopf factorization formula which admits an efficient numerical realization by means of the Fast Fourier Transform. We propose two new efficient methods for pricing first touch digital options in wide classes of Lévy processes. Both methods are based on the numerical Laplace transform inversion formulae and a numerical Wiener-Hopf factorization. The first method uses the Gaver-Stehfest algorithm, the second one deals with the Post-Widder formula. We prove the advantages of the new methods in terms of accuracy and convergence by using numerical experiments.

KumoBreake
Join date: 2016.03.18
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#6
seekers_:

heard good thing about this
seekers_
Join date: 2016.05.01
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#7

Trading Systems

We study how the phenomenon of contagion can take place in the network of the world's stock exchanges when each stock exchange acts as an integrate-and-fire oscillator. The characteristic non-linear price behavior of the integrate-and-fire oscillators is supported by empirical data and has a behavioral origin. One advantage of the integrate-and-fire dynamics is that it enables for a direct identification of cause and effect of price movements, without the need for statistical tests such as for example Granger causality tests often used in the identification of causes of contagion. Our methodology can thereby identify the most relevant nodes with respect to onset of contagion in the network of stock exchanges, as well as identify potential periods of high vulnerability of the network. The model is characterized by a separation of time scales created by a slow build up of stresses, for example due to (say monthly/yearly) macroeconomic factors, and then a fast (say hourly/daily) release of stresses through "price-quakes" of price movements across the worlds network of stock exchanges.


seekers_
Join date: 2016.05.01
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#8
We construct a statistical model for term-structure of implied volatilities of currency options based on daily historical data for 13 currency pairs in a 19-month period. We examine the joint evolution of 1 month, 2 month, 3 month, 6 month and 1 year 50-delta options in all the currency pairs. We show that there exist three uncorrelated state variables (principal components) which account for the parallel movement, slope oscillation, and curvature of the term structure and which explain, on average, the movements of the term-structure of volatility to more than 95% in all cases. We test and construct an exponential ARCH, or E-ARCH, model for each state variable. One of the applications of this model is to produce confidence bands for the term- structure of volatility.
seekers_
Join date: 2016.05.01
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#9
We propose serial correlation-robust asymptotic confidence bands for the receiver operating characteristic (ROC) curve and its functional, viz. the area under ROC curve (AUC), estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples, and confers an additional measure of robustness to non-normality. The conventional procedure is found to be markedly undersized in terms of yielding empirical coverage probabilities lower than the nominal level, especially when the serial correlation is strong. An example from macroeconomic forecasting demonstrates the importance of accounting for serial correlation when the probability forecasts for real GDP declines are evaluated using ROC.
seekers_
Join date: 2016.05.01
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#10
The Kalman Filter is a time series estimation algorithm that is applied extensively in the field of engineering and recently (relative to engineering) in the field of finance and economics. However, presentations of the technique are somewhat intimidating despite the relative ease of generating the algorithm. This paper presents the Kalman Filter in a simplified manner and produces an example of an application of the algorithm in Excel. This scaled down version of the Kalman filter can be introduced in the (advanced) undergraduate classroom as well as the graduate classroom.
seekers_
Join date: 2016.05.01
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#11
RSI is a commonly used indicator preferred by stock traders. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. In this study, we developed a trading model using a modified RSI using trend-removed stock data. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. These parameters are optimized using genetic algorithms; then the trading performance is compared against B&H and standard RSI indicator usage. 9 different ETFs are selected for evaluating trading performance. The results indicate there is a performance improvement both in profit and success rates using this new model. As future work, other indicators might be modelled in a similar fashion in order to see if it is possible to find one indicator that can work under any market condition.
seekers_
Join date: 2016.05.01
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#12
The objective of the study is to investigate, whether a refined method of the MACD indicator can outperform a benchmark of holding a riskless security as a Treasury bond or holding the underlying asset, i.e., individual stocks of the NASDAQ-100. Thus, this study is challenging the random walk hypothesis.
seekers_
Join date: 2016.05.01
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#13
Using an indicator by itself can reveal a portion of the entire picture. Combining it with another can reveal more. by Barbara Star, Ph.D. raders use technical indicators to recognize market changes. They look to indicators for signs of price direction, momentum shifts, and market volatility. Among the most sought-after indicators are those that identify price trends. Traditionally, moving averages serve that purpose, but they suffer from whipsaw action during price consolidations. However, there is another approach. This article shows how to combine two popular indicators to help traders detect not only trend direction but also trend strength. The indicators involved are the average directional index (ADX) and the moving average convergence/divergence (MACD). The ADX functions as a trend detector, rising as price strengthens into an identifiable trend and falling when price moves sideways or loses its trending power. ADX values in the 20 to 30 range indicate mild to moderate trending behavior, while values above 30 usually signify a strong trend. Unfortunately, the ADX does not reveal the trend direction. The MACD, on the other hand, indicates price momentum and can also be used to identify price direction as it rises above its trigger line or falls below its zero line. When both indicators are plotted on the same chart, trend strength and trend direction become clear. The chart of AOL Time Warner (AOL) in Figure 1 illustrates how the two indicators complement each other. The ADX in the upper panel rose from April through May 2001, indicating a trending market. The MACD rose above its dotted trigger line and its zero line, showing that price direction was up. During July and August the ADX rose once again, but the MACD was then below its trigger Stocks & Commodities V. 20:1 (22-25): Detecting Trend Direction And Strength by Barbara Star, Ph.D. Copyright (c) Technical Analysis Inc. line and its zero line, showing that a downtrend was in progress. Most traders prefer the long side of the market and look for an uptrending market. The confirming pattern identifies exactly that condition. When the ADX and MACD move up in unison, they confirm rising price direction; the Bristol-Myers Squibb Co. (BMY) chart in Figure 2 offers a good example of a confirming pattern. The ADX and MACD rose as price moved up strongly in September to December 2000. When price changed direction in January 2001, both the ADX and MACD followed suit. The falling ADX was not indicating that a downtrend had begun; merely that it no longer could find a trend. In this example, the MACD showed that price was retracing its prior upward march. But sometimes when both indicators fall, price forms a sideways trading range, rather than the more pronounced downward move seen in this chart.
seekers_
Join date: 2016.05.01
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#14
This paper assesses the state of informational efficiency in stock markets of 75 countries around the world by empirically evaluating the economically relevance of a very popular technical analysis indicator, namely the Moving Average Convergence Divergence. There are many published papers that evaluate market efficiency around the world, but none looks at as many countries as this one does. In total, 1336 companies are selected in the sample, with temporal data starting January 1st 2001 and ending December 31, 2012. The methodology used here is based on trading simulation using an optimized trading rule that is applied on out of sample quotes. To be in accordance with the latest guidelines in the field, several statistical tests, including a bootstrap based one, are performed to validate the estimators, thus ensuring bias-free results and more relevant conclusions. Several important statements can be made based on the obtained results, the most important being that traders using the MACD as an technical analysis investment method on the stock market could some times and for certain companies obtain abnormal cost and risk adjusted returns, this pointing out that the world's stock markets present important inefficiencies.
NKTrade
Join date: 2016.01.25
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#15

Dear seekers_

Is there any pdf files about your post #13 ?

Thank you