Technical Analysis for Financial Market - oscillators
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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.
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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.
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.
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.
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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.
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.
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.
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.
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.
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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.