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Technical Analysis For Financial Market - Oscillators

This is a discussion on Technical Analysis For Financial Market - Oscillators within the General Discussion forums, part of the Trading Forum category; Relative Strength Strategies for Investing - Meb Faber The purpose of this paper is to present simple quantitative methods that ...

          
   
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    Senior Member matfx's Avatar
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    Technical Analysis For Financial Market - Oscillators

    Relative Strength Strategies for Investing - Meb Faber

    The purpose of this paper is to present simple quantitative methods that improve risk-adjusted returns for investing in US equity sectors and global asset class portfolios. A relative strength model is tested on the French-Fama US equity sector data back to the 1920s that results in increased absolute returns with equity-like risk. The relative strength portfolios outperform the buy and hold benchmark in approximately 70% of all years and returns are persistent across time. The addition of a trend-following parameter to dynamically hedge the portfolio decreases both volatility and drawdown. The relative strength model is then tested across a portfolio of global asset classes with supporting results.

    Relative Strength Strategies.pdf

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    Senior Member matfx's Avatar
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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.

    Profitability of Oscillators Used in Technical Analysis for Financial Market.pdf

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    Senior Member matfx's Avatar
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    Stochastic technical analysis for decision making on the financial market

    Abstract
    We apply the well-known CUSUM and the Girshick-Rubin algorithm as trading strategies involving only mutually exclusive long positions in cash and the DAX at Frankfurt mid-day auction prices. We select optimal pairs of fixed thresholds
    for up- and down- movements from a pre-defined two-dimensional grid, hence, admitting asymmetric intervals. We show that under three different scenarios for transaction costs, the CUSUM technique not only outperforms the passive invest-
    ment in the DAX but also the alternative Girshick-Rubin algorithm.
    Keywords:
    CUSUM, Girshick-Rubin, trading algorithm, DAX

    Introduction

    One of the most critical questions in asset management and investing is the detection of changes in the current regime. The theoretical terminology refers to this as change-point detection or break-point analysis. In an economic context, models often involve a multitude of parameters, the stability of which over time has been put into question at least since Isaac and Griffin (1989). Many others such as, for example, Balding et al. (2008), Hamilton and Susmel (1994), Schaller and Van Norden (1997), Bai and Perron (1998), Hansen (2000), Dias and Embrechts (2002), Western and Kleykamp (2004) followed suit. These approaches detect change points by looking into the rearview mirror, that is, they analyze histori-
    cal time series and determine the most probable scenario concerning a change in value, or multiple changes of a particular parameter of a more or less complex model, in the past. This, however, is of limited to no value to an investor or trader
    who has to receive signals immediately if a change appears likely. Interestingly, a suitable approach has been provided by some technique developed for quality control in manufacturing, i.e. control chart techniques first developed such as Shewhart (1932). The general idea is to observe some time series until a predefined threshold is trespassed. Page (1954a) and Page (1954b) coined the term CUSUM as short for cumulative-sum where the actual value of a process, for example, a random walk is compared to some prior extreme value such as an all-time low or high, respectively. If the difference between actual and reference value is greater than the threshold, a signal is delivered. The initial approach was augmented by the moving average control chart by Roberts (2000). The CUSUM is equivalent to the filter trading rule introduced by Alexander (1961). Initial results of this rule are given, for example, by Alexander (1961), Fama and Blume (1966), or Dryden (1969) who showed that, after consideration of trading costs, the filter method cannot outperform the traditional buy-and-hold strategy. Moreover, a shortcoming of the original rule was detected in that, under
    certain circumstances, the trading rule could result in unbounded losses.

    Stochastic Technical Analysis_KITe_WP_62.pdf

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    Senior Member matfx's Avatar
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    Revisiting the Performance of MACD and RSI Oscillators

    Abstract:

    Chong and Ng (2008) find that the Moving Average Convergence–Divergence (MACD) and Relative Strength Index (RSI) rules can generate excess return in the London Stock Exchange. This paper revisits the performance of the two trading rules in the stock markets of five other OECD countries. It is found that the MACD(12,26,0) and RSI(21,50) rules consistently generate significant abnormal returns in the Milan Comit General and the S&P/TSX Composite Index. In addition, the RSI(14,30/70) rule is also profitable in the Dow Jones Industrials Index. The results shed some light on investors’ belief in these two technical indicators in different developed markets.

    MACD_RSI.pdf

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    Senior Member matfx's Avatar
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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.

    Contagion in the Worldcs Stock Exchanges Seen as a Set of Coupled Oscillators.pdf

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    Senior Member ArticleMan's Avatar
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    Forex Oscillators: How to use them for profit

    Forex Oscillators: How to use them for profit

    Article written by http://www.forextraders.com

    Forex price action moves between the theoretical bounds of zero and infinity. Although we have the intuition that prive movements will be a lot less than what is implied by these bounds most of the time, and that there is a high, and a low price for each period under consideration justifying a sell and a buy order, respectively, we don`t possess any concrete means of putting that intuition to work unless we make use of certain technical tools. The toolbox of technical analysis supplies many choices towards this goal which are classified under the heading of `forex oscillators.

    Divergence and Convergence Patters

    In spite of their benefits, one major problem with forex oscillators is that the patterns, and signals created by them do not possess a lot of staying power in the hectic action of the markets. We can derive many overbought/oversold signals in different timeframes while trading, but most of them will prove to be bogus, so to speak, because of the tendency of the price action to fluctuate wildly at inflection points. In other words, even if the signals derived from the oscillators are well-placed and timely, (which is not very frequent anyway), volatility will invalidate many of them, or make them unusable in short-term trading.

    To combat this problem, traders have been resorting to divergence/convergence patterns for a long time. The advantages of trading on this basis are numerous. Divergence/convergence signals are rarer than raw overbought/oversold signals since they are created by a multitude of the latter. They are easier to trade in contrast to a simple overbought/oversold value because of the ease of visual identification. With fewer signals, and more time to analyze them, better results can be achieved over the longer term.

    A divergence pattern is created when a trend of price highs is contradicted by the oscillator, that is, as the price registers higher highs, the oscillator creates a succession of lower highs resulting in a `divergence` between the primary trend line on the price chart, and the secondary trend on the oscillator graph.

    A convergence pattern, by contrast, would arise when lower lows, or bottoms on the price chart are contradicted by a series of higher lows on the oscillator. The series of higher oscillator lows and lower bottoms on the price chart lead to a converging formation, hence the name.

    Finally, parallel lines between the oscillator and price charts indicate that the underlying trend remains valid, with no contradicting signals presaging reversal. This does not imply that the trend is solid, but that the trader should proceed with other aspects of analysis in order to deepen his examination before reaching a conclusion. Parallel lines exist when no divergence/convergence pattern is being observed.

    Divergence/convergence patterns can arise in any type of oscillator, in any type of trend, and there is no specific oscillator especially suitable for use with this strategy. Regardless of the nature of the formation, these patterns always signal that the existing bull/bear pattern is weakening and in danger of reversal at some point.

    As with all other aspects of technical analysis, there is no certainty that an emerging pattern will not be eliminated by a sudden development in the marketplace. The value of the divergence/convergence formation is in its rarity. Seeking them allows a trader to focus his activity to a particular technical configuration that is easily identified and analyzed, preventing confusion in decision-making, and indecisiveness in execution.

    Now let`s take a look at the most popular oscillators in technical analysis, and discuss briefly the application of patterns that may arise in them.
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    I am learning FOREX as fast as possible with little money to spend on learning. What courses in college can I take and what books should I read? I am really interested in indicators and oscillators. So please help here?!?!!!

    Also, Whats the difference between indicator and oscillator?

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    Quote Originally Posted by Beverly View Post
    I am learning FOREX as fast as possible with little money to spend on learning. What courses in college can I take and what books should I read? I am really interested in indicators and oscillators. So please help here?!?!!!

    Also, Whats the difference between indicator and oscillator?
    oscillator is the indicator which is on separate window of the chart (mainly), the indicator is on any.
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    The oscillator is the indicator are very good to consider them in the operative

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