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

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

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

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

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

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?

Originally Posted by Beverly

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.

The oscillator is the indicator are very good to consider them in the operative

Traders are using a wide variety of investigative apparatus so that they can achieve profitable trading journey by ensuring practical decision making. And technical analysis is one of the mostly used analytical tools today. In case of technical analysis, any trader gives attention on short term perspective by using various technical indicators like pivot points, candle stick charting, MACD, moving average, etc. However, in using technical indicators a trader must be cautious.