Technical Analysis Indicators Research Articles

A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.

A Fuzzy Logic Based Technical Indicator for BIST 30 Index and Islamic Index

Fuzzy logic, originally introduced by Lofti Zadeh in the 1960's, resembles human reasoning in its use of approximate, vague, noisy or imprecise data/information and uncertainty to generate decisions. According to Sriram (2005), fuzzy theory was designed with a specific purpose of mathematically representing vagueness and provides formalized procedures for tackling the impreciseness inherent in many variables in a multitude of problems. Crises, bubbles, fiscal politics etc. makes investing difficult in financial markets. These issues haphazardly raise and cause irregular characteristics which also raise risk. On the other hand, traders and market participants try to reduce risk and increase returns. We try to make dependable suggestion tool which contains a few technical indicators using fuzzy logic modeling. In financial markets technical analysis is commonly used to provide trading decisions. Technical analysis presumes that there are trends and patterns in financial assets’ movements. In this study, BIST-30 and Islamic (Participation) Index data is used between March 2012 and November of 2014 taken from Borsa Istanbul. The aim of the study is to create a new technical analysis indicator using fuzzy logic method which could be an alternative to popular indicators used by traders. BUY and SELL signals given by indicators’ after closing prices are assumed to be applied in the next day opening prices when calculating the indicators’ performance. The performance of the indicator for BIST-30 and Islamic index is measured by modified sharpe ratio and compared to widely used indices like MACD, MA, RSI and OBV. The Sharpe ratio is used to calculate risk adjusted return. It shows the rate of return as opposed to risk. The asset which has the higher Sharpe Ratio is considered to yield better return for the same amount of risk.

Solvency, Indebtedness, Rates of Return and Investment Impact on Economic and Technical Analysis Indicators (Case Study)

Abstract The purpose of the performed and presented financial analyzes, in the case of the two units, does not allow major discrepancies between the estimated and achieved situation. The authors have based the research on the technical-economic and financial analysis of investments, which sets economic efficiency and investments viability and assumes forecast of revenue, expenses, profit and loss account, balance sheet and cash flow: a) static analysis of indicators; b) dynamic analysis of indicators, c) cost-benefit analysis. Analysis of economic efficiency and sustainability of investments aimes at both static indicators of investment analysis and dynamic analysis of economic efficiency. The goal of research was to improve analyzes and economic indicators for assessing the effectiveness and sustainability of investments and the objectives targeted technical and economic analysis, solvency and viability of investments and the impact of the investment on key indicators of economic and financial technical analysis. Case studies, carried out at S.C. NORD INTERMED CONSULTING GROUP S.R.L. Dorneşti, Suceava county and S.C. ANDIMIR TOP S.R.L. Mihălăşeni, Botoşani county, led to a series of measures to improve the financial analysis of economic efficiency and sustainability of investments.

The use of technical analysis indicators in the Russian stock market

The article defines and describes the effectiveness of the basic and most popular indicators of technical analysis in the stock market. It also compares them with the conservative “buy and hold” strategy, demonstrating the success of the application of strategies based on trading signals obtained by using certain indicators.

Zastosowanie wybranych wskaźników analizy technicznej w algorytmicznym systemie transakcyjnym

Zastosowanie wybranych wskaźników analizy technicznej w algorytmicznym systemie transakcyjnym

Hisse Senedi Getirisi Üzerinde Teknik Analiz Göstergelerinin Etkinliği: Bir Panel Veri Yaklaşımı

Technical analysis aims on visually identifying geometrical patterns and indicators in price charts in order to anticipate price “trends”. This paper investigates the relations between return and technical analysis indicators of Turkish commercial banking sector in BIST-BANKING index for the period 1-1-2000- 31-12-2013. Technical analysis indicators are generated by taking into account the daily stock prices. The general steps go through the identification of the relations for the securities returns and technical analysis indicators. We illustrate the proposed with a panel data methodology including several securities of banking sector. Our investigation shows that there is a statistically significant relation between technical analysis indicators and stock returns as panel.

Market Efficiency and Technical Analysis in Romania

In this paper we make a detail evaluation of stock market efficiency in Romania. First, we employ 686,243 trading models derived from 44 technical analysis indicators and determine that significant inefficiencies exist for stock prices in this country. The time varying nature of these points out that market efficiency is not improving over time, but instead fluctuates in the way consistent to the Adaptive Market Hypothesis. We show that investor success does not depend on the target investment asset, slightly depends on specific prediction models and heavily depend on the size of the implemented rule universe. Next, we focus on finding out what are the determining factors for market efficiency. Contrary to what one might expect, we find that market liquidity has an almost insignificant impact on efficiency. The main determining factor for market efficiency in Romania is price momentum, and argues that the detected anomalies are due to investor behavioral biases.

Stock Market Efficiency and the MACD. Evidence from Countries around the World

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.

Comparing abnormal returns and ranking of technical analysis indicators based on TOPSIS Technique

Comparing abnormal returns and ranking of technical analysis indicators based on TOPSIS Technique

Web-Based Decision Support Systems Application of Stock Recommendation Using Bayesian Methods

We propose an application that can support traders by providing recommendation about the right stock transaction. The expected impact from this application is to reduce the risk of loss, even achieve the maximum profit for traders who use this application. Recommendation that resulted by application is based on Bayesian methods calculation and four technical analysis indicators that most commonly used by stock experts, i.e. Bollinger Bands, Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and Stochastic Oscillator. Methodology used in this paper consists of data collection, data analysisa, application design, implementation, and testing. From the results of application testing, the accuracy of the application is 87,37%.

Entropy-Based Technical Analysis Indicators Selection for International Stock Markets Fluctuations Prediction Using Support Vector Machines

Most of works on stock price forecasting are concerned with the problem of predicting its future value. However, forecasting stock price future fluctuation trend could be easier and interesting for traders and investors to maximize profits. The purpose of this study is to predict CAC40, FTSE, NASDAQ and S&P500 price index up and down fluctuations. In particular, it aims to propose a methodology to forecast regime switches in these markets time series to assist traders and investors in decision making. In the first stage, a large set composed of twenty five technical analysis indicators is formed. They fall into four broad categories namely oscillators, stochastic measures, indexes and indicators. Entropy statistic is employed to rank the initial technical analysis indicators. Finally, in the third stage, polynomial-based kernel support vector machines (SVM) are used for predicting CAC40, FTSE, NASDAQ and S&P500 future upward and downward fluctuations. The forecasting results show that the choice of technical analysis indicators used to predict CAC40 and NASDAQ fluctuations depend on the type of risk-aversion and risk-appetite of the investor. For the S&P500 and FTSE, technical analysis indicators used in our study can detect future downshifts with high accuracy. Thus, they are suitable for market analysis and trading by risk-averse investors on these markets.

Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes

This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that movements of target crude oil prices may be related to other important crude oil markets besides the target market for the prediction time horizon since traders may find price movement information within other relevant crude oil markets useful. We also considered multiple timeframes in this study since trends may differ across different timeframes and, in fact, traders may use their own timeframes. Therefore, for forecasting target crude oil prices, this study emphasizes on features pertaining to other important crude oil markets and different timeframes in addition to features of the target crude oil market and target timeframe. Moreover, the MKL framework has been used to fuse information extracted from different sources and timeframes of the same data source. Experimental results show that out-of-sample forecasting using the MKL method is superior to benchmark methods in terms of root mean square error (RMSE) and average percentage profit (APP). They also show that the information from multiple timeframes is useful for prediction, but that from another crude oil market is not.

Could a Trader Using Only “Old” Technical Indicator be Successful at the Forex Market?

The use of technical analysis indicators for trading is widely known and discussed. Quite a large number of indicators and the long time period of their usage provide opportunities for creating profitable and successful trading strategies. On the other side technical analysis indicators were constructed for the stock market and therefore to traders on the Forex market it places the question. Can be a strategy based purely on technical analysis indicators profitable on the market with high volatility? Do we use the old, proven indicators, or use the newer ones? Will indicators generate good trading entries in time of crisis? The paper tries to find answers to some of these questions. On the basis of Moving Average Convergence Divergence (MACD) indicator, the trading strategies have been developed and back-tested at the Forex market with different timeframes. An important element of the research is to distinguish the time period of the crisis and beyond the crisis period and tested success of created strategies at the major, most traded currency pair. At the end of the paper the performance and profitability of the created strategies are discussed.

An Algorithm for Testing the Efficient Market Hypothesis

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).

Fractional Black-Scholes Model and Technical Analysis of Stock Price

In the stock market, some popular technical analysis indicators (e.g., Bollinger bands, RSI, ROC, etc.) are widely used to forecast the direction of prices. The validity is shown by observed relative frequency of certain statistics, using the daily (hourly, weekly, etc.) stock prices as samples. However, those samples are not independent. In earlier research, the stationary property and the law of large numbers related to those observations under Black-Scholes stock price model and stochastic volatility model have been discussed. Since the fitness of both Black-Scholes model and short-range dependent process has been questioned, we extend the above results to fractional Black-Scholes model with Hurst parameterH>1/2, under which the stock returns follow a kind of long-range dependent process. We also obtain the rate of convergence.

Model calibration and automated trading agent for Euro futures

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of technical indicators and design a new set of trading rules. We tested this approach with high-frequency data of the Dow Jones EURO STOXX 50 Index Futures (FESX) and the DAX Futures (FDAX) for March 2009. Our method was implemented with different learning algorithms and outperformed a combination of the same group of technical analysis indicators using the parameters typically recommended by practitioners. We incorporated this method of model calibration in a trading agent that relies on a layered structure consisting of the machine learning algorithm described above, an online learning utility, a trading strategy, and a risk management overlay. The online learning layer combines the output of several experts and suggests a short or long position. If the expected position is positive (negative), the trading agent sends a buy (sell) limit order at prices slightly lower (higher) than the bid price at the top of the buy (sell) order book less (plus) transaction costs. If the order is not 100% filled within a fixed period (i.e. 1 minute) of being issued, the existent limit orders are cancelled, and limit orders are reissued according to the new experts' forecast. As part of its risk management capability, the trading agent eliminates any weak trading signal. The trading agent algorithm generated positive returns for the two major European index futures (FESX and FDAX) and outperformed a buy-and-hold strategy.

An entropy-LVQ system for S&P500 downward shifts forecasting

Article history: Received July 20, 2011 Accepted 7 October 2011 Available online 8 October 2011 The purpose of this paper is to predict the S&P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature. © 2012 Growing Science Ltd. All rights reserved.

A profitability study on the Malaysian futures markets using a new adjustable technical analysis indicator, adjustable bands Z-test-statistics' (ABZ')

Financial markets all over the world generally exist in 2 conditions: trending and ranging. One of the most frustrating issues confronting market technicians daily is the critical definition when market is ranging and when the market is trending. Applying a trending algorithm to a ranging market will result in whipsaws that yield losses. To avoid some of these false entry signals, this research proposed to vary the moving average and standard deviation parameters to avoid some of these false entry whipsaws and yet capture a new trend early. Adaptive algorithm like the Adjustable Bands Z-Test-Statistics' (ABZ'), seeked to provide a solution to this issue. For the year 2008, ABZ' showed an abnormal return of 316 index points for Malaysian Kuala Lumpur Composite Index Futures (FKLI) compared with a negative return of -562 index points for the passive buy-and-hold strategy and 690 for Malaysian Crude Palm Oil Futures (FCPO) compared with a negative return of -1387. Results from this research interest investors world wide who are trying to overcome this baffling issue of ranging versus trending markets. Key words: Technical analysis indicators, automatically adjustable algorithm trading systems futures trends.

Stochastic volatility model and technical analysis of stock price

In the stock market, some popular technical analysis indicators (e.g. Bollinger Bands, RSI, ROC, ...) are widely used by traders. They use the daily (hourly, weekly, ...) stock prices as samples of certain statistics and use the observed relative frequency to show the validity of those well-known indicators. However, those samples are not independent, so the classical sample survey theory does not apply. In earlier research, we discussed the law of large numbers related to those observations when one assumes Black-Scholes’ stock price model. In this paper, we extend the above results to the more popular stochastic volatility model.

Supporting trading strategies by inverse fuzzy transform

Trading in finance requires to define strategies able to identify early trading buy/sell signals, i.e., conditions which suggest to enter or exit a position, in order to exploit early mover advantage. Trading signals are generally identified by looking at the time series of prices and volumes. Several technical analysis indicators and strategies have been proposed and are commonly in use. In this paper we propose inverse fuzzy transform as a means for building a new class of technical indicators. Experimental results show that this approach outperforms simple and exponential moving average when embedded in common strategies.