Trading rules and excess returns: evidence from Turkey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Massoud Metghalchi ◽  
Nazif Durmaz ◽  
Peggy Cloninger ◽  
Kamvar Farahbod
Keyword(s):  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Multiple Time ◽  
Excess Returns ◽  
Strength Index ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Content Type ◽  
Practical Implications

Purpose This paper aims to investigate popular technical trading rules (TTRs) applied to the FTSE Turkish all-cap and small-cap indexes from September 23, 2003 to August 9, 2019 to determine rules that produce net excess returns over the Buy-and-Hold strategy (B&H). Design/methodology/approach Five TTRs, namely, simple moving average, relative strength index, moving average convergence divergence, momentum, and rate of change, are applied, singly (one indicator) and in combination (two indicators) for multiple time periods. Findings For the small-cap index, some TTRs – including the famous Golden Cross, when the 50-day moving average rises above 200-day moving average – produced net annual excess returns (NAERs) over the B&H strategy, for the entire period and each sub-period, after accounting for risk and transaction costs. Results were mixed for the large-cap index. The results support Cakici and Topyan (2013). Research limitations/implications This study investigates several indicators, but future studies should examine others, especially based on volume and price. Practical implications Investors in the FTSE Turkish small-cap index may use some trading rules to earn NAERs over the B&H strategy. Originality/value This research is important because it addresses a gap in the research by examining numerous TTRs in the Turkish stock market. Studies of TTRs in Turkey are scarce.

scholarly journals Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Learning

2020 ◽  
Vol 9 (10) ◽  
pp. 484-486
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Random Forest Algorithm ◽  
Strength Index ◽  
Training Set ◽  
Trend Prediction ◽  
Unique Role

Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction.

scholarly journals Technical analysis on select stocks of banking sector

2016 ◽  
Vol 6 (3) ◽  
pp. 231-242
Author(s):  
Sharmila R ◽  
Kavitha R ◽  
Ananthi S
Keyword(s):  
Banking Sector ◽  
Weather Forecasting ◽  
Stock Exchange ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Rate Of Change ◽  
Strength Index ◽  
Price Movements ◽  
Over Time

Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is “likely” to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. This study is based on the analysis of four Nifty Bank Index stocks namely Axis Bank, Bank of Baroda, State Bank of India and ICICI bank listed in National Stock Exchange. Technical indicators such as Relative strength index (RSI), Rate of change (ROC) and Moving Average (MA) are used in the study. This paper aims at carrying out Technical Analysis of the securities of the selectedbanking stocks and to assist investment decisions in this Indian Market.

scholarly journals Analysis of Nifty Movement on Share Prices of Selected Construction Companies

2012 ◽  
Vol 1 (3) ◽  
pp. 59-65
Author(s):  
M. Gomathi ◽  
Dr.S. Nirmala
Keyword(s):  
Stock Prices ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Rate Of Change ◽  
Construction Sector ◽  
Strength Index ◽  
Share Prices ◽  
Construction Companies ◽  
Price Movements

This study aims at analyzing and predicting the price movements of construction companies stocks contributing to the NIFTY50 Index. To analyze the volatility of telecom stock and understand the behavior of stock prices in construction sector stocks i.e. (JP ASSOCIATES LIMITED, DLF LIMITED, GAMMON INDIA LIMITED, PUNJ LLOYD LIMITED, HCC LIMITED). The data for these stocks are collected from magazines, newspaper and websites. The stocks are analyzed by monitoring their respective price movements using technical tools. The technical tools used in this study are Exponential moving average, Relative strength index, Rate of change, MACD. Using these tools the trend over the recent past was deciphered. The expected trend in the immediate future was also predicted. Technical Analysis studies the price and volume movement in the market and predicts the future. It helps in identifying that the best time to buy and sell equity. Technical Analysis is a method of evaluating equities by analyzing the statistics generated by market activity, such as past prices and volume.

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Shishir Kumar Gujrati
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Future Price ◽  
Range Data ◽  
Strength Index ◽  
Price Trends ◽  
Past Data ◽  
Price Movements

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.

Candle charts for financial technical analysis

2019 ◽  
Vol 19 (1) ◽  
pp. 200-209 ◽  
Author(s):  
Mehmet F. Dicle
Keyword(s):  
Moving Average ◽  
Visual Representations ◽  
Technical Analysis ◽  
Relative Strength ◽  
Financial Industry ◽  
Strength Index ◽  
Research And Teaching ◽  
Industry Research ◽  
The Individual

Technical analysis is an important part of financial industry, research, and teaching. The methodology has two parts: i) calculation of the individual tools and ii) visual representations. In this article, I provide a community-contributed command, candlechart, to draw the most common technical analysis charts. My intent is to draw these charts similarly to industry examples. The popular candle price chart is combined with charts for volume, moving-average convergence divergence, relative strength index, and Bollinger bands.

Forecasting stock returns in Saudi Arabia and Malaysia

2018 ◽  
Vol 17 (2) ◽  
pp. 259-279 ◽  
Author(s):  
Abdelmonem Oueslati ◽  
Yacine Hammami
Keyword(s):  
Saudi Arabia ◽  
Stock Returns ◽  
Equity Premium ◽  
Return Predictability ◽  
Excess Returns ◽  
Content Type ◽  
Forecasting Method ◽  
The Us ◽  
Diffusion Index ◽  
Practical Implications

Purpose This paper aims to investigate the performance of various return forecasting variables and methods in Saudi Arabia and Malaysia. The authors document that market excess returns in Saudi Arabia are predicted by changes in oil prices, the dividend yield and inflation, whereas the equity premium in Malaysia is predicted only by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method and stock return predictability is stronger in expansions than in recessions. To interpret the findings, the authors perform two tests. The empirical results suggest irrational pricing in Malaysia and rationally time-varying expected returns in Saudi Arabia. Design/methodology/approach The authors apply the state-of-the-art in-sample and out-of-sample forecasting techniques to predict stock returns in Saudi Arabia and Malaysia. Findings The Saudi equity premium is predicted by oil prices, dividend yield and inflation. The Malaysian equity premium is predicted by the US market excess returns. In both countries, the authors find that the diffusion index is the best forecasting method. In both countries, predictability is stronger in expansions than in recessions. The tests suggest irrational pricing in Malaysia and rationality in Saudi Arabia. Practical implications The empirical results have some practical implications. The fact that stock returns are predictable in Saudi Arabia makes it possible for policymakers to better evaluate future business conditions, and thus to take appropriate decisions regarding economic and monetary policy. In Malaysia, the results of this study have interesting implications for portfolio management. The fact that the Malaysian market seems to be inefficient suggests the presence of strong opportunities for sophisticated investors, such as hedge and mutual funds. Originality/value First, there are no papers that have studied the return predictability in Saudi Arabia in spite of its importance as an emerging market. Second, the methods that combine all predictive variables such as the diffusion index or the kitchen sink methods have not been implemented in emerging markets. Third, this paper is the first study to deal with time-varying short-horizon predictability in emerging countries.

Profitability of Trading Strategies Before and During the Greek Crisis: An Empirical Study.

2017 ◽  
Vol 11 (1) ◽  
pp. 1-26
Author(s):  
Efstathios Xanthopoulos ◽  
Konstantinos Aravossis ◽  
Spyros Papathanasiou
Keyword(s):  
Stock Exchange ◽  
Statistical Tests ◽  
Moving Average ◽  
Efficient Market Hypothesis ◽  
Trading Strategies ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Greek Crisis ◽  
Athens Stock Exchange

This paper investigates the profitability of technical trading rules in the Athens Stock Exchange (ASE), utilizing the FTSE Large Capitalization index over the seven-year period 2005-2012, which was before and during the Greek crisis. The technical rules that will be explored are the simple moving average, the envelope (parallel bands) and the slope (regression). We compare technical trading strategies in the spirit of Brock, Lakonishok, and LeBaron (1992), employing traditional t-test and Bootstrap methodology under the Random Walk with drift, AR(1) and GARCH(1,1) models. We enrich our analysis via Fourier analysis technique (FFT) and more statistical tests. The results provide strong evidence on the profitability of the examined technical trading rules, even during recession period (2009-2012), and contradict the Efficient Market Hypothesis.

scholarly journals Performance comparison between Islamic and conventional stocks: evidence from Pakistan's equity market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Munazza Jabeen ◽  
Saba Kausar
Keyword(s):  
Stock Prices ◽  
Stock Exchange ◽  
Equity Market ◽  
Performance Comparison ◽  
Parametric Estimation ◽  
Total Risk ◽  
Excess Returns ◽  
Content Type ◽  
Practical Implications ◽  
Non Parametric

PurposeThis paper aims to examine the performance of Islamic and conventional stocks listed at the Pakistan Stock Exchange by using both parametric and non-parametric approaches. The motivation is to do risk-return analysis of Islamic stock prices and conventional stock prices.Design/methodology/approachIt uses various measures of performance, e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, beta, generalized auto-regressive conditional heteroskedasticity and stochastic dominance. Using the Karachi Meezan Index-30 (KMI-30) and the Karachi Stock Exchange Index-30 (KSE-30) as proxies for Islamic and conventional stock prices, respectively, it examines the performance of Islamic and conventional stocks. The daily data of KMI-30 and KSE-30, covering period from June 9, 2009 to June 20, 2020 are used.FindingsThe results show that the overall KMI-30 outperforms the KSE-30. The returns of the KMI-30 are greater than the KSE-30. However, the risk and volatility of the KMI-30 and KSE-30 are similar. Further, the KMI-30 has higher excess returns per unit of total risk than the KSE-30. But both indexes have similar excess returns per unit of systematic risk. Moreover, the KMI-30 returns have stochastically dominance over the KSE-30 returns. These results reveal that the Islamic index performs better than the conventional index.Practical implicationsThe findings provide several practical implications in financial and investment decisions making by investors, managers and policymakers such as strategies for asset allocation and investment. Further, in risk management, it provides guidance for allocating portfolios and managing risk. The investment in Islamic stocks may mitigate potential risk within asset portfolios.Originality/valueThis research is unique in its approach to the analysis of the performance comparison of conventional and Islamic stock by using comprehensive parametric and non-parametric estimation techniques. Such research has not been undertaken in the Pakistan's equity market since.

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

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Nina Sevani ◽  
Maria Ariesta
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Relative Strength ◽  
Strength Index ◽  
Web Based ◽  
Maximum Profit ◽  
Stock Recommendation ◽  
Collection Data ◽  
The Right ◽  
Stock Transaction

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%.

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