Testing Technical Trading Rules: Evidence from SAARC Countries

2018 ◽  
Vol 1 ◽  
pp. 1-36
Author(s):  
Faisal Anees ◽  
Shujahat Haider Hashmi ◽  
Muhammad Asad
Keyword(s):  
Historical Data ◽  
Moving Average ◽  
Technical Analysis ◽  
Moving Averages ◽  
Short Term ◽  
Technical Trading Rules ◽  
Average Rule ◽  
Data Representative ◽  
Simple Average

Technical analysis is widely accepted tool in professional place which is frequently used for investment decisions. Technical analysis beliefs that there exist patterns and trends and by capturing trends and patterns one can bless with above average profits. We test two technical strategies: Moving averages and Trading Range to question, either these techniques can yield profitable returns with the help of historical data. Representative daily indices of Four countries namely Pakistan, India, Srilanka, Bangladesh ranging from 1997 to 2011 have been examined. In case of Moving Average Rule, both simple and exponential averages have been examined to test eleven different short term and long term rules with and without band condition. Our results delivered that buy signals generate consistent above average returns for the all sub periods and sell signals generate lower returns than the normal returns. Intriguing observation is that Exponential average generates higher returns than the Simple Average. The results of Trading Range Break strategy are parallel with Moving average Method. However, Trading Range Strategy found not to give higher average higher return when compared with Moving Averages Rules and degree of volatility in returns is higher when compared with moving Average rule. In attempt to conclude, there exist patterns and trends that yield above average and below average returns which justify the validity of technical analysis.

scholarly journals Medias Móviles, como alternativas de inversión en un índice accionario.

2020 ◽  
Author(s):  
Gerardo Ablanedo Rosas ◽  
Lágrima De María Azcárraga Montiel ◽  
Edmundo Mejía Sánchez
Keyword(s):  
Quantitative Research ◽  
Historical Data ◽  
Moving Average ◽  
Technical Analysis ◽  
Stock Index ◽  
Back Testing ◽  
A Value ◽  
Stock Investment ◽  
Convergence And Divergence

  RESUMEN En inversión accionaria, identificar la tendencia del precio es importante para tomar decisiones de compra o venta de títulos o valores bursátiles. Dentro del análisis técnico, existen métodos que intentan predecir el comportamiento de un valor en función a datos históricos. En este trabajo se analizan dos estrategias de análisis técnico: la media móvil exponencial y la media móvil de convergencia y divergencia. Se generan datos históricos y se observa su comportamiento en el tiempo. Las estrategias se aplican a precios históricos del índice accionario del país Singapur que, por su movimiento lateral, es difícil de predecir y las medias móviles serían la herramienta que apoyaría la decisión del inversor. Además, y para fundamentar las conclusiones de éste trabajo, los resultados que se obtienen se comparan con la estrategia “buy and hold” que corresponde a comprar y mantener durante largo plazo y se exponen los efectos. Este trabajo es una investigación cuantitativa basada en el método empírico-analítico, que muestra las consecuencias de aplicar la media móvil exponencial y la media móvil de convergencia y divergencia a gráficos diarios del índice accionario mencionado. Se pretende identificar la herramienta de mayor funcionalidad que reconozca el mayor número de aciertos con posiciones largas y cortas, lo que permitiría incrementar los rendimientos de inversiones especulativas y con ello orientar al inversionista sobre la mejor estrategia. Los resultados basados en pruebas de back testing demuestran que, en ocasiones, el análisis técnico y el uso de medias móviles que son herramientas sofisticadas de trading, no generan altos rendimientos y que la estrategia más sencilla basada en comprar y esperar puede ser más rentable en el largo plazo.ABSTRACT In stock investment, identifying the price trend is important to make decisions to buy or sell securities or securities. Within the technical analysis, there are methods that try to predict the behavior of a value based on historical data. In this paper, two technical analysis strategies are analyzed: the exponential moving average and the moving convergence and divergence average. Historical data are generated and its behavior over time is observed. The strategies are applied to historical prices of the stock index of the country Singapore, which, due to its lateral movement, is difficult to predict and the moving averages would be the tool that would support the decision of the investor. In addition, and to base the conclusions of this work, the results obtained are compared with the "buy and hold" strategy that corresponds to buying and maintaining for a long term and the effects are exposed. This work is a quantitative research based on the empirical-analytical method, which shows the consequences of applying the exponential moving average and the moving average of convergence and divergence to daily charts of the aforementioned stock index. The aim is to identify the tool with the most functionality that recognizes the greatest number of successes with long and short positions, which would allow to increase the yields of speculative investments and thereby guide the investor on the best strategy. The results based on back testing prove that, at times, technical analysis and the use of mobile averages that are sophisticated trading tools do not generate high returns and that the simpler strategy based on buying and waiting can be more profitable in the long-term. Keywords: Technical analysis; Exponential moving average; Moving average of convergence and divergence; Trend. 

scholarly journals Accuracy of Moving Average Forecasting for NEPSE

2020 ◽  
Vol 13 (1) ◽  
pp. 62-76
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Graphical Representation ◽  
Moving Average ◽  
Annual Reports ◽  
Percentage Error ◽  
Moving Averages ◽  
Short Term ◽  
Time Duration ◽  
The Future

A simple moving average is one of the oldest and the simplest techniques of forecasting the trends of the stock market. The technical analysts follow mainly three types of moving averages, namely; simple, weighted, and exponential moving averages. Among these three types, as per the interest of investors, short-term and long-term time duration is used to calculate the trend using the moving average. All the mentioned moving averages are used by investors or analysts to predict the future trends of the market using historical data. Hence, for evaluating their forecasting accuracy, the paper has used both the short-term and the long-term moving average. The paper has used the NEPSE (closing) index values to calculate as well as plotted the moving averages to forecast the future trend and its accuracy with the help of Mean Absolute Percentage Error (MAPE). The paper found that there is a better crossover in the graphical representation of the moving average in the long-term moving average. In context to the Nepalese stock market, the MAPE results reflected a weekly (5-trading days) 5-SMA analysis of the market movement as the most relevant in short-term forecasting. Similarly, using the technique of moving average, 200-SMA (200-trading days of a year) was seen as the most effective to forecast long-term trends. The result of the long-term moving average MAPE pointed out that the annual reports of the listed companies better determine the trend of the market.

MULTIFRACTAL FLUCTUATIONS OF JIUZHAIGOU TOURISTS BEFORE AND AFTER WENCHUAN EARTHQUAKE

Fractals ◽  
2013 ◽  
Vol 21 (01) ◽  
pp. 1350001 ◽  
Author(s):  
KAI SHI ◽  
WEN-YONG LI ◽  
CHUN-QIONG LIU ◽  
ZHENG-WEN HUANG
Keyword(s):  
Wenchuan Earthquake ◽  
Moving Average ◽  
Long Term Memory ◽  
Short Term ◽  
Dynamical Mechanism ◽  
Before And After ◽  
Previous State ◽  
Multifractal Behavior ◽  
Multifractal Nature

In this work, multifractal methods have been successfully used to characterize the temporal fluctuations of daily Jiuzhai Valley domestic and foreign tourists before and after Wenchuan earthquake in China. We used multifractal detrending moving average method (MF-DMA). It showed that Jiuzhai Valley tourism markets are characterized by long-term memory and multifractal nature in. Moreover, the major sources of multifractality are studied. Based on the concept of sliding window, the time evolutions of the multifractal behavior of domestic and foreign tourists were analyzed and the influence of Wenchuan earthquake on Jiuzhai Valley tourism system dynamics were evaluated quantitatively. The study indicates that the inherent dynamical mechanism of Jiuzhai Valley tourism system has not been fundamentally changed from long views, although Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake. Jiuzhai Valley tourism system has the ability to restore to its previous state in the short term.

scholarly journals Stochastic and Momentum Analysis of Nepalese Stock Market

2018 ◽  
Vol 11 (1) ◽  
pp. 14-22
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Basic Concept ◽  
Moving Average ◽  
Fiscal Year ◽  
Short Term ◽  
Stochastic Oscillator ◽  
Zero Line ◽  
June July

The stochastic oscillator is one of the popular tools used by technical analysts. The tools are used mainly to find the overbought and oversold position in the stock market. The stochastic values are between 0-100 which helps to determine the market scenario. The two stochastic indicators are comprised of two lines namely; %K and %D. The investors using the short-term moving average follows %K and for long-term moving average for %D. Though, both are used for buy signal or sell signal by the investors. The basic concept is if, the value of %K is seen above %D, which reflects to sell position, which in context to Nepalese stock market, the scenario is seen during the month of June-July of every fiscal year. At the same time, momentum uses transaction signal or trade signal or the zero ‘0’ line to find the bearish or bullish trend of the market. The momentum of NEPSE index clearly pictures out the bullish and the bearish trend for a specific duration. If the momentum line touches the ‘zero line’, the NEPSE has changed its trend.

scholarly journals Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Marina Resta ◽  
Paolo Pagnottoni ◽  
Maria Elena De Giuli
Keyword(s):  
Technical Analysis ◽  
Daily Basis ◽  
Moving Averages ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Price Series ◽  
Daily Data

In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.

The Biology of the Ground Parrot, Pezoporus wallicus, in Queensland. III. Distribution and Abundance

1991 ◽  
Vol 18 (2) ◽  
pp. 199 ◽  
Author(s):  
DC McFarland
Keyword(s):  
National Park ◽  
Historical Data ◽  
Vegetation Type ◽  
Habitat Destruction ◽  
Interactive Effects ◽  
Seasonal Effects ◽  
Short Term ◽  
The West ◽  
Seed Availability

Ground parrots in Queensland were found in closed graminoid-heathlands and sedgelands between Maryborough and Coolum on the mainland, and along the west coast of Fraser I. Parrot distribution, when compared to historical data, shows a decline which is a result of habitat destruction or degradation in the northern and southern limits of the species range. The current population is estimated at 2900 birds, with the majority in the heathlands of Cooloola National Park, Wide Bay Military Reserve and the State Forest and the Great Sandy National Park on Fraser I. Ground parrot density varied between sites because of the interactive effects of vegetation type, heathland area, time since and frequency of fire, microhabitat diversity and proximity to recolonisers. Within sites, parrot numbers changed in the long term with time since fire (influence of temporal changes in vegetation structure and seed availability) peaking at 5-8 years after burning, and in the short term with the seasonal effects of dispersal and breeding. Although predators were present their impact on the main populations was considered minimal. All of these factors are, to some extent, influenced by human activities, e.g. clearing and burning of heathlands.

scholarly journals Technical trading revisited: evidence from the Asian stock markets

2014 ◽  
Vol 11 (2) ◽  
pp. 511-532 ◽  
Author(s):  
Thorben Lubnau ◽  
Neda Todorova
Keyword(s):  
Transaction Costs ◽  
Moving Average ◽  
Predictive Ability ◽  
Excess Returns ◽  
Short Term ◽  
Technical Trading ◽  
Developed Markets ◽  
Stock Indices ◽  
Bootstrap Tests

We examine the forecasting power and profitability of moving average (MA) and trading range break (TRB) rules for the daily prices of ten Asian stock indices from January 1990 to September 2012 using bootstrap tests. The results confirm the predictive ability of MA rules whereas the picture uncovered by the TRB rules is more mixed. The MA rules consistently generate positive excess returns after transaction costs, with highest magnitudes often achieved for less developed markets. However, more developed markets surprisingly seem to be far from informationally efficient as well. Furthermore, short-term variants of the trading rules outperform systematically long-term variants.

scholarly journals Causal Effects of Air Pollution on Mortality Rate in Massachusetts

2020 ◽  
Vol 189 (11) ◽  
pp. 1316-1323 ◽  
Author(s):  
Yaguang Wei ◽  
Yan Wang ◽  
Xiao Wu ◽  
Qian Di ◽  
Liuhua Shi ◽  
...  
Keyword(s):  
Air Pollution ◽  
Nitrogen Dioxide ◽  
Moving Average ◽  
National Standards ◽  
Ozone Exposure ◽  
Short Term ◽  
Multiplicative Models ◽  
Epidemiology Studies ◽  
Excess Deaths

Abstract Air pollution epidemiology studies have primarily investigated long- and short-term exposures separately, have used multiplicative models, and have been associational studies. Implementing a generalized propensity score adjustment approach with 3.8 billion person-days of follow-up, we simultaneously assessed causal associations of long-term (1-year moving average) and short-term (2-day moving average) exposure to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), ozone, and nitrogen dioxide with all-cause mortality on an additive scale among Medicare beneficiaries in Massachusetts (2000–2012). We found that long- and short-term PM2.5, ozone, and nitrogen dioxide exposures were all associated with increased mortality risk. Specifically, per 10 million person-days, each 1-μg/m3 increase in long- and short-term PM2.5 exposure was associated with 35.4 (95% confidence interval (CI): 33.4, 37.6) and 3.04 (95% CI: 2.17, 3.94) excess deaths, respectively; each 1–part per billion (ppb) increase in long- and short-term ozone exposure was associated with 2.35 (95% CI: 1.08, 3.61) and 2.41 (95% CI: 1.81, 2.91) excess deaths, respectively; and each 1-ppb increase in long- and short-term nitrogen dioxide exposure was associated with 3.24 (95% CI: 2.75, 3.77) and 5.60 (95% CI: 5.24, 5.98) excess deaths, respectively. Mortality associated with long-term PM2.5 and ozone exposure increased substantially at low levels. The findings suggested that air pollution was causally associated with mortality, even at levels below national standards.

scholarly journals A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

2020 ◽  
Vol 12 (9) ◽  
pp. 3612 ◽  
Author(s):  
Davut Solyali
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Historical Data ◽  
Sustainable Growth ◽  
Solar Irradiation ◽  
Support Vector ◽  
Short Term ◽  
Inference System ◽  
Electricity Load

Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

scholarly journals Technical Financial Analysis Tools for Stata

2017 ◽  
Vol 17 (3) ◽  
pp. 736-747 ◽  
Author(s):  
Mehmet F. Dicle ◽  
John D. Levendis
Keyword(s):  
Financial Analysis ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Analysis Tool ◽  
Investment Strategies ◽  
Moving Averages ◽  
Strength Index ◽  
Analysis Tools

In this article, we provide four financial technical analysis tools: moving averages, Bollinger bands, moving-average convergence divergence, and the relative strength index. The tftools command is used with four subcommands, each referring to a technical analysis tool: bollingerbands, macd, movingaverage, and rsi. We provide examples for each tool. tftools allows researchers to backtest their own investment strategies and will be of interest to investors, researchers, and students of finance.

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