scholarly journals Analyzing Performance of Technical Analysis on Stock Markets

2020 ◽  
pp. 53-62
Author(s):  
A. Stavytskyy ◽  
V. Taraba
Keyword(s):  
Hong Kong ◽  
Historical Data ◽  
Arima Model ◽  
Technical Analysis ◽  
Optimal Parameters ◽  
Arima Models ◽  
Optimal Model ◽  
Stock Indices ◽  
Analysis Methods ◽  
The Stability

The article analyzes the profitability of technical analysis methods for the seven stock indices during the last ten years. According to the analysis, the profitability of technical analysis has increased recently due to changes in market conditions. However, the efficiency of technical analysis methods was much lower during 2010-2018. The analysis showed that technical analysis methods demonstrated best results on the Chinese, Indian, and Hong Kong stock indices, the worst – on the American, European, and Japanese stock indices. However, the stability of these methods is quite low: their profitability varies greatly with the change of the sample. The issue of aggregation of technical analysis signals and ARIMA-model signals is also considered in this paper. The optimal parameters for the technical analysis methods were selected by testing on historical data; optimal ARIMA models were selected for each index. For 3 out of 7 indices the optimal model is WN (white noise). Most technical analysis methods showed poor results on the American (S&P 500) and European (Euronext 100) stock indices (except for the last two years). The results can be used to develop trading strategies.The analysis showed that technical analysis methods demonstrated best results on the Chinese, Indian, and Hong Kong stock indices, the worst – on the American, European, and Japanese stock indices. However, the stability of these methods is quite low: their profitability varies greatly with the change of the sample. The issue of aggregation of technical analysis signals and ARIMA-model signals is also considered in this paper. The optimal parameters for the technical analysis methods were selected by testing on historical data; optimal ARIMA models were selected for each index. For 3 out of 7 indices the optimal model is WN (white noise). Most technical analysis methods showed poor results on the American (S&P 500) and European (Euronext 100) stock indices (except for the last two years). The results can be used to develop trading strategies.

scholarly journals Analisis Teknikal Harga Saham Waskita Karya Tahun 2017 dengan Metode ARIMA

2018 ◽  
Vol 7 (1) ◽  
pp. 122-126
Author(s):  
Wahyuni Windasari
Keyword(s):  
Key Words ◽  
Stock Prices ◽  
Stock Price ◽  
Historical Data ◽  
Arima Model ◽  
Security Analysis ◽  
Technical Analysis ◽  
Price Movement

AbstractAs an investor needs to do an analysis before making a decision either in selling or buyingshares. Security analysis consist of two types of analysis, namely tecnical analysis andfundamental analysis. Technical analysis to test wheater historical data will predict stock pricesas a consideration to buy or sell an investment's instrument. One type of technical analysis isthe ARIMA method. In this research uses daily stock price of WSKT Tbk during 1 Januari–10Oktober 2017 to predict stock prices the few days. The best ARIMA model to describe WSKTstock price movement is MA(4), with MAE predict data is 480.25.Key words : forecasting, ARIMA, technical analysis, stock prices.

scholarly journals Analisis Teknikal Harga Saham Waskita Karya Tahun 2017 dengan Metode ARIMA

2018 ◽  
Vol 7 (1) ◽  
pp. 80-84
Author(s):  
Wahyuni Windasari
Keyword(s):  
Key Words ◽  
Stock Prices ◽  
Stock Price ◽  
Historical Data ◽  
Arima Model ◽  
Security Analysis ◽  
Technical Analysis ◽  
Fundamental Analysis ◽  
Price Movement

As an investor needs to do an analysis before making a decision either in selling or buying shares. Security analysis consist of two types of analysis, namely tecnical analysis and fundamental analysis. Technical analysis to test wheater historical data will predict stock prices as a consideration to buy or sell an investment's instrument. One type of technical analysis is the ARIMA method. In this research uses daily stock price of WSKT Tbk during 1 Januari–10 Oktober 2017 to predict stock prices the few days. The best ARIMA model to describe WSKT stock price movement is MA(4), with MAE predict data is 480.25.Key words : forecasting, ARIMA, technical analysis, stock prices.

SYSTEM APPROACH TO ANALYSIS OF THE REASONS FOR METHANE EXPLOSIONS AND COAL DUST ON RUSSIAN MINES

2020 ◽  
Author(s):  
Виктория Владимировна Смирнякова ◽  
Валерий Витальевич Смирняков ◽  
Федор Александрович Орлов
Keyword(s):  
Statistical Methods ◽  
System Approach ◽  
Coal Dust ◽  
Technical Analysis ◽  
Analysis Methods ◽  
Dust Explosions ◽  
Comparative Results

Авторами приведены статистические данные об авариях, связанных со взрывами газа и пыли на горных предприятиях России. Показаны сравнительные результаты оценки причин аварийных ситуаций, проведенных статистическими методами и методами технического анализа. The authors provide statistics on accidents associated with gas and dust explosions at mining enterprises in Russia. Comparative results of the assessment of the emergencies causes conducted by statistical methods and technical analysis methods are shown.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

Empirical Assessment and Modeling of Traffic-induced Air Pollution

2021 ◽  
pp. 036119812110237
Author(s):  
Zhongyang Lu ◽  
Andy H. F. Chow ◽  
Jacky Leung ◽  
Haydn Kwok ◽  
Sammy Cheung
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Road Network ◽  
Historical Data ◽  
Flow Speed ◽  
Traffic Flows ◽  
Real World Data ◽  
Empirical Assessment ◽  
Air Quality Monitoring Stations ◽  
Transport Management

Congestion and traffic-induced air pollution are associated with population growth and economic development. Compared with congestion, there are relatively few studies on modeling and assessment of traffic-induced pollution. This paper presents an empirical assessment and analysis of traffic-induced air pollution with real-world data collected from the Hong Kong Strategic Road Network. The study employed historical data of traffic flows, speeds, and emission of air pollutants collated by the Hong Kong Transport Department and Environmental Protection Department. This paper first reveals the correlation between traffic flows, speeds, and corresponding induced pollutants including nitrogen oxides (NO2, NOX) and carbon monoxide (CO). To gain further statistical insight, a regression analysis was conducted on the flow–speed–emission relationship at three air quality monitoring stations, which revealed the significance of various factors on this relationship. This study contributes to green transport management and urban sustainability.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

A Survey of Fractional-Order Neural Networks

2017 ◽  
Author(s):  
Shuo Zhang ◽  
YangQuan Chen ◽  
Yongguang Yu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Fractional Order ◽  
Computational Science ◽  
General Introduction ◽  
Analysis Methods ◽  
Recent Trends ◽  
The Stability

In this paper, the literature of fractional-order neural networks is categorized and discussed, which includes a general introduction and overview of fractional-order neural networks. Various application areas of fractional-order neural networks have been found or used, and will be surveyed and summarized such as neuroscience, computational science, control and optimization. Recent trends in dynamics of fractional-order neural networks are presented and discussed. The results, especially the stability analysis of fractional-order neural networks, are reviewed and different analysis methods are compared. Furthermore, the challenges and conclusions of fractional-order neural networks are given.

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