scholarly journals Volatility forecast with artificial neural networks as univariate time series, with examples from stock market indexes

2018 ◽  
Vol 7 (3) ◽  
pp. 291-299
Author(s):  
Fahriye Uysal ◽  
Burak Erturan
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Financial Markets ◽  
Global Economy ◽  
Historical Data ◽  
Univariate Time Series ◽  
Artificial Neural ◽  
Market Indexes

The tools that are offered to investors in financial markets are fluctuating. As this fluctuation causes losses as well as earnings, it is characterised as a risk for the investor. Especially, fluctuations that may occur in globally important markets and financial instruments have great significance, not just for investor but also for the global economy. Volatility, as a measure of fluctuations taking place in markets, is often used particularly by investors and all economic actors. Therefore, in recent years, future volatility predictions have gained importance. The aim of this research is forecasting future volatility values using the historical data of S&P 500, FTSE 100 and NIKKEI 225 stock market indexes. The progress of historical volatility values in years is presented and generated univariate time series is modelled with artificial neural networks. Future forecasts are done with the obtained model and results are interpreted. Keywords: Artificial neural networks, volatility, time series analysis, stock market indexes.

scholarly journals Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry

2020 ◽  
Vol 10 (3) ◽  
pp. 829
Author(s):  
Tomas Eloy Salais-Fierro ◽  
Jania Astrid Saucedo-Martinez ◽  
Roman Rodriguez-Aguilar ◽  
Jose Manuel Vela-Haro
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Automotive Industry ◽  
Historical Data ◽  
Series Data ◽  
Qualitative And Quantitative ◽  
Artificial Neural ◽  
Demand Forecasts

According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data.

scholarly journals ESTIMATING STOCK RETURNS USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL DESIGN WITH AN EVIDENCE FROM IRAQ STOCK EXCHANGE

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mohammed H Adnan ◽  
Mustafa Muneer Isma’eel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Stock Returns ◽  
Stock Exchange ◽  
Back Propagation ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Propagation Network

The research aims to estimate stock returns using artificial neural networks and to test the performance of the Error Back Propagation network, for its effectiveness and accuracy in predicting the returns of stocks and their potential in the field of financial markets and to rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was selected with (38) stock for a time series spanning (120) months for the years (2010_2019). The research found that there is a weakness in the network of Error Back Propagation training and the identification of data patterns of stock returns as individual inputs feeding the network due to the high fluctuation in the rates of returns leads to variation in proportions and in different directions, negatively and positively.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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