The Econometric Modelling of Financial Time Series

Franses, Ph.H.B.F.

doi:10.1016/s0169-2070(00)00046-7

Cited by 6 publications

(2 citation statements)

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“…The ARIMA model is currently a widely used class of time series prediction models [16]. This model allows you to predict the behavior of time series in the future and understand their properties [17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

Horák

Krulický

2019

SHS Web Conf.

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Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

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Section: Introductionmentioning

confidence: 99%

Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

Horák

Krulický

2019

SHS Web Conf.

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“…At present, the ARIMA model is often used for its unique predictive capability also for share prices development forecasting [7]. Frances [8] favours this statement claiming that the ARIMA models are the most frequently used models for predicting time series that can be stationary transformation. ARIMA can therefore enable identification of time series characteristics as well as predicting their behaviour in the future.…”

Section: Introductionmentioning

confidence: 99%

Prediction of stock price developments using the Box-Jenkins method

Groda

Vrbka

2017

SHS Web Conf.

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Stock prices develop in a non-linear way. Naturally, the stock price prediction is one of the most important issues at stock markets. Therefore, a variety of methods and technologies is devoted to the prediction of these prices. The present article predicts the future development of the stock price of ČEZ, a. s., on the Prague Stock Exchange using the ARIMA method -the Box-Jenkins method. The analysis employs the final price of the last trading day in a given month, from February 2012 to September 2017. The data come from the Prague Stock Exchange database. Statistica software is used for processing the data, namely advanced time series prediction methods, the ARIMA tool, and autocorrelation functions. First, the current stock development of ČEZ, a.s., was graphically evaluated, and this was followed by a stock price prediction for the next 60 days in which the shares would be traded. Lastly, the prediction residues were analysed. It was confirmed that the calculation was done correctly, but with little accuracy. The conclusion is an assertion that the Box-Jenkins method is not a suitable tool for prediction.

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Econometrics—Selected Models

Vrbka

2021

Studies in Computational Intelligence

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The Econometric Modelling of Financial Time Series

Cited by 6 publications

References 0 publications

Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

Prediction of stock price developments using the Box-Jenkins method

Econometrics—Selected Models

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