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Predicting the unpredictable - Can Artificial Neural Network replace ARIMA for prediction of the Swedish Stock Market (OMXS30)?
Mid Sweden University, Faculty of Human Sciences, Department of Economics, Geography, Law and Tourism.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

During several decades the stock market has been an area of interest forresearchers due to its complexity, noise, uncertainty and nonlinearity of thedata. Most of the studies regarding this area use a classical stochastics method,an example of this is ARIMA which is a standard approach for time seriesprediction. There is however another method for prediction of the stock marketthat is gaining traction in the recent years; Artificial Neural Network (ANN).This method has mostly been used in research on the American and Asian stockmarkets so far. Therefore, the purpose of this essay was to explore if ArtificialNeural Network could be used instead of ARIMA to predict the Swedish stockmarket (OMXS30). The study used data from the Swedish Stock Marketbetween 1991-07-09 to 2018-12-28 for the training of the ARIMA model anda forecast data that ranged between 2019-01-02 to 2019-04-26. The forecastdata of the ANN was composed of 80% of the data between 1991-07-09 to2019-04-26 and the evaluation data was composed of the remaining 20%. TheANN architecture had one input layer with chunks of 20 consecutive days asinput, followed by three Long Short-Term Memory (LSTM) hidden layers with128 neurons in each layer, followed by another hidden layer with RectifiedLinear Unit (ReLU) containing 32 neurons, followed by the output layercontaining 2 neurons with softmax activation. The results showed that theANN, with an accuracy of 0,9892, could be a successful method to forecast theSwedish stock market instead of ARIMA.

Place, publisher, year, edition, pages
2019. , p. 45
Keywords [en]
Artificial Neural Network, ARIMA, LSTM, stock market
National Category
Business Administration
Identifiers
URN: urn:nbn:se:miun:diva-36908OAI: oai:DiVA.org:miun-36908DiVA, id: diva2:1344390
Subject / course
Business Administration FE1
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Examiners
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • nn-NB
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  • Other locale
More languages
Output format
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