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The optimal training interval for amultilayer perceptron on a day to dayestimation of the Swedish OMXS30index
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Advanced level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The stock market plays a big role in our current nancial system and the uctuations onit are believed to depend on many dierent factors. One of the factors that are believedto be correlated to the stock market are macroeconomic variables, that is, variables thatindicate the status of the economical situation. Examples of such macroeconomic variablesare unemployment rate, loan interests and ination. Earlier attempts to predictthe stock market have been made by using process demanding methods such as arti-cial neural network. A multilayer perceptron is a self learning system that goes underthe category of an articial neural network. Such a network learns by being trainedon old data sets and has the capacity to identify relationships between dierent data.This method has been used in earlier studies to predict the stock market with goodresults. The problem statement of this report is to nd the optimal training interval fora multilayer perceptron on a day to day estimation of the Swedish OMXS30 index. Theinput to the algorithm consisted of 38 parameters, which in this case was a collectionof individual companies stock prices, foreign stock indexes, macroeconomic variables,previous and current values of the OMXS30 index. The results from the simulationsthat were executed on old stock data shows that 180 to 200 days of training yielded thebest results, where eight of nine periods over seven years (2007-2014) yielded prot. Theresults from the simulations during the periods with increasing index were sometimesbelow the index gain, but always with a prot. During periods of index decrease theresults were sometimes with a prot and sometimes non-prot. In the case of indexdecrease the result was always above the total index decrease. The conclusion is as theresults shows, that the optimal training interval is 180 to 200 days for the simulationsrun in the study of this report.1

Abstract [sv]

Aktiemarknaden spelar en stor roll i dagens finansiella system och fluktutionerna på börsen tros bero pa många orsaker. En av de saker som tros ha en koppling till börsen är makroekonomiska variabler, dvs sådana variabler som indikerar hur ekonomin mår. Exempel på makroekonomiska variabler ar arbetslöshet,       räntenivåer och i nation. Andra kopplingar som tros finnas till börsens utveckling är hur individuella aktier och utlandskabörser utvecklas. Tidigare försök har gjorts att forsöka forutsäga aktiemarknaden med hjalp av beräkningskrävande metoder, t. ex. Articiella neuron nät. En flerlagers perceptronar ett självlärande system som räknas som en typ av articiellt neuron nät. Nätverket lär sig genom att tränas pa gammal data och har formåagan att hitta samband mellan olika data. I tidigare studier har denna metod använts for att förutsäga aktiemarknaden med goda resultat. Problemformulering i denna rapport ar att ta reda på vilket det optimala träningsintervallet ar för en flerlagers perceptron för att, från en dag till en annan, förutsäga indexet på Stockholmsbörsen, OMXS30. Algoritmens indata bestod av totalt 38 parametrar som i detta fall var en samling av enskilda företagsaktievärden, utländska börsers index, makroekonomiska variabler, tidigare värden på OMXS30 samt det nuvarande värdet pa börsen. Resultaten från simulationerna som kördes pa gammal aktiedata visar att 180-200 dagar är det basta träningsintervallet daatta av nio stycken perioder över sju år (2007-2014) gick med vinst. Resultaten fransimulationerna under de perioder med stigande index blev i vissa fall under index, men alltid med vinst. I perioder med avtagande index sa blev resultaten i vissa fall vinstgivande och i andra fall inte vinstgivande, men i dessa fall alltid battre an den totalaindex nedgangen. Slutsatsen ar som resultaten visar att 180-200 dagar ar det optimala träningsintervallet for de simulationer som gjordes i undersökningen i denna rapport.2

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157697OAI: oai:DiVA.org:kth-157697DiVA: diva2:771145
Examiners
Available from: 2014-12-12 Created: 2014-12-12 Last updated: 2014-12-12Bibliographically approved

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