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PC Regression, Vector Autoregression, and Recurrent Neural Networks: How do they compare when predicting stock index returns for building efficient portfolios?
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
PC Regression, Vektorautoregression, och Återkopplande Neurala Nätverk: En jämförelse mellan deras förmåga att prognostisera aktieindexavkastning för att konstruera effektiva portföljer (Swedish)
Abstract [en]

This thesis examines the statistical and economic performance of modeling and predicting equity index returns by application of various statistical models on a set of macroeconomic and financial variables. By combining linear principal component regression, vector autoregressive models, and LSTM neural networks, the authors find that while a majority of the models display high statistical significance, virtually none of them successfully outperform classic portfolio theory on efficient markets in terms of risk-adjusted returns. Several implications are also discussed based on the results.

Abstract [sv]

Detta examensarbete undersöker den statistiska och ekonomiska prestationen i att modellera och prognostisera aktieindexavkastning via applikation av flertalet statistiska modeller på en datamängd bestående av makroekonomiska och finansiella variabler. Genom att kombinera linjär huvudkomponentsregression (principal component analysis), vektorautoregression och den återkopplande neurala nätverksmodellen LSTM finner författarna att även om majoriteten av modellerna påvisar hög statistisk signifikans så överpresterar praktiskt taget ingen av dem mot klassisk portföljteori på effektiva marknader, sett till riskjusterad avkastning. Flera implikationer diskuteras också baserat på resultaten

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:087
Keywords [en]
PC Regression, Vektorautoregression, och Återkopplande Neurala Nätverk: En jämförelse mellan deras förmåga att prognostisera aktieindexavkastning för att konstruera effektiva portföljer
Keywords [sv]
Huvudkomponentregression, vektorautoregression, LSTM, återkopplande neurala nätverk, portföljteori, portföljoptimering, maskininlärning, makroekonomi, finans, aktieavkastning, aktieindex
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252557OAI: oai:DiVA.org:kth-252557DiVA, id: diva2:1319879
External cooperation
Länsförsäkringar Fonder
Subject / course
Financial Mathematics
Educational program
Master of Science - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-03 Last updated: 2019-06-04Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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