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Can a trading strategy based on predictions from a nonlinear support vector machine outperform a passive investor holding the S&P500 index?

Opsahl, Kristian; Harsjøen, Marius Skyrud
Master thesis
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2287743.pdf (1.949Mb)
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http://hdl.handle.net/11250/2623038
Utgivelsesdato
2019
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  • Master of Science [1116]
Sammendrag
In this empirical research, we compare the forecasting performance of a supervised

Support Vector Machine technique to a passive buy-and-hold strategy on the S&P500

index. By introducing two investment strategies, we find evidence that the application of

a nonlinear Support Vector Machine can be superior to linear regression models, as well

as to a passive buy-and-hold strategy. The Support Vector Machine model generates both

excess returns and reduced volatility for the period between 2013 to 2019. However,

when comparing the prediction results of a Support Vector Machine model to that of a

linear regression model during the Great Recession, the results are ambiguous, although

both models have proven to explicitly outperform the passive buy-and-hold approach.
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Masteroppgave(MSc) in Master of Science in Finance - Handelshøyskolen BI, 2019
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Handelshøyskolen BI

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