A self-normalizing neural network approach to bond liquidity classication

Detta är en Master-uppsats från KTH/Matematisk statistik

Författare: Gustav Kihlström; [2018]

Nyckelord: ;

Sammanfattning: Bond liquidity risk is complex and something that every bond-investor needs to take into account. In this paper we investigate how well a selfnormalizing neural network (SNN) can be used to classify bonds with respect to their liquidity, and compare the results with that of a simpler logistic regression. This is done by analyzing the two algorithms' predictive capabilities on the Swedish bond market. Performing this analysis we find that the performance of the SNN and the logistic regression are broadly on the same level. However, the substantive overfitting to the training data in the case of the SNN suggests that a better performing model could be created by applying regularization techniques. As such, the conclusion is formed as such that there is need of more research in order to determine whether neural networks are the premier method to modelling liquidity.

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