Exploring the drivers and prediction of fund flow in the Norwegian Bond Fund Market - Deploying classic statistics and machine learning methods - Master thesis

Aug 6, 2026

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This thesis analyzes the drivers behind the flow of Norwegian bond funds and predicts their net flow one month in advance using machine learning models and macro variables. The study uses a sample of 79 bond funds, including a subset of medium credit risk funds. XGBoost feature importance and OLS regression are applied to test hypotheses on the influence of performance and macro factors. Results highlight a significant negative linear relationship with EUR/NOK changes, while XGBoost suggests non-linear patterns related to VIX and lagged net flow; however, prediction accuracy remains insufficient for effective one-month forecasting.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages49

Reading Time2 h 3 min

AccessFree to download

Abstract

Acknowledgements

Introduction

Motivation

Challenges

Literature Review

Data and Sample Description

Dependent Variable (Y)

Independent Variables (X)

Methodology

Describing OLS and Machine Learning Algorithms

Machine Learning Considerations

Results and Main Analysis

XGBoost Feature Selection

OLS Result Bond Funds

OLS Result Bond Funds with Medium Credit Risk

Net Flow Prediction with Machine Learning

Understanding the Drivers of Prediction Results in Our Machine Learning Models

Discussion

Conclusion

Contribution for Further Research

Bibliography

Appendix

EuropeW/O0Jf(ClwT23) 􀂒 Handelsh0ysllolen Bl GRA 19703 Master Thesis Thesis Master of Science 100% - W Predefinert informasjon Startdato: Sluttdato: Ellsamensform: Flowkode: Intern sensor: Delta􀂓er Navn: 09-01-2023 09:00 CET 03-07-2023 12:00 CESTT 202310ll11184IIINOOIIWIIT (Anonymisert) Termin: Vurderingsform: Rikke Nordvoll og Marte Nystad Rise 202310 Norsk 6-trinns sllala (A-F) lnformasjon fra delta􀂓er Exploring the driuers and prediction of fund flow in the Norwegian Bond Fund Market: Deploying classic statistics and machine Leaming Tittel *: methods Naun pa ueileder *: Paolo Giordani lnneholder besuarelsen Nei Kan besuarelsen Ja konfidensielt offentliggjtres?: materiale?: Cjruppe (jruppenaun: (Anonymisert) (jruppenummer: 313 Andre medlemmer i gruppen: BI Norwegian Business School Oslo, Spring 2023 Exploring the drivers and prediction of fund flow in the Norwegian Bond Fund Market: Deploying classic statistics and machine learning methods Master Thesis Marte Rise and Rikke Nordvoll Supervisor: Paolo Giordani Master of Science in Business Analytics BI NORWEGIAN BUSINESS SCHOOL Oslo, July 3, 2023 This thesis is apart of theMSc program at BI Norwegian Business School. The school takes no responsibility for the methods used, result found, or conclusions drawn. Abstract This paper aims to analyze the drivers and predict flow of Norwegian bond funds one month in advance using machine learning models and macro variables. The study investigates a sample of 79 bond funds and a subset of medium credit risk funds. We utilize XGBoost feature importance to investigate the factors that influence net flow of Norwegian bond funds. Additionally, through the use of OLS regression, we address our hypothesis concerning the significant impact of performance and macro variables on net flow of the Norwegian bond fund market. Our findings reveal only a significant negative linear relationship between change EUR/NOK and Norwegian bond funds. To harness the capabilities of machine learning models, partial dependency plots are also examined in search for nonlinear relationships. XGBoost reveals non-linear relationship among predicted net flow and changes in VIX, the models (XGBoost and MLP) show varying impacts of change EUR/NOK, and contradiction patterns in lagged net flow. Due to poor accuracy scores across prediction models, we are unable to achieve effective models for predicting bond fund flow one month ahead using top selected features. Acknowledgements First of all, we would like to thank Paolo Giordani, our supervisor, for his advice and helpful feedback on our thesis. Secondly, we want to thank Norwegian Fund and Asset Management Association (VFF) for providing us with the data on Norwegian bond funds. Finally, we would like to express our gratitude to Thomas Eitzen, Chief Analyst of Fixed Income at SEB, for his suggestion to explore capital flow of Norwegian bond funds. Content ABSTRACT ...................................................................................................................................... I ACKNOWLEDGEMENTS............................................................................................................ II INTRODUCTION............................................................................................................................ 1 MOTIVATION................................................................................................................................. 3 CHALLENGES.................................................................................................................................. 3 LITERATURE REVIEW................................................................................................................ 4 DATA AND SAMPLE DESCRIPTION ........................................................................................ 6 DEPENDENT VARIABLE (Y) .......

FAQs

How is Norwegian bond fund flow predicted in this thesis?

Net flow is predicted one month ahead using machine learning models alongside macro variables, with analysis also covering specific fund subsets such as medium credit risk funds.

Which methods are used to identify key drivers of net flow?

The thesis uses XGBoost feature importance to examine influential factors and OLS regression to test the impact of performance and macro variables on net flow.

What do the results show about exchange rate and volatility variables?

The findings reveal a significant negative linear relationship between EUR/NOK changes and Norwegian bond funds, while XGBoost indicates non-linear relationships involving predicted net flow and changes in VIX.

Aug 6, 2026

Upload by Eliana