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Publicação
Machine learning in fnancial forecasting: predicting equity volatility and assessing portfolio strategies
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| 2.86 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
In this directed research, we look to capitalize on a tree gradient boosting model for stock
volatility prediction. Through an extensive literature review we build on past research focusing
on a wide range of volatility drivers and integrate them as model features. We propose different
rebalancing techniques to the market portfolio according to our volatility predictions and assess
their viability. Finally, we arrive at an approach that offers a robust framework for equity
volatility forecasting and propose portfolio constructions that can further advance the current
understanding on the use of volatility for fund managers.
Descrição
Palavras-chave
Volatility timing Supervised learning Xgboost Portfolio rebalancing