Naver trends and stock market volatility = 네이버 트렌드와 주식시장 변동성

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Stock market volatility is a widely and deeply studied subject across academic and industrial settings. The increase in internet data usage, collection, and availability has also led to studies examining the links between internet search history and the stock market. In this paper we attempt to predict future stock market volatility, otherwise known as realized volatility, in Korean equity markets using Naver search trend data in a Long Short-Term Memory (LSTM) machine learning model. We find that LSTM networks showed an improvement in predicting realized volatility over the traditional GARCH model when compared using the mean average percentage error (MAPE) metric in both the KOSPI and KOSDAQ indices. We also find that we can improve on our original LSTM network with feature selection and expansion of the observation interval and normalization window.
Advisors
Byun, Sukjoonresearcher변석준researcher
Description
한국과학기술원 :금융공학프로그램,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 금융공학프로그램, 2020.8,[iii, 24 p. :]

Keywords

machine learning▼avolatility▼aLSTM▼arecurrent neural networks▼asearch trend▼aKOSPI▼aKOSDAQ; 기계학습▼a변동성▼a인공 신경망▼a검색 트렌드▼a코스피▼a코스닥

URI
http://hdl.handle.net/10203/285144
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=926318&flag=dissertation
Appears in Collection
KGSF-Theses_Master(석사논문)
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