Academic journal article East Asian Economic Review

Can Big Data Help Predict Financial Market Dynamics?: Evidence from the Korean Stock Market

Academic journal article East Asian Economic Review

Can Big Data Help Predict Financial Market Dynamics?: Evidence from the Korean Stock Market

Article excerpt

(ProQuest: ... denotes formulae omitted.)

I. INTRODUCTION

This paper estimates dynamic relationships between the Korean stock market index and the related online search queries within a multivariate GARCH framework. In addition, the paper also attempts to investigate whether the information from search query data can be served as a potential source for designing profitable trading strategies in the Korean stock market.

The emergence of internet and social networking services combined with the extensive dissemination of smart phones have revolutionized the way we communicate and exchange information. Consequently, big data continuously flowing from increasing online activities by users have become a buzz word for recent years because of its potentials for various uses including marketing, political predictions, disease epidemics, social dynamics, etc.1

Economists are one of the late professions who delves into the investigation of possible use of big data mainly for forecasting market dynamics and related issues.2 The seminal paper by Choi and Varian (2012) shows the use of Google search query data as a key predictor for various economic activities including auto sales, travels, etc.3 It stimulates subsequent studies in the field of economics, which mostly concentrates on exploiting big data to increase the prediction power of forecasting models for economic variables of own interests.

The idea that the search query might contain information about subsequent actions by users is based on the premise that economic agents living in a contemporary society largely rely on the prior information-search process before making important economic decisions such as the purchase of durable goods and financial investments.

However, the motivaton of information demand does not always run in this direction; the heightened information-gathering activity itself can be the manifestation of a simple endogenous response to major events in markets in quest of more information, which might yield possible effects on market developments in next rounds. These intricacies in the causal relationship between the information demand and the market outcomes make it difficult to assess correctly the real importance of big data. In spite of this complicacy, the predictive power of information generated from online big data for market activity is supported by numerous studies ranging from stock markets to housing markets.4

The empirical analysis on the Korean stock market in this study reveals that the search frequency related to the Korean stock market has negative contemporaneous correlations with the KOSPI return for the majority of time with the occasional tightening of its magnitude. Furthermore, a negative association between the search query and the one-week-ahead stock return is observed, while the stock return has no statitically significant impact on the level of the future search query.

Based on these observations, we experiment with a hypothetical trading strategy that interprets the increased level of online search activity as a negative signal for future stock returns so as to examine whether profitable trading schemes can be constructed out of big data. The result from this simple exercise demonstrates that the big data-based strategy outperforms the benchmark strategy in terms of the expected utility over wealth. The other experiment also shows that the big data-based option trading strategy can beat the market for certain KOSPI200 option contracts.

As a result, this study is the first attempt to analyse the relationship between the KOSPI return and the degree of investor attention5 using the Korean stock market data and a Korean-based internet platform. We conjecture that the contribution is not limited to quantifying the dynamic relationships between stock retums and investor attention and estimating time-varying volatilities; it also explores the potential of big data as one of the practical tools for financial investment strategies, which is rarely pursued in the related literature. …

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