Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility

Xu, Qifa; Bo, Zhongpu; Jiang, Cuixia; Liu, Yezheng

doi:10.1016/j.knosys.2018.12.025

Cited by 48 publications

(27 citation statements)

References 48 publications

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“…Another interesting text source for these text-mining forecasting tools is social media data (for example, microblogging data present on twitter) [91]. Also, Google trends can be useful indicators for stock market volatility forecasts [92]. Evidently, there is a diverse set of data (both structured and unstructured) and machine learning algorithms that have been implemented for volatility modelling.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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“…Girardin and Joyeux (2013) choose the lag length as 24 months. Xu, Bo, Jiang, and Liu (2019) determine the lag length based on the lags at which the weight is close to zero. Generally, the lag length is between 12 and 36 months according to the previous literature.…”

Section: Methodsmentioning

confidence: 99%

An empirical study on the role of trading volume and data frequency in volatility forecasting

Liu

Choo

2020

Journal of Forecasting

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This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed-Data Sampling (GARCH-MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and(3) high-frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH-MIDAS in volatility forecasting.

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“…We notice that all research works mentioned above only consider and use the dynamics of stock time series rather than other information or exogenous variables. Recently, many studies have confirmed the benefit of Google Trends, as the additional exogenous variable, for improving forecasting results [10,29,30]. Therefore, in this study, the US crisis contagion effect on developing and developed stock markets are investigated by the dynamic copula-GARCH with additional Google Trends indicator.…”

Section: Introductionmentioning

confidence: 98%

“…D'Amuri and Marcucci [9] examined the predictive power of Google-based models in forecasting US unemployment and revealed the higher performance of Google-based models during the Great Recession, with their relative performance stabilizing. Most recently, Xu et al [10] applied Google Trends to predict the volatility of the stock markets and mentioned that Google Trends is the vital source of the volatility besides macroeconomic fundamentals. According to the conclusions made by these previous papers, we can expect that the forecasting performance can be improved by incorporating the Google Trends data as an additional exogenous variable in the forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends

Maneejuk

Yamaka

2019

Mathematics

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The accuracy of contagion prediction has been one of the most widely investigated and challenging problems in economic research. Much effort has been devoted to investigating the key determinant of contagion and enhancing more powerful prediction models. In this study, we aim to improve the prediction of the contagion effect from the US stock market to the international stock markets by utilizing Google Trends as a new leading indicator for predicting contagion. To improve this contagion prediction, the dynamic copula models are used to investigate the structure of dependence between international markets and the US market, before, during, and after the occurrence of the US financial crisis in 2008. We also incorporate the Google Trends data as the exogenous variables in the time-varying copula equation. Thus, the ARMAX process is introduced. To investigate the predictive power of Google Trends, we employ the likelihood ratio test. Our empirical findings support that Google Trends is a significant leading indicator for predicting contagion in seven out of 10 cases: SP-FTSE, SP-TSX, SP-DAX, SP-Nikkei, SP-BVSP, SP-SSEC, and SP-BSESN pairs. Our Google-based models seem to predict particularly well the effect of the US crisis in 2008. In addition, we find that the contribution of Google Trends to contagion prediction varies among the different stock market pairs. This finding leads to our observation that the more volatile the market time-varying correlation, the more useful Google Trends.

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Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility

Cited by 48 publications

References 48 publications

Machine Learning for Financial Risk Management: A Survey

Machine Learning for Financial Risk Management: A Survey

An empirical study on the role of trading volume and data frequency in volatility forecasting

Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends

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