The cost of financing for investors is also related to uncertainty: High levels of uncertainty lower asset prices and drive up financing costs, leading to reduced investment and a slowdown in the economy. Economic contractions cause recessions to become more pronounced when businesses delay investment. Additionally, consumers and investors are also reluctant to make spending and investment decisions when they sense a high level of uncertainty in the economy. This uncertainty not only affects investor confidence and increases investors' holdings of short-term liquid assets such as cash, it can also cause investors to have difficulty predicting the future. Without strong regulation, this uncertainty fuels speculation and affects the stability of financial markets and even the broader economy. These events not only caused huge losses to the global economy but also had a significant impact on the Chinese stock market, resulting in large fluctuations in stock indices. In recent years, the U.S.–China trade war, the spread of COVID-19, and, most recently, the war between Russia and Ukraine, have heightened uncertainty in the global economy. Therefore, in the face of an uncertainty such as market volatility caused by the spread of the pandemic, the active release of favorable information by regulators can help guide investor sentiment, prevent sharp stock market volatility, and improve the effectiveness of policy governance. Using daily data from March 2, 2020, to March 2, 2021, our empirical findings reveal that stock returns during a pandemic lead to an increase in investor retrieval of search engine data and that uncertainty affects stock returns during a pandemic. This paper uses the uncertainty index, investor sentiment reflected by search engine data, and Chinese stock return data during the pandemic to examine the relationships among the three. When people face uncertainty, they often turn to internet search engines to obtain more information to support their investment decisions.
#GOOGLE TRENDS STOCK PREDICTION SERIES#
Using the mean absolute percentage error as a metric, the results support the view that the search volume model does have some forecast ability in produc- ing volatility estimates.In recent years, a series of uncertain events, including the spread of COVID-19, has affected the Chinese stock market. For those stocks whose search volume data proved fruitful in forecasting their volatility, a search volume model con- sisting of lags of search volume data as predictors was compared to a null model consisting of the average of the volatility as a forecast. The re- sults from the Granger causality analysis showed that some, but not all, stocks could use their search volume data from Google Trends to signifi- cantly forecast their volatility. Twelve stocks were selected from three sectors and a Granger causality analysis was performed to determine whether the search volume time series was useful in forecasting the volatility time series for a given stock. The thesis studies the effect of weekly search volume data from Google Trends on volatility measures of a portfolio of hand-picked stocks. John Joseph Hannan, Honors Advisor Keywords:
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