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Item Restricted In search of COVID-19: analysis of the impact of negative investor sentiment on ASEAN-6 stock markets using Google trends data(2023-05-12) Alcedo, Maxine C; Go, Hannah Nicole B.; Mendoza, Adrian R.This study analyzes the effects of negative retail investor sentiment regarding COVID- 19 on the stock market indices of six countries in the Association of Southeast Asian Nations (ASEAN), namely Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. We argue that investors demand more information in times of uncertainty (e.g., pandemics) to lessen their exposure to risk, which in turn influences their behavior in the stock market. Using weekly data from January 2020 to July 2022 to capture the beginning, peak, and decline of the pandemic, we analyze the abnormal increase in Google search volume relating to COVID-19 to measure uncertainty and panic in investor behavior. In particular, we used the “Coronavirus disease 2019” topic in Google Trends as a proxy for negative investor sentiment relating to the pandemic. Using pooled ordinary least squares, we found that negative investor sentiment has a significant adverse effect on ASEAN stock market returns. Our fixed effects regression showed that negative investor sentiment has a significant positive effect on ASEAN stock market volatility. Our findings also suggest that negative investor sentiment has a greater effect on ASEAN stock markets than changes in new COVID-19 cases. These results are robust after controlling for relevant factors such as government stringency measures, financial market size, oil prices, real exchange rates, and global interest rates. The growth of new COVID-19 cases has a significant negative effect on stock market returns. Faster growth in new vaccinations positively impacts returns as well.Item Restricted The search for digital gold: Google trends data as a predictor for cryptocurrency price volatility(2023-07) Maliwanag, Rafael John S.; Uligan, Ileanna April D.; Alburo, Florian A.Cryptocurrency price determination is one of the most interesting phenomena in modern finance. Polarizing as it is volatile, return and, consequently, risk in cryptocurrencies dwarf those of other traditional financial markets, and hence, constructing more holistic models of prediction undoubtedly has practical value. Proving to be useful in predicting market outcomes, the benefits of incorporating web search activity in forecasting models of cryptocurrency may be augmented due to the digital nativity of the market. Using time series data obtained from Google Trends, this paper examines the predictive capability of Google search volumes on cryptocurrency price volatility beyond prominent volatility models such as the GARCH(1,1). The study employs a two-step approach consisting of a GARCH(1,1) process and an ordinary least squares (OLS) regression. The conditional variance from the former, along with short-term and long-term indicators of Google Trends data are used as independent variables in an OLS regression of log volatility. The study has three main findings: 1.) Google Trends data predict cryptocurrency price volatility beyond the GARCH(1,1) model; 2.) Cryptocurrency price volatility is more responsive to the short-term Google Trends indicators rather than the long-term; and 3.) Among examined categories, keywords relating to the name and ticker of the coin (e.g. “Bitcoin” and “BTC”, for Bitcoin) and on widespread cryptocurrency-related terms have the most significant additional forecasting capabilities on the volatility of cryptocurrency returns beyond the GARCH(1,1). On the other hand, economic, geopolitical, and pandemic-related words seem to have little to no additional predictive capabilities. Ethereum is found to be most responsive to Google Trends data, while Cardano was found to be least responsive.Item Restricted Exploring the predictive potential of Google Trends on nowcasting unemployment rate in the Philippines(2016-12) Aclan, Christianne Mari G. ; Marfori, Marie Joyce C. ; Mendoza, Maria Nimfa F.Timely information on the current situation of the economy is very crucial in the decision making process of an economist, policy maker or even an investor. Technological developments have paved the way for such real-time information to be gathered and processed. The challenge now is how one utilizes this sort of information to make meaningful analysis and predictions. For instance, forecasting the current situation of the labor market through economic variables like unemployment rates can significantly contribute to helping government agencies and firms plan effectively particularly in determining the shortage of workers or if more workers will need to be trained in the future. Typically, unemployment rate forecasts are based on the historical time-series properties of the unemployment rate and near-term indicators of the labor market. In this study, we developed a new approach that incorporates and utilizes real-time information, specifically from Google Trends, to make a more accurate and convenient forecast of unemployment rates in the Philippines. Through an in-sample and out-of-sample comparison of forecast accuracy, we found that an Autoregressive Distributed Lag (ARDL) model which uses lagged values of the Google Index (GI) as independent variables, can improve the nowcasting performance of unemployment rates in the Philippines relative to competing benchmark models.Item Restricted Assessing the predictive capability of Google search volumes on Philippine peso exchange rates(2016-06) Aniciete, Elmar John O. ; Barchini, Christine Anne M. ; Debuque-Gonzales, MargaritaThe onset of the computer age and developments in information technology have changed the way people gather information. Web search engines such as Google and Yahoo! provide efficient platforms for acquiring relevant data. Web search query volumes are now being made available through websites such as Google Trends. This study aims to utilize time series data on weekly Google searches in order to determine whether the number of searches for selected keywords related to the foreign exchange market is a significant indicator of movements in the said market. The study applies a two-step approach consisting of GARCH (1,1) and OLS regression. Results show that the GARCH (1,1) conditional variance is not the sole and unbiased predictor of foreign exchange rate volatility. Additionally, tests show that geopolitical search terms give better predictions of foreign exchange rate volatility beyond the GARCH (1,1) model as compared to economic-related keywords based on worldwide Google search volumes, while a combination of geopolitical- and economic-related keywords give the best predictions for Philippine search volumes.Item Restricted Predictive capability of Google search in forecasting tourist arrival in the Philippines(2017-12-22) Ruiz, John Matthew D. ; Zacarias, Ysabelle T. ; Debuque-Gonzales, MargaritaGoogle Trends, a new source of data, shows the index of worldwide search volume queries in the internet. This study took advantage of this open data to look into its potentiality in improving the tourist arrival forecast for the Philippines using fifty keywords, which were divided into three categories: tourism, political and economic, and safety and order. A 12-month tourism forecast horizon were made for the top five largest sources of tourists for the Philippines, including the OFWs, using three models. A multivariate Vector Autoregression (VAR) model with Google data and two benchmark models: Autoregressive Integrated Moving Average (ARIMA) and a VAR model without Google data were compared using RMSE and MAPE criteria. This study provided evidence that the VAR model with Google data generally performed better in terms of RMSE and MAPE criteria as compared to the benchmark models. The set of keywords under the tourism category also proved to be the most significant in predicting tourist arrivals.