This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,t...This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.展开更多
A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employ...A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.展开更多
In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time pr...In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time preferences when planning to visit a destination.More and more researchers have adopted tourism-related search engine data in the field of tourism prediction.However,few studies use search engine data to conduct cluster analysis to identify residents'choice toward a tourism destination.In the present study,146 keywords related to“Beijing tourism”are obtained from Baidu index and principal component analysis(PCA)is applied to reduce the dimensionality of keywords obtained by Baidu index.Modified affinity propagation(MAP)clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing.The result shows that residents in Hebei province are most likely to travel to Beijing.The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means,linkage,and Affinity Propogation(AP)in terms of silhouette coefficient and Calinski–Harabaz index.We also distinguish the difference of residents’choice to travel to Beijing during the peak tourist season and off-season.The residents of Tianjing are inclined to travel to Beijing during the peak tourist season.The residents of Guangdong,Hebei,Henan,Jiangsu,Liaoning,Shanghai,Shandong,and Zhejiang have high attention to travel to Beijing during both seasons.展开更多
The development of the Internet and big data have made it possible to study pop-ulation migration and flow between cities.This study analyzes the probability of the population migration propensity of China’s three ma...The development of the Internet and big data have made it possible to study pop-ulation migration and flow between cities.This study analyzes the probability of the population migration propensity of China’s three major urban clusters,identi-fies the direction of population movements,and uses Markov chains to predict the probability of population migration propensity moving forward in order to assess the intercity population migration trends of urban clusters in the future.Internet search engine data is used,and a population migration propensity intensity model is used for calculations.The results show that the Pearl River Delta urban cluster and the Yangtze River Delta urban clusters are areas of active population migration,and that intercity population migration is a part of this activity.Intercity population migra-tion in the Beijing-Tianjin-Hebei urban cluster is not as active as it is in the Yangtze River Delta and the Pearl River Delta urban cluster.Although the physical distance between Beijing and surrounding cities is relatively small,the correlation degree of migration propensity is not high.In the future,Shanghai,Nanjing,and Zhoushan in the Yangtze River Delta urban cluster;Zhuhai,Shenzhen,Guangzhou,Huizhou,and Zhongshan in the Pearl River Delta urban cluster;and Tianjin in the Beijing-Tianjin-Hebei urban cluster will be the main destinations of China’s population migration.展开更多
With the arrival of the era of personal auto consumption, residents are enjoying the convenience of travel; however, problems such as environmental protection, transportation, purchasing restriction are plaguing consu...With the arrival of the era of personal auto consumption, residents are enjoying the convenience of travel; however, problems such as environmental protection, transportation, purchasing restriction are plaguing consumers at the same time. As an effective alternative to conventional vehicle, electric vehicle in recent years has been widely concerned due to its characteristics of energy saving and environmental protection. To identify the key influential factors in the development of EV industry in China is conducive to formulating reasonable industrial development strategy and meeting market demand of consumers. In this paper, the key factors in the development of EV industry are obtained by analyzing Baidu Index in each time period.展开更多
The prediction of the Baidu index for tourism demand has been increasingly focused on by scholars.However,few studies have evaluated the predictive power of the Baidu index for hotel guest arrivals in fine granularity...The prediction of the Baidu index for tourism demand has been increasingly focused on by scholars.However,few studies have evaluated the predictive power of the Baidu index for hotel guest arrivals in fine granularity at the micro level.Taking Guilin as a case study,we use the OLS regression method to quantitatively investigate the forecasting power of the Baidu index for daily hotel guest arrivals and to comprehensively evaluate the performance of the forecasting model and to optimize the forecasting model by deeply mining the hidden characteristics of tourism flow in a special case study.The contributions of this papermainly have threefold:first,to the best of our knowledge,based on the actual full-example of daily hotel guest check-in data in fine granularity,we evaluated the predictive power of the Baidu index by comparison of 5 forecasting models for the first time.Second,we proposed two metrics for forecasting:the trend forecasting index and the forecasting stability index.Finally,we introduce a kind of punishment strategy to optimize forecasting models based on the potential pattern of research objects.展开更多
基金This work is supported by the National Natural Science Foundation of China(71790594,71701150,and U1811462).
文摘This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index.Furthermore,the predictability of the Baidu Index is found to rise as the forecasting horizon increases.We also find that continuous components enhance predictive power across all horizons,but that increases are only sustained in the short and medium terms,as the long-term impact on volatility is less persistent.Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.
基金This work is supported by the National Natural Science Foundation of China(71320107003 and 71532009).
文摘A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.
基金Humanities and Social Sciences Foundation of Chinese Ministry of Education,China(No.18YJA630005)National Natural Science Foundation of China(No.71810107003).
文摘In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time preferences when planning to visit a destination.More and more researchers have adopted tourism-related search engine data in the field of tourism prediction.However,few studies use search engine data to conduct cluster analysis to identify residents'choice toward a tourism destination.In the present study,146 keywords related to“Beijing tourism”are obtained from Baidu index and principal component analysis(PCA)is applied to reduce the dimensionality of keywords obtained by Baidu index.Modified affinity propagation(MAP)clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing.The result shows that residents in Hebei province are most likely to travel to Beijing.The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means,linkage,and Affinity Propogation(AP)in terms of silhouette coefficient and Calinski–Harabaz index.We also distinguish the difference of residents’choice to travel to Beijing during the peak tourist season and off-season.The residents of Tianjing are inclined to travel to Beijing during the peak tourist season.The residents of Guangdong,Hebei,Henan,Jiangsu,Liaoning,Shanghai,Shandong,and Zhejiang have high attention to travel to Beijing during both seasons.
基金supported by National Social Science Foundation of China(19ARK001).
文摘The development of the Internet and big data have made it possible to study pop-ulation migration and flow between cities.This study analyzes the probability of the population migration propensity of China’s three major urban clusters,identi-fies the direction of population movements,and uses Markov chains to predict the probability of population migration propensity moving forward in order to assess the intercity population migration trends of urban clusters in the future.Internet search engine data is used,and a population migration propensity intensity model is used for calculations.The results show that the Pearl River Delta urban cluster and the Yangtze River Delta urban clusters are areas of active population migration,and that intercity population migration is a part of this activity.Intercity population migra-tion in the Beijing-Tianjin-Hebei urban cluster is not as active as it is in the Yangtze River Delta and the Pearl River Delta urban cluster.Although the physical distance between Beijing and surrounding cities is relatively small,the correlation degree of migration propensity is not high.In the future,Shanghai,Nanjing,and Zhoushan in the Yangtze River Delta urban cluster;Zhuhai,Shenzhen,Guangzhou,Huizhou,and Zhongshan in the Pearl River Delta urban cluster;and Tianjin in the Beijing-Tianjin-Hebei urban cluster will be the main destinations of China’s population migration.
文摘With the arrival of the era of personal auto consumption, residents are enjoying the convenience of travel; however, problems such as environmental protection, transportation, purchasing restriction are plaguing consumers at the same time. As an effective alternative to conventional vehicle, electric vehicle in recent years has been widely concerned due to its characteristics of energy saving and environmental protection. To identify the key influential factors in the development of EV industry in China is conducive to formulating reasonable industrial development strategy and meeting market demand of consumers. In this paper, the key factors in the development of EV industry are obtained by analyzing Baidu Index in each time period.
基金supported by the research on key technology of tourism destination safety warning and its application demonstration granted by No.Guike AB17195028technology development of tourist safety warning system for smart scenic spot and virtual spatiotemporal reconstruction of special culture and its application demonstration granted by No.20170220research on sustainable utility technology integration of Longji terrace landscape resources and tourism industry demonstration granted by No.20180102-2,Guangxi natural science fund by No.2018GXNSFAA138209.
文摘The prediction of the Baidu index for tourism demand has been increasingly focused on by scholars.However,few studies have evaluated the predictive power of the Baidu index for hotel guest arrivals in fine granularity at the micro level.Taking Guilin as a case study,we use the OLS regression method to quantitatively investigate the forecasting power of the Baidu index for daily hotel guest arrivals and to comprehensively evaluate the performance of the forecasting model and to optimize the forecasting model by deeply mining the hidden characteristics of tourism flow in a special case study.The contributions of this papermainly have threefold:first,to the best of our knowledge,based on the actual full-example of daily hotel guest check-in data in fine granularity,we evaluated the predictive power of the Baidu index by comparison of 5 forecasting models for the first time.Second,we proposed two metrics for forecasting:the trend forecasting index and the forecasting stability index.Finally,we introduce a kind of punishment strategy to optimize forecasting models based on the potential pattern of research objects.
