In his speech,Han Zheng said that exchanges and mutual learning among civilisations are important driving forces for the advancement of human civilisation as well as world peace and development.President Xi Jinping po...In his speech,Han Zheng said that exchanges and mutual learning among civilisations are important driving forces for the advancement of human civilisation as well as world peace and development.President Xi Jinping pointed out that civilisations become richer and more colourful with exchanges and mutual learning.As the futures of all countries are closely interconnected,inclusiveness,coexistence,exchanges and mutual learning among different civilisations will contribute to the common progress of humanity and promote global prosperity and development.展开更多
The Fourth Dialogue on Exchanges and Mutual Learning Among Civilisations was held in Dunhuang in Northwest China's Gansu Province on Friday,bringing together politicians,heads of international organisations,schola...The Fourth Dialogue on Exchanges and Mutual Learning Among Civilisations was held in Dunhuang in Northwest China's Gansu Province on Friday,bringing together politicians,heads of international organisations,scholars and experts to discuss the significance of exchanges and dialogues between different civilisations in a world facing many conflicts.展开更多
With the theme of"Timeless Dunhuang:The Symphony of History and Modernity",the exhibition brought together the efforts of four institutions,namely Dunhuang Academy,the Contemporary World Press,Duzhe Publishi...With the theme of"Timeless Dunhuang:The Symphony of History and Modernity",the exhibition brought together the efforts of four institutions,namely Dunhuang Academy,the Contemporary World Press,Duzhe Publishing Group,and Dunhuang Culture and Tourism Group.The exhibition featured four sections:the Dunhuang Culture In-Depth Learning Hall,the Contemporary Dunhuang Technology Experiences,the Dunhuang Cultural and Creative Product Market and the Dunhuang Culture and Art,which fully demonstrated the distinctive charm of Dunhuang Culture as a treasure of the integration of four major civilisations,and presented a painting of civilizations spanning thousands of years for vips both from home and abroad.展开更多
After living in China for 33 years,Associate Professor Gu Qingyang of the Lee Kuan Yew School of Public Policy(LKYSPP)at the National University of Singapore(NUS)arrived in Singapore in 1994.Over the past 31 years,he ...After living in China for 33 years,Associate Professor Gu Qingyang of the Lee Kuan Yew School of Public Policy(LKYSPP)at the National University of Singapore(NUS)arrived in Singapore in 1994.Over the past 31 years,he has remained dedicated to building bridges—initially by systematically introducing Singapore’s development experience to China,and later by fostering mutual learning between the two countries.展开更多
Civilization-related issues have attracted attention because they affect international relations,global governance and international order.World politics and the Clash of Civilizations theory proposed by Western strat...Civilization-related issues have attracted attention because they affect international relations,global governance and international order.World politics and the Clash of Civilizations theory proposed by Western strategic circles raise risks of inter-civilization conflicts,whereas China's framework of building a community with a shared future for mankind advocates peace.Although civilizations are bound to diversify,and conflicts are endemic between vastly distinct civilizations,there is aproper basis for peaceful coexistence in exchanges and mutual learning.China holds that civilizations are diverse,equal and tolerant,and that different civilizations should learn from each other's civilization concept and actively practice it.For future relations between Chinese and Westerncivilizations,China promotes the benign interaction of inter-civilization exchanges and mutual learning,and the building of a community with a shared future.展开更多
On November 28,the First Dialogue on Exchanges and Mutual Learning among Civilizations organized by the Chinese Association for International Understanding was staged in the Forbidden City.Some 100 participants from a...On November 28,the First Dialogue on Exchanges and Mutual Learning among Civilizations organized by the Chinese Association for International Understanding was staged in the Forbidden City.Some 100 participants from all over the world were present at the Dialogue.Participants made discussions themed on"building a world featuring mutual learning and harmonious coexistence among different civilizations".Ji Bingxuan,Vice Chairman of Standing Committee of the National People’s Congress and President of the Chinese Association for International Understanding attended the opening ceremony and delivered a keynote speech.展开更多
Civilisation is colourful because of communication,and civilisation is rich because of mutual learning.On October 27th,the second World Conference of Sinologists was held in Nanping City,Fujian Province,under the them...Civilisation is colourful because of communication,and civilisation is rich because of mutual learning.On October 27th,the second World Conference of Sinologists was held in Nanping City,Fujian Province,under the theme"Understanding Chinese Civilisation to Jointly Promote Global Modernisation".More than 200 sinologists at home and abroad gathered to deepen exchanges among civilisations and enhance mutual understanding and friendship.展开更多
At the invitation of CAFIU,an 11-member European-American young leaders delegation composed of politicians,think tank researchers and media workers from France,Germany,Italy,UK and US headed by Mr.Manlio di Stefano,Me...At the invitation of CAFIU,an 11-member European-American young leaders delegation composed of politicians,think tank researchers and media workers from France,Germany,Italy,UK and US headed by Mr.Manlio di Stefano,Member of House of Representatives of Italy and Chairman of the Five-Star展开更多
This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United Sta...This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.展开更多
Tolerance,coexistence,exchanges and mutual learning among different civilisations play an irreplaceable role in advancing humanity's modernisation process when the future of all countries are closely connected.
For the history of medical culture in the world,the exchange and transmission of medical knowledge has formed an important part of mutual learning among different cultures,which has also increasingly shown unique acad...For the history of medical culture in the world,the exchange and transmission of medical knowledge has formed an important part of mutual learning among different cultures,which has also increasingly shown unique academic value in the study of knowledge history.Traditional Eastern medicine(such as Chinese medicine,Indian ayurvedic medicine,Persian medicine,Arabic medicine),and other medical systems in the ancient Western world(including Greek medicine and Roman medicine)have left precious literature/texts,cultural relics(for example,pills,preparations,medical instruments),folklore and legends,which truly record the process of learning,transplantation,fusion and succession after the encounter of different medical systems at least for the past two thousand years.展开更多
Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinea...Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.展开更多
The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or m...The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.展开更多
This study explores how overseas exchange opportunities might influence Chinese students ’engagement in L2 learning activities and how far such opportunities may satisfy their motivation to study abroad. The analysis...This study explores how overseas exchange opportunities might influence Chinese students ’engagement in L2 learning activities and how far such opportunities may satisfy their motivation to study abroad. The analysis of the data, collected and filtered from carefully designed questionnaires and interviews, showed that students ’ L2 learning activities and study-abroad motivations underwent changes after their overseas experiences. Regarding the former, the overseas environment was the cause of the change because it provided students with more chances to talk with native speakers and increased the frequency of their using L2 in their daily life. Regarding the latter, the decline of the students ’ major study-abroad motivations was partly because they tended to treat L2 learning as a tool for realizing other goals and partly because the students had got other important motivations. In view of these findings, suggestions were raised to help future students get better prepared for their overseas study or short-term exchange life.展开更多
Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S.dollar(KRW/USD)exchange rate and the U.S.and Kore...This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S.dollar(KRW/USD)exchange rate and the U.S.and Korean stock market returns.We construct international asset allocation portfolios based on these forecasts and evaluate their performance.Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns,as evidenced by their superior out-of-sample R-squared values.We also identify the key factors crucial for improving the accuracy of forecasting the KRW/USD exchange rate and stock market returns.Furthermore,a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrated superior performance.Global portfolios constructed using LASSO(Sharpe ratio=3.45)and Elastic Net(Sharpe ratio=3.48)exhibit a notable performance advantage over traditional benchmark portfolios.This suggests that machine learning models outperform traditional global portfolio construction methods.展开更多
Recently,the 4th Dialogue on Exchanges and utual Learning among Civilizations Themed Book Exhibition was held in Dunhuang,Gansu Province,by Contemporary World Press,marking the beginning of a multicultural feast。
Recently,internet users have significantly increased their use of search engines,and market investors are no exception.As a result,predictive models that incorporate scattered web-based information are developing as a...Recently,internet users have significantly increased their use of search engines,and market investors are no exception.As a result,predictive models that incorporate scattered web-based information are developing as an area of forecasting.The objective of this research is to compare the predictive accuracy of fundamental macroeconomic variables,online attention series measured by the Google Trends search volume index,and a combination of both data types for the Mexican,Brazilian,Chilean,and Colombian currencies paired with the USD.The exchange rate series used in this study are sourced from a real-time platform.Four indicators capturing the fundamental macroeconomic differences between these emerging economies and the U.S.from January 2004 to March 2021(monthly)were analyzed.To assess the predictive performance of the KNN algorithm,OLS regression and the random walk with drift model were compared.Considering in-sample predictions,the results generally exhibit lower estimation errors in the random walk with drift model,but in the joint fundamental–online attention data,the KNN and OLS predictions are more accurate than those of the random walk with drift.However,the KNN predictions based on out-of-sample fit generate the lowest estimation errors and the most accurate predictions for the joint fundamental–online attention data.Additionally,performance testing indicates that the KNN extended model outperforms the out-ofsample forecast for the OLS regression and the random walk with drift model.展开更多
文摘In his speech,Han Zheng said that exchanges and mutual learning among civilisations are important driving forces for the advancement of human civilisation as well as world peace and development.President Xi Jinping pointed out that civilisations become richer and more colourful with exchanges and mutual learning.As the futures of all countries are closely interconnected,inclusiveness,coexistence,exchanges and mutual learning among different civilisations will contribute to the common progress of humanity and promote global prosperity and development.
文摘The Fourth Dialogue on Exchanges and Mutual Learning Among Civilisations was held in Dunhuang in Northwest China's Gansu Province on Friday,bringing together politicians,heads of international organisations,scholars and experts to discuss the significance of exchanges and dialogues between different civilisations in a world facing many conflicts.
文摘With the theme of"Timeless Dunhuang:The Symphony of History and Modernity",the exhibition brought together the efforts of four institutions,namely Dunhuang Academy,the Contemporary World Press,Duzhe Publishing Group,and Dunhuang Culture and Tourism Group.The exhibition featured four sections:the Dunhuang Culture In-Depth Learning Hall,the Contemporary Dunhuang Technology Experiences,the Dunhuang Cultural and Creative Product Market and the Dunhuang Culture and Art,which fully demonstrated the distinctive charm of Dunhuang Culture as a treasure of the integration of four major civilisations,and presented a painting of civilizations spanning thousands of years for vips both from home and abroad.
文摘After living in China for 33 years,Associate Professor Gu Qingyang of the Lee Kuan Yew School of Public Policy(LKYSPP)at the National University of Singapore(NUS)arrived in Singapore in 1994.Over the past 31 years,he has remained dedicated to building bridges—initially by systematically introducing Singapore’s development experience to China,and later by fostering mutual learning between the two countries.
文摘Civilization-related issues have attracted attention because they affect international relations,global governance and international order.World politics and the Clash of Civilizations theory proposed by Western strategic circles raise risks of inter-civilization conflicts,whereas China's framework of building a community with a shared future for mankind advocates peace.Although civilizations are bound to diversify,and conflicts are endemic between vastly distinct civilizations,there is aproper basis for peaceful coexistence in exchanges and mutual learning.China holds that civilizations are diverse,equal and tolerant,and that different civilizations should learn from each other's civilization concept and actively practice it.For future relations between Chinese and Westerncivilizations,China promotes the benign interaction of inter-civilization exchanges and mutual learning,and the building of a community with a shared future.
文摘On November 28,the First Dialogue on Exchanges and Mutual Learning among Civilizations organized by the Chinese Association for International Understanding was staged in the Forbidden City.Some 100 participants from all over the world were present at the Dialogue.Participants made discussions themed on"building a world featuring mutual learning and harmonious coexistence among different civilizations".Ji Bingxuan,Vice Chairman of Standing Committee of the National People’s Congress and President of the Chinese Association for International Understanding attended the opening ceremony and delivered a keynote speech.
文摘Civilisation is colourful because of communication,and civilisation is rich because of mutual learning.On October 27th,the second World Conference of Sinologists was held in Nanping City,Fujian Province,under the theme"Understanding Chinese Civilisation to Jointly Promote Global Modernisation".More than 200 sinologists at home and abroad gathered to deepen exchanges among civilisations and enhance mutual understanding and friendship.
文摘At the invitation of CAFIU,an 11-member European-American young leaders delegation composed of politicians,think tank researchers and media workers from France,Germany,Italy,UK and US headed by Mr.Manlio di Stefano,Member of House of Representatives of Italy and Chairman of the Five-Star
文摘This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.
文摘Tolerance,coexistence,exchanges and mutual learning among different civilisations play an irreplaceable role in advancing humanity's modernisation process when the future of all countries are closely connected.
文摘For the history of medical culture in the world,the exchange and transmission of medical knowledge has formed an important part of mutual learning among different cultures,which has also increasingly shown unique academic value in the study of knowledge history.Traditional Eastern medicine(such as Chinese medicine,Indian ayurvedic medicine,Persian medicine,Arabic medicine),and other medical systems in the ancient Western world(including Greek medicine and Roman medicine)have left precious literature/texts,cultural relics(for example,pills,preparations,medical instruments),folklore and legends,which truly record the process of learning,transplantation,fusion and succession after the encounter of different medical systems at least for the past two thousand years.
基金The financial support provided by the Project of National Natural Science Foundation of China(U22A20415,21978256,22308314)“Pioneer”and“Leading Goose”Research&Development Program of Zhejiang(2022C01SA442617)。
文摘Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.
文摘The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.
基金supported by a General Research Fund (#4440713) from the Research Grants Council of Hong Kong。
文摘This study explores how overseas exchange opportunities might influence Chinese students ’engagement in L2 learning activities and how far such opportunities may satisfy their motivation to study abroad. The analysis of the data, collected and filtered from carefully designed questionnaires and interviews, showed that students ’ L2 learning activities and study-abroad motivations underwent changes after their overseas experiences. Regarding the former, the overseas environment was the cause of the change because it provided students with more chances to talk with native speakers and increased the frequency of their using L2 in their daily life. Regarding the latter, the decline of the students ’ major study-abroad motivations was partly because they tended to treat L2 learning as a tool for realizing other goals and partly because the students had got other important motivations. In view of these findings, suggestions were raised to help future students get better prepared for their overseas study or short-term exchange life.
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A8055710).
文摘This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S.dollar(KRW/USD)exchange rate and the U.S.and Korean stock market returns.We construct international asset allocation portfolios based on these forecasts and evaluate their performance.Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns,as evidenced by their superior out-of-sample R-squared values.We also identify the key factors crucial for improving the accuracy of forecasting the KRW/USD exchange rate and stock market returns.Furthermore,a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrated superior performance.Global portfolios constructed using LASSO(Sharpe ratio=3.45)and Elastic Net(Sharpe ratio=3.48)exhibit a notable performance advantage over traditional benchmark portfolios.This suggests that machine learning models outperform traditional global portfolio construction methods.
文摘Recently,the 4th Dialogue on Exchanges and utual Learning among Civilizations Themed Book Exhibition was held in Dunhuang,Gansu Province,by Contemporary World Press,marking the beginning of a multicultural feast。
基金“Peso-Dollar Exchange Rate Prediction:Fundamentals vs.Internet Search Indicators”,which was sponsored by the Universidad Autonoma de Nuevo Leon,Mexico(PAICYT Project#375-CSA-2022).
文摘Recently,internet users have significantly increased their use of search engines,and market investors are no exception.As a result,predictive models that incorporate scattered web-based information are developing as an area of forecasting.The objective of this research is to compare the predictive accuracy of fundamental macroeconomic variables,online attention series measured by the Google Trends search volume index,and a combination of both data types for the Mexican,Brazilian,Chilean,and Colombian currencies paired with the USD.The exchange rate series used in this study are sourced from a real-time platform.Four indicators capturing the fundamental macroeconomic differences between these emerging economies and the U.S.from January 2004 to March 2021(monthly)were analyzed.To assess the predictive performance of the KNN algorithm,OLS regression and the random walk with drift model were compared.Considering in-sample predictions,the results generally exhibit lower estimation errors in the random walk with drift model,but in the joint fundamental–online attention data,the KNN and OLS predictions are more accurate than those of the random walk with drift.However,the KNN predictions based on out-of-sample fit generate the lowest estimation errors and the most accurate predictions for the joint fundamental–online attention data.Additionally,performance testing indicates that the KNN extended model outperforms the out-ofsample forecast for the OLS regression and the random walk with drift model.
