In view of the study of finance and economics information, we research on the real-time financial news posted on the authority sites in the world's major advanced economies. Analyzing the massive financial news of...In view of the study of finance and economics information, we research on the real-time financial news posted on the authority sites in the world's major advanced economies. Analyzing the massive financial news of different information sources and language origins, we come up with a basic theory model and its algorithm on financial news, which is capable of intelligent collection, quick access, deduplication, correction and integration with financial news' backgrounds. Furthermore, we can find out connections between financial news and readers' interest. So we can achieve a real-time and on-demand financial news feed, as well as provide a theoretical basis and verification of the scientific problems on real-time processing of massive information. Finally, the simulation experiment shows that the multilingual financial news matching technology can give more help to distinguish the similar financial news in different languages than the traditional method.展开更多
As the heart of news,financial news headline determines whetherpeople are interested in the content.The purpose of translating financial news headlines is to accurately convey the financial information of foreign medi...As the heart of news,financial news headline determines whetherpeople are interested in the content.The purpose of translating financial news headlines is to accurately convey the financial information of foreign media to domestic readers and attract domestic readers’attention by means of rhetoric,typesetting,and so on.This paper mainly discusses how the Skopos Theory is applied in translating financial news headlines through case studies.In the context of Skopos Theory,translation principles for different financial news headlines will also be explored and summarized.展开更多
Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint...Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.展开更多
How the recent progress of reasoning large language models(LLMs),especially the new open-source model DeepSeek-R1,can benefit financial services is an underexplored problem.While LLMs have ignited numerous application...How the recent progress of reasoning large language models(LLMs),especially the new open-source model DeepSeek-R1,can benefit financial services is an underexplored problem.While LLMs have ignited numerous applications within the financial sector,including financial news analysis and general customer interactions.展开更多
基金the National Social Science Foundation of China(Nos.15CTQ028 and 14@ZH036)the Social Science Foundation of Beijing(No.15SHA002)the Young Faculty Research Fund of Beijing Foreign Studies University(No.2015JT008)
文摘In view of the study of finance and economics information, we research on the real-time financial news posted on the authority sites in the world's major advanced economies. Analyzing the massive financial news of different information sources and language origins, we come up with a basic theory model and its algorithm on financial news, which is capable of intelligent collection, quick access, deduplication, correction and integration with financial news' backgrounds. Furthermore, we can find out connections between financial news and readers' interest. So we can achieve a real-time and on-demand financial news feed, as well as provide a theoretical basis and verification of the scientific problems on real-time processing of massive information. Finally, the simulation experiment shows that the multilingual financial news matching technology can give more help to distinguish the similar financial news in different languages than the traditional method.
文摘As the heart of news,financial news headline determines whetherpeople are interested in the content.The purpose of translating financial news headlines is to accurately convey the financial information of foreign media to domestic readers and attract domestic readers’attention by means of rhetoric,typesetting,and so on.This paper mainly discusses how the Skopos Theory is applied in translating financial news headlines through case studies.In the context of Skopos Theory,translation principles for different financial news headlines will also be explored and summarized.
基金partly supported by National Natural Science Foundation of China(No.71771204,72231010)the Fundamental Research Funds for the Central Universities(No.E0E48946X2).
文摘Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.
文摘How the recent progress of reasoning large language models(LLMs),especially the new open-source model DeepSeek-R1,can benefit financial services is an underexplored problem.While LLMs have ignited numerous applications within the financial sector,including financial news analysis and general customer interactions.
