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1.
Timm O. Sprenger Andranik Tumasjan Philipp G. Sandner Isabell M. Welpe 《European Financial Management》2014,20(5):926-957
Microblogging forums (e.g., Twitter) have become a vibrant online platform for exchanging stock‐related information. Using methods from computational linguistics, we analyse roughly 250,000 stock‐related messages (so‐called tweets) on a daily basis. We find an association between tweet sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility. In contrast to previous related research, we also analyse the mechanism leading to an efficient aggregation of information in microblogging forums. Our results demonstrate that users providing above average investment advice are retweeted (i.e., quoted) more often and have more followers, which amplifies their share of voice. 相似文献
2.
This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment. 相似文献
3.
Andrew Todd;James Bowden;Yashar Moshfeghi; 《International Journal of Intelligent Systems in Accounting, Finance & Management》2024,31(1):e1549
Advances in Deep Learning have drastically improved the abilities of Natural Language Processing (NLP) research, creating new state-of-the-art benchmarks. Two research streams at the forefront of NLP analysis are transformer architecture and multimodal analysis. This paper critically evaluates the extant literature applying sentiment analysis techniques to the financial domain. We classify the financial sentiment analysis literature according to the most used techniques in the area, with a focus on methods used to detect sentiment within corporate earnings conference calls, because of their dual modality (text-audio) nature. We find that the financial literature follows a similar path to NLP sentiment literature, in that more advanced techniques to define sentiment are being used as the field progresses. However, techniques used to determine financial sentiment currently fall behind state-of-the-art techniques used within NLP. Two future directions stem from this paper. Firstly, we propose that the adoption of transformer architecture to create robust representations of textual data could enhance sentiment analysis in academic finance. Secondly, the adoption of multimodal classifiers in finance represents a new, currently underexplored area of study that offers opportunities for finance research. 相似文献
4.
在语言学研究中,可以同时、长时地在一种或二种以上的语言中开展比较。现代对比语言学首先是在欧洲和美国得到发展。在中国,严复最早开始对比分析。跨学科研究,尤其是语言学和美学的结合反过来使语言研究更丰富。 相似文献
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Murat Aydogdu Hakan Saraoglu David Louton 《International Journal of Intelligent Systems in Accounting, Finance & Management》2019,26(4):153-163
We implement an efficient methodology for extracting themes from Securities Exchange Commission 13D filings using aspects of human‐assisted active learning and long short‐term memory (LSTM) neural networks. Sentences from the ‘Purpose of Transaction’ section of each filing are extracted and a randomly chosen subset is labelled based on six filing themes that the existing literature on shareholder activism has shown to have an impact on stock returns. We find that an LSTM neural network that accepts sentences as input performs significantly better, with precision of 77%, than an alternately specified neural network that uses the common bag of words approach. This indicates that both sentence structure and vocabulary are important in classifying SEC 13D filings. Our study has important implications, as it addresses the recent cautions raised in the literature that analysis of finance and accounting‐related text sources should move beyond bag‐of‐words approaches to alternatives that incorporate the analysis of word sense and meaning reflecting context. 相似文献
7.
Ferhat D. Zengul James D. Byrd Nurettin Oner Mark Edmonds Arline Savage 《International Journal of Intelligent Systems in Accounting, Finance & Management》2019,26(4):175-192
The literature on corporate governance (CG) has been expanding at an unprecedented rate since major corporate scandals surfaced, such as Enron, WorldCom, and HealthSouth. Corresponding with accounting's important role in CG, accounting scholars increasingly have investigated CG in recent years, so the body of literature is growing. Although previous attempts have been made to summarize extant literature on CG via reviews, none of these attempts has utilized recent developments in text analyses and natural language processing. This study uses latent semantic and topic analyses to address this research gap by analysing abstracts from 1,399 articles in all accounting journals that the Australian Business Deans Council (ABDC) has rated A and A*. The ABDC journal list is widely recognized as a journal‐quality indicator across many universities worldwide. The analyses revealed 10 distinct research topics on CG in the ABDC's top accounting journals. The results presented include the five most representative articles for each topic, as distinguished by topic scores. This study carries important practice and policy implications, as it reveals major research streams and exhibits how researchers respond to various CG problems. 相似文献
8.
Arezoo Hatefi Ghahfarrokhi Mehrnoush Shamsfard 《International Journal of Intelligent Systems in Accounting, Finance & Management》2020,27(1):22-37
We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks. 相似文献
9.
Timm O. Sprenger Philipp G. Sandner Andranik Tumasjan Isabell M. Welpe 《Journal of Business Finance & Accounting》2014,41(7-8):791-830
This study presents a methodology for identifying a broad range of real‐world news events based on microblogging messages. Applying computational linguistics to a unique dataset of more than 400,000 S&P 500 stock‐related Twitter messages, we distinguish between good and bad news and demonstrate that the returns prior to good news events are more pronounced than for bad news events. We show that the stock market impact of news events differs substantially across different categories. 相似文献
10.
Mahmoud El‐Haj Paul Rayson Martin Walker Steven Young Vasiliki Simaki 《Journal of Business Finance & Accounting》2019,46(3-4):265-306
We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of studies applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four tools are named entity recognition (NER), summarization, semantics and corpus linguistics. 相似文献
11.
Sunita Goel Ozlem Uzuner 《International Journal of Intelligent Systems in Accounting, Finance & Management》2016,23(3):215-239
We present a novel approach for analysing the qualitative content of annual reports. Using natural language processing techniques we determine if sentiment expressed in the text matters in fraud detection. We focus on the Management Discussion and Analysis (MD&A) section of annual reports because of the nonfactual content present in this section, unlike other components of the annual reports. We measure the sentiment expressed in the text on the dimensions of polarity, subjectivity, and intensity and investigate in depth whether truthful and fraudulent MD&As differ in terms of sentiment polarity, sentiment subjectivity and sentiment intensity. Our results show that fraudulent MD&As on average contain three times more positive sentiment and four times more negative sentiment compared with truthful MD&As. This suggests that use of both positive and negative sentiment is more pronounced in fraudulent MD&As. We further find that, compared with truthful MD&As, fraudulent MD&As contain a greater proportion of subjective content than objective content. This suggests that the use of subjectivity clues such as presence of too many adjectives and adverbs could be an indicator of fraud. Clear cases of fraud show a higher intensity of sentiment exhibited by more use of adverbs in the “adverb modifying adjective” pattern. Based on the results of this study, frequent use of intensifiers, particularly in this pattern, could be another indicator of fraud. Moreover, the dimensions of subjectivity and intensity help in accurately classifying borderline examples of MD&As (that are equal in sentiment polarity) into fraudulent and truthful categories. When taken together, these findings suggest that fraudulent MD&As in contrast to truthful MD&As contain higher sentiment content. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
12.
Daniel E. O'Leary 《International Journal of Intelligent Systems in Accounting, Finance & Management》2019,26(1):46-53
Google's Duplex is a computer‐based system with natural language capabilities that provides a human sounding conversation as it performs a set of tasks, such as making restaurant reservations. This paper analyses Google's Duplex and some of the initial reaction to the system and its capabilities. The paper does a text analysis and finds that the system‐generated text creates standardized ratings that suggest the text is analytical, authentic and possesses a generally positive tone. As would be expected for the applications for which it is being used, the text is heavily focused on the present. In addition, this analysis indicates that the text provides evidence of social processes, cognitive processes, tentativeness and affiliation. Further, this paper examines some of the characteristics of speech that Duplex uses to sound human. Those capabilities appear to allow the system pass the Turing test for some well‐structured tasks. However, this paper investigates some of the ethics of pretending to be human and suggests that such impersonation is against evolving computer codes of ethics. 相似文献
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Michael Gurstein 《Futures》1985,17(6):652-671
Artificial intelligence (AI) will be a transforming technology because it will allow old things to be done in a dramatically different way-whether cheaper, faster, or simply better. This article looks at the social impacts of computerization and discusses natural language processing, machine translation, expert systems and the overall effect of AI applications on employment. It is concluded that AI applications are likely to develop in an evolutionary sequence rather than through one or more sudden breakthroughs. However, the sum of the changes which will result from the sequence of these suboptimal systems will almost certainly transform a wide range of human activities. 相似文献
15.
This study examines consensus building in environmental and energy policies by analyzing the minutes of the safety and security committee of the Nuclear Regulation Authority (NRA) of the Japanese government, in the context of the discussion of the safe return of Fukushima evacuees after the 2011 Great East Japan Earthquake and Tsunami and the subsequent Fukushima nuclear disaster. One important issue associated with evacuation policies was the high number of evacuees, if not all, who were not willing to return to their old homes. Although the contents of governmental committee meetings are freely accessible through the internet, they have been rarely analyzed for these purposes. In this study, we used text-mining techniques to analyze NRA committee minutes quantitatively and qualitatively. We have three primary findings. First, the committee attempted to take evacuees’ feelings into account and pragmatically discuss what was needed to restore their lives and livelihoods, as well as to make its meetings transparent and open to the public by, for example, streaming them live on the internet. Second, in earlier committee meetings, government representatives insisted on specific policies made by themselves to control the return of evacuees. However, outside experts at the meetings convinced representatives that decisions regarding issues surrounding the safe return of evacuees should consider the opinions of the evacuees themselves. Third, the NRA reported the outcome of the meetings at a Cabinet meeting to accelerate policies requiring urgent implementation as well as those related to the alleviation of people’s anxieties regarding exposure to radioactivity or those related to equal treatment among those who wanted to voluntarily move away from the affected areas versus those who want to return to their homes. Finally, this analysis further identified a number of issues concerning citizen participation and governance associated with environment and energy policies, all of which need to be overcome in order to establish consensus among concerned stakeholders. 相似文献
16.
Transparency continues to interest finance scholarship, as regards not just to financial reporting, but to a host of areas. Concomitantly, there is a growing emphasis on the transparency of the finance research process, with journals initiating requirements for uploading data and codes. However, little consideration is given to the transparency of finance scholarly texts, despite new emphasis by academic institutions and accreditation bodies on articles having an impact on practitioners. We use textual analysis to investigate the readability of articles in a selection of finance journals. Results evidence that academic articles are becoming less readable. Whether readability straightforwardly implies transparency is unclear, still, we consider these issues alongside our findings. Our study should be of great interest to those concerned with the state of finance scholarship. 相似文献
17.
史美娜 《内蒙古财经学院学报(综合版)》2012,10(4):75-77
作为认知语言学发展至今影响最深远的两大范畴理论,经典范畴理论和原型范畴理论的优劣及其关系问题一直是学界关注和争议的焦点。本文从两种理论的特征出发,结合学界已有观点浅析二者的对立和互补关系,论证二者虽然存在很大分歧但并非完全对立,经典是原型的基础,原型是经典的延伸。原型范畴理论具有更大的包容性和实用性,但是经典范畴理论也仍具备其不可替代的优越性。 相似文献
18.
Jonas Poelmans Paul Elzinga Stijn Viaene Guido Dedene 《International Journal of Intelligent Systems in Accounting, Finance & Management》2010,17(3-4):167-191
We propose a human-centred process for knowledge discovery from unstructured text that makes use of formal concept analysis and emergent self-organizing maps. The knowledge discovery process is conceptualized and interpreted as successive iterations through the concept–knowledge (C–K) theory design square. To illustrate its effectiveness, we report on a real-life case study of using the process at the Amsterdam–Amstelland police in the Netherlands aimed at distilling concepts to identify domestic violence from the unstructured text in actual police reports. The case study allows us to show how the process was not only able to uncover the nature of a phenomenon such as domestic violence, but also enabled analysts to identify many types of anomaly in the practice of policing. We will illustrate how the insights obtained from this exercise resulted in major improvements in the management of domestic violence cases. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
19.
Shuhua Liu Benoit Favre 《International Journal of Intelligent Systems in Accounting, Finance & Management》2013,20(2):89-110
Economic crises are significant threats to macroeconomic stability. They can incur large costs and bring devastating effects on economies, with the effects often spilling over into other economies. Since 2007 we have witnessed the most severe and widely spread economic crisis since the Great Depression of the 1930s. In the meantime, a huge amount of ongoing media coverage, reporting, analysis and debate concerning the global economic crisis has been generated. In this study we explore the possibilities of applying text summarization tools to learn from text documents the various discussions surrounding the global economic crisis. Included in our analysis are blog posts and articles of highly influential economists, as well as official speeches and publications of government organizations. The ICSI‐ILP extractive summarizer is applied in a large number of experiments, and the summary outputs are manually examined and evaluated. The results provide us with insights into the potential and limitations of state‐of‐the‐art summarization systems when used to help us quickly learn and digest large amounts of textual information. The results also suggest different ways to break the limitations of text summarization technology. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
20.
语言是人类经济活动中一种重要且特殊的资源,具有经济属性.作为新兴产业,语言产业具有低耗能、低污染、高产出等绿色产业的特点,符合我国经济发展形势和产业发展阶段性特征,理应成为新的经济增长点.目前我国的语言产业是一个亟需政策引导和措施鼓励的领域,仍存在只重视汉语国际推广的力度而不重视汉语产业的培育、语言产业相关的信息化... 相似文献