CompSciRN: Artificial Intelligence (Topic)最新文献

{"title":"What Data Series Matter? Explaining key trends and factors generated by Artificial Intelligence","authors":"Irene E. Aldridge","doi":"10.2139/ssrn.3925856","DOIUrl":"https://doi.org/10.2139/ssrn.3925856","url":null,"abstract":"We show a simple way to let the data speak for themselves. Specifically, we show how a large mixed bag of data, potentially embedded with missing data points and collinearities, and therefore unsuitable for traditional econometric analysis, can be useful in building fast and meaningful big data and artificial intelligence analyses and predictions. What’s more, our technique helps the results of the analyses to be easily interpreted by researchers. We use these techniques to build a surprisingly profitable E-mini crude oil futures trading strategy with monthly reallocations, delivering annualized returns of 100%+ with Sharpe ratio exceeding 2.2.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130044220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROI of AI: Effectiveness and Measurement","authors":"Sandeep Pandey, Snigdha Gupta, S. Chhajed","doi":"10.2139/ssrn.3858398","DOIUrl":"https://doi.org/10.2139/ssrn.3858398","url":null,"abstract":"Knowing what an AI investment is worth and what determines that value is a pre-requisite for intelligent decision making- in choosing to invest in this field and domain. Investments in advanced transformation like AI, in deciding on the appropriate price to pay or receive in a takeover and in making financial choices when running a business are to be evaluated thoroughly. The premise of computing ROI for such investments, is that we can make reasonable estimates of value, and that the same fundamental principles determine the values of all types of assets, real tangible as well as intangibles. Some investments are easier to valuate as compared to others. Valuation process and effectiveness measurement techniques of new and highly evolving technologies such as AI vary from investment to investment. Also, the uncertainty associated with value estimates is different for different investments and assets, but the core principles and the foundation of valuations remain almost same.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127450426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SOK: An Evaluation of Quantum Authentication Through Systematic Literature Review","authors":"Ritajit Majumdar, Sanchari Das","doi":"10.2139/ssrn.3859056","DOIUrl":"https://doi.org/10.2139/ssrn.3859056","url":null,"abstract":"Quantum computers are considered a blessing to the dynamic technological world that promises to solve complex problems much faster than their known classical counterparts. Such computational power imposes critical threats on modern cryptography where it has been proven that asymmetric key cryptosystem will be rendered useless in a quantum world. However, we can utilize such a powerful mechanism for improving computer security by implementing such technology to solve complex data security problems such as authentication, secrets management, and others. Mainly, Quantum Authentication (QA) is an emerging concept in computer security that creates robust authentication for organizations, systems, and individuals. To delve deeper into the concept, for this research, we have further investigated QA through a detailed systematic literature review done on a corpus of N = 859 papers. We briefly discuss the major protocols used by various papers to achieve QA, and also note the distribution of papers using those protocols. We analyzed the technological limitations mentioned by previous researchers and highlighted the lack of human-centered solutions for such modern inventions. We emphasize the importance of research in the user aspect of QA to make the users aware of its potential advantages and disadvantages as we move to the quantum age.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116863678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics","authors":"Hans Buehler, M. Phillip, Mikko S. Pakkanen, Ben Wood","doi":"10.2139/ssrn.3808555","DOIUrl":"https://doi.org/10.2139/ssrn.3808555","url":null,"abstract":"We present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. <br><br>This approach can then be used to implement a <i>stochastic implied volatility</i> model in the following two steps:<br>1) Train a market simulator for option prices, for example as discussed in our recent work <a href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3470756\">here</a>;<br><br>2) Find a risk-neutral density, specifically in our approach the <i>minimal entropy martingale measure</i>.<br><br>The resulting model can be used for risk-neutral pricing, or for <a href=\"https://ssrn.com/abstract=3120710\">Deep Hedging</a> in the case of transaction costs or trading constraints.<br><br>To motivate the proposed approach, we also show that market dynamics are free from \"statistical arbitrage\" in the absence of transaction costs if and only if they follow a risk-neutral measure. We additionally provide a more general characterization in the presence of convex transaction costs and trading constraints. <br><br>These results can be seen as an analogue of the fundamental theorem of asset pricing for statistical arbitrage under trading frictions and are of independent interest.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126847903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Obras geradas por inteligência artificial: desafios ao conceito jurídico de autoria (Works Generated by Artificial Intelligence: Challenges to the Legal Concept of Authorship)","authors":"Marcelo Frullani Lopes","doi":"10.2139/ssrn.3874667","DOIUrl":"https://doi.org/10.2139/ssrn.3874667","url":null,"abstract":"Portuguese Abstract: Sistemas de inteligência artificial vêm sendo cada vez mais utilizados para produção de obras artísticas, como músicas, textos, pinturas e filmes, dentre outras. Nesse contexto, o conceito de autoria sofre grandes desafios. Partiu-se da hipótese de que esse conceito é inaplicável a situações em que há emprego de inteligência artificial no processo de produção artística. Para iniciar a discussão, esta dissertação tratou, no primeiro capítulo, da evolução histórica do conceito de autoria, com destaque às influências exercidas pelo Iluminismo e pelo Romantismo. Além disso, tratou-se das teorias clássicas de fundamentação dos direi-tos autorais, muito direcionadas à concepção de autor como ser humano, e que tiveram um papel significativo na tradicional distinção feita entre os sistemas francês e inglês de direi-tos autorais. Esse conceito individualista de autoria, porém, passou a ser muito questionado a partir dos últimos anos do século XIX e ao longo do XX em diversas áreas do conheci-mento. Um impacto ainda maior ocorreu no final do século XX e ao longo dos primeiros anos do XXI, quando a disseminação de computadores pessoais e da rede mundial de computadores possibilitou que todos os usuários se tornem potenciais criadores, tornando menos nítida a fronteira entre artista e público. Além disso, essas tecnologias forneceram um estímulo a criações colaborativas, que podem envolver até milhares de pessoas, sem que haja coordenação ou centralização dos esforços. Assim, a inteligência artificial não chega a um cenário em que o conceito de autoria é inquestionável. O terceiro capítulo desta disser-tação realiza uma breve análise do conceito de inteligência artificial e das principais técnicas utilizadas para a produção de obras de arte. A partir dos diversos exemplos apresentados, ficou claro que inteligência artificial serve como um termo “guarda-chuva”, que abrange uma série de técnicas muito variadas. Em algumas situações, ferramentas que empregam essas técnicas apresentam elevado nível de autonomia, podendo-se dizer que são criativas, ao menos em um sentido objetivo; já em outros casos, elas podem servir como meras assis-tentes dos humanos. Por fim, no quarto capítulo, mostrou-se que a hipótese adotada inici-almente estava parcialmente correta, pois o conceito jurídico de autoria ainda pode ser apli-cado a alguns cenários. Uma distinção importante pode ser feita entre obras geradas e obras assistidas por inteligência artificial. No entanto, em situações nas quais as máquinas apre-sentam elevado grau de autonomia, algumas propostas da doutrina para aplicação desse conceito mostram-se insatisfatórias, em especial nos países mais exigentes quanto ao nível de criatividade e em que se sustenta a existência de um vínculo entre a personalidade do autor e sua obra. Em contrapartida, nos países que seguem a tradição inglesa, em geral, há maior facilidade para flexibilizar o conceito, de modo que ele ainda pode ser aplicado mesmo em situa","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"166 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125969462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Разработка концепции создания в Российской Федерации регуляторных рамок для развития рынка технологий искусственного интеллекта (Development of a Concept for the Creation in the Russian Federation of a Regulatory Framework for the Development of the Market for Artificial Intelligence Technologies)","authors":"Antonina Levashenko, Alexander Chernovol","doi":"10.2139/ssrn.3860821","DOIUrl":"https://doi.org/10.2139/ssrn.3860821","url":null,"abstract":"<b>Russian Abstract:</b> В работе была проведена оценка текущих тенденций развития правовых отношений, связанных с развитием рынка технологий искусственного интеллекта в России и в странах ОЭСР. В результате исследования авторами выявлены существенные элементы развития правового регулирования технологии ИИ, а также выработаны предложения по формированию актуального регулятивного подхода к определению понятия искусственный интеллект и задачам государственного регулирования. <br><br><b>English Abstract:</b> The work assessed the current trends in the development of legal relations associated with the development of the market for artificial intelligence technologies in Russia and in the OECD countries. As a result of the study, the authors identified essential elements of the development of legal regulation of AI technology, and also developed proposals for the formation of an actual regulatory approach to the definition of the concept of artificial intelligence and the tasks of state regulation.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131253382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Generated Databases. Do the Creation/obtaining Dichotomy and the Substantial Investment Requirement Exclude the Sui Generis Right Provided for under the EU Database Directive? Reflections and Proposals","authors":"Paolo Burdese","doi":"10.2139/ssrn.3850662","DOIUrl":"https://doi.org/10.2139/ssrn.3850662","url":null,"abstract":"The starting point of this analysis is the position of the European Commission, which, in its 2018 evaluation of the Directive, clearly stated that the sui generis right does not apply broadly to the data economy, including Artificial Intelligence (AI). This position was justified by the spin-off theory and the lack of substantiality of the investment involved in creating the databases concerned.<br><br>According to Article 7 of the Database Directive, in order to benefit from sui generis right protection, the maker of a database has to make a qualitatively or quantitatively substantial investment in either obtaining, verifying or presenting the contents of the database. While the CJEU and domestic courts agreed that the threshold for the substantiality of the investment should be low, major issues arose in connection with the aim of this investment. Indeed, due to judgments handed down by the CJEU in 2004, investment in creating the content of a database cannot be interpreted as “obtaining” such materials and, therefore, is not relevant. This interpretation created the so-called creation/obtaining dichotomy. However, while these judgments significantly reduced the number of protectable databases, the CJEU clearly rejected the spin-off theory.<br><br>Several years later, the CJEU’s judgment in the Ryanair case made the framework even more complex, the court ruling that the legitimate user of a database not protected under Article 7 of the Directive cannot benefit from the rights granted under Articles 8 and 15. This decision gave rise to a true paradox, because an unprotected database can benefit from stronger protection by contractual arrangement.<br><br>Looking beyond the legal framework, this paper challenges the view of the European Commission and asserts that, as a matter of principle, AI-generated databases can be protected under the sui generis right. Furthermore, the fact that this kind of database is usually generated by data-recording or data-mining processes, which involves obtaining rather than creating, means that the creation/obtaining dichotomy is no longer tenable and, therefore, should be abandoned. Moreover, rejecting this dichotomy would also mitigate the negative outcomes of the Ryanair decision.<br><br>However, it seems clear that granting easier protection in this way might generate serious access-related issues, especially where sole-source databases are concerned. What is needed is a balance that, echoing the 1992 Proposal of the Directive, could be achieved by introducing compulsory licensing provisions.<br>","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114175363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning","authors":"M. Azimi, Anup Agrawal","doi":"10.2139/ssrn.3258821","DOIUrl":"https://doi.org/10.2139/ssrn.3258821","url":null,"abstract":"\u0000 We use a novel text classification approach from deep learning to more accurately measure sentiment in a large sample of 10-Ks. In contrast to most prior literature, we find that positive and negative sentiments predict abnormal returns and abnormal trading volume around the 10-K filing date and future firm fundamentals and policies. Our results suggest that the qualitative information contained in corporate annual reports is richer than previously found. Both positive and negative sentiments are informative when measured accurately, but they do not have symmetric implications, suggesting that a net sentiment measure advocated by prior studies would be less informative. (JEL C81, D83, G10, G14, G30, M41)\u0000 Received February 12, 2020; editorial decision January 5, 2021 by Editor Hui Chen","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129636986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence’s Role in Finance and How Financial Companies are Leveraging the Technology to Their Advantage","authors":"Whitney Hunt","doi":"10.2139/ssrn.3707908","DOIUrl":"https://doi.org/10.2139/ssrn.3707908","url":null,"abstract":"This research paper aims to better define artificial intelligence (AI) and its current role in financial markets. All the while discussing the subgroups of AI, how machines learn, the pro’s and con’s, it’s role in financial analytics and the future of the field. This work encompasses research from some of the top minds in the field of artificial intelligence in order to explain the relevance of AI thoroughly and its future.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125300093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Nonfarm Employment","authors":"Tarun Bhatia","doi":"10.2139/ssrn.3683459","DOIUrl":"https://doi.org/10.2139/ssrn.3683459","url":null,"abstract":"U.S. Nonfarm employment is considered one of the key indicators for assessing the state of the labor market. Considerable deviations from the expectations can cause market moving impacts.<br><br>In this paper, the total U.S. nonfarm payroll employment is predicted before the release of the BLS employment report. The content herein outlines the process for extracting predictive features from the aggregated payroll data and training machine learning models to make accurate predictions. Publicly available revised employment report by BLS is used as a benchmark. Trained models show excellent behaviour with R 2 of 0.9985 and 99.99% directional accuracy on out of sample periods from January 2012 to March 2020.<br><br>CCS Concepts<br>• Applied Computing methodologies➝Machine Learning.<br>","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129698785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}