{"title":"人工智能对配置效率的影响","authors":"Anna Grygiel-Tomaszewska, Lech Kurkliński","doi":"10.4324/9781003219958-17","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) became one of the constitutional factors of the fifth industrial revolution in the global economy. It influences the use of resources (environmental impact), reshapes the labour market, dehumanises work (social impact), and diminishes the role of human management (governance impact). This chapter investigates the impact of AI-supported high-frequency trading (HFT) on the efficiency of capital allocation in global financial markets and its consequences for the sustainability of economies. This issue has become ever more important during the COVID-19 pandemic because low interest rates and quantitative easing provoke large transfers of savings and investments into the capital markets. The aim of the chapter was fulfilled with the citations count regression model and theoretically informed analysis of the leveraged research papers. Data for the research were derived from the Web of Science Core Collection, for the timeframe of 2016-2020. The research confirms and presents the impact of HFT on price discovery, liquidity, transactional costs, bid/ask spreads, volatility, and vulnerability to flash crash emergence, all contributing to the financial market allocative efficiency and thus - to the sustainable development of economies. The last would however also require legal protection of latent traders and the requirement to use ESG criteria by HFT algorithms. © 2021 selection and editorial matter, Anna Szelagowska and Aneta Pluta-Zaremba;individual chapters, the contributors.","PeriodicalId":328849,"journal":{"name":"The Economics of Sustainable Transformation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The impact of artificial intelligence on allocative efficiency\",\"authors\":\"Anna Grygiel-Tomaszewska, Lech Kurkliński\",\"doi\":\"10.4324/9781003219958-17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) became one of the constitutional factors of the fifth industrial revolution in the global economy. It influences the use of resources (environmental impact), reshapes the labour market, dehumanises work (social impact), and diminishes the role of human management (governance impact). This chapter investigates the impact of AI-supported high-frequency trading (HFT) on the efficiency of capital allocation in global financial markets and its consequences for the sustainability of economies. This issue has become ever more important during the COVID-19 pandemic because low interest rates and quantitative easing provoke large transfers of savings and investments into the capital markets. The aim of the chapter was fulfilled with the citations count regression model and theoretically informed analysis of the leveraged research papers. Data for the research were derived from the Web of Science Core Collection, for the timeframe of 2016-2020. The research confirms and presents the impact of HFT on price discovery, liquidity, transactional costs, bid/ask spreads, volatility, and vulnerability to flash crash emergence, all contributing to the financial market allocative efficiency and thus - to the sustainable development of economies. The last would however also require legal protection of latent traders and the requirement to use ESG criteria by HFT algorithms. © 2021 selection and editorial matter, Anna Szelagowska and Aneta Pluta-Zaremba;individual chapters, the contributors.\",\"PeriodicalId\":328849,\"journal\":{\"name\":\"The Economics of Sustainable Transformation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Economics of Sustainable Transformation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4324/9781003219958-17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Economics of Sustainable Transformation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4324/9781003219958-17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
The impact of artificial intelligence on allocative efficiency
Artificial intelligence (AI) became one of the constitutional factors of the fifth industrial revolution in the global economy. It influences the use of resources (environmental impact), reshapes the labour market, dehumanises work (social impact), and diminishes the role of human management (governance impact). This chapter investigates the impact of AI-supported high-frequency trading (HFT) on the efficiency of capital allocation in global financial markets and its consequences for the sustainability of economies. This issue has become ever more important during the COVID-19 pandemic because low interest rates and quantitative easing provoke large transfers of savings and investments into the capital markets. The aim of the chapter was fulfilled with the citations count regression model and theoretically informed analysis of the leveraged research papers. Data for the research were derived from the Web of Science Core Collection, for the timeframe of 2016-2020. The research confirms and presents the impact of HFT on price discovery, liquidity, transactional costs, bid/ask spreads, volatility, and vulnerability to flash crash emergence, all contributing to the financial market allocative efficiency and thus - to the sustainable development of economies. The last would however also require legal protection of latent traders and the requirement to use ESG criteria by HFT algorithms. © 2021 selection and editorial matter, Anna Szelagowska and Aneta Pluta-Zaremba;individual chapters, the contributors.