InfoSciRN: Big Data (Sub-Topic)最新文献

Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News 利用HAR模型增强限价订单和新闻的大数据方法实现波动率预测

InfoSciRN: Big Data (Sub-Topic) Pub Date : 2020-09-01 DOI: 10.2139/ssrn.3684040

Eghbal Rahimikia, S. Poon

{"title":"Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News","authors":"Eghbal Rahimikia, S. Poon","doi":"10.2139/ssrn.3684040","DOIUrl":"https://doi.org/10.2139/ssrn.3684040","url":null,"abstract":"The study determines if information extracted from a big data set that includes limit order book (LOB) and Dow Jones corporate news can help to improve realised volatility forecasting for 23 NASDAQ tickers over the sample from 28 June 2007 to 17 November 2016. The out-of-sample forecasting results indicate that CHAR model outperformed all other models in the HAR-family of models, and there is strong evidence that news and LOB data provide statistically significant improvement in RV forecasts. Specifically, the slope of the bid-side of LOB has better predictive power than the slope from the ask-side. For normal volatility day, the ‘negative’ sentiment derived from the news has a clear impact, while ‘news count’, and to a lesser extent, ‘weak modal’, and ‘uncertainty’ can help to forecast volatility jumps. The depth of the LOB also helps to forecast volatility jumps. Indeed, the findings also suggest normal volatility and volatility jumps should be separately analysed as variables improve the forecasting performance of normal days causes a degradation in the forecasting performance of volatility jumps and vice versa. On the other hand, increasing the estimation sample size causes statistically signiﬁcant degradation in the forecasting performance of volatility on normal days, especially if it includes extreme volatility period such as the 2008 financial crisis, but a longer sample improves the forecast of volatility jumps.","PeriodicalId":283708,"journal":{"name":"InfoSciRN: Big Data (Sub-Topic)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131799650","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}

引用次数: 5

Integrating Big Data and Intellectual Capital: Resource Complementarity in Business Value Creation 整合大数据与智力资本:商业价值创造中的资源互补

InfoSciRN: Big Data (Sub-Topic) Pub Date : 2020-07-25 DOI: 10.2139/ssrn.3660390

I. Alomari, Feras Shehada, Jaber El-Daour

引用次数: 0

Selecting Smart Strategies Based on Big Data Techniques and SPACE Matrix (FASE model) 基于大数据技术和空间矩阵(FASE模型)的智能策略选择

InfoSciRN: Big Data (Sub-Topic) Pub Date : 2019-03-22 DOI: 10.2139/ssrn.3620400

Mohammad Ali Farajian, S. Mohammadi, B. Sadeghi Bigham, Farhad Shamsfakhr

{"title":"Selecting Smart Strategies Based on Big Data Techniques and SPACE Matrix (FASE model)","authors":"Mohammad Ali Farajian, S. Mohammadi, B. Sadeghi Bigham, Farhad Shamsfakhr","doi":"10.2139/ssrn.3620400","DOIUrl":"https://doi.org/10.2139/ssrn.3620400","url":null,"abstract":"Şirket yoneticileri icin en onemli sorulardan biri, sirketleri icin akilli bir stratejiyi nasil sectikleridir. Bu soruyu cevaplamak icin yoneticiler, kurumun gelecegi uzerinde etkisi olan bazi boyutlari goz onunde bulundurmali ve ardindan sirketleri icin uygun bir strateji secmelidir. Bu makale, stratejik pozisyonu degerlendirmek ve Buyuk Veri teknikleri ve UZAY matrisini temel alan akilli bir strateji secmek icin yeni bir model (FASE modeli) sunmaktadir. Piyasadaki en iyi pozisyonu elde etmek icin FASE modeli, en iyi stratejiyi secmeyi kolaylastirir: saldirgan, muhafazakâr, savunmaci ve rekabetci stratejiler. FASE modeli, Fuzzy-Cmeans, Apriori birlik kural uyaricisi, UZAY matrisi olmak uzere uc ana islemden olusur. Fuzzy-Cmeans algoritmasi, musterileri RFM degerlerine ve davranissal puanlamaya dayali olarak kumelemek icin kullanilir. Kumelenmenin sonuclari daha sonra Apriori birlik kural uyaricisi kullanilarak musterilerin ozelliklerine gore sekillendilir. Stratejik pozisyonu degerlendirmek ve akilli bir strateji secmek icin UZAY matrisi kullanilir. FASE modelini daha iyi anlamak icin, bir bankacilik vakasi secildi ve bunun uzerine FASE modeli uygulandi.","PeriodicalId":283708,"journal":{"name":"InfoSciRN: Big Data (Sub-Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128437933","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}

引用次数: 1

Digital Dictatorship: The System of Social Credit in China 数字独裁:中国的社会信用体系

InfoSciRN: Big Data (Sub-Topic) Pub Date : 2019-02-15 DOI: 10.2139/ssrn.3828279

I. Osetrova

引用次数: 0

Big Data Tools, Techniques and Technologies Looking at Insights in Smart Cities 大数据工具、技术和技术着眼于智慧城市的洞察力

InfoSciRN: Big Data (Sub-Topic) Pub Date : 2015-02-17 DOI: 10.2139/ssrn.3518392

Fahimeh Tabatabaei, T. Wani, N. Hajiheidari

引用次数: 0