{"title":"大数据分析、订单失衡和股票回报的可预测性","authors":"Erdinc Akyildirim , Ahmet Sensoy , Guzhan Gulay , Shaen Corbet , Hajar Novin Salari","doi":"10.1016/j.mulfin.2021.100717","DOIUrl":null,"url":null,"abstract":"<div><p>Financial institutions<span> have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading<span> was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.</span></span></p></div>","PeriodicalId":47268,"journal":{"name":"Journal of Multinational Financial Management","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Big data analytics, order imbalance and the predictability of stock returns\",\"authors\":\"Erdinc Akyildirim , Ahmet Sensoy , Guzhan Gulay , Shaen Corbet , Hajar Novin Salari\",\"doi\":\"10.1016/j.mulfin.2021.100717\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Financial institutions<span> have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading<span> was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.</span></span></p></div>\",\"PeriodicalId\":47268,\"journal\":{\"name\":\"Journal of Multinational Financial Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Multinational Financial Management\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1042444X21000402\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Multinational Financial Management","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1042444X21000402","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Big data analytics, order imbalance and the predictability of stock returns
Financial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.
期刊介绍:
International trade, financing and investments have grown at an extremely rapid pace in recent years, and the operations of corporations have become increasingly multinationalized. Corporate executives buying and selling goods and services, and making financing and investment decisions across national boundaries, have developed policies and procedures for managing cash flows denominated in foreign currencies. These policies and procedures, and the related managerial actions of executives, change as new relevant information becomes available. The purpose of the Journal of Multinational Financial Management is to publish rigorous, original articles dealing with the management of the multinational enterprise. Theoretical, conceptual, and empirical papers providing meaningful insights into the subject areas will be considered. The following topic areas, although not exhaustive, are representative of the coverage in this Journal. • Foreign exchange risk management • International capital budgeting • Forecasting exchange rates • Foreign direct investment • Hedging strategies • Cost of capital • Managing transaction exposure • Political risk assessment • International working capital management • International financial planning • International tax management • International diversification • Transfer pricing strategies • International liability management • International mergers.