Title :
Examination of Ex-Dividend Day Trading Using Big Data of American Depositary Receipts
Author_Institution :
Dept. of Manage. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Abstract :
This work is the first to utilize big data of American depository receipt (ADRs) to conduct economic analysis. Foreign tax liability is the minimums of the taxation imposed on ADR dividend income. Identical foreign tax rates enable ADR investors to engage in transactions during ex-dividend days. This study is the first in utilizing 5,424 ex-dividend events to simulate the impact of tax on the ADR excess trading. This work calculates 1,464,780 daily returns of ADRs using daily price differences of ADRs. This work converts unstructured trading ADR prices into structured ADR returns. This work has collected the big data of 1,464,780 ADR returns to observe whether the rate of ADR returns are significantly higher during the ex-dividend period than those during the non-ex-dividend days, which implies the sale of ADRs before ex-dividend days and the repurchase of ADRs after the ex-dividend days. In addition, this work calculates 433,920 daily volumes of ADRs by summing up the ADR trading volumes of all the transactions each day. This work converts unstructured trading ADR volumes of all transactions each day into structured daily ADR volumes. The results exhibit prominent excess ADR trading volumes on ADR ex-dividend days. Our analysis results contribute to investors in implementing investment strategies and provide insights into tax policies.
Keywords :
Big Data; commerce; investment; taxation; ADR dividend income; ADR prices; ADR trading volumes; American depository receipt; Big Data; economic analysis; ex-dividend day trading; foreign tax liability; identical foreign tax rates; investment strategies; Big data; Economics; Estimation; Finance; Frequency modulation; Instruments; Mathematical model; big data; regression; structured data; t-statistics;
Conference_Titel :
Advanced Cloud and Big Data (CBD), 2014 Second International Conference on
Print_ISBN :
978-1-4799-8086-4
DOI :
10.1109/CBD.2014.13