Results 261 to 270 of about 4,165 (288)

Editorial: Hammer or telescope? Challenges and opportunities of science-oriented AI in legal and sociolegal research. [PDF]

Front Artif Intell
Lettieri N, Pluchino A.
europepmc   +1 more source

Blockchain-Facilitated Cybersecurity for Ubiquitous Internet of Things with Space-Air-Ground Integrated Networks: A Survey. [PDF]

Sensors (Basel)
Zhao W, Yang S, Luo X.
europepmc   +1 more source

Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges. [PDF]

BMC Cancer
Nittas V, Ormond KE, Vayena E, Blasimme A.   +3 more
europepmc   +1 more source

Ethical Design of Data-Driven Decision Support Tools for Improving Cancer Care: Embedded Ethics Review of the 4D PICTURE Project.

JMIR Cancer
Bak M, Hartman L, Graafland C, Korfage IJ, Buyx A, Schermer M, 4D PICTURE Consortium.   +6 more
europepmc   +1 more source

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Insider trading and the algorithmic trading environment

International Review of Finance, 2021
AbstractWe examine how algorithmic trading (AT) changes the trading environment for corporate insiders, specifically in terms of motivation to trade and timing of trade. Using SEC Form 4 insider filings and AT computed from the limit order book, we find that AT affects insiders' decisions to buy or sell, depending on whether the trades are information ...
Millicent Chang, John Gould, Yuyun Huang, Sirimon Treepongkaruna, Joey Wenling Yang   +4 more
openaire   +2 more sources

Algorithmic Trading

Computer, 2011
In electronic financial markets, algorithmic trading refers to the use of computer programs to automate one or more stages of the trading process: pretrade analysis (data analysis), trading signal generation (buy and sell recommendations), and trade execution.
M. Mirghaemi, Chaiyakorn Yingsaeree, Giuseppe Nuti, P. Treleaven   +3 more
openaire   +2 more sources

Algorithmic Trading and Fragmentation

The Journal of Trading, 2017
Prior studies on algorithmic trading (AT) have mostly focused on a single exchange. The authors use a public dataset provided by the Securities and Exchange Commission (SEC) covering all major U.S. exchanges to study the impact of AT and its fragmentation on market liquidity.
Archana Jain, Chinmay Jain, Christine X. Jiang   +2 more
openaire   +2 more sources

Are Algorithmic Trades Informed? An Empirical Analysis of Algorithmic Trading around Earnings Announcements [PDF]

SSRN Electronic Journal, 2012
This study examines the impact of corporate earnings announcements on trading activity and speed of price adjustment, analyzing algorithmic and non–algorithmic trades during the immediate period pre– and post– corporate earnings announcements. We confirm that algorithms react faster and more correctly to announcements than non–algorithmic traders ...
P. Joakim Westerholm, Hui Zheng, Alex Frino, George H. K. Wang, Tina Viljoen   +4 more
openaire   +1 more source

Algorithmic trading review

Communications of the ACM, 2013
The competitive nature of AT, the scarcity of expertise, and the vast profits potential, makes for a secretive community where implementation details are difficult to find.
Vidhi Lalchand, Philip Treleaven, Michal Galas   +2 more
openaire   +2 more sources