Results 61 to 70 of about 6,288,155 (355)
IEEE AccessWith the development of artificial intelligence, there have been many attempts to incorporate artificial intelligence into algorithmic trading. In particular, reinforcement learning, which aims to solve dynamic decision-making problems, is attracting ...
Ji-Heon Park, Jae-Hwan Kim, Jun-Ho Huh
semanticscholar +1 more source
Molecular Oncology, EarlyView.We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck +12 more
wiley +1 more source
Does algorithmic trading improve liquidity? [PDF]
, 2008 Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic ...
Hendershott, Terrence +2 more
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Journal of Autonomous IntelligenceStock trading is a popular and important profession that requires near-to-perfect data analytical skills, mathematical and statistical knowledge, and a broad understanding of buying and selling stocks.
Sstuti D. Mehra, S. Shetty
semanticscholar +1 more source
Algorithmic Trading in Experimental Markets with Human Traders: A Literature Survey
Social Science Research Network, 2021 This chapter surveys the nascent experimental research on the interaction between human and algorithmic (bot) traders in experimental markets. We first discuss studies in which algorithmic traders are in the researcher’s hands.
T. Bao +3 more
semanticscholar +1 more source
Molecular Oncology, EarlyView.This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris +10 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah +7 more
wiley +1 more source
Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market [PDF]
, 2018 We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly ...
Calvez, Arthur le, Cliff, Dave
core +3 more sources
Applying Deep Reinforcement Learning to Algorithmic Trading
Современные информационные технологии и IT-образование, 2020 At the moment, there is a large volume of literature on exchange trading. Obviously, every year the mathematical base of work is becoming more complicated along with an increase in computing power, machines can process more metrics from year to year and ...
Petr Nikitin +3 more
doaj +1 more source
Algorithmic trading in a microstructural limit order book model [PDF]
, 2019 We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox ...
Abergel, Frédéric +2 more
core +1 more source