High frequency source depth localization using deep neural network

2020 ◽  
Vol 148 (4) ◽  
pp. 2590-2590
Author(s):  
Seung Hyun Yoon ◽  
Haesang Yang ◽  
Woojae Seong
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Deep Neural Network ◽  
Source Depth ◽  
Frequency Source ◽  
Depth Localization

scholarly journals Real-Time Detection of Volatility in Liquidity Provision

2020 ◽  
Vol 9 ◽  
pp. 143-156
Author(s):  
Matt Brigida
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Frequency ◽  
Prediction Error ◽  
Deep Neural Network ◽  
Price Volatility ◽  
Market Participant ◽  
Liquidity Provision ◽  
Real Time Detection

Previous research has found that high-frequency traders will vary the bid or offer price rapidly overperiods of milliseconds. This is a benefit to fast traders who can time thier trades with microsecondprecision, however it is a cost to the average market participant due to increased trade execution priceuncertainty. In this analysis we attempt to construct real-time methods for determining whether theliquidity of a security is being altered by high-frequency traders. We find a four-state Markov switchingmodel identifies a state consistent with high-frequency traders affecting liquidity. Moreover, we find thisstate is positicely corrrelated with the prediction error from a deep neural network. This state can beused as a signal to delay market participant orders until the price volatility subsides. This delay wouldonly last tens of milliseconds, and so would not be noticable by the average market participant.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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