Integration of a Predictive, Continuous Time Neural Network into Securities Market Trading Operations

06/04/2014
by   Christopher S Kirk, et al.
0

This paper describes recent development and test implementation of a continuous time recurrent neural network that has been configured to predict rates of change in securities. It presents outcomes in the context of popular technical analysis indicators and highlights the potential impact of continuous predictive capability on securities market trading operations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/13/2021

RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural Network-based Forecasting System

Consistent alpha generation, i.e., maintaining an edge over the market, ...
research
02/15/2020

Deep Learning for Asset Bubbles Detection

We develop a methodology for detecting asset bubbles using a neural netw...
research
02/02/2022

CTMSTOU driven markets: simulated environment for regime-awareness in trading policies

Market regimes is a popular topic in quantitative finance even though th...
research
12/02/2022

NFT Wash Trading in the Ethereum Blockchain

Non-Fungible Token (NFT) marketplaces on the Ethereum blockchain saw an ...
research
10/21/2021

Optimal trading: a model predictive control approach

We develop a dynamic trading strategy in the Linear Quadratic Regulator ...
research
11/25/2011

Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

Though machine learning has been applied to the foreign exchange market ...
research
01/17/2018

The Data Market: Policies for Decentralised Visual Localisation

This paper presents a mercantile framework for the decentralised sharing...

Please sign up or login with your details

Forgot password? Click here to reset