An approach using Artificial Neural Network and Genetic Algorithm for Day Trade Portfolio Selection

Portfolio Selection ◽

Financial Market ◽

Financial Strategy ◽

Nsga Ii ◽

Financial Investments ◽

Artificial Neural ◽

Investment Portfolios

In the financial market there are several types of investors, from the most conservative to the most daring, who are subject to greater risks in the expectation of greater returns on their investments. However, the concept of risk, in investment portfolios, makes it possible to measure it in different ways. This paper aims to present a method created to select portfolios for Day Trade financial investments using different metric risks, such as CVaR, EWMA and GARCH, and the ensemble of Genetic Algorithm NSGA-II and LSTM Artificial Neural Network, comparing it’s selected portfolios’ performance with another method which uses only NSGA-II and Buy and Hold financial strategy. The results show that the proposed method, with LSTM ANN achieved better returns in the year of 2019.

IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007) ◽

Application of genetic algorithm in artificial neural network for fault classification in parallel transmission lines

10.1049/ic:20070644 ◽

2007 ◽

Cited By ~ 1

Author(s):

R. Rajeswari ◽

N. Kamaraj

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Transmission Lines ◽

Fault Classification ◽

Parallel Transmission ◽

An experimental study of objective pain measurement using pupillary response based on genetic algorithm and artificial neural network

Applied Intelligence ◽

10.1007/s10489-021-02458-4 ◽

2021 ◽

Author(s):

Li Wang ◽

Yikang Guo ◽

Biren Dalip ◽

Yan Xiao ◽

Richard D. Urman ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Experimental Study ◽

Pain Measurement ◽

Pupillary Response ◽

Tri-objective constrained optimization of pulsating DC sourced magnetic abrasive finishing process parameters using artificial neural network and genetic algorithm

Materials and Manufacturing Processes ◽

10.1080/10426914.2020.1866196 ◽

2021 ◽

pp. 1-15

Author(s):

Shadab Ahmad ◽

Ranganath M. Singari ◽

R.S. Mishra

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Constrained Optimization ◽

Process Parameters ◽

Magnetic Abrasive Finishing ◽

Finishing Process ◽

Abrasive Finishing ◽

Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithm: Pilot study

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2020.104045 ◽

2020 ◽

Vol 126 ◽

pp. 104045

Author(s):

Katarina M. Rajković ◽

Kata Dabić-Stanković ◽

Jovan Stanković ◽

Miodrag Aćimović ◽

Nina Đukanović ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Pilot Study ◽

Dose Rate ◽

Prostate Carcinoma ◽

High Dose Rate ◽

High Dose ◽

Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

Water Resources Management ◽

10.1007/s11269-021-02818-2 ◽

2021 ◽

Author(s):

Amir Molajou ◽

Vahid Nourani ◽

Abbas Afshar ◽

Mina Khosravi ◽

Adam Brysiewicz

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Feature Selection ◽

Optimal Design ◽

Rainfall Runoff ◽

Runoff Modeling ◽

Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

Scientific Reports ◽

10.1038/s41598-018-27858-4 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 16

Author(s):

Mohammad Mehdi Arab ◽

Abbas Yadollahi ◽

Maliheh Eftekhari ◽

Hamed Ahmadi ◽

Mohammad Akbari ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Culture Medium ◽

In Vitro Rooting ◽

2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies ◽

Short circuit current forecast of large scale power grid based on improving BP Artificial Neural Network combined with Genetic Algorithm

10.1109/drpt.2008.4523583 ◽

2008 ◽

Cited By ~ 1

Author(s):

Bo Liu ◽

Yan Zhang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Large Scale ◽

Short Circuit ◽

Power Grid ◽

Short Circuit Current ◽

Bp Artificial Neural Network ◽

Artificial Neural ◽

Grid Based

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1343 ◽

2009 ◽

Vol 4 (1) ◽

Cited By ~ 2

Author(s):

Sandip K Lahiri ◽

Kartik Chandra Ghanta

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Operating Conditions ◽

Misclassification Error ◽

Wide Range ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Pipeline Design ◽

Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.