scholarly journals Improving Quality of Long-Term Bond Price Prediction Using Artificial Neural Networks

2021 ◽  
Vol 25 (1) ◽  
pp. 103-123
Author(s):  
Robert Verner ◽  
Michal Tkáč ◽  
Michal Tkáč
Keyword(s):  
Neural Network ◽  
Learning Algorithms ◽  
Coefficient Of Determination ◽  
Complex Nature ◽  
Bond Price ◽  
Prediction Ability ◽  
Price Prediction ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Purpose: The aim of this paper is to propose nonlinear autoregressive neural network which can improve quality of bond price forecasting.         Methodology/Approach: Due to the complex nature of market information that influence bonds, artificial intelligence could be accurate, robust and fast choice of bond price prediction method. Findings: Our results have reached a coefficient of determination higher than 95% in the training, validation and testing sets. Moreover, we proposed the nonlinear autoregressive network with external inputs using 50 year interest-rate swaps denominated in EUR and volatility index VIX as two external variables. Research Limitation/Implication: Our sample of daily prices between 4th January 2016 and 13th January 2021 (totally 1,270 trading days) suggest that both Levenberg-Marquardt and Scaled conjugate gradient learning algorithms achieved excellent results. Originality/Value of paper: Despite the fact that both learning algorithms achieved satisfying outcomes, implementation of an independent variable into the autoregressive neural network environment had no significant impact on prediction ability of the model.

SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

2021 ◽  
pp. 1-28
Author(s):  
Yanbo Che ◽  
Yibin Cai ◽  
Hongfeng Li ◽  
Yushu Liu ◽  
Mingda Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Variable Order ◽  
Nonlinear Correlation ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

scholarly journals Artificial neural network model in predicting the quality of fresh tomato genotypes

2021 ◽  
Vol 48 (1) ◽  
pp. 9-21
Author(s):  
Mladenka Pestorić ◽  
Jasna Mastilović ◽  
Žarko Kevrešan ◽  
Lato Pezo ◽  
Miona Belović ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sensory Properties ◽  
Coefficient Of Determination ◽  
Eating Quality ◽  
Ann Model ◽  
Artificial Neural ◽  
Fresh Tomato

Sensory analysis is the best mean to precisely describe the eating quality of fresh foods. However, it is expensive and time-consuming method which cannot be used for measuring quality properties in real time. The aim of this paper was to contribute to the study of the relationship between sensory and instrumental data, and to define a proper model for predicting sensory properties of fresh tomato through the determination of the physicochemical properties. Principal Component Analysis (PCA) was applied to the experimental data to characterize and differentiate among the observed genotypes, explaining 73.52% of the total variance, using the first three principal components. Artificial neural network (ANN) model was used for the prediction of sensory properties based on the results obtained by basic chemical and instrumental determinations. The developed ANN model predicts the sensory properties with high adequacy, with the overall coefficient of determination of 0.859.

scholarly journals An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weiwei Hao ◽  
Hongyan Gao ◽  
Zongqing Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Equation ◽  
Evaluation Index System ◽  
Corporate Debt ◽  
Investment Efficiency ◽  
Neural Network Approach ◽  
Ownership Reform ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

This paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises (SOE). Secondly, a set of investment efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage.

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