Merger Premium Predictions Using a Neural Network Approach

2005 ◽  
Vol 2 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Tara J. Shawver
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Network Approach ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Bank Merger ◽  
Financial Variables ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Merger Premiums

Over 80 percent of mergers fail to achieve projected financial, strategic, and operational synergies (Marks and Mirvis 2001). It is critical for management to find accurate models to price merger premiums. Management has an interest to protect stakeholders by acquiring companies that can add value to their investments at the most favorable price. Published studies in the area of pricing mergers have not attempted to use expert systems in the decision-making process. This paper is the first of its kind that describes the development and testing of neural network models for predicting bank merger premiums accurately. A neural network prediction model provides a tool that can filter through noise and recognize patterns in complicated financial relationships. The results confirm that a neural network approach provides more explanation between the dependent and independent financial variables in the model than a traditional regression model. The higher level of accuracy provided by a neural network approach can provide practitioners with a competitive advantage in pricing merger offers.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Plant Disease Detection and Classification using CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 152-156
Author(s):  
Rinu R ◽  
◽  
Manjula S H ◽  
Keyword(s):  
Neural Network ◽  
Economic Status ◽  
Network Models ◽  
Training Model ◽  
Input Image ◽  
Plant Diseases ◽  
Human Beings ◽  
Neural Network Models ◽  
Neural Network Approach

Agriculture is one field which has a high impact on life and economic status of human beings. Improper management leads to loss in agricultural products. Diseases are detrimental to the plant’s health which in turn affects its growth. To ensure minimal loss to the cultivated crop, it is crucial to supervise its growth. Convolutional Neural Network is a class of Deep learning used majorly for image classification, other mainstream tasks such as image segmentation and signal processing. The main aim of the proposed work is to find a solution to the problem of 38 different classes of plant diseases detection using the simplest approach while making use of minimal computing resources to achieve better results compared to the traditional models. VGG16 training model is deployed for detection and classification of plant diseases. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94.8% indicating the feasibility of the neural network approach even under unfavorable conditions.

scholarly journals Neural Network Based Simulation of Micro Creeping Fibrous Composites SiC/Al6061 for Plastic Behaviour

2017 ◽  
Vol 47 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Vahid Monfared
Keyword(s):  
Neural Network ◽  
Optical Fibers ◽  
Creep Behaviour ◽  
Turbine Blades ◽  
Short Fiber ◽  
Solution Procedure ◽  
Neural Network Approach ◽  
Plastic Behaviour ◽  
Neural Network Prediction ◽  
Network Prediction

Abstract The present work presents a new approach based on neural network prediction for simple and fast estimation of the creep plastic behaviour of the short fiber composites. Also, this approach is proposed to reduce the solution procedure. Moreover, as a significant application of the method, shuttles and spaceships, turbine blades and discs are generally subjected to the creep effects. Consequently, analysis of the creep phenomenon is required and vital in different industries. Analysis of the creep behaviour is required for failure, fracture, fatigue, and creep resistance of the optoelectronic/photonic composites, and sensors. One of the main applications of the present work is in designing the composites with optical fibers and devices. At last, a good agreement is seen among the present prediction by neural network approach, finite element method (FEM), and the experimental results.

scholarly journals Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 122
Author(s):  
Sungjin Lee ◽  
Soo Cho ◽  
Seo-Hoon Kim ◽  
Jonghun Kim ◽  
Suyong Chae ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Low Income ◽  
Prediction Accuracy ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Input Variables ◽  
Heating Energy Consumption

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy (R2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy (Cv(RMSE)) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.

Two Types of Neural Network Prediction Models and Time Complexity

2014 ◽  
Vol 548-549 ◽  
pp. 985-989
Author(s):  
Ji Min Zhang ◽  
Liang Zhu ◽  
Subhash Rekheja
Keyword(s):  
Neural Network ◽  
Time Complexity ◽  
Prediction Models ◽  
Lateral Acceleration ◽  
Neural Network Models ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction

The linear adaptive neural network and RBF neural network, according to the measured low-pass filter lateral acceleration signal, was used to establish the reference lateral acceleration applied for the input of tilting train control system. This paper presents the two types of neural network models and prediction algorithms, and studies the time complexity of the two types of network algorithms. The results show that time complexity of the neural network prediction is closely related to its parameters, the neural network structure also can lead to the difference in their calculation time, and RBF prediction neural network spends the minimum time.

CNN: A SPEAKER RECOGNITION SYSTEM USING A CASCADED NEURAL NETWORK

1996 ◽  
Vol 07 (02) ◽  
pp. 203-212 ◽  
Author(s):  
M. ZAKI ◽  
A. GHALWASH ◽  
A.A. ELKOUNY
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Network Models ◽  
Recognition System ◽  
Conventional Technique ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Unsupervised Neural Network ◽  
And Performance ◽  
Filtration Stage

The main emphasis of this paper is to present an approach for combining supervised and unsupervised neural network models to the issue of speaker recognition. To enhance the overall operation and performance of recognition, the proposed strategy integrates the two techniques, forming one global model called the cascaded model. We first present a simple conventional technique based on the distance measured between a test vector and a reference vector for different speakers in the population. This particular distance metric has the property of weighting down the components in those directions along which the intraspeaker variance is large. The reason for presenting this method is to clarify the discrepancy in performance between the conventional and neural network approach. We then introduce the idea of using unsupervised learning technique, presented by the winner-take-all model, as a means of recognition. Due to several tests that have been conducted and in order to enhance the performance of this model, dealing with noisy patterns, we have preceded it with a supervised learning model—the pattern association model—which acts as a filtration stage. This work includes both the design and implementation of both conventional and neural network approaches to recognize the speakers templates—which are introduced to the system via a voice master card and preprocessed before extracting the features used in the recognition. The conclusion indicates that the system performance in case of neural network is better than that of the conventional one, achieving a smooth degradation in respect of noisy patterns, and higher performance in respect of noise-free patterns.

scholarly journals Neural network models in the problems of semantic analysis of natural language texts.

2020 ◽  
Vol 11 (8-2020) ◽  
pp. 91-101
Author(s):  
M.G. Shishaev ◽  
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Semantic Analysis ◽  
Network Models ◽  
Stage Structure ◽  
Subject Area ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Subject

The paper deals with the problem of text analysis focused on the formation of a semantic model of the subject area. A two-stage structure of the problem of semantic analysis is proposed, and the typology of text models used to determine features and form a target model is considered. Examples of the application of the neural network approach to various problems of the analysis of natural language texts are given.

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