Stock Market Prediction Performance of Neural Networks: A Literature Review

In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

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Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms

CogITo Smart Journal ◽

10.31154/cogito.v3i1.40.10-19 ◽

2017 ◽

Vol 3 (1) ◽

pp. 10

Author(s):

Debby E. Sondakh

Keyword(s):

Neural Networks ◽

Multilayer Perceptron ◽

Mean Squared Error ◽

Absolute Error ◽

Error Rates ◽

Large Dataset ◽

Training Time ◽

Healthcare Data ◽

Squared Error ◽

Hidden Layer

Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.

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Estimating modulus of elasticity (MOE) of particleboards using artificial neural networks to reduce quality measurements and costs

Drvna industrija ◽

10.5552/drvind.2019.1840 ◽

2019 ◽

Vol 70 (3) ◽

pp. 257-263

Author(s):

Rıfat Kurt ◽

Selman Karayilmazlar

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Artificial Neural Networks ◽

Modulus Of Elasticity ◽

Mean Squared Error ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Squared Error ◽

Artificial Neural

There are a large number of costs that enterprises need to bear in order to produce the same product at the same quality for a more affordable price. For this reason, enterprises have to minimize their expenses through a couple of measures in order to offer the same product for a lower price by minimizing these costs. Today, quality control and measurements constitute one of the major cost items of enterprises. In this study, the modulus of elasticity values of particleboards were estimated by using Artificial Neural Networks (ANN) and other mechanical properties of particleboards in order to reduce the measurement costs in particleboard enterprises. In addition to that, the future values of modulus of elasticity were also estimated using the same variables with the purpose of monitoring the state of the process. For this purpose, data regarding the mechanical properties of the boards were randomly collected from the enterprise for three months. The sample size (n) was: 6 and the number of samples (m): 65 and a total of 65 average measurement values were obtained for each mechanical property. As a result of the implementation, the low Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Error (MSE) performance measures of the model clearly showed that some quality characteristics could easily be estimated by the enterprises without having to make any measurements by ANN.

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An Open Innovation Intraday Implied Volatility for Pricing Australian Dollar Options

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc7010023 ◽

2021 ◽

Vol 7 (1) ◽

pp. 23

Author(s):

Thi Le ◽

Ariful Hoque ◽

Kamrul Hassan

Keyword(s):

Implied Volatility ◽

Mean Squared Error ◽

Absolute Error ◽

Currency Options ◽

Essential Information ◽

Australian Dollar ◽

Pricing Options ◽

Error Measures ◽

Squared Error ◽

Foreign Exchange Volatility

This study introduces the intraday implied volatility (IV) for pricing the Australian dollar (AUD) options. The IV is estimated using the at-the-money one-month, two-month, and three-month maturity AUD options traded in the opening, midday, and closing period of a trading day. The Mincer-Zarnowitz regression test evaluates the predictive power of IV to forecast the foreign exchange volatility for the within-week, one-week, and one-month horizon. The mean absolute error, mean squared error, and root mean squared error measures are employed to assess the performance of IV in estimating the price of currency options for the within-week, one-week, and one-month horizon. This study reveals four critical findings. First, a three-month maturity IV does not contain vital information for pricing options. Second, IV incorporated information is not relevant to compute the value of options for a horizon of less than a week. Third, IV in the closing period of Monday or Tuesday subsumes most of the essential information to estimate options price. Fourth, the shorter (longer) maturity IV provides critical information to price options for the shorter (longer) horizon. The intraday IV is a new dimension of unobservable volatility in accurately pricing currency options for researchers and practitioners.

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Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

American Journal of Business Education (AJBE) ◽

10.19030/ajbe.v6i3.7815 ◽

2013 ◽

Vol 6 (3) ◽

pp. 347-360 ◽

Cited By ~ 5

Author(s):

Handanhal V. Ravinder

Keyword(s):

Operations Management ◽

Mean Squared Error ◽

Forecast Error ◽

Management Science ◽

Exponential Smoothing ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Squared Error ◽

Optimal Values ◽

The Impact

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants.

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Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production byAcinetobactersp.

BioMed Research International ◽

10.1155/2014/361732 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Sirisha Edupuganti ◽

Ravichandra Potumarthi ◽

Thadikamala Sathish ◽

Lakshmi Narasu Mangamoori

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Feed Forward Neural Network ◽

Feed Forward ◽

Squared Error ◽

Feed Forward Neural Networks ◽

Alpha Galactosidase

Alpha-galactosidase production in submerged fermentation byAcinetobactersp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), andR2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

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Determining The Optimal Values Of Exponential Smoothing Constants – Does Solver Really Work?

American Journal of Business Education (AJBE) ◽

10.19030/ajbe.v9i1.9574 ◽

2016 ◽

Vol 9 (1) ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Handanhal V. Ravinder

Keyword(s):

Operations Management ◽

Mean Squared Error ◽

Forecast Error ◽

Management Science ◽

Exponential Smoothing ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Squared Error ◽

Optimal Values ◽

The Impact

A key issue in exponential smoothing is the choice of the values of the smoothing constants used. One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE). We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants. We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants.

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A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Sustainability ◽

10.3390/su13147612 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7612

Author(s):

Mahdis sadat Jalaee ◽

Alireza Shakibaei ◽

Amin GhasemiNejad ◽

Sayyed Abdolmajid Jalaee ◽

Reza Derakhshani

Keyword(s):

Hybrid Method ◽

Computational Intelligence ◽

Energy Demand ◽

Mean Squared Error ◽

Absolute Error ◽

Coal Consumption ◽

Squared Error ◽

Artificial Neural ◽

Socio Economic Variables ◽

Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽

10.3390/electronics10070861 ◽

2021 ◽

Vol 10 (7) ◽

pp. 861

Author(s):

Kyeung Ho Kang ◽

Mingu Kang ◽

Siho Shin ◽

Jaehyo Jung ◽

Meina Li

Keyword(s):

Physical Activity ◽

Energy Expenditure ◽

Mean Squared Error ◽

Absolute Error ◽

Ensemble Model ◽

Direct Calorimetry ◽

Patch Type ◽

Mortality And Morbidity ◽

Squared Error ◽

Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

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A hybrid recommender system based-on link prediction for movie baskets analysis

Journal Of Big Data ◽

10.1186/s40537-021-00422-0 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mohammadsadegh Vahidi Farashah ◽

Akbar Etebarian ◽

Reza Azmi ◽

Reza Ebrahimzadeh Dastjerdi

Keyword(s):

Recommender System ◽

Link Prediction ◽

Mean Squared Error ◽

Spatial Clustering ◽

Randomized Algorithm ◽

Similarity Criterion ◽

Absolute Error ◽

Similarity Criteria ◽

Online Systems ◽

Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

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Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

Sustainability ◽

10.3390/su10124863 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4863 ◽

Cited By ~ 6

Author(s):

Chao Huang ◽

Longpeng Cao ◽

Nanxin Peng ◽

Sijia Li ◽

Jing Zhang ◽

...

Keyword(s):

Power Plants ◽

Mean Squared Error ◽

Absolute Error ◽

Multilayer Perception ◽

Squared Error ◽

The Mean ◽

Effectiveness And Efficiency ◽

Mlp Network ◽

Grid Operation ◽

Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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