Predictability of HK-REITs returns using artificial neural network

2019 ◽  
Vol 38 (4) ◽  
pp. 291-307
Author(s):  
Wei Kang Loo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hong Kong ◽  
Capital Asset Pricing ◽  
Content Type ◽  
Forecasting Performance ◽  
Annual Time Series ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Annual Time

Purpose The purpose of this paper is to determine if artificial neural network (ANN) works better than linear regression in predicting Hong Kong real estate investment trusts’ (REITs) excess return. Design/methodology/approach Both ANN and the regression were applied in this study to forecast the Hong Kong REITs’ (HK-REITs) return using the capital asset pricing model and Fama and French’s three-factor models. Each result was further split into annual time series as a measure to investigate the consistency of the performance across time. Findings ANN had produced a better forecasting results than the regression based on their trading performance. However, the forecasting performance varied across individual REITs and time periods. Practical implications ANN should be considered for use when one were to attempt forecasting the HK-REITs excess returns. However, the trading performance should be always compared with buy and hold strategy prior to make any investment decisions. Originality/value This paper tested the predicting power of ANN on the HK-REITs and the consistency of its predicting power.

Textile pattern reconstruction and innovation using artificial neural network

2019 ◽  
Vol 31 (1) ◽  
pp. 103-114
Author(s):  
Chen Tao ◽  
Yafeng Duan ◽  
Xinghua Hong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pattern Generation ◽  
Digital Innovation ◽  
Color Distribution ◽  
Content Type ◽  
Output Layer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sample Set

Purpose The purpose of this paper is to advance a digital technology that is intended to bring about innovations on the existing textile patterns. Design/methodology/approach The pattern is deemed as a relation function between colors and positions which can be learnt by the artificial neural network (ANN). The outputs of the ANN are used for the reconstruction of the pattern and the innovation is performed by interceptors in the input/output layer. The ANN is carried out with one input layer, one output layer and several hidden layers, and the capacity of the architecture is adjusted by the scale of hidden layers to accommodate different function relations of the patterns. The training is conducted repeatedly on a sample set extracted from the pixels of the pattern image to minimize the error, and the chromatic outputs of the architecture are replaced to their origins so as to rebuild the pattern. Then, the interceptors are installed into the input and output layers to modulate the positions and the colors, and consequently the innovations are achieved on the geometric formation and color distribution of the pattern. Findings It has turned out that the precision of reconstruction is concerned with network scale, training epochs and color mode of the sample set. Four primary innovative effects including stripes, twisters, sandification and overprints have been qualified in terms of interceptors. Originality/value This study introduces ANN into textile pattern generation and provides a novel way to perform digital innovation of textile patterns.

scholarly journals Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yuehjen E. Shao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Forecast Model ◽  
Hybrid Models ◽  
Explanatory Variables ◽  
Forecasting Performance ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN) and multiple regression (MR) components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

A novel respiration pattern biometric prediction system based on artificial neural network

Sensor Review ◽  
2020 ◽  
Vol 40 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Rafiu King Raji ◽  
Michael Adjeisah ◽  
Xuhong Miao ◽  
Ailan Wan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Learning Rate ◽  
Prediction System ◽  
Ann Model ◽  
Content Type ◽  
Respiration Pattern ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Purpose The purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN). Design/methodology/approach Respiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting. Findings Findings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances. Practical implications The findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment. Originality/value This is the first time respiratory patterns have been applied in biometric prediction system design.

The prediction of optimal conditions for the surface grafting of β-cyclodextrin onto silk fabrics by an artificial neural network (ANN)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abolfazl Zare ◽  
Pedram Payvandy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Crosslinking Agent ◽  
Blue Dye ◽  
Content Type ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Silk Fabrics ◽  
Recovery Angle

Purpose The purpose of this study is the chemical grafting of β-Cyclodextrin (β-CD) onto silk fabrics by the use of butane tetracarboxylic acid (BTCA) as a crosslinking agent and nano-TiO2 (NTO) as a catalyst. The effects of different parameters involved in this particular process, e.g. β-CD, BTCA and NTO concentrations, are examined using the artificial neural network (ANN). The method is evaluated for its ability to predict certain properties of treated fabrics, including grafting yield, dry crease recovery angle (DCRA) and wet crease recovery angle (WCRA), tensile strength, elongation at break and methylene blue dye absorption. Design/methodology/approach This study was conducted to describe the cross-linking of silk with 1,2,3,4-BTCA as a crosslinking agent in a wet state at low temperatures using NTO catalyst to improve the dry and wet wrinkle recovery (DCRA and WCRA) of silk fabrics. An ANN was also used to model and analyze the effects of BTCA, β-CD and NTO concentrations on the grafting percentage and some properties of the treated samples. Findings According to the results, the wet and dry wrinkle recovery of the silk fabrics was improved for about 38% and 11%, respectively, as compared to the non-cross-linked fabrics, without significantly affecting the tensile strength retention of the fabrics. Originality/value This research model and analyze the effects of BTCA, β-CD and NTO concentrations on the grafting percentage and some properties of the treated samples for the first time.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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