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Analysis of herding behavior in individual investor portfolios using machine learning algorithms

RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE 2022年 62卷

作者： Mavruk, Taylan Univ Gothenburg Ctr Finance Dept Business Adm Box 610 S-40530 Gothenburg Sweden

This paper examines the determinants of herding at both stock and individual investor levels and studies the portfolio performance of herd vs. non-herd portfolios using machine learning algo-rithms. The disposition effect and the attention effect seem to explain herding behavior at the stock level. At the individual investor level, the cumulative number of buys and portfolio values reduce the prediction of herding behavior, while high values of portfolio return lead to a small increase in herding. Individuals who herd do not outperform either market or non-herd portfolios, suggesting that herding is a behavioral bias. Thus, such behavior seems to destabilize stock markets, creating temporary discrepancies in stock prices followed by reversals back to funda-mentals. The most predictive factor in the performance tests of individual portfolios is the market risk premium and using equally-weighted factors rather than value-weighted factors seem to provide more consistent results in the portfolio performance analyses.

关键词： Herding Portfolio performance machine learning algorithms

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The use of machine learning algorithms for image recognition

The use of machine learning algorithms for image recognition

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Radioelectronic Systems Conference (RSC)

作者： Matuszewski, Jan Rajkowski, Adam Mil Univ Technol Fac Elect Inst Radioelect 2 Gen Sylwestra Kaliskiego St PL-00908 Warsaw Poland Sandomierz Radoszki JW 35333 Brt Sandomierz Poland

ISBN: (纸本)9781510636729

The article presents a way of using machine learning algorithms to recognize objects in images. To implement this task, an artificial neural network was used, which has a high adaptability and allows work with a very large set of input data. The neural network was described using a program written in the MATLAB simulation environment. The basic problem faced by the designer of objects recognition is to collect a sufficient training set of images to achieve the high probability of correct recognition. The set of learning patterns in the artificial neural networks may contain from several dozen thousands to one million training samples. In this article at the beginning the neural network was pre-trained trained based on the images included in the publicly available CIFAR 100 database, which are characterized by a small size of 32x32 pixels. It contains 70 000 images assigned to 10 basic categories. Then the author's database, consisting from 1000 pedestrians, cars and road signs was used. The article contains a description of applied algorithm, method of supervised learning and correction of weight coefficients, selection of activation function and operation on max pooling filter. The results of proposed solution are presented in the form of screenshots from calculations and in figures depicting results of recognized objects. Attention was also paid to the impact of used database for learning the network on the speed of calculations and recognition efficiency. The proper selection of number and types of layers, number of neurons, activation function and the value of the learning factor is very important in designing the neural network in application to objects recognition contained in the images. The problems occurring in the process of learning the neural networks and suggestions for their further improvement are also presented.

关键词： image recognition deep neural network machine learning algorithms

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Predicting the academic performance of middle- and high-school students using machine learning algorithms

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Social Sciences and Humanities Open 2022年第1期6卷

作者： Rajendran, Suchithra Chamundeswari, S. Sinha, Akhouri Amitanand Department of Industrial and Manufacturing Systems Engineering University of Missouri Columbia 65211 MO United States Department of Marketing University of Missouri Columbia 65211 MO United States N.K.T College of Education for Women University of Madras Chennai 600005 India

This research is one of the first to predict the academic performance of middle- and high-school students using machine learning algorithms (MLAs) based on numerous socio-demographic (such as age, gender, obesity, average household income, family size, and marital status of parents), school-related (type of gender education and academic level), and student-related (stress and lifestyle) variables. The Grade Point Average (GPA), which is a reflection of academic performance, is considered to be the model output. Five different MLAs are considered to identify and rank the parameters affecting academic performance: multinomial logistic regression, artificial neural network, random forest, gradient boosting and stacking methods. To evaluate the performance of the MLAs, three metrics are utilized: precision, recall, and F1-score. It is observed that the gradient boosting method outperformed the other techniques by generating superior results, followed by random forest. From the model analysis, it is concluded that a health-conscious lifestyle positively correlates to academic performance, whereas the existence of stress has a negative impact. However, gender is not found to be a significant predictor of a student's academic performance. © 2022 The Authors

关键词： Academic performance machine learning algorithms School-related factors Socio-demographic variables Student-related features

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Alzheimer Disease Prediction using machine learning algorithms 6

Alzheimer Disease Prediction using Machine Learning Algorith...

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6th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Neelaveni, J. Devasana, M. S. Geetha Sri Ramakrishna Engn Coll Comp Sci & Engn Dept Coimbatore 641022 Tamil Nadu India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151977;9781728151960

Alzheimer disease is the one amongst neurodegenerative disorders. Though the symptoms are benign initially, they become more severe over time. Alzheimer's disease is a prevalent sort of dementia. This disease is challenging one because there is no treatment for the disease. Diagnosis of the disease is done but that too at the later stage only. Thus if the disease is predicted earlier, the progression or the symptoms of the disease can be slow down. This paper uses machine learning algorithms to predict the Alzheimer disease using psychological parameters like age, number of visit, MMSE and education.

关键词： Aizheimer disease mild cognitive impairment machine learning algorithms psychological parameters

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Evaluation of machine learning algorithms for Automated Management of Wireless Links 1

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21st International Conference on Intelligent Data Engineering and Automated learning

作者： Frias, Francisca Marcal, Andre R. S. Prior, Rui Moreira, Waldir Oliveira, Antonio, Jr. Fraunhofer Portugal AICOS Porto Portugal Univ Porto Fac Ciencias Porto Portugal Inst Telecomunicacoes Porto Portugal Univ Fed Goias Inst Informat Goiania Go Brazil

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

machine learning, a subfield of artificial intelligence, has been widely used to automate tasks usually performed by humans. Some applications of these techniques are understanding network traffic behavior, predicting it, classifying it, fixing its faults, identifying malware applications, and preventing deliberate attacks. The goal of this work is to use machine learning algorithms to classify, in separate procedures, the errors of the network, their causes, and possible fixes. Our application case considers the WiBACK wireless system, from which we also obtained the data logs used to produce this paper. WiBACK is a collection of software and hardware with auto-configuration and self-management capabilities, designed to reduce CAPEX and OPEX costs. A principal components analysis is performed, followed by the application of decision trees, k nearest neighbors, and support vector machines. A comparison between the results obtained by the algorithms trained with the original data sets, balanced data sets, and the principal components data is performed. We achieve weighted F1-score between 0.93 and 0.99 with the balanced data, 0.88 and 0.96 with the original unbalanced data, and 0.81 and 0.89 with the Principal Components Analysis.

关键词： machine learning algorithms Principal components analysis Traffic classification Wireless links management

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Assessment of machine learning algorithms for Near-Sensor Computing under Radiation Soft Errors 27

Assessment of Machine Learning Algorithms for Near-Sensor Co...

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27th IEEE International Conference on Electronics, Circuits and Systems (IEEE ICECS)

作者： Trindade, M. Garay Garibotti, R. Ost, L. Letiche, M. Beaucour, J. Bastos, R. Possamai Univ Grenoble Alpes CNRS Grenoble INP TIMAInst Engn F-38000 Grenoble France Pontificia Univ Catolica Rio Grande do Sul Sch Technol BR-90619900 Porto Alegre RS Brazil Loughborough Univ Loughborough LE11 3TU Leics England Inst Laue Langevin F-38042 Grenoble France

ISBN: (纸本)9781728160443

machine learning (ML) algorithms have been regaining momentum thanks to their ability to analyze substantial and complex data, supporting artificial intelligence decisions in cloud computing but also in near-sensor computing in end-point devices. Both cloud and near-sensor computing are liable to radiation-induced soft errors, especially in automotive and aerospace safety-critical applications. In this regard, this paper contributes by comparing the accuracy of two prominent machine learning algorithms running on a low-power processor upset by radiation-induced soft errors. Both ML algorithms have been assessed with the help of a fault injection-based method able to natively emulate soft errors directly in a development board. In addition, neutron radiation test results suggest the most critical situations in which mitigation solutions should address.

关键词： Radiation-induced soft errors machine learning algorithms low-power processors

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Sarcasm detection using machine learning algorithms in Twitter: A systematic review

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INTERNATIONAL JOURNAL OF MARKET RESEARCH 2020年第5期62卷 578-598页

作者： Sarsam, Samer Muthana Al-Samarraie, Hosam Alzahrani, Ahmed Ibrahim Wright, Bianca Univ Sains Malaysia Sch Commun Gelugor 11800 Penang Malaysia Coventry Univ Sch Media & Performing Arts Coventry W Midlands England King Saud Univ Dept Comp Sci Riyadh Saudi Arabia

Recognizing both literal and figurative meanings is crucial to understanding users' opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted machine learning algorithms (AMLA) and Customized machine learning algorithms (CMLA). The review results revealed that Support Vector machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.

关键词： machine learning algorithms sarcasm detection trolling twitter

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Application of machine learning algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj

Application of Machine Learning Algorithms for Patient Lengt...

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Hijry, Hassan Olawoyin, Richard Oakland Univ Ind & Syst Engn Dept Rochester MI 48063 USA

ISBN: (纸本)9781728162867

Hospital emergency departments (EDs) in vital locations face high patient demand during peak events such as the annual Islamic pilgrimage (the Hajj event) in Mecca, Saudi Arabia, the New Year celebration ceremony in New York, and the World Cup, etc. Variable patient arrival rates and hospital conditions, particularly the availability of beds for inpatients, impacts long waiting times and length of stay (LOS), causing pain and dissatisfaction to patients. Patient length of stay is chosen to be a measure of ED overcrowding as a compliance measure set by most hospitals. Clinicians need to get an opportunity to be proactive in ED overcrowding crises, specifically in the case of peak days. For this purpose, the research aims to build a model to forecast Hajj patient LOS, using machine learning algorithms through predictive input factors such as patient age, mode of arrival, and patient's type of condition in the ED. Therefore, using machine learning algorithms, such as artificial neural networks, linear and logistic regressions, to forecast ED LOS allows clinicians to prepare for high levels of congestion and provide insights to determine the LOS of patients during vital times.

关键词： machine learning algorithms Overcrowding Length of Stay Emergency Department Hajj event

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Assessing the democratic legitimacy of public decisions based on machine learning algorithms 7

Assessing the democratic legitimacy of public decisions base...

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7th Swiss Conference on Data Science (SDS)

作者： Jimenez, Maria Carolina Univ Geneva Dept Polit Sci Geneva Switzerland

ISBN: (纸本)9781728171777

This paper explores how issues around the opacity and fairness of machine learning algorithms may affect the legitimacy of their use in the context of public decision-making. It identifies shortcomings in current research attempting to ground the justification of the decisions of machine learning algorithms on the general design of such models, as well as limitations of current research on algorithmic fairness. The paper concludes that even if challenges related to opacity and fairness cannot be completely overcome through technical means, the legitimacy of machine learning algorithms and their decisions can be enhanced through political processes based on the deliberative democratic paradigm.

关键词： machine learning algorithms opacity fairness legitimacy democracy public decision-making

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Intrusion Detection Study of Nuclear Power Plant Electronic Systems Based on machine learning Optimization algorithms 3

Intrusion Detection Study of Nuclear Power Plant Electronic ...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Zhang, Xiaoyan School of Management Shanghai University Shanghai China

ISBN: (纸本)9798350359961

In modern society, the rapid development of information technology makes a variety of electronic devices are widely used, and the electronic systems in these devices are often the key to the control system, and their stability and security are crucial to the operation of the whole system. This paper studies and compares the prediction ability of various machine learning algorithm models in the intrusion detection of electronic systems in nuclear power plants. The results show that the decision tree model and random forest model have the best prediction ability, which is far better than the other models. The linear regression model and KNN model have poor prediction ability, while the BP neural network model has poor prediction effect and is not suitable for intrusion detection of nuclear power plant electronic system. Intrusion detection in electronic systems of nuclear power plants is an important security problem, so it is important to study the prediction ability of different machine learning models for this problem. In this paper, through the experimental comparison of multiple machine learning models, we found that the decision tree model and random forest model have the best prediction ability, which indicates that these two models have high prediction ability and accuracy in nuclear power plant electronic system intrusion detection. The linear regression model and KNN model have poorer predictive ability, which may be due to the inability of these two models to capture the complex relationships in the intrusion detection of nuclear power plant electronic systems. And the BP neural network model has poor predictive effect, which may be due to the fact that the model is prone to overfitting phenomenon, resulting in inaccurate prediction results. The findings of this paper show that these conclusions provide valuable references for intrusion detection in electronic systems of nuclear power plants. © 2023 IEEE.

关键词： Decision Tree machine learning algorithms Random Forest

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