scholarly journals Deep learning for decision making and the optimization of socially responsible investments and portfolio

2019 ◽  
Vol 124 ◽  
pp. 113097 ◽  
Author(s):  
Nhi N.Y. Vo ◽  
Xuezhong He ◽  
Shaowu Liu ◽  
Guandong Xu
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Socially Responsible ◽  
Socially Responsible Investments

A Study on How Investors Decide on Socially Responsible Investments: Classification of Investment Behavior According to Companies’ Environmental Activities

2014 ◽  
Vol 8 (5) ◽  
pp. 677-687
Author(s):  
Nariaki Nishino ◽  
◽  
Kaoru Kihara ◽  
Kenju Akai ◽  
Tomonori Honda ◽  
...  
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Investment Decision ◽  
Sustainable Production ◽  
Socially Responsible ◽  
Responsible Investment ◽  
Investment Decision Making ◽  
Socially Responsible Investments ◽  
People’S Attitudes

Environmental problems must be solved urgently, and sustainable production activities are desired. This study focuses on environmental finance, which is a method of promoting sustainable corporation activities. Environmental finance allows socially responsible investment to directly contribute to corporate activities and sustainable production activities. To clarify the mechanism of eco-friendly investment decision making, 4,843 respondents took a questionnaire survey on investment decision making, based on the framework of prospect theory. The results showed that prospect theory did not always work for environment issues and that people’s attitudes when they decide on eco-friendly investments could be classified to four clusters.

scholarly journals Socially responsible investments

2021 ◽  
pp. 91-106
Author(s):  
Marzena Remlein
Keyword(s):  
Decision Making ◽  
Investment Strategy ◽  
The Other ◽  
Financial Resources ◽  
Socially Responsible ◽  
Responsible Investing ◽  
Socially Responsible Investments ◽  
The World ◽  
Sustainable Investment ◽  
The Right

Socially responsible investing (SRI) is a decision making process concerning the allocation of free financial resources, where the investor aims at maximization of profit and minimization of risk on one part, and includes the socio-ethical and environmental-ecological considerations on the other. We can find four types of motives, describing them as mobilizing forces to undertake SRI. These are psychological and social, legal, economic and strategic, financial. Investors invest their funds in such investments by choosing the right investment strategy for them. We can find many different classifications relating to strategies and actions within the framework of SRI. The most important classifications of the SRI strategy were prepared by Global Sustainable Investment Review and Eurosif. These two organizations prepare also reports on SRI in the world and in Europe. The European market has the largest share in the global SRI market but the most dynamically developing market is Japan.

scholarly journals Are People Altruistic When Making Socially Responsible Investments? Evidence From a tDCS Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaolan Yang ◽  
Wenting Meng ◽  
Shu Chen ◽  
Mei Gao ◽  
Jian Zhang
Keyword(s):  
Decision Making ◽  
Socially Responsible ◽  
Responsible Investment ◽  
Temporoparietal Junction ◽  
Charitable Donations ◽  
Anodal Stimulation ◽  
Socially Responsible Investments ◽  
The Right ◽  
Cathodal Stimulation

Socially responsible investment (SRI) is an emerging philosophy that integrates social and environmental impacts into investment considerations, and it has gradually developed into an important form of investment. Previous studies have shown that both financial and non-financial motivations account for SRI behaviors, but it is unclear whether the non-financial motive to adopt SRI derives from investors’ altruism. This study uses neuroscientific techniques to explore the role of altruism in SRI decision-making. Given that existing evidence has supported the involvement of the right temporoparietal junction (rTPJ) in altruism and altruistic behaviors, we used transcranial direct current stimulation (tDCS) to temporarily modulate activity in the rTPJ and tested its effect on charitable donations and SRI behaviors. We found that anodal stimulation increased the subjects’ donations, while cathodal stimulation decreased them, suggesting that tDCS changed the subjects’ levels of altruism. More importantly, anodal stimulation enhanced the subjects’ willingness to make SRIs, while cathodal stimulation did not have a significant impact. These findings indicate that altruism plays an important role in SRI decision-making. Furthermore, cathodal stimulation changed the subjects’ perceived effectiveness of charitable donation but not that of socially responsible fund. This result may help explain the inconsistent effects of cathodal stimulation on charitable donations and SRI behaviors. The main contribution of our study lies in its pioneering application of tDCS to conduct research on SRI behaviors and provision of neuroscientific evidence regarding the role of altruism in SRI decision-making.

scholarly journals Modeling Psychology in Islamic Wealth Management

2017 ◽  
Vol 9 (10) ◽  
pp. 64
Author(s):  
Ahmed Hamed Al-Abbadi ◽  
Adam Abdullah
Keyword(s):  
Decision Making ◽  
Behavioral Finance ◽  
Psychological Factors ◽  
Individual Investors ◽  
Financial Advisors ◽  
Socially Responsible ◽  
Client Experience ◽  
Wealth Management ◽  
Socially Responsible Investments ◽  
Ultimate Objective

This paper proposes a conceptual framework for modeling psychology in Islamic wealth management. Incorporating psychology into finance would significantly contribute to our understanding of the behavior of individual investors as well as market behavior. Utilizing the findings of behavioral finance and financial therapy, along with industry megatrends, Islamic wealth management could step further in fulfilling its ultimate objective of promoting social welfare. This is can be achieved by exploring and identifying the psychological factors that affect the clients’ decision-making and then behaviorally and cognitively helping them to engage in socially-responsible investments, projects and initiatives. In operationalizing this model, financial advisors/wealth managers should adopt a comprehensive client discovery and profiling method and apply Fintech innovations in producing complex analytics and thereby enriching the client experience.

scholarly journals Inertia and discounting in the selection of socially responsible investments

2016 ◽  
Vol 2 (1) ◽  
pp. 29-47 ◽  
Author(s):  
Pat Auger ◽  
Timothy Devinney ◽  
Grahame Dowling ◽  
Christine Eckert
Keyword(s):  
Decision Making ◽  
Experimental Approach ◽  
Socially Responsible ◽  
Content Type ◽  
Responsible Investment ◽  
Individual Level ◽  
Risk Return ◽  
Socially Responsible Investments ◽  
Developed World ◽  
Selection Of

Purpose Socially responsible investment (SRI) funds have grown dramatically as an investment alternative in most of the developed world. The paper aims to discuss this issue. Design/methodology/approach This study uses a structured experimental approach to determine if the decision-making process of investors to invest in SRIs is consistent with the process used for conventional investments. The theoretical framework draws on two widely studied concepts in the decision making and investment literature, namely, inertia and discounting. Findings The authors find that inertia plays a significant role in the selection of SRI funds and that investors systemically discount the value of SRIs. Research limitations/implications The results suggest that SRIs need to be designed to cater to the risk/return profiles of investors and that these investors need to be better informed about the performance of SRIs vs conventional investments to reduce their systematic discounting. Originality/value Unique experimental approach applied to investment alternatives in a manner that captures individual level variation.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1052
Author(s):  
Leang Sim Nguon ◽  
Kangwon Seo ◽  
Jung-Hyun Lim ◽  
Tae-Jun Song ◽  
Sung-Hyun Cho ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Network Model ◽  
Endoscopic Ultrasonography ◽  
Data Augmentation ◽  
Clinical Information ◽  
Training Data ◽  
Fine Tuning ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

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