Investment Decision Support on Precious Metal Market with Use of Binary Representation

2019 ◽  
pp. 423-438
Author(s):  
Krzysztof Piasecki ◽  
Michał Dominik Stasiak ◽  
Żaneta Staszak
Keyword(s):  
Decision Support ◽  
Precious Metal ◽  
Investment Decision ◽  
Binary Representation ◽  
Metal Market

Building Investment Decision Support (BIDSTM) for green building technologies

2017 ◽  
pp. 108-132
Author(s):  
Vivian Loftness ◽  
Volker Hartkopf ◽  
Azizan Aziz ◽  
Megan Snyder ◽  
Joonho Choi ◽  
...  
Keyword(s):  
Decision Support ◽  
Investment Decision ◽  
Green Building

Decision Support System for Project Selection

2011 ◽  
pp. 209-226
Author(s):  
Prasanta Kumar Dey
Keyword(s):  
Decision Support ◽  
Environmental Impact ◽  
Decision Support System ◽  
Impact Assessment ◽  
Support System ◽  
Social Impact ◽  
Financial Analysis ◽  
Investment Decision ◽  
Multiple Attribute Decision Making ◽  
Analytic Hierarchy

The evaluation and selection of industrial projects before investment decision is customarily done using marketing, technical, and financial information. Subsequently, environmental impact assessment and social impact assessment are carried out mainly to satisfy the statutory agencies. Because of stricter environment regulations in developed and developing countries, quite often impact assessment suggests alternate sites, technologies, designs, and implementation methods as mitigating measures. This causes considerable delay to complete project feasibility analysis and selection as complete analysis requires to be taken up again and again until the statutory regulatory authority approves the project. Moreover, project analysis through the above process often results in suboptimal projects as financial analysis may eliminate better options as more environment friendly alternative will always be cost intensive. In this circumstance, this study proposes a decision support system which analyses projects with respect to market, technicalities, and social and environmental impact in an integrated framework using analytic hierarchy process, a multiple attribute decision-making technique. This not only reduces duration of project evaluation and selection, but also helps select an optimal project for the organization for sustainable development. The entire methodology has been applied to a cross-country oil pipeline project in India and its effectiveness has been demonstrated.

Influence Diagram for Investment Portfolio Selection

2011 ◽  
pp. 62-78 ◽  
Author(s):  
Chui-Che Tseng
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Portfolio Selection ◽  
Investment Decision ◽  
Influence Diagram ◽  
Intelligence System ◽  
News Releases ◽  
Expert Opinions ◽  
Investment Decision Making ◽  
Investment Returns

The goal of an artificial intelligence decision support system is to provide the human user with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is the investment domain—the goal of investment decision making is to select an optimal portfolio that satisfies the investor’s objective or, in other words, to maximize the investment returns under the constraints given by investors. The investment domain contains numerous and diverse information sources, such as expert opinions, news releases, economic figures and so on. This presents the potential for better decision support but also poses the challenge of building a decision support agent for selecting, accessing, filtering, evaluating and incorporating information from different sources, and for making final investment recommendations. In this study we use an artificial intelligence system called influence diagram for portfolio selection. We found that the system outperform human portfolio managers and the market in the year of 1998 to 2002.

scholarly journals Membangun Decision Support System Berbasis Financial Technology Dalam Berinvestasi Saham

2020 ◽  
Vol 11 (02) ◽  
Author(s):  
Lucky Kartanto
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Financial Institutions ◽  
Support System ◽  
Stock Exchange ◽  
Investment Decision ◽  
Minimal Risk ◽  
Stock Selection ◽  
Index Model ◽  
Financial Technology

At present, investment is well known in Indonesian society, investment awareness by the public has begun to increase along with the existence of several investment instruments that are widely offered by bank financial institutions, non-bank financial institutions, as well as various types of investment options on the Indonesia Stock Exchange. According to Sophar Lumbantoruan (1996), the notion of investment is equity participation in other companies. One form of investment known to the general public is shares traded on the Indonesia Stock Exchange. Investing always considers the results and risks that will be faced by Investors. Not all investors understand the theory of investing in stocks, especially in selecting shares in a portfolio in order to produce a certain rate of return with minimal risk. This study aims to find a decision support system (DSS) based on Financial Technology that will provide information related to stock recommendations that should be bought by investors. Stock Selection in this study are shares of listed companies listed on the Kompas 100 Index, the Analysis Technique used in this study is the Single Index Model. This research can produce recommendations for investors to buy shares in a portfolio that will provide certain benefits with minimal risk. Keyword- Investment, Decision Support System, Financial Technology, Single Index Model, Porfolio

scholarly journals Construction of Corporate Investment Decision Support Model Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jian-tao Song
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Prediction Model ◽  
Quality Evaluation ◽  
Investment Decision ◽  
Corporate Investment ◽  
Evaluation Index System ◽  
Decision Support Model ◽  
Model Based ◽  
Stock Quality

Aiming at the problem of corporate investment decision support, this paper proposes and constructs a stock quality evaluation model based on deep learning and applies it to the stock quality evaluation of e-commerce enterprises. Firstly, LSTM neural network is used to construct the evaluation and prediction model. Secondly, the evaluation index system is constructed. Finally, the structure and parameters of the model are designed, and the prediction model is tested and evaluated through simulation experiments. The experiments prove that the model is reasonable and feasible, which can provide a reference for investors to make decisions.

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