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

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.

scholarly journals Pengaruh Cluster Emiten terhadap Return Saham JSX Berbasis Parameter Rasio Analisa Fundamental

2021 ◽  
Vol 2 (4) ◽  
pp. 279-291
Author(s):  
Berlian Karlina ◽  
◽  
Ario Menak Sanoyo ◽  
Keyword(s):  
Cluster Analysis ◽  
Risk Management ◽  
Decision Support ◽  
Clustering Algorithm ◽  
Stock Exchange ◽  
Investment Decision ◽  
Central Area ◽  
Alternative Form ◽  
Stock Selection ◽  
Algorithmic Approaches

Abstract Purpose: This research aimed to find the effect of cluster techniques in determining stock selection to maximize return and minimize risk in the stock market. Research Methodology: The methodology consists of two of several algorithmic approaches of the clustering method to find hidden patterns in a group of datasets, i.e., Partitioning clusters (k-means) defined by the dataset object and its central area, and hierarchical clusters that group data through varying scales to be implemented into cluster trees or dendrogram. Dataset summary analysis of the fundamental ratio of stocks in the study was obtained from IDX stock data. Results: This study's classification has been obtained that consists of three zones: green, blue and red zone. The significance obtained provides an alternative form of stock categorization, creating an investment decision support system based on Cluster Analysis, the search for correlations and patterns between ratios of the Financial Statements as complementary tools of Investment Risk Management. Limitations: This research uses only two clustering algorithm methods to analyze the effect of clustering in maximizing return and minimizing risk and only used variables of financial reports for the company listed on the Indonesia Stock Exchange. Contribution: The risk management portfolio is a crucial part of being analyzed for investors and management to improve financial performance. As a complement to decision support, the risk management systems have to be analyzed based on cluster analysis and subsequent data mining to know the potential stock valuation in the market.

scholarly journals Credit Assessment in Determining The Feasibility of Debtors Using Profile Matching

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Financial Institutions ◽  
Support System ◽  
Customer Data ◽  
Profile Matching ◽  
System Method ◽  
History Of ◽  
Statistical Program ◽  
Application Data

Credit assessment is a method used by banks or other financial institutions that are useful to determine whether a prospective debtor is feasible or not get a loan. The way is to collect customer data taken from the application data customer lending other than by using a statistical program that contains a history of loan among other things on how the payment cycle is billing the customer, if the customer pays bills on time or not, how many credits are still in progress. This assessment helps the banks to analyze credit applications besides other qualitative factors. If the customer has a problem in the smooth payment, the information will be known by funders. Profile Matching is the decision support system method to rank the client feasibility. It can assess based on particular parameters given. There are several parameters to be considered. It helps banks or other financial agents to pass the client borrowing money.

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