Applying Artificial Intelligence to Financial Investing

2019 ◽  
pp. 1-16
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence, this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this chapter summarizes key contributions of applying AI to financial investing as appears in the academic literature.

Applying Artificial Intelligence to Financial Investing

2018 ◽  
pp. 1-14
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

Artificial Intelligence and Investing

2011 ◽  
pp. 237-240 ◽  
Author(s):  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Asset Valuation ◽  
Stock Valuation ◽  
Knowledge Based ◽  
Processing Techniques

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.

A KNOWLEDGE-BASED APPROACH TO COOPERATIVE RELATIONAL DATABASE QUERYING

2000 ◽  
Vol 14 (01) ◽  
pp. 73-90 ◽  
Author(s):  
JOSÉ LUÍS BRAGA ◽  
ALBERTO H. F. LAENDER ◽  
CLAUDINEY VANDER RAMOS
Keyword(s):  
Artificial Intelligence ◽  
Relational Database ◽  
Knowledge Bases ◽  
Application Domain ◽  
Query Formulation ◽  
Database Querying ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based

We present in this paper an approach to providing cooperativeness in database querying using artificial intelligence techniques. The main focus is a cooperative interface that assists nonexperienced and casual users in extracting useful answers from a relational database. Our approach relies on an architecture that comprises two knowledge bases which store rules that describe the application domain and guide the process of query formulation and answering. A subset of SQL is used for expressing queries, and the cooperative interface relieves the user from knowing its full syntax and the structure of the database.

scholarly journals Role of Artificial Intelligence in Medicine and Clinical Research

2021 ◽  
Vol 9 (8) ◽  
pp. 1512-1518
Author(s):  
Jhumpa Sarma
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Clinical Settings ◽  
Business Sector ◽  
Process Automation ◽  
Artificial Intelligence Techniques ◽  
Fuzzy Expert Systems ◽  
Artificial Neural ◽  
Time Required

Abstract: Artificial Intelligence is a branch of computer science that enables to analyse complex medical data. The proficiency of artificial intelligence techniques has been explored to a great extent in the field of medicine. Most of the medications go to the business sector after a long tedious process of drug development. It can take a period of 10-15 years or more to convey a medication from its introductory revelation to the hands of the patients. Artificial Intelligence can significantly reduce the time required and can also cut down the expenses by half. Among the methods, artificial neural network is the most widely used analytical tool while other techniques like fuzzy expert systems, natural language processing, robotic process automation and evolutionary computation have been used in different clinical settings. The aim of this paper is to discuss the different artificial intelligence techniques and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on medical education, physicians, healthcare institutions and bioethics. Keywords: Artificial intelligence, clinical trials, medical technologies, artificial neural networks, diagnosis.

scholarly journals Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

2021 ◽  
Vol 7 ◽  
pp. e488
Author(s):  
Amir Masoud Rahmani ◽  
Elham Azhir ◽  
Saqib Ali ◽  
Mokhtar Mohammadi ◽  
Omed Hassan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimization Theory ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based ◽  
Complete Survey ◽  
The Internet Of Things

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

Research on Ontology Knowledge Based System of Meteorological and Hydrological Support

2013 ◽  
Vol 373-375 ◽  
pp. 1027-1030
Author(s):  
Wan Li ◽  
Bi Hua Zhou ◽  
Qi Zhang ◽  
Ya Peng Fu ◽  
Tao Wang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Sharing ◽  
Knowledge Base ◽  
Prototype System ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based ◽  
Class Testing ◽  
Ontology Language ◽  
Testing Algorithm ◽  
Positioning Algorithm

According to knowledge sharing&reusing problem and system extensibility&portability problem in meteorological and hydrological support, the knowledge concepts, attributes, instances, hierarchy and relationships were clarified; the ontological knowledge base was established by OWL ontology language and SWRL rule language. With the help of Racer, Protégé, Jess, and class positioning algorithm, class testing algorithm, rule testing algorithm, the meteorological and hydrological support prototype system was implemented by Eclipse, ProtegeInEclipse plug-in and Jena. The system includes the server, the client and the processing controller. It provides artificial intelligence techniques means for knowledge maintenance and usage in meteorological and hydrological field.

scholarly journals Challenges of Financial Risk Management: AI Applications

2021 ◽  
Author(s):  
Vesna Bogojevic Arsic
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Credit Risk ◽  
Financial Risk ◽  
Market Risk ◽  
Credit Risk Management ◽  
Financial Industry ◽  
Financial Risk Management ◽  
Artificial Intelligence Techniques ◽  
Financial Technology

Research Question: This paper reviews different artificial intelligence (AI) techniques application in financial risk management. Motivation: Financial technology has significantly changed the business operations which required transformation of financial industry. The financial risk management needs to be restructured because the methods that have been used in the past became low effective. The artificial intelligence techniques proved their efficiency and contributed to fast, low–cost and improved financial risk management in both financial institutions and companies. Idea: The aim of this paper is to present a state of AI techniques application in financial risk management, as well as to point out the direction in which further application and development could be expected. Data: The analysis was conducted by reviewing various papers, books and reports on AI applications in financial risk management. Tools: The relevant literature systematization was used to provide answers to the question to what extent AI techniques (especially machine learning) could be used in managing financial risk management. Findings: Artificial intelligence largely improved the market risk and credit risk management through data preparation, modelling risk, stress testing and model validation. Artificial intelligence techniques can be useful in data quality assurance, text-mining for data augmentation and fraud detection. The financial technology will continue to affect the financial sector through requiring the adaption to new environment and new business models. Because of that, it could be expected that artificial intelligence will become part of the financial risk management framework. Contribution: This paper provides a review of artificial intelligence applications in market risk management, credit risk management and operational risk management. The paper identified the key AI techniques that could be used for financial risk management improvement because of financial industry transformation.

Sign in / Sign up

Export Citation Format

Share Document