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.

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.

scholarly journals What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

2019 ◽  
Vol 33 ◽  
pp. 6674-6681 ◽  
Author(s):  
Irene Li ◽  
Alexander R. Fabbri ◽  
Robert R. Tung ◽  
Dragomir R. Radev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Courses ◽  
Specific Topic ◽  
Reading List ◽  
University Courses

Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of “what should one learn first,”we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

scholarly journals Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 218-239 ◽  
Author(s):  
Ravikumar Patel ◽  
Kalpdrum Passi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
World Cup ◽  
Part Of Speech ◽  
Twitter Data ◽  
Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

scholarly journals Computerized Answer Grading

2021 ◽  
Vol 9 (VI) ◽  
pp. 618-619
Author(s):  
Anurag Langan
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Technology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grade Student ◽  
Processing Techniques

Grading student answers is a tedious and time-consuming task. A study had found that almost on average around 25% of a teacher's time is spent in scoring the answer sheets of students. This time could be utilized in much better ways if computer technology could be used to score answers. This system will aim to grade student answers using the various Natural Language processing techniques and Machine Learning algorithms available today.

scholarly journals Answer Script Evaluation using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 849-852
Author(s):  
Dr. K. Suresh
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Methods ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Extraction ◽  
Processing Techniques

The current way of checking answer scripts is hectic for the college. They need to manually check the answers and allocate the marks to the students. Our proposed system uses Machine Learning and Natural Language Processing techniques to beat this. Machine learning algorithms use computational methods to find out directly from data without hopping on predetermined rules. NLP algorithms identify specific entities within the text, explore for key elements during a document, run a contextual search for synonyms and detect misspelled words or similar entries, and more. Our algorithm performs similarity checking and also the number of words associated with the question exactly matched between two documents. It also checks whether the grammar is correctly used or not within the student's answer. Our proposed system performs text extraction and evaluation of marks by applying Machine Learning and Natural Language Processing techniques.

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