scholarly journals Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market

2019 ◽  
Vol 5 (4) ◽  
pp. 81 ◽  
Author(s):  
Mariano Méndez-Suárez ◽  
Francisco García-Fernández ◽  
Fernando Gallardo
Keyword(s):  
Artificial Intelligence ◽  
Data Driven ◽  
Investment Strategies ◽  
Network Modelling ◽  
Modelling Framework ◽  
Out Of Sample ◽  
Active Data ◽  
Input Variables ◽  
Using Data ◽  
Trading Simulation

Financial innovation by means of Fintech firms is one of the more disruptive business model innovations from the latest years. Specifically, in the financial advisor sector, worldwide assets under management of artificial intelligence (AI)-based investment firms, or robo-advisors, currently amount to US$975.5 B. Since 2008, robo-advisors have evolved from passive advising to active data-driven investment management, requiring AI models capable of predicting financial asset prices on time to switch positions. In this research, an artificial neural network modelling framework is specifically designed to be used as an active data-driven robo-advisor due to its ability to forecast with today’s copper prices five days ahead of changes in prices using input data that can be fed automatically in the model. The model, tested using data of the two periods with a higher volatility of the returns of the recent history of copper prices (May 2006 to September 2008 and September 2008 to September 2010) showed that the method is capable of predicting in-sample and out-of-sample prices and consequently changes in prices with high levels of accuracy. Additionally, with a 24-day window of out-of-sample data, a trading simulation exercise was performed, consisting of staying long if the model predicts a rise in price or switching to a short position if the model predicts a decrease in price, and comparing the results with the passive strategies, buy and hold or sell and hold. The results obtained seem promising in terms of both statistical and trading metrics. Our contribution is twofold: 1) we propose a set of input variables based on financial theory that can be collected and fed automatically by the algorithm. 2) We generate predictions five days in advance that can be used to reposition the portfolio in active investment strategies.

scholarly journals Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework

2013 ◽  
Vol 17 (7) ◽  
pp. 2827-2843 ◽  
Author(s):  
N. J. Mount ◽  
C. W. Dawson ◽  
R. J. Abrahart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Goodness Of Fit ◽  
Relative Sensitivity ◽  
Data Driven ◽  
Network Modelling ◽  
Core Element ◽  
Mechanistic Modelling ◽  
Modelling Framework ◽  
Artificial Neural

Abstract. In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-box characteristics, which prohibit adequate understanding of their mechanistic behaviour and restrict their wider heuristic value. In response, presented here is a new generic data-driven mechanistic modelling framework. The framework is significant because it incorporates an evaluation of the legitimacy of a data-driven model's internal modelling mechanism as a core element in the modelling process. The framework's value is demonstrated by two simple artificial neural network river forecasting scenarios. We develop a novel adaptation of first-order partial derivative, relative sensitivity analysis to enable each model's mechanistic legitimacy to be evaluated within the framework. The results demonstrate the limitations of standard, goodness-of-fit validation procedures by highlighting how the internal mechanisms of complex models that produce the best fit scores can have lower mechanistic legitimacy than simpler counterparts whose scores are only slightly inferior. Thus, our study directly tackles one of the key debates in data-driven, hydrological modelling: is it acceptable for our ends (i.e. model fit) to justify our means (i.e. the numerical basis by which that fit is achieved)?

scholarly journals MACHINE LEARNING: AN OVERVIEW

2021 ◽  
Vol 9 (7) ◽  
pp. 343-348
Author(s):  
Adya Trisal ◽  
Dheeraj Mandloi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Driven ◽  
Future Prospects ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Machine Learning Methods ◽  
Using Data ◽  
Computer Power

Given the tremendous availability of data and computer power, there is a resurgence of interest in using data driven machine learning methods to solve issues where traditional engineering solutions are hampered by modeling or algorithmic flaws. The purpose of this      article is to provide a comprehensive review of machine learning, including its history, types, applications, limitations and future prospects. In addition to this, the article also discusses the main point of difference between the field of artificial intelligence and machine learning.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

The Oxford Handbook of Ethics of AI

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Rapid Development ◽  
Data Driven ◽  
International Human Rights Law ◽  
Human Knowledge ◽  
Human Rights Law ◽  
Ai Ethics ◽  
And Behavior ◽  
Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
Using Data

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

2021 ◽  
Author(s):  
Marina Johnson ◽  
Rashmi Jain ◽  
Peggy Brennan-Tonetta ◽  
Ethne Swartz ◽  
Deborah Silver ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Driven
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