Computational Finance

Yingsaeree, Chaiyakorn; Nuti, Giuseppe; Treleaven, Philip

doi:10.1109/mc.2010.343

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…In order to be concrete as regards the methodology, the EEFE Platform & Infrastructure will take the advantage of the embryonic version developed at the UCL centre platform and infrastructure. The Algorithmic Trading & Risk Analytics Development Environment (ATRADE) developed at UCL is a 'financial wind tunnel', originally developed for algorithmic trading but now used for risk: systematic, institutional and high-frequency (HFT) [89,90]. The ATRADE platform was initially developed to speed up the development of our algorithmic trading systems.…”

Section: Methodsmentioning

confidence: 99%

An economic and financial exploratory

Cincotti

Sornette

Treleaven

et al. 2012

Eur. Phys. J. Spec. Top.

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An economic and financial exploratoryCincotti, S.; Sornette, D.; Treleaven, P.; Battiston, S.; Caldarelli, G.; Hommes, C.H.; Kirman, A. Published in:The European Physical Journal -Special Topics DOI:10.1140/epjst/e2012-01699-6Link to publication Citation for published version (APA):Cincotti, S., Sornette, D., Treleaven, P., Battiston, S., Caldarelli, G., Hommes, C. H., & Kirman, A. (2012). An economic and financial exploratory. The European Physical Journal -Special Topics, 214(1), 361-400. DOI: 10.1140/epjst/e2012-01699-6 General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract. This paper describes the vision of a European Exploratory for economics and finance using an interdisciplinary consortium of economists, natural scientists, computer scientists and engineers, who will combine their expertise to address the enormous challenges of the 21st century. This Academic Public facility is intended for economic modelling, investigating all aspects of risk and stability, improving financial technology, and evaluating proposed regulatory and taxation changes. The European Exploratory for economics and finance will be constituted as a network of infrastructure, observatories, data repositories, services and facilities and will foster the creation of a new cross-disciplinary research community of social scientists, complexity scientists and computing (ICT) scientists to collaborate in investigating major issues in economics and finance. It is also considered a cradle for training and collaboration with the private sector to spur spin-offs and job creations in Europe in the finance and economic sectors. The Exploratory will allow Social Scientists and Regulators as well as Policy Makers and the private sector to conduct realistic investigations with real economic, financial and social data. The Exploratory will (i)

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Section: Methodsmentioning

confidence: 99%

An economic and financial exploratory

Cincotti

Sornette

Treleaven

et al. 2012

Eur. Phys. J. Spec. Top.

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“…With the increasing availability of financial data, a significant amount of computing power is devoted to financial forecasting [7]. Financial forecasting seeks to predict future values of financial variables with the aim of supporting investment and trading decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Financial forecasting seeks to predict future values of financial variables with the aim of supporting investment and trading decisions. A forecasting problem can be formulated using either a regression or classification method [7]. Regression-based forecasting techniques, such as computational statistics, model and analyze a relation between a dependent variable and one or more independent variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge-guided genetic algorithm for financial forecasting

Rada

2012

2012 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Machine learning algorithms, such as the genetic algorithm, have often been applied to financial problems, but not enough is known about how to systematically incorporate financial knowledge into these generic learning algorithms. The general hypothesis of this paper is that semantic similarity among financial concepts can be exploited in a hybrid genetic algorithm. A Knowledge-guided Genetic Algorithm for Forecasting is introduced to predict the values of financial statement variables. The mutation operation is guided by domain knowledge to make small or large changes in an organism. The algorithm makes a bigger (or smaller) change in the organism when the variables being forecast have higher (or lower) variability. The specific hypothesis is that the use of problem-specific knowledge improves the prediction accuracy. The experimental results show that the use of domain knowledge improves the performance of the algorithm. The knowledge used in this experiment would reasonably be extended in various ways to be used by a refined genetic algorithm.

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“…K-SVD and OMP are a set of computer algorithms operating with finite-precision floating-point arithmetic that employ numerical analysis and linear algebra to obtain approximate answers to questions in mathematics of continuous variables [5]. Such algorithms are generally referred to as solvers and are key to solving engineering and computational science problems such as image and signal processing [6], data mining [7], bioinformatics [8], computational finance [9] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Linear Neural Network as a Fast Solver for Dictionary Learning

Sathish¹,

Sheet²,

Khare³

2023

Preprint

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<p>Traditional numerical solvers for linear algebra problems such as dictionary learning (DictL) operating on large data face challenges because of super-linear computational complexity and massive memory requirement. This does not make it easily solvable on commonly available computers. A linear neural network (NN) is proposed to be employed as an alternate solver for such problems. Specifically, we demonstrate a linear fully-connected (FC) NN-based solver for DictL. It is employed to learn the dictionary atoms instead of the classically used k-singular value decomposition (K-SVD), while the sparse coefficients are learned using the classical orthogonal matching pursuit (OMP) approach. We compare the computational complexity of the classical vs our proposed FCNN-based approach implemented on both CPU and GPU using synthetically generated datasets with varying sizes. Further, we demonstrate practical utility in image denoising with DictL while comparing the equivalence of solution obtained from our FCNN-based solver with the traditional approach with K-SVD. We achieve a notable speedup compared to the traditional technique on both CPU and GPU implementations.</p>

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Computational Finance

Cited by 10 publications

References 7 publications

An economic and financial exploratory

An economic and financial exploratory

Knowledge-guided genetic algorithm for financial forecasting

Linear Neural Network as a Fast Solver for Dictionary Learning

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