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MCA
Special Issues
Intelligent Computation and Its Applications in Financial Technology
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Intelligent Computation and Its Applications in Financial Technology

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Social Sciences".

Deadline for manuscript submissions: closed (15 August 2020) | Viewed by 14199

Special Issue Editors

Prof. Dr. Fei Ma

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Guest Editor
Applied Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, China
Interests: patten recognition; quantative finance
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Niu

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Guest Editor
Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: large sparse matrix eigenvalue problems; high performance preconditioning techniques for large system of linear equations; iterative methods; information retrieval and image processing
Prof. Dr. Conghua Wen

E-Mail Website
Guest Editor
School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
Interests: stochastic process; numerical solutions on stochastic models; model estimation and model selection
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Zong

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Guest Editor
Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: mathematical modeling of financial derivatives; financial problems; quantitative methods
Prof. Dr. Gemai Chen

E-Mail Website
Guest Editor
1. Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
2. Department of Mathematics and Statistics, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
Interests: quality control; parametric and non-parametric regression; oil and gas discovery process and natural resource assessment; monitoring the irregularity of high-speed train tracks

Special Issue Information

Dear Colleagues,

Intelligent computation is an emerging cross-disciplinary field that enables machines to solve complex real-life problems by learning from data. In the combination of mathematical/computational models, data science, optimization, and algorithms, intelligent computation has become one of the most rapidly developing areas with varied applications in both science and society. Due to the industrial permeation of computational intelligence, the finance sector—a key unit in the global economy—is experiencing a transformation with the rise of financial technologies (or Fintech) that are being developed to compete with the operation of traditional financial services and decision-making processes.

This Special Issue targets the dissemination of the most recent advances in the field of intelligent computation with an emphasis on its applications in financial technology. We welcome full-length original research papers as well as review articles and short reports on new methodologies/applications. The main topics of this Special Issue include but are not limited to:

  • Scientific computing and applications;
  • Soft computing and applications;
  • Optimization algorithms;
  • Artificial-intelligence-oriented financial/social applications;
  • Advanced financial models;
  • Big data and knowledge engineering;
  • Medical image processing ;
  • Natural language processing in science and social science.

Dr. Fei Ma
Dr. Qiang Niu
Dr. Conghua Wen
Dr. Lu Zong
Prof. Dr. Gemai Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematical and Computational Applications is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent computation
  • financial technology
  • scientific computing
  • big data
  • artificial intelligence

Published Papers (3 papers)

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Research

30 pages, 656 KiB  
Article
Arbitrage Bounds on Currency Basket Options
by Yi Hong
Math. Comput. Appl. 2020, 25(3), 60; https://doi.org/10.3390/mca25030060 - 17 Sep 2020
Viewed by 2156
Abstract
This article exploits arbitrage valuation bounds on currency basket options. Instead of using a sophisticated model to price these options, we consider a set of pricing models that are consistent with the prices of available hedging assets. In the absence of arbitrage, we [...] Read more.
This article exploits arbitrage valuation bounds on currency basket options. Instead of using a sophisticated model to price these options, we consider a set of pricing models that are consistent with the prices of available hedging assets. In the absence of arbitrage, we identify valuation bounds on currency basket options without model specifications. Our results extend the work in the literature by seeking tight arbitrage valuation bounds on these options. Specifically, the valuation bounds are enforced by static portfolios that consist of both cross-currency options and individual options denominated in the numeraire currency. Full article
(This article belongs to the Special Issue Intelligent Computation and Its Applications in Financial Technology)
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20 pages, 1510 KiB  
Article
Volatility Forecasting Based on Cyclical Two-Component Model: Evidence from Chinese Futures Markets and Sector Stocks
by Conghua Wen and Junwei Wei
Math. Comput. Appl. 2020, 25(3), 59; https://doi.org/10.3390/mca25030059 - 10 Sep 2020
Viewed by 1891
Abstract
This article aims to study the schemes of forecasting the volatilities of Chinese futures markets and sector stocks. An improved method based on the cyclical two-component model (CTCM) introduced by Harris et al. in 2011 is provided. The performance of CTCM is compared [...] Read more.
This article aims to study the schemes of forecasting the volatilities of Chinese futures markets and sector stocks. An improved method based on the cyclical two-component model (CTCM) introduced by Harris et al. in 2011 is provided. The performance of CTCM is compared with the benchmark model: Heterogeneous Autoregressive model of Realized Volatility type (HAR-RV type). The impact of open interest for futures market is included in HAR-RV type model. We employ 3 different evaluation rules to determine the most efficient models when the results of different evaluation rules are inconsistent. The empirical results show that CTCM is more accurate than HAR-RV type in both estimation and forecasting. The results also show that the realized range-based tripower volatility (RTV) is the most efficient estimator for both Chinese futures markets and sector stocks. Full article
(This article belongs to the Special Issue Intelligent Computation and Its Applications in Financial Technology)
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16 pages, 760 KiB  
Article
The Application of Stock Index Price Prediction with Neural Network
by Penglei Gao, Rui Zhang and Xi Yang
Math. Comput. Appl. 2020, 25(3), 53; https://doi.org/10.3390/mca25030053 - 18 Aug 2020
Cited by 38 | Viewed by 9768
Abstract
Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we [...] Read more.
Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones. Full article
(This article belongs to the Special Issue Intelligent Computation and Its Applications in Financial Technology)
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