Nature‐inspired soft computing for financial option pricing using high‐performance analytics

Thulasiram, Ruppa K.; Thulasiraman, Parimala; Prasain, Hari; Jha, Girish Kumar

doi:10.1002/cpe.3360

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…Other examples of quantitative modelling include: service architecture for capital market systems management (Rabhi & Benatallah, 2002); managing metadata in financial analytics software (Flood, 2009); identifying successful initial public offerings (Martens et al, 2011); high-frequency financial data mining (Sun & Meinl, 2012); identifying drivers of firm value (Kuzey, Uyar, & Delen, 2014); sentiment analysis for predicting economic variables (Levenberg, Pulman, Moilanen, Simpson, & Roberts, 2014); volatility of returns (Sun, Chen, & Yu, 2015); option pricing (Thulasiram, Thulasiraman, Prasain, & Jha, 2016;Xiao, Ma, Li, & Mukhopadhyay, 2016); and market basket analysis (Videla-Cavieres & Rios, 2014), which is the identification of sets of products or services that are sold together.…”

Section: Stock Market Prediction and Quantitative Modellingmentioning

confidence: 99%

Big data techniques in auditing research and practice: Current trends and future opportunities

Gepp¹,

Linnenluecke²,

O'Neill³

et al. 2018

Journal of Accounting Literature

227

150

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This paper analyzes the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

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Section: Stock Market Prediction and Quantitative Modellingmentioning

confidence: 99%

Big data techniques in auditing research and practice: Current trends and future opportunities

Gepp¹,

Linnenluecke²,

O'Neill³

et al. 2018

Journal of Accounting Literature

227

150

View full text Add to dashboard Cite

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“…In their experiments, they use both the DE and PSO metaheuristics. Finally, Sharma et al [2013] and Thulasiram et al [2014] use PSO to price options.…”

Section: Option Pricing Problemmentioning

confidence: 99%

“…In fact, most of the works use reasonably low computing times -in the order of seconds or a few minutes-, to solve instances of realistic size. Secondly, Sharma et al [2013] and Thulasiram et al [2014] already use modern graphical process units (GPUs), which benefit from massive multi-thread parallelization of a computational task. Indeed, these GPUs can help achieve significant reductions in the execution times, often to just a few seconds even for complex problems, such as option pricing.…”

Section: Computational Issuesmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

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“…We encourage the readers to review to gain insight into the breadth and depth of problems and innovative solutions about high performance and reliable computing in the context of big data, such as a new platform for ubiquitous computing , a new application of soft computing for financial option pricing , and a new graph data processing system .…”

mentioning

confidence: 99%

“…Jinson Zhang et al[6] present a new density approach that can produce a new model for analyzing and visualizing big data analysis. Guiyi Wei et al[7] target on the service cooperation in VANETs and introduce a game-based incentive model to improve the service cooperation.We encourage the readers to review [8-10] to gain insight into the breadth and depth of problems and innovative solutions about high performance and reliable computing in the context of big data, such as a new platform for ubiquitous computing [8], a new application of soft computing for financial option pricing [9], and a new graph data processing system [10].…”

mentioning

confidence: 99%

Security and reliability in big data

Xiang

Mueller

Zhang

2016

Concurrency and Computation

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The purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2013). This annual conference brings together researchers and practitioners in the world from both academia and industry who are working on trusted computing and communications in computer systems and networks, in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions.Big data is now invading in every aspect of our daily life and promise to revolutionize our life. For example, human beings create about 2.5 quintillion bytes of data every day in 2012, which come from sensors, individual archives, social networks, Internet of Things, enterprise, and Internet in all scales and formats. We face the most challenging issue, that is, how to effectively manage such a large amount of data and identify new ways to analyze large amounts of data and unlock information. This special issue presents many examples of how researchers, scholars, vendors, and practitioners are collaborating to address security and reliability research challenges.The scope of this special issue is broad and is representative of the multi-disciplinary nature of security and reliability. In addition to submissions that deal with security challenges, reliability issues, theoretical analysis, algorithms, and practical experience in the context of big data, this issue also includes articles that address practical challenges with handling large-scale data and applications.The effective protection of information and privacy in the context of big data is a central topic of this issue. Gwan-Hwan Hwang et al.[1] present a scheme to fulfill mutual nonrepudiation for cloud storage that is now a popular way in people's daily life. [2,4] describes a new way to protect outsourced data in cloud computing through access management. Yulei Wu et al. [3] provide the insights into the issue in IPv6 adoption and show a special view from the Chinese IPv6 Application mix. To apply the encryption technique to protect the real-world data, key management is an essential issue. Guojun Wang et al. [4] propose to use the multilinear forms to achieve decentralized Group Key Management for Hierarchical Access Control. Chang Liu et al.[5] develop a hierarchical key exchange scheme for secure scheduling and auditing of big data.How to effectively analyze and utilize large-scale data is an interesting topic of this issue. Jinson Zhang et al.[6] present a new density approach that can produce a new model for analyzing and visualizing big data analysis. Guiyi Wei et al. [7] target on the service cooperation in VANETs and introduce a game-based incentive model to improve the service cooperation.We encourage the readers to review [8][9][10] to gain insight into the breadth and depth of problems and innovative solutions about high performance and reliable computing in the context of big data, such a...

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Nature‐inspired soft computing for financial option pricing using high‐performance analytics

Cited by 12 publications

References 38 publications

Big data techniques in auditing research and practice: Current trends and future opportunities

Big data techniques in auditing research and practice: Current trends and future opportunities

A Survey on Financial Applications of Metaheuristics

Security and reliability in big data

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