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New Trends in Algorithmic Trading
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New Trends in Algorithmic Trading

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 45188

Special Issue Editor

Dr. Francisco Guijarro

E-Mail Website1 Website2
Guest Editor
Faculty of Business Administration and Management, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: financial markets; trading strategies; valuation; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Financial markets are complex and non-linear dynamic systems, facilitating the increased popularity of new products and tools among those investors interested in quantitative trading and machine learning strategies. Consequently, algorithmic trading based on large time series databases has received considerable interest both from academics and practitioners. New and faster technologies have given rise to the rapid development of tools and investment opportunities for investors and speculators. This Special Issue will gather original research in the field of new trends in algorithmic trading, focusing on cutting edge technologies, methodologies and algorithms.

Suitable topics include, but are not limited to, the following: Algorithmic Trading, Commodities Trading, Cryptocurrencies Trading, Cyber Threats, Data Analytics, Expert Systems, Forex Market Strategies, High Frequency Trading, Index Tracking, Machine Learning, Market Efficiency, Natural Language Processing (NLS), New Derivative Products, Pattern Recognition, Quantitative Trading, Reversal Strategies, Risk Analysis, Robo-Advisors, Social Trading, and Text Mining.

Prof. Francisco Guijarro
Guest Editor

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. International Journal of Financial Studies is an international peer-reviewed open access quarterly 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 1800 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

  • Quantitative Trading
  • Investment Strategies
  • Social Trading
  • Expert Systems

Published Papers (1 paper)

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Review

22 pages, 494 KiB  
Review
Stock Market Analysis: A Review and Taxonomy of Prediction Techniques
by Dev Shah, Haruna Isah and Farhana Zulkernine
Int. J. Financial Stud. 2019, 7(2), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/ijfs7020026 - 27 May 2019
Cited by 207 | Viewed by 43082
Abstract
Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in [...] Read more.
Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field. Full article
(This article belongs to the Special Issue New Trends in Algorithmic Trading)
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