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Technical Analysis of Financial Markets
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Technical Analysis of Financial Markets

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 39713

Special Issue Editor

Dr. Reza Tajaddini

E-Mail Website
Guest Editor
Department of Accounting, Economics and Finance, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: technical analysis of stock markets; cultural finance and economics; real estate investment

Special Issue Information

Dear Colleagues,

Technical analysis investigates the price movement and patterns of security. It is based on the belief that all known information about a financial security is reflected in its price and volume. By scrutinizing a security's past price action, investors hope to anticipate what is likely to happen to prices.

Participants in different financial markets widely use technical analysis to forecast future price trends; however, technical analysis has not received substantial support by academics. This Special Issue strives to provide a platform for a more in-depth discussion about the theoretical and empirical implications of technical analysis in financial markets.

This Special Issue invites submissions that expand our understanding of technical analysis performance in equity, currency, and cryptocurrency markets. The topics covered in this Special Issue will include but are not limited to:

  • Technical vs. fundamental analysis;
  • Technical analysis tools and financial decision-making during periods of high market volatility;
  • Applications of machine learning tools and computational intelligence techniques in analyzing financial market trends;
  • Stock market trading rules based on pattern recognition;
  • Market trend prediction using hybrid methods.

Dr. Reza Tajaddini
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. Journal of Risk and Financial Management is an international peer-reviewed open access monthly 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

  • Technical analysis
  • Machine learning methods
  • Pattern recognition
  • Trend-following and mean‐reversal indicators
  • Sentiment analysis
  • Stock selection method
  • Efficient market

Published Papers (3 papers)

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Research

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25 pages, 4440 KiB  
Article
Technical Analysis of Tourism Price Process in the Eurozone
by Sergej Gričar and Štefan Bojnec
J. Risk Financial Manag. 2021, 14(11), 517; https://doi.org/10.3390/jrfm14110517 - 27 Oct 2021
Cited by 1 | Viewed by 1998
Abstract
This study is a specific contribution to investigating normalities in prices to a well-established cointegrated vector autoregressive model (VAR). While the role of prices in computational economics has been investigated, the real prices vis-à-vis nominal prices in the decision process has been neglected. [...] Read more.
This study is a specific contribution to investigating normalities in prices to a well-established cointegrated vector autoregressive model (VAR). While the role of prices in computational economics has been investigated, the real prices vis-à-vis nominal prices in the decision process has been neglected. The paper investigates the transition from nominal to real time-series of prices without losing information in the data set when deflating or de-seasonalizing. The likelihood approach is based on careful specifications of the (co)integration characteristics of tourism prices. The results confirm that the transmission of tourism prices in the Eurozone positively impacts Slovenian tourism prices when the spatial consolidated cointegrated VAR model is used. The theoretical-conceptual and empirical contribution is twofold: first, the study develops and empirically applies bona fide divisor of normality consolidation for time-series in levels instead of routinely utilised inflation integers, and second, the study introduces perfection of prices on a long-run time-series treatment. Full article
(This article belongs to the Special Issue Technical Analysis of Financial Markets)
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31 pages, 25078 KiB  
Article
Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature
by Thierry Warin and Aleksandar Stojkov
J. Risk Financial Manag. 2021, 14(7), 302; https://doi.org/10.3390/jrfm14070302 - 2 Jul 2021
Cited by 13 | Viewed by 7899
Abstract
Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural [...] Read more.
Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing. Full article
(This article belongs to the Special Issue Technical Analysis of Financial Markets)
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Review

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34 pages, 1583 KiB  
Review
Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda
by Ritika Chopra and Gagan Deep Sharma
J. Risk Financial Manag. 2021, 14(11), 526; https://doi.org/10.3390/jrfm14110526 - 4 Nov 2021
Cited by 29 | Viewed by 25946
Abstract
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock [...] Read more.
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship. Full article
(This article belongs to the Special Issue Technical Analysis of Financial Markets)
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