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Advances in Behavioral Finance
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Advances in Behavioral Finance

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

Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 8552

Special Issue Editor

Dr. Ioannis C. Pragidis

E-Mail Website
Guest Editor
Department of Economics, Democirtus University of Thrace, 69100 Komotini, PC, Greece
Interests: fiscal and monetary policy; time series analysis; bond markets; textual analysis

Special Issue Information

Dear Colleagues,

Behavioral finance explains why and how financial markets might be inefficient. Bounded rationality, loss aversion, overconfidence, herding behavior and other forms of biases are some of the issues that behavioral finance deals with. According to Shefrin (2001), behavior finance is the study of how psychology affects financial decision making process and financial markets. Behavioral finance includes more than three decades of intensive research providing useful information of the markets functioning and of investors investment behavior. In recent years, communications and information have become available worldwide in seconds speed. This influences both the behavior and the perception of the agents/investors in a different way that used to be in previous years.

This Special Issue is dedicated in papers focusing on the functioning of the financial markets and of the economy through the lens of behavioral finance. New methods and new research questions examining possible deviations from rational behavior are highly encouraged. It is also of highly interest papers that take into account the impact of the increased information through textual and sentiment analysis on the investment behavior and on the financial markets in general.

Dr. Ioannis C. Pragidis
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

  • Behavioral Finance
  • Textual analysis
  • Sentiment analysis
  • Financial markets
  • Commodities, stock market prediction

Published Papers (2 papers)

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Research

16 pages, 2223 KiB  
Article
Topological Network Analysis Based on Dissimilarity Measure of Multivariate Time Series Evolution in the Subprime Crisis
by Mansooreh Kazemilari and Ali Mohamadi
Int. J. Financial Stud. 2018, 6(2), 47; https://doi.org/10.3390/ijfs6020047 - 05 May 2018
Cited by 5 | Viewed by 4039
Abstract
Correlation network based on similarity is the common approach in financial network analyses where the Minimal Spanning Tree (MST) is used to filter the important information contained in the network. In this paper, by considering a distance matrix based on dissimilarities among multivariate [...] Read more.
Correlation network based on similarity is the common approach in financial network analyses where the Minimal Spanning Tree (MST) is used to filter the important information contained in the network. In this paper, by considering a distance matrix based on dissimilarities among multivariate time series of currency, a topological network was analyzed. A topological network can explain to what extent two or more multi-dimensional currency structures are different from each other. For this purpose, we examined the topological network of currency market from 2005 to 2011 in terms of the subprime crisis. After that, the multivariate time series evolution of MSTs were analyzed in terms of the structural changes for three periods (before, during, and after the crisis). Moreover, since the clusters of currencies in network analysis are due to regional factors, by considering each region, which is composed of a number of currencies, as an element on the financial system, we attempted to determine how a region interacts with the other regions in crisis periods. This motivated us to introduce a region-based network analysis of currencies. Since each region consisted of a different number of currencies compared to the others, the appropriate network analysis was in multivariate setting. Finally, the applications of the method were presented with the situation of a currencies crisis behavior. The results indicate significant changes in the topological structures of MSTs when their properties are compared to each other. Full article
(This article belongs to the Special Issue Advances in Behavioral Finance)
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12 pages, 237 KiB  
Article
A Closer Look at the Halloween Effect: The Case of the Dow Jones Industrial Average
by Peter Arendas, Viera Malacka and Maria Schwarzova
Int. J. Financial Stud. 2018, 6(2), 42; https://doi.org/10.3390/ijfs6020042 - 12 Apr 2018
Cited by 7 | Viewed by 4056
Abstract
The Halloween effect is one of the most famous calendar anomalies. It is based on the observation that stock returns tend to perform much better over the winter half of the year (November–April) than over the summer half of the year (May–October). The [...] Read more.
The Halloween effect is one of the most famous calendar anomalies. It is based on the observation that stock returns tend to perform much better over the winter half of the year (November–April) than over the summer half of the year (May–October). The vast majority of studies that investigated the Halloween effect over the recent decades focused only on stock indices. This means that they evaluated whether a stock index follows the Halloween effect pattern, but they omitted digging a little deeper and analyze the Halloween effect on the individual stocks level. This paper investigates to what extent the blue-chips stocks included in the Dow Jones Industrial Average are affected by the Halloween effect and whether the Halloween effect is widespread or the behavior of the whole index is driven by only a handful of stocks that are strongly affected by the Halloween effect. The results show that, although the strength of the Halloween effect varies quite rapidly from stock to stock, the vast majority of analyzed stocks experienced a notably higher average winter period than summer period returns over the 1980–2017 period. Moreover, in 18 out of 35 cases, the Halloween effect was statistically significant. Full article
(This article belongs to the Special Issue Advances in Behavioral Finance)
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