International Journal of Financial Engineering and Risk Management
- Editor in Chief
- Prof. Constantin Zopounidis
- ISSN online
- ISSN print
- 4 issues per year
IJFERM is a scholarly peer-reviewed international journal covering all aspects of the theory and practice of financial engineering and risk management. IJFERM is particularly interested in promoting research related to the development and implementation of new quantitative models leading to operational decision aids in finance. This perspective is motivated by the increasing complexity of financial decisions and the rapidly increasing use of quantitative methods for the design and implementation of innovative financial instruments, processes and solutions to financial decision-making problems.
Topics covered include
- Asset pricing
- Asset-liability management
- Capital budgeting and financial planning
- Computational and mathematical finance
- Corporate performance and efficiency analysis
- Decision support systems for financial decision making
- Financial forecasting and econometrics
- Fund management
- Hedging and trading strategies
- Interest rate modelling
- Investment appraisal and management
- Operations research/management science models in finance
- Quantitative behavioural finance
- Project finance
- Risk metrics and risk management
The purpose of IJFERM is to publish innovative, original and high-standard research aiming to provide as broad a coverage as possible of different relevant modelling approaches and paradigms stemming from traditional fields such as probability theory, stochastic calculus, statistics, econometrics, as well as from new approaches based on management science/operations research and artificial intelligence.
IJFERM is of interest to a broad audience including academics, researchers, professionals and policy makers.
IJFERM publishes high quality original and review papers as well as case studies. Special issues devoted to important topics relevant to the quantitative aspects of financial engineering financial risk management and financial decision-making will occasionally be published.
Editor in Chief
- Zopounidis, Constantin, Audencia Nantes School of Management, France and Technical University of Crete, Greece
- Doumpos, Michael, Technical University of Crete, Greece
- Andriosopoulos, Kostas, ESCP Europe, UK
- D'Ecclesia, Rita, “La Sapienza” University of Rome, Italy
- Galariotis, Emilios, Audencia Nantes School of Management, France
- Spronk, Jaap, Erasmus University Rotterdam, Netherlands
- Tsekrekos, Andrianos, Athens University of Economics and Business, Greece
Regional Editor Asia and Oceania
- Chen, Shu-Heng, National Chengchi University, Taiwan
Regional Editor North America
- Koutmos, Gregory, Fairfield University, USA
Editorial Board Members
- Angilella, Silvia, University of Catania, Italy
- Atiya, Amir, Cairo University, Egypt
- Bancel, Franck, ESCP Europe, France
- Brabazon, Anthony, University College Dublin, Ireland
- Chevalier, Alain, ESCP Europe, France
- Consigli, Giorgio, University of Bergamo, Italy
- Dimitras, Augustinos I., Hellenic Open University, Greece
- Fabozzi, Frank J., EDHEC Business School, USA
- Frydman, Halina, New York University, USA
- Gaganis, Chrysovalantis, University of Crete, Greece
- Gil Aluja, Jaime, University of Barcelona, Spain
- Gougeon, Patrick, ESCP Europe, UK
- Hasan, Iftekhar, Fordham University, USA
- Ioannidis, Christos, University of Bath, UK
- Kosmidou, Kiriaki, Aristotle University of Thessaloniki, Greece
- Matarazzo, Benedetto, University of Catania, Italy
- Nagurney, Anna, University of Massachusetts at Amherst, USA
- Nomikos, Nikos, City University, London, UK
- Pardalos, Panos M., University of Florida, USA
- Pasiouras, Fotios, Technical University of Crete, Greece
- Peccati, Lorenzo, Bocconi University, Italy
- Satchell, Steve, University of Cambridge, UK
- Siriopoulos, Kostas, University of Patras, Greece
- Siskos, Yannis, University of Piraeus, Greece
- Slowinski, Roman, Poznan University of Technology, Poland
- Steuer, Ralph, University of Georgia, USA
- Stulz, René M., Ohio State University, USA
- Tapiero, Charles S., New York University Polytechnic Institute, USA
- Uryasev, Stan, University of Florida, USA
- Van Der Wijst, Nico, Norwegian University of Science and Technology, Norway
- Ziemba, William T., University of British Columbia, Canada
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
- There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.
- All articles for this journal must be submitted using our online submissions system.
- Submit here.
Googling stockmarket success
22 January, 2019
Researchers in the USA have found a way to extract information from the well-known internet search engine, Google, that can be used to assist with understanding trading on the stock market. The approach follows, what the team refers to as "a long short-term memory approach". Writing in the International Journal of Financial Engineering and Risk Management, Joseph St. Pierre, Mateusz Klimkiewicz, Adonay Resom and Nikolaos Kalampalikis of the Worcester Polytechnic Institute, in Worcester, Massachusetts, explain how they have extracted Google search indices from a Google trends tracking website. This allows them to study the putative investor interest in stocks listed on the Dow Jones index (Dow 30). Essentially, they accomplish this task by using a long short-term memory network that finds correlations between changes in the search volume for a given asset with changes in the actual trade volume for that asset [...]More details...