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The Journal of Finance and Data Science is a peer-reviewed journal published by Elsevier, covering Financial Markets and Investment Strategies, Stock Market Forecasting Methods, Market Dynamics and Volatility.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, The Journal of Finance and Data Science 12 (2023) 45–58.
Formats any DOI in The Journal of Finance and Data Science style. No sign-up. |
| Publishes research in | Financial Markets and Investment Strategies Stock Market Forecasting Methods Market Dynamics and Volatility Financial Risk and Volatility Modeling Complex Systems and Time Series Analysis |
| ISSN | 2405-9188 |
| Citation impact (2-yr) | 2.85 |
| h-index | 31 |
| i10-index | 74 |
| Total citations | 4,586 |
| Article processing charge | $100 |
| Open access | Yes |
| Top institutions publishing here | Southern University of Science and Technology |
| Journal website | www.sciencedirect.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Economists face method selection problem while working with time series data. As time series data may possess specific properties such as trend and structural break, common methods used to analyze other types of data may not be appropriate for the analysis of time series data. This paper discusses the properties of time series data, compares…
The purpose of predictive stock price systems is to provide abnormal returns for financial market operators and serve as a basis for risk management tools. Although the Efficient Market Hypothesis (EMH) states that it is not possible to anticipate market movements consistently, the use of computationally intensive systems that employ machine learning algorithms is increasingly…
Government of India aims at making the Indian Banks internationally competitive. In the wake of intense competition and changing global and national business environment, the efficiency issues have emerged as an important pillar of success in the Indian banking sector. Therefore, it is an essential task to comprehend the efficiency levels of the overall Indian…
In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN) and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and…
Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, either linear or nonlinear, either supervised or unsupervised, either “shallow” or “deep”. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification…
