Special Issue "Second Edition of Data Analysis for Financial Markets"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 November 2022 | Viewed by 849

Special Issue Editor

Special Issue Information

Dear Colleagues,

Data analysis plays a key role in the decisions made by participants in financial markets. Rapid advances in computing high amounts of data from stock exchanges have enabled the design of algorithms, which currently lead a considerable proportion of volume in international stock markets. Important research has recently addressed different approaches to take advantage of intraday data, but also any information provided by countless websites, posts on Twitter, corporate reports, or daily news announcements. Extracting insights from unstructured data is also part of ongoing research.

This Special Issue will contribute to bringing original research to the field of data analysis in financial markets. Suitable topics include, but are not limited to, the following: big data, business intelligence, sentiment analysis, text mining, financial volatility, real-time analytics, machine learning, fraud detection, operational efficiency, financial trading, high-frequency data, trading rules, stock markets, bankruptcy, and financial shocks.

Dr. 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. Data 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.

Published Papers (1 paper)

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Research

Article
A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network
Data 2022, 7(5), 51; https://doi.org/10.3390/data7050051 - 20 Apr 2022
Viewed by 648
Abstract
Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and [...] Read more.
Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work. Full article
(This article belongs to the Special Issue Second Edition of Data Analysis for Financial Markets)
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