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Second Edition of Data Analysis for Financial Markets
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Second Edition of Data Analysis for Financial Markets

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 32667

Special Issue Editor

Dr. Francisco Guijarro

E-Mail Website1 Website2
Guest Editor
Faculty of Business Administration and Management, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: financial markets; trading strategies; valuation; sustainability
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (4 papers)

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Research

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12 pages, 1318 KiB  
Article
Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam
by Kim Long Tran, Hoang Anh Le, Thanh Hien Nguyen and Duc Trung Nguyen
Data 2022, 7(11), 160; https://doi.org/10.3390/data7110160 - 14 Nov 2022
Cited by 22 | Viewed by 6807
Abstract
The past decade has witnessed the rapid development of machine learning applied in economics and finance. Recent evidence suggests that machine learning models have produced superior results to traditional statistical models and have become the driving force for dramatic improvement in the financial [...] Read more.
The past decade has witnessed the rapid development of machine learning applied in economics and finance. Recent evidence suggests that machine learning models have produced superior results to traditional statistical models and have become the driving force for dramatic improvement in the financial industry. However, a much-debated question is whether the prediction results from black box machine learning models can be interpreted. In this study, we compared the predictive power of machine learning algorithms and applied SHAP values to interpret the prediction results on the dataset of listed companies in Vietnam from 2010 to 2021. The results showed that the extreme gradient boosting and random forest models outperformed other models. In addition, based on Shapley values, we also found that long-term debts to equity, enterprise value to revenues, account payable to equity, and diluted EPS had greatly influenced the outputs. In terms of practical contributions, the study helps credit rating companies have a new method for predicting the possibility of default of bond issuers in the market. The study also provides an early warning tool for policymakers about the risks of public companies in order to develop measures to protect retail investors against the risk of bond default. Full article
(This article belongs to the Special Issue Second Edition of Data Analysis for Financial Markets)
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11 pages, 930 KiB  
Article
A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network
by Srivinay, B. C. Manujakshi, Mohan Govindsa Kabadi and Nagaraj Naik
Data 2022, 7(5), 51; https://doi.org/10.3390/data7050051 - 20 Apr 2022
Cited by 35 | Viewed by 11570
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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23 pages, 2214 KiB  
Data Descriptor
Technology Transfer from Nordic Capital Parenting Companies to Lithuanian and Estonian Subsidiaries or Joint Capital Companies: The Analysis of the Obtained Primary Data
by Agnė Šimelytė and Manuela Tvaronavičienė
Data 2022, 7(10), 139; https://doi.org/10.3390/data7100139 - 14 Oct 2022
Cited by 2 | Viewed by 1914
Abstract
Scientific literature describes various factors that influence knowledge transfer and successful adoption, assimilation, transformation, and exploitation. These four components are mostly related to the absorptive capacity of the company. However, more factors influence both developments of innovations or patents and the lack of [...] Read more.
Scientific literature describes various factors that influence knowledge transfer and successful adoption, assimilation, transformation, and exploitation. These four components are mostly related to the absorptive capacity of the company. However, more factors influence both developments of innovations or patents and the lack of ability to use external and internal information (knowledge). Using external knowledge is often associated with previous experience, or even a point of view towards investment in innovation or developing patents. Thus, the companies might be divided into innovators and imitators. The research addresses several problems (questions). What external factors are influencing knowledge transfer and further development of innovation? What factors are influencing absorptive capacity? What factors are essential in cooperation and knowledge transfer to switch from a linear to a circular economy? To collect data, a computer-assisted telephone interviewing method was used. The survey was addressed to subsidiaries, joint companies, Lithuanian-Nordic, Estonian-Nordic capital companies, or companies in close collaboration with the Nordic countries. A total of 158 companies from Estonia and Lithuania agreed to answer all the questions. The survey involves companies of various sizes and ages from different business sectors. Reliability was denoted, as Cronbach’s Alpha was estimated. The KMO test was used to measure whether the data were suitable for principal component analysis. Additionally, PCA was performed. PCA reduced the number of variables into an extracted number of components. The separate row of the component defined a linear composite of the component score that would be the expected value of the associated variable. The dataset may be used to develop interlinkages among the research mentioned above questions, and the results of introducing innovation, the company’s size, and age might be used as control variables. The article aims to analyze the factors that determine innovation development and their interlinkages while technology is transferred from Nordic parenting companies to the subsidiaries. The article’s results contribute to the interdisciplinary knowledge transfer, innovations, and internationalization field. Full article
(This article belongs to the Special Issue Second Edition of Data Analysis for Financial Markets)
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8 pages, 219 KiB  
Data Descriptor
A Dataset for the Vietnamese Banking System (2002–2021)
by Tu D. Q. Le, Tin H. Ho, Thanh Ngo, Dat T. Nguyen and Son H. Tran
Data 2022, 7(9), 120; https://doi.org/10.3390/data7090120 - 25 Aug 2022
Cited by 11 | Viewed by 11104
Abstract
This data article describes a dataset that consists of key statistics on the activities of 45 Vietnamese banks (e.g., deposits, loans, assets, and labor productivity), operated during the 2002–2021 period, yielding a total of 644 bank-year observations. This is the first systematic compilation [...] Read more.
This data article describes a dataset that consists of key statistics on the activities of 45 Vietnamese banks (e.g., deposits, loans, assets, and labor productivity), operated during the 2002–2021 period, yielding a total of 644 bank-year observations. This is the first systematic compilation of data on the splits of state vs. private ownership, foreign vs. domestic banks, commercial vs. policy banks, and listed vs. nonlisted banks. Consequently, this arrives at a unique set of variables and indicators that allow us to capture the development and performance of the Vietnamese banking sector over time along many different dimensions. This can play an important role for financial analysts, researchers, and educators in banking efficiency and performance, risk and profit/revenue management, machine learning, and other fields. Full article
(This article belongs to the Special Issue Second Edition of Data Analysis for Financial Markets)
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