Aggregating multiple types of complex data in stock market prediction: A model-independent framework

Wang, Huiwen; Lu, Shan; Zhao, Jichang

doi:10.1016/j.knosys.2018.10.035

Cited by 48 publications

(27 citation statements)

References 63 publications

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“…Recently, CoDa has been applied in finance and accounting to answer research questions concerning relative magnitudes. Examples include crowdfunding (Davis et al., 2017), financial markets (Kokoszka et al., 2019; Ortells et al., 2016; Wang et al., 2019), municipal budgeting (Voltes-Dorta et al., 2014), investment portfolios (Boonen et al., 2019; Belles-Sampera et al., 2016), product portfolios (Joueid and Coenders, 2018) and insurance pricing (Verbelen et al., 2018). CoDa has been successfully applied in multivariate descriptive studies of financial statements (Linares-Mustarós et al., 2018; Carreras-Simó and Coenders, 2019, in press).…”

Section: Methodsmentioning

confidence: 99%

Corporate social responsibility and financial profile of Spanish private hospitals

Creixans-Tenas

Coenders

Serrat

2019

Heliyon

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In the context of the recent economic crisis, the financial situation of the Spanish health sector has become a major concern for responsible actors from the public and private spheres, because of a decline in public spending and increased demand due to population growth and ageing. The public health system seeks collaborative synergies with the private health system to achieve better health care results, cut waiting lists and cope with financial pressure. The private health sector currently owns 452 hospitals in Spain (57% of the country's total). This paper analyses the financial statements of hospital companies in the Spanish private healthcare system, using compositional data (CoDa) methodology. It identifies a significant relationship between financial statement structure and corporate social responsibility (CSR). Relevant CSR indicators include a sustainability report according to the Global Reporting Initiative (GRI), accreditation by the Joint Commission International (JCI) and ISO 14001, 50001 and 26000 accreditations. These indicators reduce asset turnover and increase debt quality: the proportion of long-term debt within total liability. From a methodological perspective, the paper proposes the first application CoDa in statistical models to predict financial statements from non-financial variables, and the first use of CoDa in partial-least-squares structural equation models (PLS-SEM). Our methodological approach reduces the asymmetry, redundancy and outliers encountered in standard statistical models that predict financial ratios.

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Section: Methodsmentioning

confidence: 99%

Corporate social responsibility and financial profile of Spanish private hospitals

Creixans-Tenas

Coenders

Serrat

2019

Heliyon

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“…Nowadays, CoDa spans almost all of the hard sciences and has started to be used in several management fields. Besides Linares et al 2018, examples include crowdfunding (Davis et al, 2017), financial markets (Ortells et al, 2016;Wang et al, 2019), investment portfolios (Belles-Sampera et al, 2016;Boonen et al, 2019;Glassman & Riddick, 1996), municipality budgets (Voltes-Dorta et al, 2014), product portfolios (Joueid & Coenders, 2018), market segmentation (Ferrer-Rosell & Coenders, 2018;Ferrer-Rosell et al, 2016a), market share (Morais et al, 2018), advertisement (Mariné-Roig & Ferrer-Rosell, 2018), consumer research (Ferrer-Rosell et al, 2015;Ferrer-Rosell et al, 2016b;Vives-Mestres et al, 2016a), quality management (Vives-Mestres et al, 2014; 2016b), organizational culture (Van Eijnatten, et al, 2015), and management education (Batista-Foguet et al, 2015;Mateu-Figueras et al, 2016).…”

Section: Definition and Purposementioning

confidence: 99%

Principal component analysis of financial statements. A compositional approach

Simó

Coenders

2020

RMCE

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Financial ratios are often used in principal component analysis and related techniques for the purposes of data reduction and visualization. Besides the dependence of results on ratio choice, ratios themselves pose a number of problems when subjected to a principal component analysis, such as skewed distributions. In this work, we put forward an alternative method drawn from compositional data analysis (CoDa), a standard statistical toolbox for use when data convey information about relative magnitudes, as financial ratios do. The method, referred to as the CoDa biplot, does not rely on any particular choice of financial ratio but allows researchers to visually order firms along the pairwise financial ratios for any two accounts. Non-financial magnitudes and time evolution can be added to the visualization as desired. We show an example of its application to the top chains in the Spanish grocery retail sector and show how the technique can be used to depict strategic management differences in financial structure or performance, and their evolution over time.

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“…In their opinion, the positive or negative direction of the opening return rather than the absolute value of the opening return itself is of the foremost interest in reality because it can provide advice on the direction of trading. The authors used zero as the cut-off point and transformed y ∈{0,1} (which denotes the label) into a binary variable [ 53 ]. Mojtaba Nabipour et al trained four groups of machine learning models by comparing the stock data at 1, 2, 5, 10, 15, 20, and 30 days in advance [ 54 ].…”

Section: Introductionmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation given, a definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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Aggregating multiple types of complex data in stock market prediction: A model-independent framework

Cited by 48 publications

References 63 publications

Corporate social responsibility and financial profile of Spanish private hospitals

Corporate social responsibility and financial profile of Spanish private hospitals

Principal component analysis of financial statements. A compositional approach

A Labeling Method for Financial Time Series Prediction Based on Trends

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