Entropy correlation distance method. The Euro introduction effect on the Consumer Price Index

Miśkiewicz, Janusz

doi:10.1016/j.physa.2009.12.031

Cited by 12 publications

(4 citation statements)

References 12 publications

(23 reference statements)

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“…The question on reliability of data along BL can be debated on theoretical grounds as mentioned here above [5,6,21]. In order to complement such a study we propose to examine another measure, -the distance between distributions as discussed by Cho and Gaines or by Miskiewicz [31,32]. A distance measure can be constructed as the Euclidean distance between two distributions: this distance measure d * is defined as…”

Section: Distribution Distancementioning

confidence: 99%

Benford’s law first significant digit and distribution distances for testing the reliability of financial reports in developing countries

Shi

Ausloos

Zhu

2018

Physica A: Statistical Mechanics and its Applications

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We discuss a common suspicion about reported financial data, in 10 industrial sectors of the 6 so called "main developing countries" over the time interval [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014]. These data are examined through Benford's law first significant digit and through distribution distances tests.It is shown that several visually anomalous data have to be a priori removed. Thereafter, the distributions much better follow the first digit significant law, indicating the usefulness of a Benford's law test from the research starting line. The same holds true for distance tests. A few outliers are pointed out.

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Section: Distribution Distancementioning

confidence: 99%

Benford’s law first significant digit and distribution distances for testing the reliability of financial reports in developing countries

Shi

Ausloos

Zhu

2018

Physica A: Statistical Mechanics and its Applications

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“…Entropy is new to the field of entrepreneurship, with only few studies focused on this research area of entrepreneurship and most literature only introduces the concept of entropy without directly modelling it in empirical studies [6,38,62,77].…”

Section: Discussionmentioning

confidence: 99%

“…In other words, entropy is an index to measure the uncertainty that is expressed by a probability distribution [4]. Recent studies [5][6][7] link entropy with economic activities. These studies have shifted the attention from one way analysis to a multidisciplinary approach based on Econophysics [8].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Entropy, Corporate Entrepreneurship and Organizational Capabilities in Romanian Medium Sized Enterprises

Ceptureanu

Popescu

2017

Entropy

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This paper analyses the relations between entropy, organizational capabilities and corporate entrepreneurship. The results indicate strong links between strategy and corporate entrepreneurship, moderated by the organizational capabilities. We find that companies with strong organizational capabilities, using a systematic strategic approach, widely use corporate entrepreneurship as an instrument to fulfil their objectives. Our study contributes to the limited amount of empirical research on entropy in an organization setting by highlighting the boundary conditions of the impact by examining the moderating effect of firms' organizational capabilities and also to the development of Econophysics as a fast growing area of interdisciplinary sciences.

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“…The entropy characteristics of time series are usually covered in the following three categories: binned entropy [ 35 ], approximate entropy [ 36 ] and sample entropy [ 37 ]. Miśkiewicz proposed a entropy correlation distance based method (ECDM), which was applied to several time series, and the results proved that the entropy correlation distance measure is more suitable in detecting significant changes of the time series [ 38 ], which can also be used to test some economy globalization processes [ 39 ]. Machine learning can better quantify various characteristics, make quantitative decisions, and respond quickly to market information and can predict trend changes more quickly, thereby guiding decision makers to conduct corresponding risk control and make investment decisions.…”

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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Entropy correlation distance method. The Euro introduction effect on the Consumer Price Index

Cited by 12 publications

References 12 publications

Benford’s law first significant digit and distribution distances for testing the reliability of financial reports in developing countries

Benford’s law first significant digit and distribution distances for testing the reliability of financial reports in developing countries

Relationship between Entropy, Corporate Entrepreneurship and Organizational Capabilities in Romanian Medium Sized Enterprises

A Labeling Method for Financial Time Series Prediction Based on Trends

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