The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings In particular, the changing behaviors towards sustainability issues of the global community and the availability of environmental, social and governance (ESG) indicators are attracting investors to socially responsible investment decisions Furthermore, whereas the strategic importance of ESG metrics has been particularly studied for private enterprises, little attention have received public companies To address this gap, the present work has three aims—1 To predict the accuracy of main financial indicators such as the expected Return of Equity (ROE) and Return of Assets (ROA) of public enterprises in Europe based on ESG indicators and other economic metrics; 2 To identify whether ESG initiatives affect the financial performance of public European enterprises; and 3 To discuss how ESG factors, based on the findings of aims #1 and #2, can contribute to the advancements of the current debate on Corporate Social Responsibility (CSR) policies and practices in public enterprises in Europe To fulfil the above aims, we use a combined approach of machine learning (ML) techniques and inferential (ie, ordered logistic regression) model The former predicts the accuracy of ROE and ROA on several ESG and other economic metrics and fulfils aim #1 The latter is used to test whether any causal relationships between ESG investment decisions and ROA and ROE exist and, whether these relationships exist, to assess their magnitude The inferential analysis fulfils aim #2 Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial indicators In addition, the existing relationship appears more evident when companies invest in environmental innovation, employment productivity and diversity and equal opportunity policies As a result, to fulfil aim #3 useful policy insights are advised on these issues to strengthen CSR strategies and sustainable development practices in European public enterprises

https://www.mdpi.com/2071-1050/12/13/5317/pdf

Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe

A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms.

Covid-19 pandemic and spillover effects in stock markets: A financial network approach

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 was given, the 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.

https://www.mdpi.com/1099-4300/22/10/1162/pdf

A Labeling Method for Financial Time Series Prediction Based on Trends.

Time-frequency connectedness of crude oil, economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis

Machine learning as an early warning system to predict financial crisis

Are Timber and Water Investments Safe-Havens? A Volatility Spillover Approach and Portfolio Hedging Strategies for Investors

Financial contagion in real economy: The key role of policy uncertainty

Empirical investigation of co-authorship in the field of finance: A network perspective

This paper discusses the volatility spillovers between the Greek Debt crisis and the Cypriot financial crisis. Cyprus was in the spotlight of financial markets due to significant problems stemming from the banking sector, which were dealt with by EU regulators with a bail-in on bank deposits. The current analysis aims to shed light on the reasons behind the implementation of this novel approach to bank distress. The study uses a Dynamic Conditional Correlation model on the returns of the stock markets of the two countries, which shows strong spillover effects during the period leading up to the 2013 Cypriot crisis, but a significant decrease of these effects from then on. The results confirm the close interdependence of the Greek and Cypriot economies before 2013 but also show that this interdependence was limited from that point onwards. This would indicate that since the risk of contagion to the Eurozone had diminished, regulators were able to test the bail-in solution in Cyprus in 2015. The current work contributes to the discussion on the interdependence of European economies. The paper’s findings can also be applied to other emerging European economies.

/pdf/spillover-effects-between-greece-and-cyprus-a-dcc-model-on-4posilubod.pdf

Elias Kampouris

Papers

Machine learning as an early warning system to predict financial crisis

Are Timber and Water Investments Safe-Havens? A Volatility Spillover Approach and Portfolio Hedging Strategies for Investors

Financial contagion in real economy: The key role of policy uncertainty

Empirical investigation of co-authorship in the field of finance: A network perspective

Spillover effects between Greece and Cyprus: a DCC model on the interdependence of small economies