Browsing by Author "Eratalay, Mustafa Hakan"

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Deep diving into the S&P Europe 350 index network and its re-action to COVID-19
(2021) Cortés Ángel, Ariana Paola; Eratalay, Mustafa Hakan
In this paper, we analyse the dynamic partial correlation network of the constituent stocks of S&P Europe 350. We focus on global parameters such as radius, which is rarely used in financial networks literature, and also the diameter and distance parameters. The first two parameters are useful for deducing the force that economic instability should exert to trigger a cascade effect on the network. With these global parameters, we hone the boundaries of the strength that a shock should exert to trigger a cascade effect. In addition, we analysed the homophilic profiles, which is quite new in financial networks literature. We found highly homophilic relationships among companies, considering firms by country and industry. We also calculate the local parameters such as degree, closeness, betweenness, eigenvector, and harmonic centralities to gauge the importance of the companies regarding different aspects, such as the strength of the relationships with their neighbourhood and their location in the network. Finally, we analysed a network substructure by introducing the skeleton concept of a dynamic network. This subnetwork allowed us to study the stability of relations among constituents and detect a significant increase in these stable connections during the Covid-19 pandemic.
The effects of the ECB communications on financial markets before and during COVID-19 pandemic
(2022) Alfieri, Luca; Eratalay, Mustafa Hakan; Lapitskaya, Darya; Sharma, Rajesh
The paper aims to estimate the effects of the European Central Bank communications on the sectoral returns of STOXX Europe 600 from 2013 to 2021. Previous literature has investigated the effects of communications of central banks and checked their effects on macroeconomics and financial data. New opportunities offered by text mining analysis allow us to find new insights into these aspects. However, studies focusing on how text mining indices derived from central banks’ communications can affect different financial sectors are more limited. In this paper, we use different sentiment and topic indices derived from the European Central Bank’s speeches. The paper shows how these different topics and sentiment indices affect the returns on different financial sectors. Our results indicate that the topic of communications is more influential on returns of sectoral indices than the type of communications. Moreover, we find that monetary policy and financial stability topics are the most relevant. We also find that during the COVID-19 time, the number of negative speeches is relevant for almost all the sectoral index returns.
The impact of ESG ratings on the systemic risk of European blue-chip firms
(2022) Eratalay, Mustafa Hakan; Cortés Ángel, Ariana Paola
There are diverging results in the literature on whether engaging in ESG related activities increases or decreases the financial and systemic risks of firms. In this paper we explore whether maintaining higher ESG ratings would reduce the systemic risks of firms in a stock market context. For this purpose we analyse the systemic risk indicators of the constituent stocks of S&P Europe 350 for the period of January 2016 - September 2020, which also partly covers the Covid-19 period. We apply a VAR-MGARCH model to extract the volatilities and correlations of the return shocks of these stocks. Then we obtain the systemic risk indicators by applying a principle components approach to the estimated volatilities and correlations. Our focus is on the impact of ESG ratings on systemic risk indicators, while we consider network centralities, volatilities and financial performance ratios as control variables. We use fixed effects and OLS methods for our regressions. Our results indicate that (1) the volatility of a stock’s returns and its centrality measures in the stock network are the main sources contributing to the systemic risk measure (2) firms with higher ESG ratings face up to 7.3% less systemic risk contribution and exposure compared to firms with lower ESG ratings, (3) Covid-19 augmented the partial effects of volatility, centrality measures and some financial performance ratios. When considering only the Covid-19 period, we found that social and governance factors have statistically significant impacts on systemic risk.
Predicting stock return and volatility with machine learning and econometric models: A comparative case study of the Baltic stock market
(2021) Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; Sharma, Rajesh
For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In this paper, we make a thorough analysis of the predictive accuracy of different machine learning and econometric approaches for predicting the returns and volatilities on the OMX Baltic Benchmark price index, which is a relatively less researched stock market. Our results show that the machine learning methods, namely the support vector regression and k-nearest neighbours, predict the returns better than autoregressive moving average models for most of the metrics, while for the other approaches, the results were not conclusive. Our analysis also highlighted that training and testing sample size plays an important role on the outcome of machine learning approaches.