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    Comparative analysis of distance measures in stock network construction and cluster analysis
    (Inderscience Enterprises Ltd, 2025) Alkan, Serkan
    The mutual information (MI) metric and the Pearson correlation metric are both widely used in cluster analysis and stock network construction. This paper presents a detailed comparison between the MI metric and the Pearson correlation metric. To detect nonlinear relationships, polynomial and natural cubic spline regressions are proposed as alternatives to the MI metric. The methodology for computing model-fitting indices for determining network adjacencies is explained in detail, along with a comparison of the results with the MI methodology. This study employs two data sets derived from the log returns of the daily adjusted closing prices of 402 stocks in the S&P500 index to measure the impact of a financial crisis on nonlinearity: one covering the crisis period from January 2007 to December 2009, and the other covering the non-crisis period between January 2012 and December 2015. The local and global properties of hierarchical stock networks are compared using the minimum spanning tree for each distance measure. The graph-theoretic internal cluster validity indices and external indices are also used to investigate the relationship between the performance of the community detection algorithm and the selection of metrics.
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    Evaluating the Hierarchical Contagion of Economic Policy Uncertainty among the Leading Developed and Developing Economies
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Alkan, Serkan; Akdağ, Saffet; Alola, Andrew Adewale
    An array of global events, including the global financial crisis, natural disasters, and the recent coronavirus pandemic, have consistently shown the vulnerability of global systems and humans to externally undesirable contagions. In order to further provide alternative approaches to information valuation, this study utilized the economic policy uncertainty (EPU) of 21 leading developed and developing economies (Australia, Brazil, Canada, Chile, China, Colombia, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Korea, Netherlands, Russia, Spain, Sweden, the United Kingdom, and the United States of America) over the period January 1997 to May 2021. The information theory reveals the hierarchy of degrees of randomness in the EPU indices; it shows the information flow among the EPU indices through the mutual information metric and the graphical illustration of the information flows using network theory. Importantly, the Entropy measures indicate higher predictability of the Netherlands and Ireland’s EPU indices, suggesting that they have less randomness than other indices. Contrarily, Greece and the United Kingdom share the lowest predictability of the EPU indices. Moreover, the complex networks analysis shows that the EPU indices is generally shaped by geographic location. In order of significance, the United States of America’s EPU index exhibits the strongest correlation with other countries’ EPU indices and followed by the EPU indices of France, the United Kingdom (UK), and Germany. In general, the result of the investigation communicates relevant policy measures that potentially ameliorate shocks from external contagions. © 2023 by the authors.
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    Industry classifications and identification of important industry groups
    (Inderscience Publishers, 2023) Alkan, Serkan
    Researchers in finance, managers, and people interested in the stock market observe that stock prices generally move together. Financial analysts and academic researchers use different techniques to construct homogeneous stock groups as in the Global Industry Classification System (GICS). This study analyses how homogeneity at each aggregation level of the GICS scheme changes over time. It identifies industries with more homogeneous structure than others in terms of stock returns by applying internal cluster validation statistics. A technique derived from complex network theory is proposed to illustrate how to construct a network whose nodes are financial industry groups to examine the evolution of the relationships among these groups over time. The findings indicate that stock networks undergo regular structural shifts in their interaction structure, and such structural changes become more intense during financial crises. Certain industry groups dominate the market at certain times and undergo significant changes during different market regimes. © 2023 Inderscience Enterprises Ltd.
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    Liquidity and Market Efficiency in Borsa Istanbul
    (2024) Alkan, Serkan
    The Borsa Istanbul has experienced a significant increase in investor participation in the past few years, and the growing number of companies are opting to raise capital through IPOs (Initial Public Offerings). In the context of this transformation, the goal of this research is to investigate the connection between the market efficiency and liquidity of 397 stocks traded on Borsa Istanbul by using the daily data over the period from 1 January 2022 to 18 August 2023, including the new stocks that have been listed in recent years. The stocks are ranked in accordance with the degree of informational efficiency using a sample entropy (SampEn) approach. The analysis shows that all stocks exhibit different levels of informational complexity and illiquidity, and many stocks display evidence of autocorrelation and non-independence. Further, it is revealed that entropy and liquidity have a significant relationship on a cross-sectional basis, suggesting that liquidity has an important impact on both inefficiency and predictability.
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    MULTI-SCALE SAMPLE ENTROPY ANALYSIS OF THE TURKISH STOCK MARKET EFFICIENCY
    (2023) Alkan, Serkan
    This study evaluates the market efficiency of the market index and five main sector indices in the Turkish stock market: BIST 100 (XU100), BIST Industrials (XUSIN), BIST Services (XUHIZ), BIST Transportation (XULAS), BIST Financials (XUMAL), and BIST Technology (XUTEK) for the pre-and post-COVID-19 pandemic, covering the period from January 2017 to July 2022. Market efficiency is calculated using a multiscale entropy-based method for the scales of 1 to 20 business days. Entropy can yield a relative degree of efficiency, by contrast with previous methods that dealt with the efficiency question in all-or-nothing form. On a daily scale, during the pre-COVID-19 pandemic period, the XUHIZ, XU100 and XULAS indices exhibit the highest efficiency. However, in the post-COVID-19 pandemic period, the XUMAL and XU100 indices demonstrate the highest efficiency. The findings suggest that the efficiency of all indices has declined due to the COVID-19 pandemic, with the XULAS index showing the most significant decrease in informational efficiency. A general tendency of reduced informational efficiency levels is found as the time scale increases in both periods. Therefore, the indices are partially efficient for certain time scales, indicating that they are not fully efficient.