/library/oar/handle/123456789/18881 2026-09-14T10:30:19Z 2026-09-14T10:30:19Z Petropoulos, Theofanis Liapis, Konstantinos Thalassinos, Eleftherios /library/oar/handle/123456789/148685 2026-08-25T11:07:25Z 2023-01-01T00:00:00Z

Title: Optimal structure of real estate portfolio using EVA : a stochastic Markowitz model using data from Greek real estate market Authors: Petropoulos, Theofanis; Liapis, Konstantinos; Thalassinos, Eleftherios Abstract: The purpose of this paper is to examine the issue of portfolio optimization. Optimization consists of minimizing the risk for a given rate of return or achieving a bigger return for a given level of risk. We use historical data from the Bank of Greece to calculate the net return and the standard deviation (std) for each type of property that is available. The objective is to maximize the economic value added (EVA) of a property’s assets portfolio under a specific rate of standard deviation, following the classic Markowitz model (M-V). The stochastic procedure entry in the model uses the Monte Carlo Simulation method with debt to equity (DTE) following PERT distribution for the portfolio’s invested budget, and the net return for the normal distribution with the mean of the expected return and std are taken from historical data, correspondingly. The returns verify that they follow the base assumption of normality through the Lilliefors test in the Greek real estate market. We observe the maximization of EVA and the expected return maximizing concurrently, but the minimizing risk of EVA is diversified with the minimization of portfolio risk. We observe that the max weight that a residential asset takes is 22.7% because a bigger percent reduces both mean and std. The study provides an explicit portfolio optimization procedure under uncertainty in the real estate market and enriches the academic debate about EVA and revenue.

2023-01-01T00:00:00Z Thalassinos, Eleftherios Khan, Naveed Ahmed, Shakeel Zada, Hassan Ihsan, Anjum /library/oar/handle/123456789/146203 2026-05-07T07:35:24Z 2023-01-01T00:00:00Z

Title: A comparison of competing asset pricing models : empirical evidence from Pakistan Authors: Thalassinos, Eleftherios; Khan, Naveed; Ahmed, Shakeel; Zada, Hassan; Ihsan, Anjum Abstract: In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan’s stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth’s regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions.

2023-01-01T00:00:00Z Norena-Chavez, Diego Thalassinos, Eleftherios /library/oar/handle/123456789/146084 2026-04-30T12:56:42Z 2023-01-01T00:00:00Z

Title: Impact of big data analytics in project success : mediating role of intellectual capital and knowledge sharing Authors: Norena-Chavez, Diego; Thalassinos, Eleftherios Abstract: Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

2023-01-01T00:00:00Z Ojaghlou, Mortaza Ugurlu, Erginbay Kadłubek, Marta Thalassinos, Eleftherios /library/oar/handle/123456789/145878 2026-04-24T06:16:41Z 2023-01-01T00:00:00Z

Title: Economic activities and management issues for the environment : an environmental Kuznets curve (EKC) and STIRPAT analysis in Turkey Authors: Ojaghlou, Mortaza; Ugurlu, Erginbay; Kadłubek, Marta; Thalassinos, Eleftherios Abstract: The emission of air pollutants from energy production and consumption is a major cause of environmental problems. In addition, urbanisation and CO2 emissions have become major environmental concerns that are closely related to climate change and sustainable economic growth. The purpose of this paper is to investigate the long-run relationship among CO2 emissions, energy consumption, economic activities, and management issues for Turkey for the period between 1980 and 2021. The STIRPAT hypothesis and the environmental Kuznets curve (EKC) hypothesis were employed by using dynamic conditional correlation (DCC) and ARDL bound methodologies for these goals. The findings indicate that there is a long-run relationship between variables of the STIRPAT model. The coefficient of economic expansion and energy consumption affected CO2 emissions positively, which means that energy consumption and the expansion of economic activity have significant effects on environmental degradation. Those results are also confirmed by the environmental Kuznets curve (EKC) model. In addition, the N-shaped environmental Kuznets curve (EKC) is developed for Turkey. The DCC model also shows that economic growth increases CO2 emissions significantly, and energy productivity can be considered for decreasing CO2 emissions.

2023-01-01T00:00:00Z