Search Results (36)

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

This study investigates the presence of the MAX effect, as defined by Bali et al. (2011), in the stock market of Borsa Istanbul, aiming to validate and extend previous findings in international markets. A comprehensive analysis of 439 firms from December 2013 to November 2023 reveals that stocks with low performance in previous periods tend to show strong performance in subsequent periods. This finding indicates that the MAX effect is also applicable to Borsa Istanbul and suggests that this effect can significantly influence stock price movements in the market. Additionally, this study highlights that past maximum returns, especially those accumulated over long periods, have a distinct impact on future returns. These findings contribute to a deeper understanding of the MAX effect’s presence in and impact on financial markets and offer valuable guidance for market participants. Full article

This study aims to investigate the impact of environmental accounting disclosures on the financial performance of banks listed on Borsa Istanbul (BIST). In this study, sustainability and integrated reports for 2019–2023 are analyzed, and environmental accounting disclosures are classified into two categories as operational and financial activities. Using the Environmental Accounting Reporting Score, the relationship with financial performance indicators such as return on assets, return on equity, earnings per share, and profit margin is analyzed using the seemingly unrelated regression (SUR) method. The results show that environmental accounting disclosures do not have a direct and statistically significant effect on financial performance. However, control variables such as bank size, debt-to-asset ratio, and loan-to-asset ratio are found to have a positive effect on financial performance. In particular, larger banks tend to have higher profitability and earnings per share, while higher non-interest expenses have a negative impact on profitability. The study shows that the direct contribution of environmental accounting practices to financial performance is limited, but that banks’ operational and financial structures are greater determinants of performance. These findings highlight the need for improvements in areas such as standardization of sustainability reporting, stakeholder awareness, and environmental risk management for policy makers and banks. Full article

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul—industrial, services, technology, banking, and electricity—using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman–Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods ($k=1,2,3$) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks—such as inflation surges, interest rate hikes, and currency depreciation—vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators. Full article

This study examines the impact of the Kahramanmaraş Earthquake on four key sectors of Borsa Istanbul: Basic Metal, Insurance, Non-Metallic Mineral Products, and Wholesale and Retail Trade using neutral delayed fractional differential equations. Employing the Chebyshev collocation method, we numerically solved the neutral delayed fractional differential equations with initial conditions scaled by each sector’s log difference standard deviation to accurately reflect market volatility. Fractional orders were derived from the Hurst exponent, and time delays were identified using average mutual information, autocorrelation function, and partial autocorrelation function methods. The results reveal significant changes post-earthquake, including reduced market persistence and increased volatility in the Basic Metal and Insurance sectors, contrasted by enhanced stability in the Non-Metallic Mineral Products sector. Neutral delayed fractional differential equations demonstrated superior performance over traditional models by effectively capturing memory and delay effects. This work underscores the efficacy of neutral delayed fractional differential equations in modeling financial resilience amid external shocks. Full article

This study explores the impact of corporate governance on firms’ environmental, social, and governance (ESG) performance, with a focus on board characteristics and ownership structures. Using a panel dataset of 6 financial and 16 non-financial firms listed on the Borsa Istanbul (BIST) from 2013 to 2021, the study investigates how ownership (blockholder, foreign, or institutional) and board composition (size, gender diversity, and foreign directors) influence ESG disclosures. The analysis distinguishes between financial and non-financial firms, revealing that corporate governance mechanisms affect ESG performance differently across sectors. Foreign ownership and the presence of foreign and female board members are positively associated with higher ESG disclosures, while ownership concentration is negatively correlated with ESG performance. These findings suggest caution when comparing firms across sectors based solely on ESG disclosures, as governance factors influence outcomes differently in financial and non-financial contexts. This study provides a detailed analysis of effective corporate governance mechanisms in Türkiye, emphasizing the crucial roles of ownership structure and board composition in enhancing ESG transparency. The results offer valuable insights for regulators and investors, contributing to a nuanced understanding of how governance structures shape ESG performance in both financial and non-financial firms in Türkiye. Full article

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective. Full article

Earnings quality is crucial to provide investors and lenders with accurate information about the economic health of the firm and to help them make the right decisions. This paper examines whether the pooling of financial resources and internal funds allocation in corporate groups has a positive effect on earnings quality through reduced earnings management practices in affiliated firms. It is hypothesized that the funding benefits of pooling financial resources in corporate groups allow affiliated firms to reduce solvency problems arising from higher leverage, which in turn reduces incentives for earnings management. The study is based on a balanced panel data set of 95 non-financial firms traded on Borsa Istanbul covering the period between 2015 and 2022 (8 years) with a total of 760 observations. Using management’s discretionary accruals as a proxy variable to measure management’s flexibility to engage in earnings management, this study finds that being affiliated to a business group reduces earnings management incentives in group affiliates when firm’s leverage increases. The business group’s support on the debt-leveraged firm alleviates the motivation for earnings management practices. Full article

Multi-criteria decision analysis (MCDA) applications consist of techniques that enable the decision maker to make clearer decisions in scenarios where there is more than one alternative and criterion. The general approach for sensitivity analysis in MCDA applications implies sensitivity to the weight coefficient. In this study, as an alternative approach, we reinterpret sensitivity by using the statistical relationship between the final ranking produced by an MCDA method and a constant external factor. Thus, we both verify through an anchor and reveal to what extent the change in the weight coefficient changes the external relations of MCDA. The motivation for this study is to propose an alternative sensitivity methodology. On the other hand, brand value is a parameter that contains critical information about the future of the company, which has not integrated into financial performance studies made with MCDAs before. To that end, the financial performance of 31 companies with the highest brand value in Turkey and trading on Borsa Istanbul between 2013 and 2022 was analyzed with seven different MCDA applications via integrating brand value into the criteria for the first time. The study’s findings revealed that the proposed innovative sensitivity tests produced similarly robust results as traditional tests. In addition, brand value has been proved to be an advantageous criterion to be implemented into MCDAs for financial performance problems through the sensitivity analysis made. Full article

Assuming that investors can be foreign or local, do high-frequency trading (HFT) or not, and submit orders through a bank-owned or non-bank-owned broker, we associated trades to various investors. Then, building a panel vector autoregressive model, we analyzed the dynamic relation of these investors with returns and among each other before and during the COVID-19 market crash. Results show that investor groups have influence on each other. Their net purchases also interact with returns. Moreover, during the turmoil caused by the pandemic, except foreign investors not involved in HFT, the response of any investor group (retail/institutional, domestic investors doing HFT and those not doing HFT, and foreign investors doing HFT) significantly altered. This shows that the interrelation among investor groups is dynamic and sensitive to market conditions. Full article

Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were created. Word embedding and pre-trained word embedding were used for tweet representation. As classifiers, Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN models were used in this study. The best results for binary and multi-class datasets were observed with pre-trained word embedding with the CNN model (83.02%, 72.73%). When word embedding was employed, the Neural Network model had the best results on the multi-class dataset (63.85%) and GRU-CNN had the best results on the binary dataset (80.56%). Full article

Climate change is the reason behind most contemporary economic problems. The rising inflationary pressures in the food sector are one of these problems, and stable food prices are a necessity for economic development and social cohesion in societies. Therefore, this study analyzes the relationship between food prices and climate change in Nigeria by using various non-linear and quantile-based methods and data from 2008m5 to 2020m12. The empirical findings indicate that (i) there is a time- and frequency-based dependence between food prices and some explanatory variables, including climate change (i.e., temperature). (ii) At higher quantiles, temperature, oil prices, food exports, monetary expansion, global food prices, agricultural prices, and fertilizer prices stimulate food prices. (iii) The increase in food prices due to the rise in temperature and the difficulties in agriculture indicate that the heatflation phenomenon is present in Nigeria. The evidence outlines that Nigerian decisionmakers should adopt a national food security policy that considers environmental, agricultural, and monetary factors to stabilize food prices. Full article

This study explores the connection between the fractal dimensions of time series representing sentiments regarding economic news and the fractal dimensions of correlation networks among companies listed in the Borsa Istanbul star section. While there have been many studies on the correlation between different time series, the investigation into the impact of fractal dimensions on correlation networks’ dynamics has been somewhat restricted. This study investigates the correlation networks among companies listed in the Borsa Istanbul Stars segment, employing distance and topological filters. The network fractional dimensions are evaluated using the box counting and information dimension techniques. A convolutional neural network is employed to perform analysis of sentiments regarding on 2020 Turkish economic news. The network is trained on user comments and specifically built to identify fluctuations in news editorials. The Zemberek natural language processing framework is beneficial for data preprocessing. Identical analytical methods are employed to quantify the fractal dimensions of each sentiment time series. Experiments are performed on these measurements using various sliding window widths to ascertain both independence and causality. The findings indicate a substantial correlation between market behavior and the feelings expressed in economic news. Full article

The main purpose of this study is to conduct an evaluation based on listed companies traded in the energy sector sub-market of Borsa Istanbul (BIST). This evaluation is conducted on a sample of 27 companies between 2016 and 2021. In this study, corporate governance indicators are used as independent variables, while financial performance indicators are used as dependent variables. Initially, descriptive statistics of the sample and correlations between variables were calculated and interpreted in the analysis, and the Panel Data Analysis method is applied for the interaction between variables. This study emphasizes the importance of global economic and social crises, rapid changes in communication technologies, and the concept of sustainability for businesses. The sustainability of financing is highlighted as vital for companies. The findings of the study may serve as a valuable resource for understanding the performance of companies operating in the energy sector sub-market of BIST and their relationships with sustainability. Full article

The aim of this study is to investigate the influence of integrated reporting (IR) on the cost of financing within the Turkish capital market. Specifically, we analyze the effects of IR on the weighted average cost of capital (WACC), cost of equity (COE), and cost of debt (COD) for companies listed on Borsa Istanbul. Additionally, we explore how IR moderates the relationship between environmental, social, and governance (ESG) scores and the cost of financing. Our panel data analysis reveals a positive association between IR and both WACC and COD, while the impact on COE is not statistically significant. However, the findings suggest that the utilization of IR by companies to enhance the communication of their value-creating activities can mitigate WACC and COD, thus indicating a moderating effect on the relationship between ESG factors and the cost of financing. Full article