Borsa Istanbul Research Articles

The INVESTIGATION OF ASYMMETRIC DYNAMICS OF BORSA ISTANBUL INDEX WITH QUANTILE UNIT ROOT TEST

In this study, we apply the quantile unit root test, which provides robust inferences for non-normal processes based on the quantile autoregression approach, to examine the asymmetric dynamic process of the BIST100 Borsa Istanbul index. The quantile autoregression approach allows the measurement of the persistency of shocks of different magnitudes and signs affecting the stock market index and can capture the adjustment of asymmetric dynamics in the long-run equilibrium of the index. Therefore, quantile unit root tests add new approaches to index dynamics compared to traditional unit root methodologies based on the least squares regression method. Our results show that the index not only returns to the mean but also exhibits asymmetric behavior in its dynamic structure. Compared to traditional unit root tests, quantile unit root tests provide more evidence to support the efficiency of the stock market index and show that the stock market index at different frequencies does not consistently have a unit root. Asymmetric inferences in shock magnitude and sign play an important role in asset pricing and forecasting in the securities market.

A single-valued neutrosophic CIMAS-CRITIC-RBNAR decision support model for the financial performance analysis: A study of technology companies

Financial performance stands as a fundamental performance indicator that reflects companies' current financial conditions, providing information to investors and stakeholders about companies' financial well-being. In this research, a decision support system is developed for identifying the financial performance of companies traded on stock exchanges. This decision support system is based on a multicriteria decision-making (MCDM) approach and neutrosophic logic. The research introduces a novel method for calculating and ranking financial performance, using financial ratio indicators as selection criteria. The importance levels of financial ratio indicators are weighted through two different approaches. In the first approach, expert opinions and criteria importance assessment (CIMAS)-based on single-valued neutrosophic (SVN) sets are utilized for calculation. In the second approach, criteria importance through intercriteria correlation (CRITIC)-based on reference-based normalization processes is employed for criteria weighting. Subsequently, the results of the two criterion weightings are combined to calculate the final criterion weights. The SVN reference-based normalization alternative ranking (RBNAR) method is presented for ranking companies based on their financial performance. Thus, SVN-CIMAS-CRITIC-RBNAR is developed and its algorithm is presented. The novel hybrid decision support model is applied to a case study of technology companies traded on the Borsa Istanbul. The research results support the applicability of the SVN-CIMAS-CRITIC-RBNAR hybrid method. The results of the case study and sensitivity analyses affirm the applicability and robustness of the SVN-CIMAS-CRITIC-RBNAR hybrid model. The research provides detailed implications and insights for financial managers.

The portfolio optimization with simulated annealing algorithm: An application of Borsa Istanbul

The portfolio optimization with simulated annealing algorithm: An application of Borsa Istanbul

Modeling the link between environmental, social, and governance disclosures and scores: the case of publicly traded companies in the Borsa Istanbul Sustainability Index

This study constructs a proposed model to investigate the link between environmental, social, and governance (ESG) disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability (XUSRD) index. In this context, this study considers 66 companies, examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron (MLP) artificial neural network algorithm. The relevant results are fourfold. (1) The MLP algorithm has explanatory power (i.e., R2) of 79% in estimating companies’ ESG scores. (2) Common, environment, social, and governance pillars have respective weights of 21.04%, 44.87%, 30.34%, and 3.74% in total ESG scores. (3) The absolute and relative significance of each ESG reporting principle for companies’ ESG scores varies. (4) According to absolute and relative significance, the most effective ESG principle is the common principle, followed by social and environmental principles, whereas governance principles have less significance. Overall, the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies’ ESG scores; instead, companies should focus on the ESG principles that have the highest relative significance. The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index.

Relationship between monetary policy and financial asset returns in Türkiye: Time, frequency, and quantile-based effects

This study analyzes the effect of monetary policy, which are proxied by weighted average cost of funding (WACF) and Borsa Istanbul repurchase interest rate (REPO), on the returns of the main financial assets of monetary policy in Türkiye. Using daily data between January 4, 2011 and August 31, 2023, the study applies novel nonlinear time-series methods, such as wavelet coherence (WC) and quantile-on-quantile regression (QQ) as baseline methods and quantile regression (QR) for robustness. The findings demonstrate that (i) monetary policy has a stronger effect on financial asset returns at middle and higher frequencies across different periods; (ii) monetary policy has mainly declines (increases) effect on financial asset returns at lower and middle (higher) quantiles; (iii) the robustness of the outcomes is confirmed. Thus, the outcomes show that monetary policy has a significant effect on financial asset returns, and the effects vary across times, across frequencies, quantiles, and financial assets.

INVESTIGATING THE ROLE OF FIRM LIFE CYCLE ON FINANCIAL DISTRESS: EVIDENCE FROM BORSA ISTANBUL

This paper aims to examine whether acting non-compatible with life cycle has impact on firm’s financial distress. Considering every life stage requires unique priorities, firms should consider their life phases when determining their strategical decisions. We handle the causes of financial distress from a broad and strategical perspective on corporate finance ground. In this regard, we seek the relationships of investment, finance, and dividend policies with financial distress, considering firm life cycles. We applied panel data analysis examining 154 Borsa Istanbul (BIST) firms in manufacturing sector using their last seven-year data. We find that the ratio of investment to equity is positively correlated with financial distress for growth and decline firms at different levels. Additionally, we find that leverage increases financial vulnerability in all life cycles, however, the impact of leverage is larger for growth firms and decline firms. These findings are significant to navigate managers in the right direction.

The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach

This article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy.

An Alternative Sensitivity Analysis for the Evaluation of MCDA Applications: The Significance of Brand Value in the Comparative Financial Performance Analysis of BIST High-End Companies

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.

Impact of the COVID-19 Market Turmoil on Investor Behavior: A Panel VAR Study of Bank Stocks in Borsa Istanbul

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.

Tapping into long term value: a comprehensive overview of BIST 30 index companies future potential

Purpose - This article aims to establish a correlation between the valuation potential of companies listed on the Borsa Istanbul 30 index and their enduring competitive advantage, focusing on microeconomic Key Performance Indicators (KPIs) and corporate governance dimensions. Methodology - The study conducted an extensive analysis of the financial statements of BIST 30 companies spanning three years. Employing a unique approach utilizing Triangular Spherical Fuzzy Sets, it assessed the significance of these KPIs and corporate dimensions, chosen for their capacity to handle imprecise data. Three experts were consulted over three years to assign importance scores to each indicator, which were then used to calculate cumulative scores for individual stocks. Findings - The application of the Triangular Spherical Fuzzy Sets method provided a comprehensive understanding of these indicators, ena-bling a nuanced evaluation of the cumulative score for each stock. Notably, a clear distinction emerged between service and manufacturing companies, suggesting a potential glass ceiling effect that favors production-oriented enterprises. The observed patterns in certain stocks aligned with the framework developed in this study. Conclusion - This study illustrates the use of fuzzy logic in evaluating stock valuation potential, revealing insights into their sustained growth prospects. Aligned with Warren Buffett's value investing principles, it advances modeling approaches within the Turkish public stock exchange. Offering a unique perspective on BIST 30 stocks' long-term valuation potential, it provides valuable insights for investors, stakeholders, and analysts in the dynamic finance landscape. Emphasizing the understanding of companies' enduring competitive strength in an evolving market is pivotal. Keywords: Value investing, triangular spherical fuzzy sets, long term competitive advantage, corporate governance.

Analyzing Profitability Performance with the Integrated MEREC-COBRA Method: The Case of BIST Retail Companies

The purpose of this study is to evaluate the financial performance of retail trade enterprises whose shares are traded on Borsa Istanbul by using profitability ratios. Within the scope of the study, the data of ten retail trade enterprises covering the period 2021-2022 were used. The weight of the performance criteria used in the study was determined by the MEREC method. COBRA method was used to determine the performance ranking among the firms. A two-stage sensitivity analysis was conducted to determine the consistency of the analysis results. In the first stage, seven different multi-criteria decision-making methods were used to rank the firms according to their performance. The consistency between the results of the study and the results of other methods was determined by Spearman correlation coefficients. In the second stage, scenarios with different criteria weights were tested. Sensitivity analysis showed that the results were stable and consistent. According to the results of the study, MIPAZ ranked first in profitability performance for both years.

DETERMINATION OF EFFICIENCIES OF INFORMATION TECHNOLOGY SECTOR COMPANIES TRADED ON BORSA ISTANBUL IN TERMS OF FINANCIAL PERFORMANCES UNDER THE EFFECT OF COVID-19 PANDEMIC BY USING DEA WINDOW ANALYSIS

Window analysis is a time-dependent variant of Data Envelopment Analysis (DEA) and it is based on the moving average method. It is used for showing the trend of Decision Making Units’ (DMUs') performance over various times. The efficiency changes of 13 companies operating in Borsa Istanbul (BIST) Information Technology (IT) sector for quarterly periods between 2019:03 and 2021:06 are evaluated by DEA Window Analysis. The input-oriented CCR model and the input-oriented BCC Windows analysis models are applied on data set. When the CCR model and BCC model window analysis results are evaluated together, one of the first two periods with the highest average efficiency for both of them is 2021:06, and the period with the lowest average efficiency for both of them is 2019:09. DESPC company has the highest average efficiency for both models.

The Effects of Environmental, Social, and Governance Performance on Financial Performance: The Case of Borsa Istanbul

The Effects of Environmental, Social, and Governance Performance on Financial Performance: The Case of Borsa Istanbul

Effect of the Russian–Ukrainian Crisis on Borsa Istanbul Tourism Index

Effect of the Russian–Ukrainian Crisis on Borsa Istanbul Tourism Index

A Comparison of Home Purchase and Liquid Lazy Portfolio Returns in Turkey

This study presents a comprehensive comparison between the effectiveness of home purchase and liquid Lazy portfolio strategies for generating historical returns in the Turkish market. The primary aim is to develop a liquid Lazy portfolio that outperforms residential property investments. The study covers the period from January 2010 to June 2023, focusing on high-liquidity assets including the Borsa Istanbul 100 Index, currency exchange rates, gold prices, real estate investment trusts, and major stock market indices. Methodologically, the research involves data pre-processing, logarithmic transformations, and cumulative time series creation. Portfolio optimization centers around maximizing the Sharpe ratio with three test portfolios developed. Findings indicate that all portfolios consistently outperform the benchmark Residential Property Price Index, displaying higher returns and Sharpe ratios despite increased volatility. These insights are relevant for both academic researchers and investors seeking practical applications for portfolio management. It's important to note that historical performance offers insights but not future guarantees. In conclusion, the study highlights the potential benefits of constructing scalable, liquid, and high-return Lazy portfolios as alternatives to traditional home purchases

Determining the financial performance of the firms in the Borsa Istanbul sustainability index: integrating multi criteria decision making methods with simulation

Regardless of the industry in which a company operates, evaluating corporate performance is one of the most critical and vital processes; the most essential and prominent performance evaluation is related to financial performance. Appropriate performance analysis is complex and critical for decision-makers in different financial performance factors; thus, a methodological framework is needed to solve such complex decision problems. Therefore, this research aims to rank the companies included in the sustainability index (excluding banks) in Turkey by considering their financial performance. The criteria weights were determined using the full consistency method (FUCOM) by considering the evaluations of four experts. The firms were ranked using nine multi-criteria decision-making methods. The consensus among the nine rankings was ensured with the Copeland technique. The decision matrix includes financial ratios and the stock market performance of the firms; 100,000 FUCOM weights were created with random evaluations to validate the results. The results indicate that the most crucial criterion is the current ratio by considering expert evaluations. Weight simulation indicates that alternative 16 (alternative 21) is superior (inferior) to the other alternatives, even though the weights are determined with random evaluations. Ranking with expert evaluations is similar to the mean of the weight simulation results. The results demonstrate that the proposed framework can be performed as a basis for financial performance ranking.

Untangling the Influence of Corporate Sustainability on Export Intensity: The Moderating Role of R&D Intensity

Growing global environmental and social issues have imposed increased pressure on firms to address sustainability challenges in international markets, with a particular focus on improving their export performance. This is of significant importance for emerging market firms aiming to expand their presence in international markets, as they are compelled to bolster their environmental and social sustainability capacity to enhance their export intensity. This study delves into the relationship between corporate sustainability and export intensity through a longitudinal examination of 141 firms listed on Borsa Istanbul from 2014 to 2021. The results suggest that corporate sustainability positively influences export intensity, and this influence is further positively moderated by research and development (R&D) intensity. Additionally, post hoc analysis employing supplementary data pertaining to the environmental, social, and governance dimensions of corporate sustainability reveals that environmental performance plays a positive role in shaping export intensity, with R&D intensity positively moderating this relationship. In summary, the findings underscore that exporting firms that effectively integrate impactful R&D intensity into their international business operations are likely to harness their sustainability strategies, particularly those related to the natural environment, to achieve higher export intensity.

Is the performance of shares in the stock market affected by monetary policy change and the transmission channel of exchange rate? - The case of Turkey

In this study, in case that the objectives are price and financial stability, it is investigated whether the transmission channel based on exchange rate due to changes in monetary policy affects the performance of shares due to external debt stock. Borsa Istanbul is chosen as the stock market and the performance of nine sectors is examined during the high and low foreign exchange rate periods due to monetary policy change. The performance of the sectors is defined by portfolio performance measurement methods, and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analysis based on entropy is used for multi-criteria decision making. It is concluded that the monetary policy change affects the performance of the sectors, which constitutes a substantial proportion of external debt stock.

Proposal of an innovative MCDA evaluation methodology: knowledge discovery through rank reversal, standard deviation, and relationship with stock return

Financial performance analysis is of vital importance those involved in a business (e.g., shareholders, creditors, partners, and company managers). An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results. Integrated performance measurement, by its nature, consists of multiple criteria with different levels of importance. Multiple Criteria Decision Analysis (MCDA) methods have become increasingly popular for solving complex problems, especially over the last two decades. There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods. This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA, CRITIC, and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance. In this study, we propose "stock returns" as a benchmark in comparing and evaluating MCDA methods. Moreover, we calculate the "rank reversal performance of MCDA methods". Finally, we performed a "standard deviation" analysis to identify the objective and characteristic trends for each method. Interestingly, all these innovative comparison procedures suggest that PROMETHEE II (preference ranking organization method for enrichment of evaluations II) and FUCA (Faire Un Choix Adéquat) are the most suitable MCDA methods. In other words, these methods produce a higher correlation with share price; they have fewer rank reversal problems, the distribution of scores they produce is wider, and the amount of information is higher. Thus, it can be said that these advantages make them preferable. The results show that this innovative methodological procedure based on 'knowledge discovery' is verifiable, robust and efficient when choosing the MCDA method.

Stock Price Forecasting with Deep Learning Techniques

In this study, LSTM (Long-Short Term Memory) and GRU (Gated Recurrent Unit) techniques of deep learning, which are among the latest advanced technologies, were applied in the Google Colab software program for stock price forecasting. The dataset used in the study was obtained from Yahoo Finance and covers the dates between 02/01/2013 and 30/12/2022. Forecast models were created by considering 5 companies belonging to the XELKT (Electricity Market in Borsa Istanbul) index, which is part of BIST (Borsa Istanbul). Subsequently, the success of these forecast models was tested with the calculated model performance criteria, aiming to determine whether the techniques used were successful in stock price forecasting. Additionally, based on the results of MSE (Mean Squared Error) and MAPE (Mean Absolute Percentage Error) among the calculated model performance criteria, the techniques used were compared with each other, aiming to determine which of these techniques provided forecasts with less error. Then, through the analysis conducted on four different days, an attempt was made to identify the day that yielded the most successful forecasts. As a final step, the goal was to find a model with the least error based on techniques, epoch number, and the number of days forecasted, considering both MSE and MAPE for stocks. Since the model performance criteria outputs obtained from these analyses are below 1 for MSE and below 5% for MAPE, it can be concluded that both techniques demonstrate successful stock price forecasting. Consequently, in the comparison between these two techniques, it is observed that the LSTM technique is slightly more successful than the GRU technique.