The Impact of Operational Performance on Financial Results in the Storage and Transportation Sector

Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 79 ISRG PUBLISHERS Abbreviated Key Title: Isrg J Econ Bus Manag ISSN: 2584-0916 (Online) Journal homepage: https://isrgpublishers.com/isrgjebm/ Volume – III Issue - VI (November-December) 2025 Frequency: Bimonthly The Impact of Operational Performance on Financial Results in the Storage and Transportation Sector Ahmet KARACA Pamukkale University, Department of International Trade and Logistics, Denizli, Turkey | Received: 23.08.2025 | Accepted: 27.08.2025 | Published: 23.11.2025 *Corresponding author: Ahmet KARACA Pamukkale University, Department of International Trade and Logistics, Denizli, Turkey 1. Entrance Numerous factors influence a company's profitability in both the short and long term. Comprehending these factors facilitates more effective management of a company's assets. Internal determinants encompass accounting policies, product quality, market responsiveness, successful product innovations, investments in human resources, expenditures on research and development, innovative customer service initiatives, cost reduction strategies, efficient management practices, and market operations. External determinants include the business cycle, exchange rates, mergers, favorable global economic conditions, high Gross Domestic Product (GDP) growth, political and legal environments, social and demographic trends, the company's sector, suppliers, customers, competitors, the global environment, economic and technological factors, social and cultural aspects, shareholders, strategic Abstract This study explores how operational performance affects the financial results of transportation and warehousing companies. Data was gathered from 2012 to 2024 from the Public Disclosure Platform (KAP). Using panel data regression analysis, the research examined the relationship between operational performance indicators and financial outcomes. Operational performance was assessed using metrics such as receivables turnover, inventory turnover, payables turnover, cash conversion cycle, fixed asset turnover, return on equity (ROE), and return on assets (ROA). Financial performance was measured by earnings per share (EPS). The results show that, at a statistically significant level, a one-unit increase in ROE corresponds to a 0.8760 rise in EPS. Conversely, a one-unit increase in the cash conversion cycle (CCC) is linked to a decrease of 1.0106 in EPS, which is also statistically significant. Overall, the findings reveal a negative relationship between EPS and CCC, and a positive relationship between EPS and ROE, with both being statistically significant. Keywords: Operational performance, Logistics, Panel data regression, Cash conversion cycle Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 80 alliances, labor unions, and financial resources. Additionally, external factors comprise government regulations, banking institutions, purchasers, the local community, material resources, and financial assets (Parkitna & Sadowska, 2011). Effective management of any business requires the careful use of both fixed and current assets. Managing working capital is especially vital because it directly affects profitability and liquidity. A study by Singh and Pandey (2008) examined the components of working capital and how its management impacts Hindalco's profitability, along with the relationships between liquidity, profitability, and Profit Before Tax (PBT). The researchers used secondary data from Hindalco's annual reports from 1990 to 2007. Their analysis included ratio analysis, percentage calculations, and correlation coefficients. They also used multiple regression analysis to identify key factors influencing Hindalco's profitability. Additionally, the study evaluated microeconomic profitability using indicators such as the current ratio, liquid ratio, receivables turnover ratio, and the ratio of working capital to total assets (Singh & Pandey, 2008). Factors that positively affected profitability included effective inventory management, optimal debt levels, financial leverage, and capital efficiency. These findings also point out areas where performance can be improved. Managing business operations effectively involves optimizing current assets, which make up most of the total assets. Increasing inventory and receivables turnover rates can boost efficiency and profits. Furthermore, strategic allocation of resources to finance operations without sacrificing financial independence is advisable. Increasing equity turnover can create more value and lead to higher profits. Finally, reducing operating expenses has been identified as having the greatest impact on profitability (Burja, 2011). Profitability measures a company's financial success. To enhance economic performance, firms must efficiently execute their operational, investment, and financing activities. While research indicates that operational performance influences profitability in service operations, the primary emphasis has traditionally been on the relationship between productivity and profitability, or between service quality and profitability. Scholars in marketing have predominantly examined the link between quality and profitability, as evidenced by studies conducted by Nelson et al. (1992), Fornell (1992), Anderson et al. (1994), Rust et al. (1995), and Loveman (1998). Conversely, researchers in accounting and operations management have concentrated on the impact of productivity on profitability, as discussed by Schefczyk (1993) and Smith and Reece (1999), among others. Our investigation examines the relationship between operational performance and the earnings per share ratio, aiming to identify the primary factors influencing these variables. The study underscores the significance of operational activities in maintaining the competitiveness of the transportation and warehousing industry and their influence on profitability. The central research inquiry is: Does operational performance genuinely impact profitability? Moreover, does enhancing operational performance result in increased profits? We employ objective metrics to quantify profitability and operational efficiency, evaluating how operational effectiveness influences the financial outcomes of companies within the BIST Transportation and Warehousing sector. To assess operational performance, we analyze indicators such as receivables conversion cycle, inventory conversion cycle, payables conversion cycle, cash conversion cycle, fixed asset turnover, return on assets, and return on equity. Concurrently, we measure financial performance through the earnings per share ratio. 2. Operational Performance Operational activities such as investment and financing necessitate comprehensive research to attain optimal financial outcomes. In assessing current asset investments in these domains, cash and cash equivalents are of paramount importance. Excessive investment in cash and cash equivalents can elevate costs and potentially result in failures (Ceylan & Korkmaz, 2015, p. 287). Cash management encompasses forecasting a company's cash requirements and surpluses during the planning period, determining appropriate cash levels, devising strategies to accelerate cash inflows and decelerate cash outflows, and making informed decisions regarding the allocation of available funds between liquid assets and securities (Akgüç, 1998, pp. 229-230). Operational performance is assessed through key financial metrics like fixed asset turnover, return on assets, return on equity, and the cash conversion cycle. These metrics provide valuable insights into a company's overall performance. Asset utilization ratios indicate operational efficiency, with the fixed asset turnover ratio showing whether investments in property, plant, and equipment are generating enough returns. This is calculated by dividing net income by fixed asset value (Treadwell, 2015, p. 65). Return on equity measures shareholder returns by dividing net income by shareholders’ equity, while return on assets evaluates profitability and asset efficiency by dividing net profit by total assets (Karaca, 2022, p. 105). Operational performance is measured using well-established metrics based on research, such as fixed asset turnover, return on assets, return on equity, and cash conversion cycle. These metrics give a solid foundation for informed decision-making by assessing a company's performance from multiple angles. In essence, asset utilization ratios measure operational efficiency and offer valuable insights into how effectively businesses use their assets and resources. The fixed asset turnover ratio shows whether a company's investment in fixed assets, typically property, plant, and equipment, is paying off. It's calculated by dividing net income by the current value of fixed assets (Treadwell, 2015, p. 65). Return on equity is determined by dividing net income by shareholders' equity, reflecting the returns earned by shareholders from their investments and the after-tax net income generated. Return on assets is calculated by dividing net profit by total assets to assess a company's investment profitability and efficiency in using its assets (Karaca, 2022, p. 105). Cycle analysis involves examining the difference between the operating cycle and the debt payment cycle. The operating cycle measures how a company manages receivables and inventories, with companies having strong liquidity positions able to reduce financing costs, enhance profitability, and maintain a competitive advantage. The operating cycle involves steps like receiving raw materials, semi-finished goods, and parts into inventory; processing them through production; and converting them into finished products. Input prices are paid to suppliers on time, deducted from inventory upon sale, and product prices are collected from customers (Özkan et al., 2021, p. 28). For a business, forecasting future cash flows involves estimating the cash it needs and managing existing cash by expediting inflows and decelerating outflows. Factors such as size, management approach, operational framework, and auditing procedures influence a company's cash holdings (Ercan & Ban, 2005, p. 288). Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 81 To fulfill its financial obligations, enhance operational efficiency, and augment its value, an organization must accurately evaluate its cash requirements and meticulously administer its cash to sustain a healthy balance. Companies need to calculate their cash conversion cycle to manage liquidity effectively. This is done by subtracting days payable outstanding from the sum of inventory outstanding and days sales outstanding [CCC = (DIO + DSO) – DPO]. As the cash conversion cycle lengthens, an organization's funding needs increase (Ceylan & Korkmaz, 2015, p. 287). A positive cash conversion cycle means cash outflows happen before inflows, requiring more funding and driving up financing costs and reducing profit margins. On the other hand, a negative cycle means inflows come before outflows, allowing companies to boost profit margins by generating income from surplus funds each operational cycle (Özkan et al., 2021, p. 28). 3. Literature Operational performance refers to the measurable aspects of an organization's output that influence key business metrics like market share and customer satisfaction (Voss et al., 1997, p. 3). Profitability measures a company's ability to generate profit, defined as the ratio of profit earned during a specific period to the invested capital (Şimşek & Çelik, 2023, p. 136). Profit is the amount remaining from revenue after subtracting expenses directly related to revenue generation, including manufacturing and operational costs. Although research has mainly examined the relationship between productivity and profitability or performance quality and profitability, the overall impact of operational performance on profitability has received limited attention (Tsikriktsis, 2007). Performance measurement systems help achieve strategic objectives, set organizational goals, and provide control. They also serve as tools to assist management in forecasting a company's economic performance and developing strategies to adapt to future changes based on these forecasts (Nanni et al., 1990; Otley, 1999). Choosing the right performance measures is one of the biggest challenges businesses face (Ittner & Larcker, 1998). Poorly selected measures can mislead managers, leading to poor decisions and negative outcomes (Ferguson & Leistikow, 1998). Operational performance can be evaluated using various ratios, such as receivables turnover, fixed asset turnover, inventory turnover, asset turnover, return on assets, and return on equity. These ratios offer different insights into how effectively a company uses its assets and resources, helping assess financial performance from multiple angles. The receivables turnover ratio is calculated by dividing net income by average accounts receivable and shows how often a company collects cash from credit sales. The inventory turnover ratio is determined by dividing the cost of sales by average inventory, indicating how quickly inventory is converted into cash. The fixed asset turnover ratio measures whether the company's spending on equipment and facilities—property, plant, and equipment creates value. It is calculated by dividing the company's net income by the current value of its fixed assets. Yücel and Kurt (2002) explored the link between the cash conversion cycle, a key tool in working capital management, and measures of profitability, liquidity, and debt structure. They analyzed data from 167 companies listed on the Istanbul Stock Exchange (ISE) from 1995 to 2000. Their research compared the cash conversion cycle, profitability, liquidity, and debt structure across different periods, sectors, and company sizes. The results showed a positive correlation between the cash conversion cycle and the current ratio, along with a negative correlation with profitability ratios. Recently, scholars in operations management have begun exploring this area. Zhao and colleagues (2004) studied the relationship between service quality systems and business performance through case studies in China. According to Zeithaml and colleagues (1996), understanding the complex connection between service quality and profitability requires analyzing other factors at the same time, such as the link between productivity and profitability. The existing literature on the connection between productivity and profitability in services is limited. Schefczyk (1993) examined how productivity affects financial performance in the airline industry. Using data envelopment analysis, the study combined multiple outputs and inputs for 15 international airlines, finding that productivity is related to return on equity. Smith and Reece (1999) investigated the relationship among strategy, productivity, and financial performance through field research in a wholesale distribution service environment, discovering that productivity influences economic performance. A common theme across these studies is that they examined productivity's effect on profitability without considering the potential influence of quality. According to Schefczyk (1993), efficiency alone does not represent overall performance, especially when results are measured without accounting for operational factors important to customers, such as punctual flights and undamaged baggage, which do not truly reflect efficiency. Shin and Soenen (1998) discovered a negative relationship between cash conversion cycles and profit margins in food businesses in Greece. Lyroudi and Lazaridu (2000) examined the link between cash conversion cycles and liquidity ratios. Their study found a positive relationship between liquidity ratios and cash conversion cycles (Sakarya, 2008, p. 229). In operations management, practices like Total Quality Management or Just-in-Time Manufacturing are viewed as methods to improve operational and financial performance. The literature on operations management, including both theoretical and empirical studies, indicates a positive relationship between these practices and performance. However, findings from studies, even with a sample of 1,200 companies, generally do not show a clear positive link between operational practices and financial results such as growth and profitability (Duarte et al., 2011). In addition to financial metrics like sales, current ratio, debt-to-equity ratio, and net profit margin, factors such as human capital investment, past performance, and industry diversification also significantly influence profitability. The wider market sector in which a company operates is also a key factor in determining profitability (Azim et al., 2015, p. 66). In their study, Gümüş et al. (2016) examined data from five food companies listed on the BIST 100 index between 2006 and 2015. The study found that these companies mainly produce and sell retail and fast-moving consumer goods, resulting in a negative cash conversion cycle and no funding issues related to their cash conversion cycle. Tsikriktsis (2007) studied how operational performance affects profitability in the US domestic airline industry and also investigated how focus influences profitability across different service types. He used quarterly data from all major carriers Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 82 available since the mandatory reporting of service indicators to the US Department of Transportation. The analysis showed that while "focused" airlines had a relationship between late arrivals and profitability, this link was not seen in full-service airlines. Additionally, capacity utilization was a more important factor for profitability in full-service airlines than for focused airlines. Furthermore, he discovered that focused airlines outperformed the rest of the industry in profitability. Özkan et al. (2021) examined how cash conversion cycles influence the financial performance of cement companies listed on the BIST (Istanbul Stock Exchange) by analyzing their cash conversion cycles. The study found that the cash conversion cycle significantly affects financing costs. 4. Data and Method 4.1. Purpose and importance of the research The research aims to examine how the cash conversion cycle, fixed asset turnover, and profitability ratios affect earnings per share for companies in the BIST Transportation and Warehousing sector and to understand the nature of their influence. An important goal for effective financial management is to reduce the receivables collection period and extend the payment period. Cash management generally depends on the cash conversion cycle, which is calculated by subtracting the trade payables payment period from the sum of the inventory conversion period and the receivables conversion period (Yücel & Kurt, 2002, p. 2). In a competitive environment, a company's ability to survive, grow, manage risks, and maintain strong relationships with financial markets relies on its capacity to meet debt obligations and its overall operational performance. 4.2. Original value of the research This study examines the role of operations in competition within the transportation and warehousing industry and their impact on profitability. The aim is to analyze how operational performance affects the financial outcomes of firms in the BIST Transportation and Warehousing sector, using objective measures of profitability and efficiency. The literature reviews the connection between efficiency and profitability in service industries. In related research, Zhao et al. (2004) explored the relationship between service quality systems and operational performance through case studies in China. Zeithaml et al. (1996) studied the link between service quality and profitability. Schefczyk (1993) assessed how efficiency influences financial performance in the airline industry using data envelopment analysis. Shin and Soenen (1998) examined effective working capital management and operating profitability, introduced the concept of the net commercial conversion cycle as an alternative to the cash conversion cycle, and analyzed its relationship with profitability. Lyroudi and Lazaridu (2000) investigated the connection between the cash conversion cycle and liquidity indicators such as the current ratio and liquidity ratio in food industry companies. Duarte et al. (2011) researched the relationship between operational practices and financial performance, including growth and profitability. Gümüş et al. (2016) analyzed how the cash conversion cycle relates to a firm's economic structure and liquidity position. Tsikriktsis (2007) assessed the impact of operational performance on profitability within the U.S. domestic airline industry. Unlike other studies, this research examined the relationship between companies' operational performance, cash conversion cycle, return on equity, return on assets, fixed asset turnover, and earnings per share using panel regression analysis, focusing on the long-term effects from 2012 to 2024. 4.3. Scope and limitations of the research Within the scope of the research, the operational performance of companies was assessed using the receivables turnover cycle, inventory turnover cycle, payables turnover cycle, cash conversion cycle, fixed asset turnover ratio, return on assets ratio, and return on equity ratio. Financial performance was evaluated with the earnings per share ratio. A limitation of the study was that longterm data were only available for seven companies in the BIST Transportation and Storage sector, and related studies were limited. 4.4. Research method The study examined the relationship between operational performance and financial performance of enterprises using panel data analysis, with variables from Table 2 acting as dependent and independent variables. Table 1 Dependent and Independent Variables Dependent Variable HBK Earnings Per Share Independent Variables NDS Cash Conversion Cycle DVDS Fixed Asset Conversion Period ROE (Return on Equity) Return on Equity ROA (Return on Assets) Return on Assets Earnings per Share (EPS) reflect a company's profitability. It indicates the portion of the company's profit allocated to each outstanding share of common stock. EPS was calculated using equation no. 1 (Azim et al, 2015, p. 69): (1) The cash conversion cycle (CCC) measures management efficiency. It shows how well a company handles its receivables, inventory, and payables—usually, a shorter cash cycle results in better operations and higher profits. The CCC is calculated using equations 2, 3, 4, and 5 (Azim et al., 2015, p. 69; Lazol, 2014). Days Inventory Outstanding = (2) Days Sales Outstanding 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 3) Days Payable Outstanding = (4) Cash Conversion Cycle = (Days Inventory Outstanding + Days Sales Outstanding – Days Payable Outstanding) (5) The Fixed Asset Turnover Ratio (FATR) is calculated using equation no. 6 as a measure of how efficiently a company's fixed assets (property, plant, and equipment) are used (Azim et al., 2015, p. 69): 𝐴 (6) Return on Equity (ROE) shows a company's profitability by indicating how much profit it earns from shareholders' investments. ROE is calculated as shown in equation 7: Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 83 (7) Return on Assets (ROA) indicates how effectively and efficiently a company's assets are being used, indicating how profitable the company is relative to its total assets. ROA is calculated as in Equation 8. 𝐴 (8) 4.5. Research model and hypotheses The relationship between operational performance and financial performance, which are variables in the research model, was examined using panel data regression. The panel data regression model is shown in equation 9 (Gujarati, 2006, p. 219; Das, 2019, p. 43). (9) In the equation, Y (dependent variable), Xₖᵢₜ (independent variables), α is the constant term, βₖᵢₜ are the slope coefficients, and µᵢₜ is the error term, with subscript i indicating units and t representing time (Altunışık et al., 2010; Tafri et al., 2009). Consistent with the research purpose, the following hypotheses were developed: H1. Fixed Asset Turnover, Return on Equity, and Return on Assets significantly influence Earnings Per Share. H2. There is a negative correlation between Earnings Per Share and Cash Conversion Cycle. 4.6. Findings of the research Included businesses operating within the BIST Transportation and Storage sector. The selection focused on companies with a sufficient period for panel data analysis. Table 2 BIST Transportation and Storage Sector Companies Order Code Company Name 1 WHITE WHITE FLEET AUTO RENTAL INC. 2 CLEBI CELEBI AVIATION SERVICES INC. 3 GSDDE GSD SHIPPING REAL ESTATE CONSTRUCTION INDUSTRY AND TRADE INC. 4 PGSUS PEGASUS AIR TRANSPORTATION INC. 5 RYSAS REYSAŞ TRANSPORTATION AND LOGISTICS TRADE INC. 6 TLMAN TRABZON PORT MANAGEMENT INC. 7 THYAO TURKISH AIRLINES AO This part of the study includes data calculated using ratios related to the financial and operational performance of companies in the BIST Transportation and Storage Sector. Table 3 Earnings per Share Values of Companies in the BIST Transportation and Storage Sector YEARS WHITE CLEBI GSDDE PGSUS RYSAŞ THYAO TLMAN MEAN 2012 0.02 0.86 0.04 1.68 0.00 0.96 - 0.51 2013 0.07 0.13 -0.19 0.90 0.25 0.49 - 0.24 2014 -0.23 2.25 -0.16 1.40 0.19 1.32 - 0.68 2015 1.17 3.42 -0.29 1.11 -0.27 2.17 - 1.04 2016 0.73 1.10 -0.54 -1.31 -0.28 -0.03 1.35 0.15 2017 0.31 3.51 -0.30 4.91 -0.13 0.46 1.51 1.47 2018 0.00 8.42 0.68 4.96 -0.61 2.93 1.81 2.60 2019 0.15 7.92 -0.11 13.03 0.05 3.29 2.48 3.83 2020 0.38 -6.96 -0.33 -19.21 -0.18 -4.05 2.79 -3.94 2021 0.38 21.95 0.93 -19.28 -0.23 5.95 2.94 1.81 2022 0.23 44.44 0.42 69.41 1.00 34.37 10.13 22.86 2023 0.78 68.63 -2.03 204.37 1.48 118.11 7.59 56.99 2024 -0.38 146.77 -0.23 26.57 0.70 82.14 5.58 37.31 AVERAGE 0.28 23.26 -0.16 22.20 0.15 19.09 2.78 9.66 MAXIMUM 1.17 146.77 0.93 204.37 1.48 118.11 10.13 69.00 MINIMUM -0.38 -6.96 -2.03 -19.28 -0.61 -4.05 0.00 -4.76 Table 3 presents the companies' earnings per share statistics. CLEBİ ranks first with an average EPS of 23.26, PGSUS ranks second with an average EPS of 22.20, and THYAO ranks third with an average EPS of 19.09. The companies' overall average EPS performance was the lowest in 2023 at 56.99, while in 2020 it was -3.94. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 84 Figure 1 BIST Transportation and Storage Enterprises Earnings Per Share Trend Figure 1 displays the average earnings per share of companies from 2012 to 2024. Figure 2 BIST Earnings Per Share Trends for BIST Transportation and Storage Companies Figure 2 displays the trends in earnings per share for each company. Earnings per share reached a low in 2020-2021 and a high in 2023. Table 4 Cash Conversion Cycles of BIST Transportation and Storage Enterprises YEARS WHITE CLEBI GSDDE PGSUS RYSAŞ THYAO TLMAN MEAN 2012 58.64 18.05 0.00 -20.77 33.79 -1.11 - 12.66 2013 57.02 14.75 0.00 -9.32 14.86 -2.30 - 10.72 2014 18.64 6.29 0.00 2.00 -8.82 -3.65 - 2.07 2015 3.00 3.94 0.00 3.66 -20,20 -5.75 - -2.19 2016 12.58 10.56 38.30 -2.39 -30.50 -4.33 -25.94 -0.25 2017 39.21 9.05 21.65 -12.55 -27.83 -5.96 -43.60 -2.86 2018 49.04 9.28 15.37 -13.79 -11.21 -10.26 -40.14 -0.25 2019 48.55 10.23 25.13 -15.87 -11.21 -12.45 -44.49 -0.02 2020 46.16 9.72 30.07 -21.68 -11.80 -6.88 -48.36 -0.39 2021 32.74 0.96 8.61 -35.29 2.22 1.53 -16.94 -0.88 2022 28.75 -1.36 -1.89 -23.87 4.94 -0.99 -26.72 -3.02 MEAN -50.00 0.00 50.00 100.00 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 MEAN -50.00 0.00 50.00 100.00 150.00 200.00 250.00 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Earnings Per Share BEYAZ CLEBİ GSDDE PGSUS RYSAŞ THYAO TLMAN Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 85 2023 25.60 1.00 -8.91 -22.81 8.96 -4.08 -14.24 -2.07 2024 15.62 -1.39 -19.21 -17.88 -3.50 -2.64 -11.89 -5.84 AVERAGE 33.50 7.01 8.39 -14.66 -4.64 -4.53 -20.95 0.59 THE BIGGEST 58.64 18.05 38.30 3.66 33.79 1.53 0.00 22.00 SMALLEST 3.00 -1.39 -19.21 -35.29 -30.50 -12.45 -48.36 -20.60 When analyzing the data in Table 4, TLMAN has the shortest cash conversion cycle at -20.95. In other words, TLMAN quickly converts its receivables into cash while delaying debt payments, ensuring debts are paid later. PGSUS NDS ranks second, just behind TLMAN, with a - 14.66 figure, while RYSAŞ ranks third at -4.64. Figure 3 Cash Conversion Cycles of Businesses in the BIST Transportation and Storage Sector Figure 3 illustrates the NDS performance trends for each company individually. As shown in the chart, TLMAN, PGSUS, and RYSAŞ have lowered payment-related risks by maintaining negative cash conversion cycles. Companies with positive cycles have receivables and inventory turnover periods that surpass their payables turnover cycles, which increases liquidity risk. Figure 4 Average Cash Conversion Cycles of BIST Transportation and Storage Enterprises by Year Figure 4 shows the average NDS values of businesses over the years. NDSs of companies peaked in 2012 and again in 2024. In other words, the graph indicates that the receivables and inventory turnover periods of businesses decreased relative to their payables turnover periods after 2014, thereby enhancing their ability to pay debts. Table 5 Fixed Asset Turnover Periods of BIST Transportation and Storage Enterprises YEARS WHITE CLEBI GSDDE PGSUS RYSAŞ THYAO TLMAN MEAN 2012 0.68 1.50 0.00 1.03 0.90 0.99 - 0.73 2013 0.71 1.45 0.00 1.04 0.77 0.90 - 0.70 -60.00 -40.00 -20.00 0.00 20.00 40.00 60.00 80.00 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 NDS BEYAZ CLEBİ GSDDE PGSUS RYSAŞ THYAO TLMAN -10.00 -5.00 0.00 5.00 10.00 15.00 2010 2012 2014 2016 2018 2020 2022 2024 2026 MEAN Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 86 2014 2.10 1.58 0.00 1.52 0.53 0.95 - 0.95 2015 3.08 1.75 0.00 1.54 0.43 0.75 - 1.08 2016 5.30 1.48 0.10 0.89 0.43 0.56 0.68 1.35 2017 2018 13.99 1.66 0.17 1.07 0.43 0.72 1.47 2.79 16.93 1.49 0.35 0.89 0.47 0.74 1.80 3.24 2019 85.53 1.18 0.17 0.74 0.55 0.63 1.43 12.89 2020 2021 53.39 19.36 0.84 0.72 0.16 0.33 0.20 0.27 0.42 0.37 0.30 0.34 2.14 2.25 8.21 3.38 2022 21.54 1.25 0.40 0.57 0.69 0.70 0.88 3.72 2023 39.41 1.19 0.16 0.46 0.99 0.63 0.86 6.24 2024 24.89 1.70 0.25 0.52 0.75 0.70 0.56 4.20 AVERAGE 22.07 1.37 0.16 0.83 0.59 0.69 0.93 3.80 THE BIGGEST 85.53 1.75 0.40 1.54 0.99 0.99 2.25 13.35 SMALLEST 0.68 0.72 0.00 0.20 0.37 0.30 0.00 0.32 Looking at the data in Table 5, BEYAZ has the highest fixed asset turnover rate, averaging 22.07. This indicates BEYAZ converts its fixed assets into cash 22 times a year. Meanwhile, GSDDE records the lowest rate, with an average of 0.16. Figure 5 Average Fixed Asset Turnover Trends of BIST Transportation and Storage Enterprises As shown in Figure 5, although WHITE has the highest DVDH, the other companies have similar values. Figure 6 Average Fixed Asset Turnover Rate Trends of BIST Transportation and Storage Sector Enterprises by Year When the DVDH of the enterprises is reviewed annually, it is observed that the highest values occurred in 2019, 2020, and 2023, while the lowest values were between 2012 and 2016. 0.00 50.00 100.00 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 DVDH BEYAZ CLEBİ GSDDE PGSUS RYSAŞ THYAO TLMAN 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 MEAN Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17686716 87 Table 6 Return on Equity Ratios of Enterprises in the BIST Transportation and Storage Sector YEARS WHITE CLEBI GSDDE PGSUS RYSAŞ THYAO TLMAN MEAN 2012 0.01 0.33 0.02 0.39 0.00 0.21 - 0.14 2013 0.11 0.07 -0.08 0.08 0.10 0.10 - 0.05 2014 -0.35 0.51 -0.09 0.12 0.07 0.20 - 0.07 2015 0.64 0.58 -0.15 0.08 -0.13 0.21 - 0.17 2016 0.29 0.26 -0.29 -0.09 -0.17 0.00 0.38 0.05 2017 0.13 0.51 -0.17 0.20 -0.09 0.03 0.48 0.16 2018 0.00 0.58 0.27 0.14 -1.25 0.13 0.50 0.05 2019 0.12 0.34 -0.04 0.25 0.10 0.11 0.56 0.21 2020 0.24 -0.38 -0.10 -0.36 -0.04 -0.14 0.43 -0.05 2021 0.19 0.32 0.23 -0.29 -0.05 0.09 0.44 0.13 2022 0.04 0.33 0.04 0.39 0.22 0.26 0.33 0.23 2023 0.11 0.32 -0.18 0.38 0.17 0.36 0.20 0.19 2024 -0.04 0.46 -0.02 0.18 0.14 0.17 0.09 0.14 AVERAGE 0.12 0.32 -0.04 0.11 -0.07 0.13 0.26 0.12 THE BIGGEST 0.64 0.58 0.27 0.39 0.22 0.36 0.56 0.43 SMALLEST -0.35 -0.38 -0.29 -0.36 -1.25 -0.14 0.00 -0.40 When examining the values in Table 6, the company with the highest return on equity is CLEBİ, ranking first with an average of 0.32, followed by TLMAN in second place with an average of 0.26, and THYAO in third with an average of 0.13. In other words, CLEBİ delivers the highest return on the capital invested. Figure 7 Average Return on Equity Trends of Enterprises in the BIST Transportation and Storage Sector Figure 7 shows the return on equity trends for each company separately. Figure 8 Annual Average Return on Equity Trends of Enterprises in the BIST Transportation and Storage Sector -1.50 -1.00 -0.50 0.00 0.50 1.00 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 ROE BEYAZ CLEBİ GSDDE PGSUS RYSAŞ THYAO TLMAN