The steel industry is a key factor in achieving the economic and infrastructural development of Afghanistan. In the recent years, a number of domestic steel companies have started operating in the hope of boosting national self-sufficiency, curbing the dependence on imported steel from neighboring countries, and making a contribution to the overall economic stability. Current estimates by the industry show that over 220,000 tons of steel have been produced by five major companies in the recent fiscal year, with an estimated national iron-ore reserves of approximately two billion tons. These are some of the indicators that there will be significant potential for growth and a strategic opportunity for Afghanistan to strengthen its industrial base.Despite such developments, the overall business environment in Afghanistan is still very volatile. Economic fluctuations, political instability, and pressure from external markets put steel firms at risk for a variety of financial and operational risks. As global markets become more interconnected, steel companies in Afghanistan simultaneously benefit from greater market access and are vulnerable to more disruptive risk stemming from exchange rate fluctuations, commodity price volatility, and disruptions of the supply chain. Previous research highlights the fact that poor risk management practices have played a major role in the failure of big companies across industries while good financial risk management practices can lead to increased competitiveness and long-term sustainability (Moeller,2007).

The steel industry is in particular subject to large price fluctuations for both raw materials and finished goods. This exposes firms to a large commodity price and currency risks. Internationally, steel producers use a range of financial instruments, such as futures, swaps, options and forward contracts, to hedge their market exposures and stabilize cash flows (Chorn & Croft,2000). However, the adoption of such risk-mitigation practices in Afghan steel firms is still limited, owing to infrastructural constraints, underdevelopment of the market and disruptions brought about by decades of conflict.

Additionally, Afghanistan's logistics and transportation infrastructure has been badly affected by years of war. Damaged highways, security issues and limited regional connectivity make supply chain operations more complicated and more likely to be hit by unexpected disruptions. These conditions increase the importance of effective risk management systems of steel companies to ensure continuity of production, reliable distribution and access to the domestic and foreign markets.

Given these challenges, there is a critical need to study the financial risk exposures of steel companies in Afghanistan, as well as the extent to which risk management practices are being implemented. Such an examination does not only bring to light the vulnerabilities that are already present, but also gives insights into how firms can build resilience, become more operational efficient, and contribute to the national economic development.

Accordingly, the focus of this study is to analyze the major financial risks of the steel companies in Afghanistan, assess existing risk management practices, and find out strategic measures to improve the risk mitigation processes. The findings are expected to make a contribution to the literature on financial risk management in emerging and fragile economies as well as provide practical recommendations for policymakers and industry stakeholders. The rest of this paper is organized as follows: Section 2 is a literature review, Section 3 is the research methodology, Section 4 is the results and discussion, and Section 5 is the conclusion with implications and recommendations.

 Research Background

Risk has been defined in many different ways in the academic literature, most often as the likelihood of adverse outcomes or negative departures from expected results (Olsson,2002, pp.78-93). Yet risk is not completely negative, and risk can also create favorable deviations, which means that uncertainty can lead to both threats and opportunities for firms (Herman & Head,2002, pp. 34-56). From a risk management point of view, risk is a reflection of uncertainty about whether an event will or will not happen and what the consequences will be (Olsson,2002). Lucouw (2004, pp. 93-107) holds that total avoidance of risk limits business growth and hence risks must be controlled at an acceptable level where potential benefits outweigh potential losses. In the economic world, risk is defined as a condition where decision-makers are aware of the possible outcomes of decisions and its probabilities (Lam,2011, pp. 78-98). Scholars generally classify risks as credit, market and operational risks. Alijoyo (2002, pp. 98-99) identifies financial risks such as market, credit, operational and reputational risks and non-financial risks while Doherty (1985, pp. 76-78) identifies marketing, financial, resource management and environmental risks as major categories of risks that affect organizations. These classifications give a basis for analysis and prioritization of risks in companies.

In response to such risks, risk management is considered as a systematic process of identifying, analyzing, evaluating and treating risks to attain organizational objectives under the state of uncertainty. The international standards define risk management as value-creating, structured, systematic, based on the best available information, tailored to the organization and inclusive of human and cultural factors (Head,2009, pp. 112-117). Effective risk management strategies can be used to reduce the volatility in the performance of the corporate entities, stabilize the operations and market transactions, reduce the chances of failure, and improve the internal and external image of the firm (Urciuoli & Crenca,1989, pp. 341-370). Kawamoto (2001, pp. 57-73) explains risk management as the process of recognizing, analyzing and determining how to deal with specific risks faced by an entity, including financial risks.

Enterprise Risk Management (ERM) extends the traditional risk management practices by incorporating various risks into a single, firm-wide framework. Meulbroek (2002, pp. 122-130) highlights the fact that ERM does not deal with each risk separately but tries to integrate different types of risks and apply integrated tools and methods across the organization. According to Gordon et al. (2009), ERM is a structured process implemented by the board, management and staff, whose purpose is to identify the potential events, manage the risk within risk appetite of the firm, and provide reasonable assurance on the accomplishment of organizational objectives. Searle (2008, pp. 21-29) adds that ERM is useful in reconciling investor expectations with operational difficulties by maintaining constant awareness of risk exposure and facilitating strategic changes.

Financial risk is the potential impact of the variability of cash flows, returns, or financing conditions on the ability of a firm to meet its obligations. Punithavathy (2009, pp. 51-63) points out that there is a great relationship between financial risk and a firm's capital structure: The greater the leverage, the greater the financial risk. Liquidity constraints, irregular cash-flow patterns, and debtor defaults are the major contributors to financial risk (Kiragu,2014, pp. 117-121).

Tapiero (2004) defines financial risk management as the creation of economic value through the management of the exposure to financial risks, especially to credit risk and market risk. Holton (2004) in a similar vein defines financial risk as unexpected volatility of returns which includes credit risk, liquidity risk, market risk. Consequently, financial risk management practices constitute policies and procedures to protect organizations from these risks and may generally be classified into credit-risk management, liquidity-risk management and market-risk management (Kithinji,2010). Effective financial risk management provides a steady flow of cash, limits the chance for financial distress and may even ultimately improve profitability.

The risk management process generally involves risk identification, assessment, control and monitoring. The identification stage has been consistently emphasized in the literature.

Tchankova (2002) and Barton et al. (2002) describe risk identification as the first and critical step which involves the recognition of key areas internally and externally where major risks may arise. Responsibilities for identifying specific risks will need to be allocated appropriately; for example, interest rate and foreign exchange risks are generally the responsibility of the finance department. Scenario analysis and risk mapping are popular methods which can help to classify risks according to their frequency and severity, which can be used to prioritize them effectively by firms (Barton et al., 2002).

Fuser (1999) makes a case for classifying risks based on the potential damage they may inflict and the likelihood that they will occur, allowing firms to distinguish risks that threaten their survival from those that will cause minor harm and aid in the efficient allocation of resources.

Monitoring and control ensure that the practices of risk management are consistent with the company's goals and risk appetite. Al-Tamimi and Al-Mazrooei (2007) emphasize the importance of continuous monitoring to detect the deviation in its initial stages. Since it is not possible for top management to do in-depth oversight, organizations typically have separate risk or internal-audit departments to oversee the system and report deficiencies to the board (Tseng,2007, pp. 120-131).

Foundational theories in financial economics are also used to inform the relationship between risk and return. The Capital Asset Pricing Model (CAPM), developed by Sharpe (1964, pp. 231-240) based on Markowitz's portfolio theory (1952;1959) makes the distinction between systematic (market-wide) and unsystematic (firm-specific) risk. Systematic risk, which is affected by macroeconomic conditions, cannot be diversified and is thus priced by the market, while unsystematic risk can be reduced by diversification. Arbitrage Pricing Theory (APT), introduced by Ross (1976, pp. 73-80; 97-105), extends this analysis, in that it models asset returns as a linear function of a number of macroeconomic factors such as inflation, industrial production and interest rates. Both models emphasize the need for higher expected returns to offset higher systematic risk - a crucial point for industries such as steel, where exposure to commodity-price volatility and exchange rate fluctuations is high.

Hedging forms an integral part of financial risk management and enables firms to balance their exposure to adverse fluctuations in key variables such as interest rate, exchange rate, and price of commodities (Okochi,2008, pp. 56-63). Derivative instruments such as futures, forwards, options and swaps are important in shifting market risk to other market participants (Selvi & Asli,2010, pp. 143-152). Futures and forward contracts lock in future prices; options offer asymmetric protection, whilst preserving upside potential; and swaps help firms manage interest rate and currency exposures. These instruments are particularly important in commodity-intensive industries such as steel where the time lag between procurement of raw materials and the sale of finished products exposes firms to substantial price risk. Chorn and Croft (2000) report widespread use of swaps, futures, options, and forwards in the steel industry in markets in which such instruments are available, although there is less use of such instruments in emerging or fragile economies.

A significant amount of empirical research has been done on the relationship between risk management practices and firm performance. Shahroudi et al., 2012, pp 91-111 Risk prevention policies, monitoring, and control have a positive effect on financial performance by reducing losses and stabilizing returns. As cited by Mudaki et al. (2012, pp. 111-119), Ernst & Young report that companies that have credible risk management frameworks benefit from higher revenues, greater efficiency, and better return on assets. Several studies support a strong positive correlation between risk management and organizational performance (La & Choi,2012, pp. 76-81), with Asemeit and Abuda (cited in La & Choi,2012, pp. 12-23) claiming that there is a direct, positive, correlation between risk management processes and financial performance. However, there are researchers who argue that the evidence is mixed, and that managerial quality and strategic decision-making may have a stronger influence on performance than formal systems for managing risk in some contexts. Overall, the dominant finding is that good financial risk management increases profitability by reducing downside risk, stabilizing cash flows and improving resource allocation - and this is especially the case in volatile environments.

Despite the large body of literature, most empirical studies are on developed markets, large financial institutions or relatively stable economies. Evidence is still limited on the impact of financial risk management practices on the profitability of firms operating in highly volatile and institutionally fragile environments. Moreover, the steel industry - with its commodity-price volatility, exchange-rate risk and complex supply chains - has received comparatively less attention than the banking and financial sectors. In Afghanistan, there is a definite research gap about the nature of financial risk exposure in steel companies, types of financial risk management practices adopted, and empirical relationship between risk management practices and firm profitability. The present study tries to address this gap by investigating the impact of financial risk management practices on the profitability of steel milling firms in Afghanistan. By examining a strategic industry that has received little study in a fragile economy, this research seeks to have both academic and practical applications to managers and decision makers.

 The conceptual model demonstrates the correlations between financial risk management practices, which are presented in four key elements, including understanding risk and risk management (URM), risk identification (RI), risk analysis and assessment (RAA), and risk monitoring (RM) and financial performance of steel companies expressed in terms of return on assets (ROA). The control variables that impact on the dependent variable are firm size and capital structure.

Research Methodology

This study investigates the effect of financial risk management practices on the financial performance of steel milling companies in Afghanistan. The present chapter describes the research design, target population and sampling approach, data collection procedures, and the methods used for data analysis and model specification.

The study adopts an explanatory, quantitative research design. A quantitative approach is appropriate because the objective is to measure the relationships between clearly defined independent and dependent variables and to test hypotheses using numerical data. In line with the positivist paradigm, the study assumes that the phenomena under investigation can be observed, measured, and analyzed objectively through statistical techniques (Lavrakas,2008, pp. 122-126; Sekaran & Bougie,2011, pp. 35-41). The design focuses on explaining how variations in financial risk management practices are associated with variations in firm profitability among steel companies operating in Afghanistan at a given point in time.

The target population of the study consists of all steel milling companies that are actively operating in Afghanistan. For the purposes of this research, the population comprises five steel companies that dominate the domestic steel market. Following the definition by Kilani and Kobziev (2016, pp. 77-82), the population is understood as the entire group of entities sharing common, observable characteristics relevant to the study--in this case, Afghan steel firms engaged in production and sales activities. Because the number of such firms is small, the study applies a census approach at the firm level, including all five companies in the analysis.

Within these companies, the units of observation are key personnel involved in finance and risk-related functions (e.g., finance managers, risk managers, senior accountants). A purposive (judgmental) sampling strategy is employed to select respondents who are directly involved in or knowledgeable about financial risk management practices in their respective firms. This non-probability sampling technique is suitable when the researcher seeks information from individuals who possess specific expertise and are willing to participate, thereby increasing the relevance and accuracy of responses (Kombo & Tromp,2009, pp. 121-127).

The study relies on both primary and secondary data. Primary data are collected through a structured, self-administered questionnaire designed to capture financial risk management practices in the steel companies. The questionnaire consists mainly of closed-ended items measured on a Likert scale, supplemented by a limited number of open-ended questions where clarification is necessary. The instrument is organized to gather information on respondents' understanding of risk and risk management, the firm's risk identification processes, risk analysis and assessment procedures, and risk monitoring practices. To ensure clarity and to facilitate accurate responses, the questionnaire was prepared in English and translated into local languages (Dari and Pashto), then back-translated into English to maintain consistency.

Secondary data are obtained from the audited financial statements of the sampled steel companies. Specifically, annual financial data for a multi-year period (e.g., 2015-2018) are collected in order to compute the firms' financial performance indicators, particularly return on assets (ROA). The combination of survey-based data on financial risk management practices and archival financial data enables the study to empirically test the effect of these practices on firm profitability.

Data collection follows a drop-and-pick (drop-off and later collection) procedure for the questionnaires. The researcher delivered the questionnaires to the selected respondents in each company and later collected the completed forms. This procedure allowed respondents sufficient time to provide thoughtful answers in their normal working environment. The financial statements were obtained from company records, with permission from management, and key financial figures were extracted into a structured spreadsheet for analysis.

Both descriptive and inferential statistical methods are used to analyze the data. After the completed questionnaires are received, responses are checked for completeness, edited, coded, and entered into the Statistical Package for Social Sciences (SPSS). Descriptive statistics--such as means, standard deviations, minimum and maximum values--are computed to summarize the characteristics of the variables and to provide an overview of financial risk management practices and firm performance across the five companies. Tables and charts are used to present the results in a clear and interpretable manner.

Inferential statistics are then employed to examine the relationship between financial risk management practices and firm profitability. Correlation analysis is first used to explore the strength and direction of the association between the independent variables (financial risk management dimensions) and the dependent variable (ROA). Subsequently, a linear regression model is estimated to assess the effect of financial risk management practices on financial performance while controlling for firm-specific factors such as size and capital structure. In this framework, the financial performance of steel companies is treated as the dependent variable, and financial risk management practices are the main explanatory variables.

Financial risk management practices (FRMP) are conceptualized as a multi-dimensional construct comprising four key components: (i) understanding of risk and risk management, (ii) risk identification, (iii) risk analysis and assessment, and (iv) risk monitoring. These dimensions are measured using composite indices derived from the Likert-scale questionnaire items. Firm size and capital structure are included as control variables, measured respectively by the ratio of turnover to total assets and the debt-equity ratio.

The functional relationship can be expressed as:

and the econometric model linking financial performance to financial risk management practices is specified as:

where:

ü       =financial performance of firm , measured by return on assets;

ü     = intercept term;

ü       = regression coefficients;

ü       =understanding of risk and risk management in firm ;

ü       =risk identification practices in firm ;

ü       =risk analysis and assessment practices in firm ;

ü       =risk monitoring practices in firm ;

ü       =size of firm , proxied by the ratio of turnover to total assets;

ü       =capital structure of firm , measured by the debt–equity ratio;

ü       =error term capturing unobserved factors.

This methodological framework is designed to provide a rigorous and coherent basis for assessing the impact of financial risk management practices on the profitability of steel milling companies in Afghanistan, using a combination of survey evidence and financial statement data and relying primarily on quantitative, explanatory analysis.

 Findings and Analysis

This section presents the empirical findings of the study and their interpretation in line with the research objectives. Primary data were collected using a structured questionnaire based on a five-point Likert scale, while secondary data on financial performance (ROA) were obtained from the audited financial statements of the sampled steel companies. Descriptive statistics, correlation analysis, and multiple regression analysis were used to examine the effect of financial risk management practices on the profitability of steel milling companies in Afghanistan.

The target population consisted of five steel companies operating in Afghanistan. A total of 40 questionnaires were distributed to finance-related staff across these firms, of which 32 were completed and returned, yielding a response rate of 80%. This response rate is considered excellent for survey research and sufficient to provide reliable insights into the research problem. The distribution of respondents by position is shown in Table 1 Most of the respondents were finance managers, finance officers, and credit analysts, positions that are directly involved in financial decision-making and risk-management activities within the firms and therefore appropriate for this study.

 Table 1. Position and Response Rate in Steel Firms

Source: Research findings

With respect to the existence of formal risk management systems (RMS), 55.8% of respondents indicated that their companies have a formal RMS in place, while 31.3% reported that no formal system exists in their organizations; the remaining respondents were neutral. This suggests that a majority of steel firms have institutionalized some form of structured risk management, although a sizeable minority still operate without fully formalized systems. Regarding the duration of these systems, 37.5% of respondents reported that a structured RMS has been in place for more than ten years, 25.0% indicated a duration of 7-10 years, 21.9% reported 4-6 years, and 15.6% indicated 1-3 years. Overall, these results show that for many firms, risk management is a well-established function, while others remain at relatively early stages of development.

The study further examined which parties are involved in developing risk management strategies in the steel firms. Respondents rated, on a five-point Likert scale, the extent of involvement of various factors such as executive management, directors, finance managers, credit specialists, consultants, and employees. The results are summarized in Table 2 Credit specialists, executive management, and external consultants are involved to a large extent in the formulation of risk management strategies, as reflected by mean scores above 3.5. Directors and finance managers are involved to a moderate extent, while suggestions from employees and other parties are relatively less influential. These findings suggest that risk management strategy in Afghan steel companies is primarily driven by senior finance professionals and top management, with limited bottom-up participation.

 Table 2. Parties Involved in Developing RM Strategies

Source: Research findings

The main functions of financial risk management in the sampled firms were also investigated. Respondents evaluated the extent to which different roles are considered major functions of financial risk management. As presented in Table 3, enhancing financial strength, implementing and ensuring security, and establishing business continuity programs are rated very highly. Ensuring employees’ protection is also seen as an important role, while managing risk-transfer programs is perceived as moderately important. Overall, financial risk management is viewed as integral to both financial resilience and operational continuity in Afghan steel companies.

 Table 3. Major Roles of Financial Risk Management

Source: Research findings

The study also explored factors influencing the firms’ overall approach to financial risk management. Respondents rated the influence of several contextual and strategic factors. As shown in Table 4, regulatory requirements and industry competition are the most influential drivers of financial risk management, both with mean scores above 4.0. The desire to achieve high returns is also a strong driver, while the influence of the board of directors is moderate. Competition in the broader economy appears to have a relatively weaker impact on the firms’ risk-management posture. These results underline the importance of external pressures and compliance obligations in shaping the adoption and intensity of risk-management practices.

 Table 4. Factors Affecting the Financial Risk Management Approach

Source: Research findings

Regarding risk identification, respondents evaluated the extent to which their firms focus on different types of risks. The results are summarized in Table 5 The firms place the greatest emphasis on market risk and interest rate risk, followed by transaction risk and technology risk. Liquidity risk and foreign-exchange risk receive moderate attention. In contrast, political risk and price risk exposure are rated relatively low despite the highly uncertain political and economic environment in Afghanistan and the volatility of steel prices. This indicates a potential gap in the risk identification process, where some critical external risks may be underemphasized.

 Table 5. Risk Identification (RI)

Source: Research findings

The means through which awareness of financial risks is raised within the firms were also examined. As indicated in Table 6, regular meetings are the most commonly used method and are rated as highly effective in creating awareness among staff. Regular training programs are used to a moderate extent, while one-on-one sessions are relatively rare. This suggests that group-based communication is the dominant channel for disseminating risk-related information in Afghan steel firms.

 Table 6. Means of Creating Awareness on Financial Risk Management

Source: Research findings

The effectiveness of specific risk-monitoring practices was then assessed. Table 7 shows that monitoring of foreign currency prices and world steel prices is perceived as effective to very effective. Strict monitoring and adherence to customer credit limits are also rated as effective. In contrast, the regular review of debtors’ aging and working capital is seen as less effective. This indicates that while external price and currency monitoring is relatively strong, internal monitoring related to short-term liquidity and receivables management could be strengthened.

 Table 7. Risk Monitoring Practices

Source: Research findings

In terms of risk-management techniques, respondents rated the effectiveness of different tools and practices in influencing financial performance. The results in Table 8 indicate that strict credit policies, regular appraisal of debtors using the 5C’s, and incorporating foreign-exchange fluctuations into the cost model are perceived as highly effective techniques. Forecasting exposure to exchange-rate risk, using forwards and futures, price-risk management, and enhanced information sharing between hedging units and supply chain management are also considered effective. On the other hand, commodity price hedging and margin transaction hedging are rated as only slightly effective or infrequently used. This suggests that while Afghan steel firms actively apply basic financial risk-management techniques, the use of more sophisticated derivative-based hedging for commodity prices is still limited.

 Table 8. Risk Management Techniques

Source: Research findings

To investigate the relationship between financial risk-management practices and financial performance, Pearson correlation coefficients were computed for financial performance (ROA) and the main explanatory variables: understanding risk and risk management (URM), risk identification (RI), risk analysis and assessment (RAA), risk monitoring (RM), firm size, and capital structure. As shown in Table 9, financial performance is positively correlated with URM, RI, RAA, RM, and especially firm size, while it is negatively correlated with capital structure. These results suggest that stronger risk-management practices and larger firm size are associated with higher profitability, whereas higher leverage tends to be associated with lower financial performance.

 Table 9. Pearson Correlation Coefficients

Source: Research findings

A multiple linear regression model was estimated to determine the joint effect of these explanatory variables on financial performance (ROA). The model specification is:

The model summary in Table 10 shows that the coefficient of determination (R²) is 0.843, indicating that approximately 84.3% of the variation in financial performance is explained jointly by understanding risk, risk identification, risk analysis and assessment, risk monitoring, firm size, and capital structure. The model is statistically significant at the 5% level (p = 0.001), implying a good overall fit.

 Table 10. Model Summary

Source: Research findings

The ANOVA results in Table 11 confirm that the overall regression model is statistically significant, indicating that the set of predictors collectively explains a significant proportion of the variation in ROA.

  Table 11. ANOVA

Source: Research findings

The estimated regression coefficients are reported in Table 12 The resulting equation is:

where = understanding risk and risk management, = risk identification, = risk analysis and assessment, = risk monitoring, = firm size, and = capital structure.

 Table 12. Regression Coefficients