Exchange rate risk management method, system and device and storage medium

Abstract

The invention provides an exchange rate risk management method, system and device and a storage medium, and relates to the technical field of cross-border trade, and the method comprises the steps: obtaining the operation data of an enterprise, and the payment time of a foreign enterprise for paying a settlement amount to the enterprise; generating a plurality of lock sink time nodes according to the payment time; according to the operation data, generating a risk undertaking level and a revenue demand level of the enterprise; generating an initial lock sink proportion of each lock sink time node in combination with the risk bearing level and the revenue demand level; obtaining exchange rate fluctuation data within a preset time length, and generating risk parameters according to the exchange rate fluctuation data; and adjusting the initial lock sink proportion according to the risk parameters, and generating a target lock sink proportion of each lock sink time node. The method has the technical effect that the lock sink strategy is matched with the actual demand of an enterprise.

Description

Technical Field

[0001] This application relates to the technical field of cross-border trade, and particularly to an exchange rate risk management method, system, device, and storage medium. Background Art

[0002] In international trade, enterprises often face settlement risks brought about by exchange rate fluctuations. Especially when foreign enterprises pay the settlement amount to domestic enterprises in foreign currency, how to reasonably arrange the lock-in exchange rate time and lock-in exchange rate ratio within the payment period to balance the enterprise's risk tolerance and revenue requirements is an important issue in enterprise financial management.

[0003] Currently, a fixed lock-in exchange rate plan is usually adopted, that is, several fixed lock-in exchange rate time nodes are preset according to the enterprise's payment period, and lock-in exchange rate operations are carried out at these time nodes according to the preset ratio. Although this method is simple to operate and the process is clear, it lacks the ability of personalized customization and dynamic adjustment, resulting in the mismatch between the lock-in exchange rate strategy and the actual needs of the enterprise. Summary of the Invention

[0004] This application provides an exchange rate risk management method, system, device, and medium for making the lock-in exchange rate strategy match the actual needs of the enterprise.

[0005] In a first aspect, this application provides an exchange rate risk management method, which includes: obtaining the business data of the enterprise and the payment time when a foreign enterprise pays the settlement amount to the enterprise; generating multiple lock-in exchange rate time nodes according to the payment time; generating the risk tolerance level and revenue requirement level of the enterprise according to the business data; combining the risk tolerance level and the revenue requirement level to generate the initial lock-in exchange rate ratio for each of the lock-in exchange rate time nodes; obtaining the exchange rate fluctuation data within a preset time period, and generating a risk parameter according to the exchange rate fluctuation data; adjusting the initial lock-in exchange rate ratio according to the risk parameter to generate the target lock-in exchange rate ratio for each of the lock-in exchange rate time nodes.

[0006] By adopting the above technical solution, the risk tolerance level and revenue requirement level are generated by analyzing the enterprise's business data, and the initial lock-in exchange rate ratio is determined by combining these two levels, so that the lock-in exchange rate plan can adapt to the personalized characteristics of the enterprise. At the same time, the system generates a risk parameter by real-time obtaining the exchange rate fluctuation data, and dynamically adjusts the initial lock-in exchange rate ratio accordingly to generate the target lock-in exchange rate ratio, so that the lock-in exchange rate plan can respond to market environment changes in a timely manner. This way of combining enterprise characteristic analysis and market risk assessment makes the lock-in exchange rate strategy match the actual needs of the enterprise.

[0007] Optionally, the business data includes the enterprise scale and industry category. Generating the risk-taking level and revenue requirement level of the enterprise based on the business data includes: combining the enterprise scale and the industry category to generate the risk-taking level of the enterprise; combining the risk-taking level and the settlement amount to generate the revenue requirement level of the enterprise.

[0008] By adopting the above technical solution, by taking the enterprise scale and industry category as specific indicators of business data, the system can more accurately evaluate the comprehensive strength and industry characteristics of the enterprise, thereby generating a more objective risk-taking level. At the same time, by combining the risk-taking level with the settlement amount to determine the revenue requirement level, the system can further balance the revenue requirements of the enterprise on the basis of considering the enterprise's risk tolerance, so that the finally generated foreign exchange hedging plan not only conforms to the enterprise's risk management ability but also meets its actual business development needs.

[0009] Optionally, combining the enterprise scale and the industry category to generate the risk-taking level of the enterprise includes: generating the initial risk level of the enterprise according to the enterprise scale, and there is a positive correlation between the enterprise scale and the initial risk level; according to the industry category, matching the corresponding industry risk coefficient from the preset industry risk coefficient table, and adjusting the initial risk level according to the industry risk coefficient to generate the risk-taking level of the enterprise.

[0010] By adopting the above technical solution, by establishing a positive correlation between the enterprise scale and the initial risk level, the basic risk-taking ability of the enterprise is first established, reflecting the objective law that the larger the enterprise, the stronger its risk resistance ability. Subsequently, by introducing the preset industry risk coefficient to adjust the initial risk level, the system can take into account the unique business risks and market volatility characteristics of the industry, thereby generating a more comprehensive and accurate risk-taking level, providing a more reliable risk assessment basis for formulating the foreign exchange hedging plan subsequently.

[0011] Optionally, combining the risk-taking level and the settlement amount to generate the revenue requirement level of the enterprise includes: obtaining the risk-taking amount corresponding to the risk-taking level, and there is a positive proportional relationship between the risk-taking level and the risk-taking amount; calculating the difference between the risk-taking amount and the settlement amount; if the difference is positive, determining that the revenue requirement level of the enterprise is the low requirement level, and determining the corresponding first requirement level of the enterprise in the low requirement level according to the size of the difference; if the difference is negative, determining that the revenue requirement level of the enterprise is the high requirement level, and determining the corresponding second requirement level of the enterprise in the high requirement level according to the absolute value size of the difference.

[0012] By adopting the above technical solution, by establishing a direct proportional relationship between the risk-bearing level and the risk-bearing amount, the risk tolerance of the enterprise is first quantified, and then by comparing the difference between the risk-bearing amount and the actual settlement amount, the revenue demand level of the enterprise is dynamically determined. When the difference is positive, it indicates that the enterprise has sufficient risk tolerance, and the system correspondingly assigns a low demand level; when the difference is negative, it indicates that the enterprise needs more active revenue to balance the risk, and the system then assigns a high demand level. This segmentation mechanism based on the size of the difference enables the system to formulate more targeted revenue strategies for enterprises with different funding gap situations while overall considering the risk tolerance of the enterprise, thus achieving a better balance between risk and return.

[0013] Optionally, generating an initial hedging ratio for each of the hedging time nodes in combination with the risk-bearing level and the revenue demand level includes: determining a benchmark hedging ratio for each of the hedging time nodes according to the risk-bearing level, where the risk-bearing level is directly proportional to the benchmark hedging ratio; obtaining an adjustment coefficient corresponding to the revenue demand level, and adjusting the benchmark hedging ratio according to the adjustment coefficient to obtain a first hedging ratio for each of the hedging time nodes; calculating the time interval between each of the hedging time nodes and the payment time, and performing a time decay process on the first hedging ratio according to the time interval to generate a second hedging ratio for each of the hedging time nodes, where the second hedging ratio for each of the hedging time nodes is inversely proportional to the time interval; and using the second hedging ratio as the initial hedging ratio for each of the hedging time nodes.

[0014] By adopting the above technical solution, by establishing a direct proportional relationship between the risk-bearing level and the benchmark hedging ratio, it is ensured that enterprises with stronger risk tolerance can adopt more flexible hedging strategies. Then, the benchmark hedging ratio is optimized using the adjustment coefficient corresponding to the revenue demand level to generate the first hedging ratio, realizing the response to the enterprise's revenue demand. Finally, by introducing a time interval for decay processing, the hedging node farther away from the payment time has a smaller second hedging ratio. This dynamic adjustment in the time dimension not only considers the time value of funds but also reduces the uncertainty risk of forward hedging. Such a triple optimization mechanism enables the finally determined initial hedging ratio to comprehensively balance the enterprise's risk tolerance, revenue demand, and time value factors, providing a scientific and reasonable basis for subsequent risk parameter adjustment.

[0015] Optionally, generating a risk parameter according to the exchange rate fluctuation data includes: determining the maximum exchange rate and the minimum exchange rate within the preset time period according to the exchange rate fluctuation data; calculating the exchange rate difference between the maximum exchange rate and the minimum exchange rate; and determining a risk parameter according to the exchange rate difference, where the exchange rate difference is directly proportional to the risk parameter.

[0016] By adopting the above technical solution, the degree of market volatility is quantified by calculating the difference between the maximum and minimum exchange rates within a preset time period, and a direct proportional relationship is established between the exchange rate difference and the risk parameter, so that the greater the market volatility, the greater the corresponding risk parameter. This risk assessment mechanism based on actual market volatility can timely capture changes in the market environment and convert them into quantifiable risk indicators, providing an objective basis for subsequent adjustment of the initial exchange rate locking ratio, so that the final exchange rate locking scheme can dynamically adapt to the market volatility situation.

[0017] Optionally, adjusting the initial exchange rate locking ratio according to the risk parameter to generate the target exchange rate locking ratio for each of the exchange rate locking time nodes includes: obtaining a risk adjustment coefficient corresponding to the risk parameter, where the risk parameter is inversely proportional to the risk adjustment coefficient; performing an adjustment operation on the risk adjustment coefficient and the initial exchange rate locking ratio of each of the exchange rate locking time nodes to generate the target exchange rate locking ratio for each of the exchange rate locking time nodes.

[0018] By adopting the above technical solution, by establishing a negative correlation between the risk parameter and the risk adjustment coefficient, the greater the market volatility, the smaller the corresponding risk adjustment coefficient, so that a greater degree of adjustment is made to the initial exchange rate locking ratio in the adjustment operation. This design ensures that when the market risk rises, the system can control the risk exposure by reducing the previous exchange rate locking ratio; while when the market is relatively stable, a higher previous exchange rate locking ratio is maintained to lock in the exchange rate earlier. This dynamic adjustment mechanism based on market risk enables the finally generated target exchange rate locking ratio to not only reflect the real-time market environment but also maintain the continuity and operability of the exchange rate locking strategy.

[0019] In a second aspect, the present application provides an exchange rate risk management system, the system includes: an acquisition module, a first generation module, a second generation module, a combination module, a third generation module and an adjustment module; wherein, The acquisition module is used to acquire the business data of the enterprise and the payment time when a foreign enterprise pays the settlement amount to the enterprise; the first generation module is used to generate a plurality of exchange rate locking time nodes according to the payment time; the second generation module is used to generate the risk-bearing level and the revenue demand level of the enterprise according to the business data; the combination module is used to combine the risk-bearing level and the revenue demand level to generate the initial exchange rate locking ratio for each of the exchange rate locking time nodes; the third generation module is used to acquire the exchange rate fluctuation data within a preset time period and generate a risk parameter according to the exchange rate fluctuation data; the adjustment module is used to adjust the initial exchange rate locking ratio according to the risk parameter to generate the target exchange rate locking ratio for each of the exchange rate locking time nodes.

[0020] In a third aspect, the present application provides an electronic device, adopting the following technical solution: including a processor, a memory, a user interface and a network interface, the memory is used for storing instructions, the user interface and the network interface are used for communicating with other devices, and the processor is used for executing the instructions stored in the memory, so that the electronic device executes a computer program of any one of the above exchange rate risk management methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: storing a computer program that can be loaded and executed by a processor for any one of the above exchange rate risk management methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By analyzing the enterprise operation data to generate a risk-taking level and a revenue requirement level, and combining these two levels to determine the initial hedging ratio, the hedging plan can adapt to the personalized characteristics of the enterprise. At the same time, the system generates risk parameters by obtaining exchange rate fluctuation data in real time, and dynamically adjusts the initial hedging ratio based on this to generate the target hedging ratio, so that the hedging plan can respond to market environment changes in a timely manner. This way of combining enterprise characteristic analysis with market risk assessment makes the hedging strategy match the actual needs of the enterprise. Description of the Drawings

[0023] Figure 1 is a flowchart of an exchange rate risk management method provided by an embodiment of the present application; Figure 2 is a structural diagram of an exchange rate risk management system provided by an embodiment of the present application; Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0024] Description of the reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Embodiments

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0026] In the description of the embodiments of the present application, words such as "exemplary", "for example", or "for illustration" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary", "for example", or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example", or "for illustration" is intended to present related concepts in a specific manner.

[0027] Figure 1 is a schematic flow chart of an exchange rate risk management method provided by an embodiment of the present application. As Figure 1 shown, the method includes S101 - S106: S101, obtain the business data of the enterprise and the payment time for the foreign enterprise to pay the settlement amount to the enterprise.

[0028] Before conducting locked - exchange rate risk management, it is first necessary to obtain the basic business data of the enterprise and the settlement payment information. Among them, the business data of the enterprise includes the enterprise scale and industry category. The enterprise scale can be measured by indicators such as the registered capital, annual turnover, and number of employees of the enterprise. For example, the registered capital of an enterprise is 100 million yuan, the annual turnover is 500 million yuan, and the number of employees is 500. The industry category refers to the specific industrial field to which the enterprise belongs, such as the electronic manufacturing industry, the textile and garment industry, etc. By obtaining this business data, the business strength and risk - resistance ability of the enterprise can be comprehensively understood, providing a basic basis for the subsequent assessment of the risk level.

[0029] At the same time, it is also necessary to obtain the payment time for the foreign enterprise to pay the settlement amount to this enterprise. In practical applications, the payment time is usually agreed upon by both parties in the trade contract. For example, a foreign enterprise will pay 1 million US dollars in goods to this enterprise on December 31, 2025. The determination of the payment time is crucial for locked - exchange rate risk management because during the period from the current time to the payment time, the fluctuation of the exchange rate will directly affect the actual income of the enterprise. By accurately grasping the payment time, the locked - exchange rate time node can be reasonably planned and a basis can be laid for formulating differentiated locked - exchange rate strategies in the future.

[0030] These two types of information, namely the business data of the enterprise and the payment time, can be obtained through the enterprise's internal management system or relevant business departments. For example, the business data can be obtained from the enterprise's financial system, human resources system, etc., and the payment time can be obtained from the international trade contract or the accounts receivable management system. After these data are obtained, they will be input into the exchange rate risk management system, and the system will standardize these original data to ensure that the data format is unified and convenient for subsequent calculation and processing.

[0031] By obtaining and processing the basic data in this way, the accuracy and reliability of subsequent risk assessment and forward exchange contract strategy formulation can be ensured. This is an important foundation for the entire exchange rate risk management method and provides the necessary data support for implementing a scientific and reasonable forward exchange contract plan.

[0032] S102. Generate multiple forward exchange contract time nodes according to the payment time.

[0033] After obtaining the basic data, it is necessary to generate multiple forward exchange contract time nodes according to the payment time. The forward exchange contract time node refers to the specific time point at which an enterprise can choose to conduct foreign exchange locking. Reasonably setting these time nodes can help an enterprise disperse exchange rate risks and avoid excessive losses caused by the forward exchange contract decision at a single time point.

[0034] When specifically implemented, the system will first start from the payment time and calculate forward according to a preset time interval to generate multiple forward exchange contract time nodes. For example, if an enterprise's payment time is December 31, 2025, the system can be set to generate a forward exchange contract time node every other month. Calculating 6 months forward from the payment time, 6 forward exchange contract time nodes can be generated, including July 31, 2025, August 31, 2025, September 30, 2025, October 31, 2025, November 30, 2025, and December 31, 2025. The setting of the time interval can be adjusted according to the specific needs of the enterprise. For example, it can be set to every two weeks or every quarter, etc.

[0035] When generating forward exchange contract time nodes, the system will consider the trading calendar of the foreign exchange market and automatically avoid non-trading days such as legal holidays and weekends. If the calculated time node exactly falls on a non-trading day, the system will automatically adjust this time node to the nearest trading day. This can ensure that the generated forward exchange contract time nodes are all valid trading times, facilitating the actual operation of the enterprise.

[0036] In addition, the system will also determine the number of forward exchange contract time nodes in combination with the specific business characteristics of the enterprise. For an enterprise with a large business scale and strong risk tolerance, more forward exchange contract time nodes can be set to conduct risk management more flexibly; while for an enterprise with a small business scale and weak risk tolerance, the number of forward exchange contract time nodes can be appropriately reduced to simplify the management process.

[0037] S103. Generate the enterprise's risk tolerance level and revenue requirement level according to the business data.

[0038] After determining the time node for locking the exchange rate, the system needs to evaluate the enterprise's risk-bearing ability and revenue demand based on the enterprise's business data. The risk-bearing level reflects the enterprise's ability to resist exchange rate fluctuation risks, while the revenue demand level reflects the urgency of the enterprise's foreign exchange earnings. The accurate evaluation of these two indicators is crucial for formulating a reasonable exchange rate locking strategy.

[0039] First, the system will generate the enterprise's risk-bearing level by combining the enterprise scale and industry category. Specifically, the system first generates an initial risk level based on the enterprise scale. The larger the enterprise scale, the higher the initial risk level. For example, an enterprise has a registered capital of 100 million yuan, an annual turnover of 500 million yuan, and 500 employees. Based on the comprehensive evaluation of these scale indicators, its initial risk level can be preliminarily determined as level 4 (assuming the risk level is divided into levels 1-5, and the larger the number, the stronger the risk-bearing ability). Then, the system will match the risk coefficient of the industry to which the enterprise belongs from the preset industry risk coefficient table. For example, if the enterprise belongs to the electronic manufacturing industry and its industry risk coefficient is 1.2, then multiply the initial risk level 4 by the industry risk coefficient 1.2 to finally determine the enterprise's risk-bearing level as 4.8.

[0040] Then, the system will generate the enterprise's revenue demand level by combining the determined risk-bearing level and the settlement amount. First, obtain the risk-bearing amount corresponding to the risk-bearing level. For example, the risk-bearing amount corresponding to the risk-bearing level of 4.8 is 1.2 million US dollars. Compare this risk-bearing amount with the actual settlement amount (such as 1 million US dollars), and calculate the difference as a positive 200,000 US dollars. Since the difference is positive, it means that the enterprise's risk-bearing ability is higher than the amount it actually needs to bear. Therefore, the system will determine the enterprise's revenue demand level as a low demand level. Further, based on the specific value of the difference of 200,000 US dollars, determine the enterprise's specific demand level as level 2 in the low demand level (assuming the low demand level is divided into levels 1-3, and the larger the number, the higher the demand degree).

[0041] Based on the above embodiments, as an alternative implementation, in S103, the business data includes the enterprise scale and industry category. Generating the enterprise's risk-bearing level and revenue demand level according to the business data specifically includes S31-S32: S31, generate the enterprise's risk-bearing level by combining the enterprise scale and industry category.

[0042] First, the system combines the enterprise scale and industry category to generate the enterprise's risk - bearing level. Enterprise scale is a fundamental indicator for evaluating an enterprise's risk - resistance ability, while the industry category reflects the special risk characteristics of the industry in which the enterprise is located. Taking an electronic manufacturing enterprise as an example, the enterprise has a registered capital of 100 million yuan, an annual turnover of 500 million yuan, and 500 employees. These scale indicators can comprehensively reflect the enterprise's operating strength. The system generates an initial risk level of 4 (assuming the risk level is divided into 1 - 5 levels) based on these scale data. At the same time, since this enterprise belongs to the electronic manufacturing industry, the system matches the corresponding industry risk coefficient of 1.2 from the preset industry risk coefficient table. Multiply the initial risk level 4 by the industry risk coefficient 1.2, and finally determine the enterprise's risk - bearing level to be 4.8. This calculation method not only considers the enterprise's basic strength but also reflects the impact of industry characteristics on the risk - bearing ability.

[0043] Based on the above - mentioned embodiment, as an optional implementation manner, in S31, combining the enterprise scale and industry category to generate the enterprise's risk - bearing level specifically includes S311 - S312: S311, generate the initial risk level of the enterprise according to the enterprise scale, and there is a positive correlation between the enterprise scale and the initial risk level.

[0044] In specific implementation, the system determines the enterprise scale by comprehensively evaluating the data of three dimensions: the registered capital, annual turnover, and number of employees of the enterprise. The system first quantifies and scores the data of these three dimensions respectively: For the registered capital, it is divided into 5 levels according to the capital scale, corresponding to 1 - 5 points respectively (for example, less than 10 million yuan is 1 point, 10 million - 50 million yuan is 2 points, 50 million - 100 million yuan is 3 points, 100 million - 500 million yuan is 4 points, and more than 500 million yuan is 5 points); for the annual turnover, it is also divided into 5 levels, corresponding to 1 - 5 points respectively (for example, less than 50 million yuan is 1 point, 50 million - 200 million yuan is 2 points, 200 million - 500 million yuan is 3 points, 500 million - 1 billion yuan is 4 points, and more than 1 billion yuan is 5 points); for the number of employees, it is divided into 5 levels, corresponding to 1 - 5 points respectively (for example, less than 100 people is 1 point, 100 - 300 people is 2 points, 300 - 500 people is 3 points, 500 - 1000 people is 4 points, and more than 1000 people is 5 points).

[0045] S312, match the corresponding industry risk coefficient from the preset industry risk coefficient table according to the industry category, and adjust the initial risk level according to the industry risk coefficient to generate the enterprise's risk - bearing level.

[0046] Then, the system calculates the weighted average of the scores in these three dimensions. The weight of the registered capital is 0.4, the weight of the annual turnover is 0.4, and the weight of the number of employees is 0.2. According to the formula: Initial Risk Level = Registered Capital Score × 0.4 + Annual Turnover Score × 0.4 + Number of Employees Score × 0.2, the initial risk level of the enterprise can be obtained (with a full score of 5).

[0047] S32. Combine the risk-bearing level and the settlement amount to generate the revenue demand level of the enterprise.

[0048] Then, the system combines the determined risk-bearing level and the actual settlement amount of the enterprise to generate the revenue demand level. Suppose the enterprise has a settlement amount of 1 million US dollars. The system first calculates the corresponding risk-bearing amount of 1.2 million US dollars according to the risk-bearing level of 4.8 (determined through a preset mapping relationship). Compare the risk-bearing amount of 1.2 million US dollars with the actual settlement amount of 1 million US dollars to obtain a positive difference of 200,000 US dollars. Since the difference is positive, it indicates that the enterprise's risk-bearing ability exceeds the amount it actually needs to bear. Therefore, the system determines the revenue demand level of this enterprise as a low demand level. Further, according to the position of the difference of 200,000 US dollars within the preset range, determine that the specific level of this enterprise in the low demand level is level 2 (assuming the low demand level is divided into levels 1 - 3).

[0049] Based on the above embodiments, as an alternative implementation, in S32, combining the risk-bearing level and the settlement amount to generate the revenue demand level of the enterprise specifically includes S321 - S324: S321. Obtain the risk-bearing amount corresponding to the risk-bearing level, and the risk-bearing level is directly proportional to the risk-bearing amount.

[0050] The system first obtains the corresponding risk-bearing amount according to the enterprise's risk-bearing level. The corresponding relationship between the risk-bearing level (levels 1 - 5) and the risk-bearing amount is: level 1 corresponds to 200,000 US dollars, level 2 corresponds to 500,000 US dollars, level 3 corresponds to 1 million US dollars, level 4 corresponds to 2 million US dollars, and level 5 corresponds to 5 million US dollars. Intermediate values can be calculated by linear interpolation. For example, when the risk-bearing level is 3.5, the corresponding risk-bearing amount is 1.5 million US dollars.

[0051] S322. Calculate the difference between the risk-bearing amount and the settlement amount.

[0052] S323. If the difference is positive, determine that the revenue demand level of the enterprise is a low demand level, and determine the corresponding first demand level of the enterprise in the low demand level according to the size of the difference.

[0053] Next, the system compares the obtained risk-bearing amount with the actual settlement amount of the enterprise and calculates the difference between the two. This difference reflects the matching degree between the enterprise's risk-bearing ability and its actual business scale. If the difference is positive, it indicates that the enterprise's risk-bearing ability exceeds the amount it actually needs to bear. At this time, the system will determine the enterprise's revenue demand level as a low-demand level and determine the specific level within the low-demand level according to the specific value of the difference. For example, when the difference is between 0 and 300,000 US dollars, it is the low-demand level 1; between 300,000 and 600,000 US dollars, it is the low-demand level 2; and above 600,000 US dollars, it is the low-demand level 3.

[0054] S324, if the difference is negative, then determine that the enterprise's revenue demand level is a high-demand level, and determine the corresponding second demand level of the enterprise within the high-demand level according to the absolute value of the difference.

[0055] On the contrary, if the difference is negative, it indicates that the enterprise's actual business scale exceeds its risk-bearing ability. At this time, the system will determine the enterprise's revenue demand level as a high-demand level and determine the specific level within the high-demand level according to the absolute value of the difference. For example, when the absolute value of the difference is between 0 and 300,000 US dollars, it is the high-demand level 1; between 300,000 and 600,000 US dollars, it is the high-demand level 2; and above 600,000 US dollars, it is the high-demand level 3.

[0056] This evaluation method based on the difference can objectively reflect the enterprise's revenue demand situation. For example, when an enterprise's risk-bearing level is level 4 (corresponding to a risk-bearing amount of 2 million US dollars) and the actual settlement amount is 1.5 million US dollars, the difference is positive 500,000 US dollars. The system will determine it as the low-demand level 2, indicating that the enterprise can adopt a relatively flexible foreign exchange hedging strategy. On the contrary, if the actual settlement amount is 2.5 million US dollars and the difference is negative 500,000 US dollars, the system will determine it as the high-demand level 2, indicating that the enterprise needs to adopt a more prudent foreign exchange hedging strategy.

[0057] S104, combine the risk-bearing level and the revenue demand level to generate the initial foreign exchange hedging ratio for each foreign exchange hedging time node.

[0058] After completing the evaluation of the enterprise's risk-bearing level and revenue demand level, the system needs to determine a suitable initial foreign exchange hedging ratio for each foreign exchange hedging time node. The foreign exchange hedging ratio refers to the ratio of the foreign exchange amount that needs to be hedged at a certain time node to the total settlement amount. The sum of the foreign exchange hedging ratios of all time nodes must be 100%, ensuring that all settlement amounts are hedged.

[0059] The system first determines the benchmark hedging ratio for each hedging time node according to the enterprise's risk tolerance level. Taking the aforementioned example enterprise as an example, its risk tolerance level is 4.8, which is at a relatively high level. Therefore, the system will set a relatively balanced benchmark hedging ratio. Specifically, if the enterprise has a receivable of 1 million US dollars on December 31, 2025, the system will allocate 100% of the amount among 6 hedging time nodes. For enterprises with a relatively high risk tolerance level, the early-stage hedging ratio can be relatively low. The risk tolerance level reflects the enterprise's ability to resist exchange rate risks. A relatively high risk tolerance level means that the enterprise has strong financial strength and anti-risk ability, the enterprise has more sufficient cash flow to cope with exchange rate fluctuations, and the enterprise's financial condition allows it to bear more market risks. Therefore, for enterprises with a relatively high risk tolerance level: more flexible hedging strategies can be adopted, there is no need to lock in a large amount of exchange rates in the early stage, more funds can be left for the later stage, the enterprise has the ability to wait for a better exchange rate level, and can seize opportunities when favorable fluctuations occur in the market.

[0060] For example, the benchmark hedging ratios for July 31, August 31, September 30, October 31, November 30, and December 31, 2025 can be set as 10%, 15%, 15%, 20%, 20%, and 20% respectively.

[0061] Next, the system adjusts the benchmark hedging ratio according to the enterprise's revenue demand level. The enterprise's revenue demand level is level 2 in the low-demand level, and the corresponding adjustment coefficient is 0.9.

[0062] Then, the adjustment is carried out, and the adjustment method needs to reflect the following principles: reduce the adjustment coefficient for the early-stage nodes to reflect the low-demand characteristics; keep the mid-stage nodes basically stable; appropriately increase the ratio of the later-stage nodes to maintain the total of 100%; ensure that the adjusted ratio distribution still maintains an overall increasing trend.

[0063] On the premise of keeping the total ratio unchanged at 100%, the benchmark hedging ratios for each time node are reallocated, and the first hedging ratios can be 8%, 13%, 15%, 20%, 22%, and 22% respectively.

[0064] Then, the system will consider the impact of time factors and perform time decay processing on the first exchange rate locking ratio. The system calculates the interval between each exchange rate locking time node and the payment time, and dynamically adjusts the exchange rate locking ratio according to this time interval. For example, for earlier exchange rate locking time nodes, since they are farther from the payment time, the system will appropriately reduce their exchange rate locking ratios and allocate this part of the ratio to later time nodes. Suppose after time decay, the second exchange rate locking ratios of the six time nodes are adjusted to 5%, 10%, 15%, 20%, 25%, and 25%. The introduction of time decay processing is based on the following economic principle: the farther the exchange rate locking time point is from the payment time, the higher the market uncertainty, and the exchange rate locking ratio should be reduced to control risks. The longer the time interval, the larger the decay factor, and the lower the corresponding second exchange rate locking ratio.

[0065] The system uses the second exchange rate locking ratio as the initial exchange rate locking ratio for each exchange rate locking time node. The specific locked amount for each time node can be obtained by multiplying the total settlement amount by the corresponding exchange rate locking ratio. For example, at the first exchange rate locking time node, the amount that the enterprise needs to lock is $1,000,000 × 5% = $50,000. This method of generating the exchange rate locking ratio based on multi-dimensional factors provides scientific and reasonable exchange rate locking strategy suggestions for enterprises, helps enterprises flexibly adjust exchange rate locking operations according to their own characteristics and market conditions while ensuring basic returns, and achieves an effective balance between risks and returns.

[0066] Based on the above embodiments, as an alternative implementation, in S104, generating the initial exchange rate locking ratio for each exchange rate locking time node by combining the risk tolerance level and the revenue demand level specifically includes S41 - S44: S41, determine the benchmark exchange rate locking ratio for each exchange rate locking time node according to the risk tolerance level, and the risk tolerance level is directly proportional to the benchmark exchange rate locking ratio.

[0067] First, the system determines the benchmark exchange rate locking ratio for each exchange rate locking time node according to the enterprise's risk tolerance level. Suppose an enterprise's risk tolerance level is level 4 (1 - 5 levels), and the system will set the benchmark exchange rate locking ratios for the six exchange rate locking time nodes according to the preset corresponding relationship. The higher the risk tolerance level, the lower the early exchange rate locking ratio and the higher the later exchange rate locking ratio. For example, the benchmark exchange rate locking ratio corresponding to the 4 - level risk tolerance level can be set to 10%, 15%, 15%, 20%, 20%, and 20%. If it is a 3 - level risk tolerance level, the allocation ratio may correspond to 15%, 15%, 17%, 17%, 18%, and 18%, showing a more balanced distribution.

[0068] S42, obtain the adjustment coefficient corresponding to the revenue demand level, and adjust the benchmark exchange rate locking ratio according to the adjustment coefficient to obtain the first exchange rate locking ratio for each exchange rate locking time node.

[0069] Next, the system obtains the corresponding adjustment coefficient according to the revenue demand level of the enterprise. The revenue demand levels include two major categories: low demand and high demand, and each category is divided into three levels (levels 1 - 3). The adjustment coefficient corresponding to the low demand level is less than 1, specifically: level 1 of low demand corresponds to 0.8, level 2 of low demand corresponds to 0.9, and level 3 of low demand corresponds to 0.95; the adjustment coefficient corresponding to the high demand level is greater than 1, specifically: level 1 of high demand corresponds to 1.05, level 2 of high demand corresponds to 1.1, and level 3 of high demand corresponds to 1.2. The setting of the adjustment coefficient reflects the influence degree of different revenue demand levels on the foreign exchange locking strategy.

[0070] Next, the system obtains the corresponding adjustment coefficient according to the revenue demand level of the enterprise. Assume that the revenue demand level of the enterprise is level 2 of low demand, and the corresponding adjustment coefficient is 0.9. The system multiplies the benchmark foreign exchange locking ratio of each time node by this adjustment coefficient, and at the same time ensures that the sum of the adjusted ratios is still 100%, to obtain the first foreign exchange locking ratio. For example, the first foreign exchange locking ratios of the six adjusted time nodes may become 8%, 13%, 15%, 20%, 22% and 22%.

[0071] S43. Calculate the time interval between each foreign exchange locking time node and the payment time, and according to the time interval, perform time decay processing on the first foreign exchange locking ratio to generate the second foreign exchange locking ratio of each foreign exchange locking time node, where the second foreign exchange locking ratio of each foreign exchange locking time node is inversely proportional to the time interval.

[0072] Then, the system considers the influence of time factors on the foreign exchange locking strategy. Assume that the payment time is December 31, 2025, and the six foreign exchange locking time nodes are July 31, 2025, August 31, 2025, September 30, 2025, October 31, 2025, November 30, 2025, and December 31, 2025, and the corresponding time intervals are 5 months, 4 months, 3 months, 2 months, 1 month, and 0 months respectively. The system performs decay processing on the first foreign exchange locking ratio according to these time intervals. The longer the time interval, the greater the decay degree. Specifically, the decay coefficient = (1 - time interval × 0.05) can be used for calculation. The decay coefficients of the six time nodes are 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00 respectively. Multiply the first foreign exchange locking ratio by the corresponding decay coefficient, and ensure that the sum of the final ratios is 100% to obtain the second foreign exchange locking ratio, for example, it may be adjusted to 5%, 10%, 15%, 20%, 25% and 25%.

[0073] The basic principle of attenuation is as follows: attenuate the early nodes and increase the later nodes correspondingly to ensure that the total remains 100%. The longer (earlier) the time interval, the more the nodes are attenuated, and the later nodes receive compensatory increases. Specific adjustment rules: Divide the six time nodes into two groups: the first three nodes (July - September) are attenuated, and the last three nodes (October - December) receive compensatory increases. For the attenuation of the first three nodes: Calculate using the attenuation coefficient: 1 - time interval × 0.05, and multiply the original proportion by the corresponding attenuation coefficient. For the compensation of the last three nodes: Calculate the total proportion reduced after the attenuation of the first three nodes, and distribute the reduced proportion to the last three nodes according to the original proportion weights of the last three nodes.

[0074] This adjustment method ensures that: it reflects the impact of time attenuation, maintains the overall increasing trend, and the adjustment range is reasonable and controllable.

[0075] S44, use the second foreign exchange locking ratio as the initial foreign exchange locking ratio for each foreign exchange locking time node.

[0076] Finally, the system uses the calculated second foreign exchange locking ratio as the initial foreign exchange locking ratio for each foreign exchange locking time node. This multi-step calculation process enables the final foreign exchange locking ratio to fully consider the enterprise's risk tolerance, income requirements, and the impact of time factors. For the example enterprise, this means that its settlement amount of 1 million US dollars will be distributed among six time nodes in the amounts of 50,000 US dollars, 100,000 US dollars, 150,000 US dollars, 200,000 US dollars, 250,000 US dollars, and 250,000 US dollars.

[0077] S105, obtain the exchange rate fluctuation data within a preset time period, and generate risk parameters based on the exchange rate fluctuation data.

[0078] After determining the initial foreign exchange locking ratio for each foreign exchange locking time node, the system needs to consider the impact of market exchange rate fluctuations on the foreign exchange locking strategy. Generating risk parameters by analyzing historical exchange rate fluctuation data can make the foreign exchange locking plan better adapt to the current market environment and improve the practicality and effectiveness of the plan.

[0079] The system first obtains the exchange rate fluctuation data within a preset time period. The preset time period usually selects a historical cycle similar to the payment period. For example, the system will obtain the daily exchange rate data of the US dollar against the RMB in the past 6 months. Assume that during the period from January 1, 2025 to June 30, 2025, the historical data of the US dollar against the RMB shows that the highest exchange rate reaches 7.3000 and the lowest value is 6.8000. The system calculates that the exchange rate difference during this period is 0.5000 (7.3000 - 6.8000 = 0.5000).

[0080] Next, the system determines the risk parameter based on the calculated exchange rate difference. The larger the exchange rate difference, the more volatile the market is, and the corresponding risk parameter should also be larger. When calculating specifically, the ratio of the exchange rate difference to the benchmark exchange rate (such as the average exchange rate of 7.0500 during the period) can be used as the basis, and then the risk parameter is generated according to the preset calculation rules. In this example, the proportion of the exchange rate difference of 0.5000 to the benchmark exchange rate of 7.0500 is approximately 7.09%. Based on this proportion, the system can set the risk parameter to 1.4.

[0081] This method of generating risk parameters based on actual market data can timely reflect the volatility characteristics of the current foreign exchange market. A larger risk parameter means that the market is volatile, and enterprises need to adopt a more cautious exchange rate locking strategy; on the contrary, a smaller risk parameter indicates that the market is relatively stable, and enterprises can adopt a relatively flexible exchange rate locking strategy. This risk parameter will be used to adjust the initial exchange rate locking ratio subsequently to help enterprises optimize the exchange rate locking plan in the actual market environment.

[0082] Based on the above embodiments, as an alternative implementation, in S105, generating the risk parameter according to the exchange rate fluctuation data specifically includes S51 - S53: S51, determine the maximum exchange rate and the minimum exchange rate within the preset time period according to the exchange rate fluctuation data.

[0083] The system first obtains the historical exchange rate data corresponding to the payment period. For example, for a payment period of 6 months, the system will obtain the daily exchange rate data for the past 6 months. Among these historical data, the system identifies the maximum and minimum exchange rates. Assume that within the analysis period, the highest point of the US dollar against the RMB exchange rate reaches 7.3000, and the lowest point is 6.8000. These two extreme values reflect the fluctuation range of the exchange rate during this period.

[0084] S52, calculate the exchange rate difference between the maximum exchange rate and the minimum exchange rate.

[0085] Next, the system calculates the difference between the maximum exchange rate and the minimum exchange rate. In the above example, the exchange rate difference is 0.5000 (7.3000 - 6.8000 = 0.5000). This difference intuitively reflects the volatility of the market. The larger the difference, the more volatile the market is, and the higher the exchange rate risk faced by enterprises.

[0086] S53, determine the risk parameter according to the exchange rate difference, and the exchange rate difference is proportional to the risk parameter.

[0087] Then, the system determines the risk parameter based on the calculated exchange rate difference. Specifically, the system first calculates the percentage of the exchange rate difference in the average exchange rate during the period, and then maps this percentage to the corresponding risk parameter. Suppose the average exchange rate during the period is 7.0500, and the fluctuation ratio is 0.5000 / 7.0500 ≈ 7.09%. The system can set a linear mapping relationship: when the fluctuation ratio is between 0 - 3%, the risk parameter is 1.0; between 3% - 6% is 1.2; between 6% - 9% is 1.4; between 9% - 12% is 1.6; above 12% is 1.8. According to this mapping relationship, the risk parameter corresponding to a fluctuation ratio of 7.09% is 1.4.

[0088] S106. Adjust the initial locked exchange rate ratio according to the risk parameter to generate the target locked exchange rate ratio for each locked exchange time node.

[0089] After generating the risk parameter, the system needs to finally adjust the initial locked exchange rate ratio according to the risk parameter to generate a target locked exchange rate ratio that better conforms to the current market environment. This step combines the market fluctuation risk with the enterprise's specific locked exchange needs to form the final locked exchange plan.

[0090] The system first obtains the corresponding risk adjustment coefficient according to the risk parameter 1.4. There is an inverse relationship between the risk parameter and the risk adjustment coefficient, that is, the larger the risk parameter, the smaller the risk adjustment coefficient, which reflects that a more conservative locked exchange strategy needs to be adopted when market fluctuations intensify. Through the preset mapping relationship, the system converts the risk parameter 1.4 into a risk adjustment coefficient of 0.8. This risk adjustment coefficient will be used to adjust the initial locked exchange rate ratio generated previously.

[0091] Next, the system performs an adjustment operation on the risk adjustment coefficient and the initial locked exchange rate ratio of each locked exchange time node. Taking the previous example, the initial locked exchange rate ratios of the six locked exchange time nodes are 5%, 10%, 15%, 20%, 25% and 25% respectively. Multiply these ratios by the risk adjustment coefficient 0.8 respectively, and at the same time ensure that the sum of the adjusted ratios is still 100%. Finally, the target locked exchange rate ratios are 4%, 8%, 12%, 24%, 26% and 26% respectively. This adjustment further reduces the locked exchange rate ratio in the early stage and correspondingly increases the locked exchange rate ratio in the later stage, reflecting that a more prudent locked exchange strategy should be adopted when market fluctuations are large.

[0092] The target locked exchange rate ratio determined in this way makes the specific locked exchange amounts of the enterprise's $1 million settlement amount at each time node be $40,000, $80,000, $120,000, $240,000, $260,000 and $260,000 respectively. This allocation plan not only takes into account the enterprise's risk - bearing ability and revenue needs, but also adapts to the current market fluctuation situation, providing an equilibrium locked exchange strategy for the enterprise.

[0093] This dynamic adjustment mechanism based on risk parameters can help enterprises adjust their foreign exchange locking strategies in a timely manner when market fluctuations intensify, reduce the initial foreign exchange locking ratio, and increase the later foreign exchange locking ratio. Thus, while ensuring necessary returns, it reserves more room for seizing market opportunities. At the same time, since the target foreign exchange locking ratio is generated after comprehensively considering enterprise characteristics and market environment, it has strong practical guiding significance and can help enterprises better cope with exchange rate fluctuation risks.

[0094] Based on the above embodiments, as an optional implementation manner, in S106, adjusting the initial foreign exchange locking ratio according to the risk parameters to generate the target foreign exchange locking ratio for each foreign exchange locking time node specifically includes S61 - S62: S61, obtaining a risk adjustment coefficient corresponding to the risk parameter, where the risk parameter is inversely proportional to the risk adjustment coefficient.

[0095] After generating the risk parameters, the system needs to incorporate market fluctuation risk factors into the formulation process of the foreign exchange locking plan and generate the final target foreign exchange locking ratio by adjusting the initial foreign exchange locking ratio. This process ensures that the foreign exchange locking plan can adapt to the current market environment.

[0096] The system first obtains the corresponding risk adjustment coefficient according to the risk parameter. There is a preset mapping relationship between the risk parameter and the risk adjustment coefficient: when the risk parameter is 1.0, the corresponding risk adjustment coefficient is 1.0; when the risk parameter is 1.2, the corresponding risk adjustment coefficient is 0.9; when the risk parameter is 1.4, the corresponding risk adjustment coefficient is 0.8; when the risk parameter is 1.6, the corresponding risk adjustment coefficient is 0.7; when the risk parameter is 1.8, the corresponding risk adjustment coefficient is 0.6. This negative correlation ensures that when market fluctuations intensify, the system will adopt a more conservative foreign exchange locking strategy.

[0097] S62, performing an adjustment operation on the risk adjustment coefficient and the initial foreign exchange locking ratio of each foreign exchange locking time node to generate the target foreign exchange locking ratio of each foreign exchange locking time node.

[0098] After obtaining the risk adjustment coefficient, the system needs to apply it to the initial foreign exchange locking ratio through specific adjustment operations to generate the target foreign exchange locking ratio reflecting market risks. This adjustment process needs to adjust the exposure allocation of each time node according to the risk situation while maintaining the overall foreign exchange locking ratio of 100%.

[0099] The adjustment operation is carried out in the way of front - end contraction and back - end compensation. First, the initial exchange - locking ratios of the first three time nodes are adjusted for risk, and they are multiplied by the risk adjustment coefficient respectively. For example, when the initial exchange - locking ratios are [10%, 15%, 15%, 20%, 20%, 20%] and the risk adjustment coefficient is 0.8, after adjustment, the first three nodes become [8%, 12%, 12%], and the total reduced ratio is 8% (from the original 40% to 32%). Then, the system redistributes this reduced 8% to the last three nodes according to the original ratio weights (20:20:20) of the last three nodes. Each node increases by about 2.67%, making the ratios of the last three nodes become [22.67%, 22.67%, 22.67%]. Finally, appropriate rounding is performed on the results, and the target exchange - locking ratios can be [8%, 12%, 12%, 23%, 23%, 22%].

[0100] This adjustment operation method not only maintains the effectiveness of risk adjustment but also sustains the operability of the exchange - locking plan. When the market risk rises, a smaller risk adjustment coefficient will reduce the front - end exchange - locking ratio and transfer more funds to the later nodes, enabling the enterprise to moderately control risks while maintaining a sufficient observation period. When the market risk is low, a larger risk adjustment coefficient allows maintaining a higher front - end exchange - locking ratio, helping the enterprise to lock in favorable exchange rates earlier. Through this dynamic adjustment mechanism, the system can generate an exchange - locking plan that both reflects market risks and has practical operability.

[0101] The technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0102] Taking the foreign exchange exchange - locking risk management of an enterprise as an example, this enterprise has the following basic characteristics: registered capital of 100 million yuan, annual turnover of 500 million yuan, 500 employees, and the industry category is electronic manufacturing. The enterprise needs to conduct exchange - locking risk management on a payment of 1 million US dollars expected to be received on December 31, 2025.

[0103] First, the system obtains the enterprise operation data and conducts a risk - bearing level assessment. According to the enterprise scale scoring standard: a registered capital of 100 million yuan corresponds to 4 points (in the range of 100 million - 500 million), an annual turnover of 500 million yuan corresponds to 4 points (in the range of 500 million - 1 billion), and 500 employees correspond to 3 points (in the range of 300 - 500). Using the weighted calculation formula: initial risk level = registered capital score × 0.4 + annual turnover score × 0.4 + employee number score × 0.2, the initial risk level of this enterprise is 3.8 points. Then, combined with the industry risk coefficient table, the industry risk coefficient corresponding to electronic manufacturing is 1.2, and the final risk - bearing level = 3.8×1.2 = 4.56.

[0104] Next, the system evaluates the revenue demand level based on the enterprise's risk tolerance level of 4.56 and the settlement amount of $1 million. The calculated risk tolerance amount is $1.2 million (obtained through the proportional relationship mapping table between the risk tolerance level and the risk tolerance amount). Since the risk tolerance amount of $1.2 million is greater than the settlement amount of $1 million, with a positive difference of $200,000, the enterprise's revenue demand level is determined to be the low demand level, specifically low demand level 2.

[0105] The system generates 6 forward exchange locking time nodes based on the payment date of December 31, 2025: July 31, 2025, August 31, 2025, September 30, 2025, October 31, 2025, November 30, 2025, and December 31, 2025. Based on the risk tolerance level of 4.56, the benchmark forward exchange locking ratios for each node are set at 10%, 15%, 15%, 20%, 20%, 20%. Considering the revenue demand level is low demand level 2, with a corresponding adjustment coefficient of 0.9, the adjusted first forward exchange locking ratios are 8%, 13%, 15%, 20%, 22%, 22%.

[0106] Then, time decay processing is performed using the decay coefficient calculation formula: (1 - time interval × 0.05). The six time nodes are 5, 4, 3, 2, 1, 0 months away from the payment date, and the corresponding decay coefficients are 0.75, 0.80, 0.85, 0.90, 0.95, 1.00 respectively. After decay processing, the second forward exchange locking ratios of 5%, 10%, 15%, 20%, 25%, 25% are obtained as the initial forward exchange locking ratios.

[0107] The system obtains the USD / CNY exchange rate data from January 1, 2025 to June 30, 2025. The highest value is 7.3000, the lowest value is 6.8000, and the exchange rate difference is 0.5000. Based on the average exchange rate of 7.0500 during the period, the calculated fluctuation ratio is approximately 7.09%. According to the fluctuation ratio mapping table, the risk parameter corresponding to 7.09% is 1.4. The risk adjustment coefficient corresponding to the risk parameter of 1.4 is 0.8.

[0108] Finally, the risk adjustment coefficient of 0.8 is applied to the initial forward exchange locking ratios, and adjustment is performed in the way of front-end contraction and back-end compensation. The forward exchange locking ratios of the first three time nodes are adjusted by 0.8 times, and the reduced ratio is redistributed according to the original ratio weights of the last three nodes. The final target forward exchange locking ratios are 4%, 8%, 12%, 24%, 26%, 26%. This means that the specific forward exchange locking amounts for the enterprise's $1 million settlement amount at each forward exchange locking time node are $40,000, $80,000, $120,000, $240,000, $260,000, and $260,000 respectively.

[0109] In addition, the system also provides an automatic early warning function. When the actual exchange rate locking ratio at a certain time node exceeds the preset threshold (50% for US dollars and 20% for other currencies), the system automatically sends early warning messages to relevant personnel. At the same time, 7 days and 3 days before the contract expiration date, the system will also automatically send reminder notices.

[0110] In the above embodiments, the exchange rate locking business management of the electronic manufacturing enterprise involves the coordinated cooperation of multiple departments and positions. The specific division of labor is as follows: Business department: mainly includes the business division, business department and accounting group. Business personnel are responsible for inputting and updating customer detailed information in the system in a timely manner, and must update the order receiving information once a week on Friday. The business department manager queries the locked exchange amount, exposure amount and exchange rate locking ratio in the net exchange receipt plan ledger through the system to determine the amount of exchange rate locking application required. For orders that do not receive US dollars, it is also necessary to query the locked exchange amount, exposure amount and exchange rate locking ratio in the payable exchange ledger to determine the amount of exchange rate locking application.

[0111] Comprehensive team leader of the business division (or designated exchange rate locking specialist): As the executor of the exchange rate locking business of the business department, initiate exchange rate locking applications, closing applications and rollover applications in the system according to the requirements of the department manager. Among them, the exchange rate locking application needs to be submitted to the general manager of the business division for approval, and the closing and rollover applications need to be submitted after confirmation with the same business.

[0112] General manager of the business division: responsible for approving the exchange rate locking applications, closing applications and rollover applications of this business division. Approval operations can be carried out through the system and the mobile terminal.

[0113] Finance: As the representative of the finance department stationed in the business department, has the authority to initiate exchange rate locking applications. When it is found that the exposure of the business department does not meet the company's regulations, after communicating with the business department, an exchange rate locking application can be initiated, but it requires dual approval from the finance department manager and the general manager of the business division.

[0114] Finance fund administrator: is the core execution position of the exchange rate locking business, and the main responsibilities include: summarizing the exchange rate locking applications of each business division, classifying and processing them according to the exchange rate locking market, currency system and exchange rate locking month; consulting the bank about the expected price through WeChat or phone; generating or preparing transaction applications in the system and submitting them to the finance department manager for approval; registering transaction information in the system after receiving the bank transaction contract; handling transaction delivery, closing and rollover operations.

[0115] Finance department manager: responsible for approving transaction applications, delivery applications, closing applications and rollover applications. For the forward foreign exchange business (finance and finance) self-operated by the finance department, if the cumulative position amount exceeds 5 million US dollars, it also requires the approval of the company's general manager and chairman.

[0116] The system sets corresponding functional permissions according to different departments and positions: business personnel can view the detailed statement of the foreign exchange receipt plan, the ledger of the net foreign exchange receipt plan, etc.; the comprehensive team leader can operate applications for locking foreign exchange, closing positions, extending periods, etc.; the financial BP can view all ledgers and has the right to initiate applications for locking foreign exchange; the financial funds position has all operation permissions; leaders have corresponding approval permissions according to their ranks.

[0117] At the same time, the system also establishes a perfect early warning mechanism: when there are records of exceeding the standard locking exchange rate ratio (50% for US dollars and 20% for other currencies) in the ledger of the net foreign exchange receipt plan, the system automatically pushes early warning information to the head of the accounting group, the person in charge of the business unit, the financial BP, and the financial funds position at 10:00 am every Monday.

[0118] For contracts approaching maturity, the system automatically sends reminder notices to the designated person for locking foreign exchange in the business unit and the financial BP 7 days and 3 days before the maturity date.

[0119] The system automatically checks the trading balance of contracts expiring in 7 days at 2:00 am every day, compares it with the deliverable amount, and automatically sends a notice to remind business processing if there is a difference.

[0120] Based on the above method, this application also discloses an exchange rate risk management system, as Figure 2 shown, Figure 2 is a schematic structural diagram of an exchange rate risk management system provided by an embodiment of this application. The system includes: an acquisition module, a first generation module, a second generation module, a combination module, a third generation module, and an adjustment module; among them, The acquisition module is used to acquire the business data of the enterprise and the payment time for a foreign enterprise to pay the settlement amount to the enterprise; the first generation module is used to generate multiple locking exchange rate time nodes according to the payment time; the second generation module is used to generate the risk-bearing level and revenue demand level of the enterprise according to the business data; the combination module is used to combine the risk-bearing level and revenue demand level to generate the initial locking exchange rate ratio of each locking exchange rate time node; the third generation module is used to acquire the exchange rate fluctuation data within a preset time period and generate risk parameters according to the exchange rate fluctuation data; the adjustment module is used to adjust the initial locking exchange rate ratio according to the risk parameters to generate the target locking exchange rate ratio of each locking exchange rate time node.

[0121] It should be noted that: when the system provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0122] Please refer toFigure 3 , This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. As Figure 3 shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0123] Among them, the communication bus 1002 is used to implement connection communication between these components.

[0124] Among them, the user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface.

[0125] Among them, the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0126] Among them, the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire server using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005, it performs various functions of the server and processes data. Optionally, the processor 1001 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 1001 may integrate one or several combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 1001 and may be implemented separately by a single chip.

[0127] Among them, the memory 1005 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 1005 may also be at least one storage device located far from the aforementioned processor 1001. As Figure 3 shown, in the memory 1005 as a computer storage medium, it may include an operating system, a network communication module, a user interface module, and an application program of an exchange rate risk management method.

[0128] In Figure 3 the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user to obtain the data input by the user; and the processor 1001 can be used to call the application program of an exchange rate risk management method stored in the memory 1005. When executed by one or more processors, the electronic device is caused to execute one or more of the methods as described in the above embodiments.

[0129] An electronic device-readable storage medium stores instructions. When executed by one or more processors, the electronic device is caused to execute one or more of the methods as described in the above embodiments.

[0130] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0131] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0133] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0135] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0136] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and practicing the present disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An exchange rate risk management method, characterized in that, The method includes: Obtaining the business data of the enterprise and the payment time when a foreign enterprise pays the settlement amount to the enterprise; Generating multiple forward exchange locking time nodes according to the payment time; Generating the risk-bearing level and revenue demand level of the enterprise according to the business data; Combining the risk-bearing level and the revenue demand level to generate the initial forward exchange locking ratio for each of the forward exchange locking time nodes; Obtaining the exchange rate fluctuation data within a preset time period and generating a risk parameter according to the exchange rate fluctuation data; Adjusting the initial forward exchange locking ratio according to the risk parameter to generate the target forward exchange locking ratio for each of the forward exchange locking time nodes.

2. The exchange rate risk management method according to claim 1, characterized in that The business data includes the enterprise scale and industry category. The generating the risk-bearing level and revenue demand level of the enterprise according to the business data includes: Combining the enterprise scale and the industry category to generate the risk-bearing level of the enterprise; Combining the risk-bearing level and the settlement amount to generate the revenue demand level of the enterprise.

3. The exchange rate risk management method according to claim 2, characterized in that The combining the enterprise scale and the industry category to generate the risk-bearing level of the enterprise includes: Generating the initial risk level of the enterprise according to the enterprise scale, and the enterprise scale is positively correlated with the initial risk level; Matching the corresponding industry risk coefficient from a preset industry risk coefficient table according to the industry category, and adjusting the initial risk level according to the industry risk coefficient to generate the risk-bearing level of the enterprise.

4. The exchange rate risk management method according to claim 2, wherein The combining the risk-bearing level and the revenue demand level to generate the revenue demand level of the enterprise includes: Obtaining the risk-bearing amount corresponding to the risk-bearing level, and the risk-bearing level is proportional to the risk-bearing amount; Calculating the difference between the risk-bearing amount and the settlement amount; If the difference is positive, determining that the revenue demand level of the enterprise is a low demand level, and determining the corresponding first demand level of the enterprise in the low demand level according to the magnitude of the difference; If the difference is negative, determining that the revenue demand level of the enterprise is a high demand level, and determining the corresponding second demand level of the enterprise in the high demand level according to the absolute value magnitude of the difference.

5. The exchange rate risk management method according to claim 1, characterized in that The combining the risk-bearing level and the revenue demand level to generate the initial forward exchange locking ratio for each of the forward exchange locking time nodes includes: Determining the benchmark forward exchange locking ratio for each of the forward exchange locking time nodes according to the risk-bearing level, and the risk-bearing level is proportional to the benchmark forward exchange locking ratio; Obtaining the adjustment coefficient corresponding to the revenue demand level, and adjusting the benchmark forward exchange locking ratio according to the adjustment coefficient to obtain the first forward exchange locking ratio for each of the forward exchange locking time nodes; Calculating the time interval between each of the forward exchange locking time nodes and the payment time, and performing time decay processing on the first forward exchange locking ratio according to the time interval to generate the second forward exchange locking ratio for each of the forward exchange locking time nodes, wherein the second forward exchange locking ratio for each of the forward exchange locking time nodes is inversely proportional to the time interval; Taking the second forward exchange locking ratio as the initial forward exchange locking ratio for each of the forward exchange locking time nodes.

6. The exchange rate risk management method according to claim 1, characterized in that Generating a risk parameter according to the exchange rate fluctuation data, including: Determining a maximum exchange rate and a minimum exchange rate within the preset time period according to the exchange rate fluctuation data; Calculating an exchange rate difference between the maximum exchange rate and the minimum exchange rate; Determining a risk parameter according to the exchange rate difference, and the exchange rate difference is directly proportional to the risk parameter.

7. The exchange rate risk management method according to claim 1, characterized in that Adjusting the initial locked exchange rate ratio according to the risk parameter to generate a target locked exchange rate ratio for each locked exchange time node, including: Obtaining a risk adjustment coefficient corresponding to the risk parameter, and the risk parameter is inversely proportional to the risk adjustment coefficient; Performing an adjustment operation on the risk adjustment coefficient and the initial locked exchange rate ratio of each locked exchange time node to generate a target locked exchange rate ratio for each locked exchange time node.

8. An exchange rate risk management system, characterized in that, The system includes: an acquisition module, a first generation module, a second generation module, a combination module, a third generation module and an adjustment module; wherein, The acquisition module is configured to acquire the business data of the enterprise and the payment time for the foreign enterprise to pay the settlement amount to the enterprise; The first generation module is configured to generate a plurality of locked exchange time nodes according to the payment time; The second generation module is configured to generate a risk bearing level and a revenue demand level of the enterprise according to the business data; The combination module is configured to combine the risk bearing level and the revenue demand level to generate an initial locked exchange rate ratio for each locked exchange time node; The third generation module is configured to obtain exchange rate fluctuation data within a preset time period and generate a risk parameter according to the exchange rate fluctuation data; The adjustment module is configured to adjust the initial locked exchange rate ratio according to the risk parameter to generate a target locked exchange rate ratio for each locked exchange time node.

9. An electronic device, characterized in that, It includes a processor, a memory, a user interface and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored that can be loaded and executed by a processor to execute the method according to any one of claims 1-7.

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