Published Online: Feb 05, 2025
Received: Sep 11, 2024
Accepted: Dec 28, 2024
DOI: https://doi.org/10.2478/amns-2025-0049
Keywords
© 2025 Linmeng Liu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
With the popularization of innovative technologies such as Big Data and Artificial Intelligence, countries, regions, and even enterprises around the world are actively adopting these technologies, promoting the transformation of the global economy into the era of digital economy. These technologies have enhanced the efficiency of socio-economic operations and have made the success or failure of countries and regions in competition increasingly dependent on their level of digital development [1–2]. Digitization has penetrated all fields of social production and has given rise to massive data resources. These data resources have attracted a great deal of attention from domestic and foreign academics, who have gradually begun to recognize the value of data as a factor of production and confirmed its important role in promoting economic growth. Based on this recognition, scholars put forward the concept of “data asset”, emphasizing the status of data as a value-added resource [3–5].
With the booming of the digital industry, experts have begun to explore how to use data-based tools to promote the progress of society. However, the effective management and utilization of massive data in this process has been relatively neglected, failing to fully explore the potential value contained in these data resources [6–7]. In order to solve this problem, the Ministry of Finance issued the Guiding Opinions on Strengthening the Management of Data Assets, which covers three aspects, such as general requirements, main tasks and implementation guarantees, totaling eighteen specific contents. The opinion emphasizes the core objectives of realizing the common sharing of the fruits of the digital economy by all people, maximizing the potential value of data assets, and insisting on enhancing the efficiency of the circulation and use of data assets under the premise of ensuring their compliance [8–9]. By promoting the process of data assetization in an orderly manner and strengthening the management of the whole life cycle of data assets, we aim to ensure that data assets can maximize their due value and provide strong support for the development of related fields. At the same time, China has also set up a number of data trading platforms, which provide convenient conditions for the circulation of data products. In the current context of data trading center construction continues to promote, the reasonable valuation of data assets is particularly critical [10–11]. Kingsoft Office, as a large software service enterprise, has been on the “Top 100 Digital Transformation Driving Enterprises of 2022” selected by Internet Weekly, which is enough to prove its leading position in the software service industry. In its development history, whether it is the traditional PC to mobile to cloud transformation or a comprehensive shift to mobile and cloud and collaborative office, a series of strategies proposed are in line with the wind direction of the times of decision-making, has a good leading role, therefore, reasonable and fair valuation of its data assets have a certain reference significance [12–15].
Data has become a new type of production factor and strategic resource in the production and operation of enterprises, but how to reasonably estimate the value of data assets is still a difficult problem in the integration and development of the digital economy and the real economy. Based on the previous research on how to objectively and accurately disclose enterprise data assets, literature [16] tries to measure the value of data assets by drawing on Chen’s model and artificial neural network model, aiming to provide a reference value for the theory and method of the actual processing and accounting of enterprise data assets. Literature [17] discusses the value of data as an asset and data itself, and takes Google as a practical case to explore how it configures and values digital data as an asset, and concludes that its digital asset assessment method is to monetize users. Literature [18] highlights the importance of digital personal data, and on the basis of analyzing the transformation of personal data into assets, it is found that large technology companies transform “users” and “user engagement” into assets primarily by measuring, governing, and valuing the performance of user metrics. Literature [19] points out that data value assessment affects the internal management and external circulation of data assets and combs through the existing literature and specific practices of data asset value assessment, value assessment object, value assessment system, and value assessment method, in order to propose a data asset value assessment method. Literature [20] indicated that data assets cannot simply be regarded as intangible assets, and created a value model of data assets in order to facilitate the measurement of data assets and bring profits to enterprises. Literature [21] constructed a data asset value assessment model suitable for power grid enterprises by using the cost method and verified the practicality of the proposed model through actual case analysis, which is of great significance for the management and application of enterprise data assets. Literature [22] identified the market approach as the optimal valuation model for maritime data assets by using the hierarchical analysis method (AHP) for the maritime domain and indicated that the characteristics of maritime data are the most important considerations when choosing a valuation model.
The purpose of the article is to conduct a comprehensive and systematic valuation of Kingsoft Office’s data assets by utilizing advanced valuation methods and tools. First, the composition, characteristics and influencing factors of digital asset value are systematically analysed, and based on the special characteristics of data assets, the multi-period excess return method of the income approach is chosen as the valuation method. Subsequently, the data asset value of Kingsoft Office is selected as an appraisal case. On the one hand, a gray prediction model is introduced to predict some data to make it smoother and reduce the chances and volatility of data asset valuation prediction as much as possible. On the other hand, through an in-depth study of the annual report and research report of the enterprise, some of the data of the enterprise are reasonably adjusted so that they can reflect the actual situation and reduce the prediction error. Finally, the value of the data asset is assessed using the multi-period excess return model.
A data asset is a data resource that is legally owned or controlled by an organisation (government agency, enterprise, institution, etc.), recorded electronically or otherwise, e.g., structured or unstructured data such as text, images, voice, video, web pages, databases, sensing signals, etc., that can be measured or traded, and that can directly or indirectly bring economic and social benefits. However, not all data can be considered a data asset; only data resources that can create value for the organization can be considered data assets.
The value components vary across the four stages of the data value chain. Figure 1 shows the data asset chain and its value components. In the data generation phase, the value of a company’s data assets is derived from two main sources: first, the capture and documentation of source data on a large scale, which may be subject to physical, economic or legal constraints. The second advantage gained in the data generation phase is the competitive advantage gained through the scale and exclusivity of the acquired data. In the data collection phase, firms can create data value through the ability to transmit data with high degree of network coverage, reliability, security, and performance. In the data analysis phase, companies can create data value by innovating, developing, and deploying their intellectual property to build attractive, defensible, and more profitable services. In the data trading phase, if the company chooses to use it for its purposes, the value of the data asset is in improving its business development capabilities and improving existing business efforts. If the company chooses to sell the data asset, its value is determined by the quality, scale, and business effort of the data service.
Data asset chain and its value composition
Data shelf life
The value of data assets is highly time-sensitive and has a “data shelf life”, which can be generally divided into three periods: the effective decision-making period, the decisionsupport period and the final effective period. The effective decision-making period refers to the period of time that data assets can provide a direct and effective reference for enterprise decision-making, the decision-support period refers to the period of time that data assets can provide a certain degree of effective reference for enterprise decision-making, and the final validity period refers to the period of time that data assets can provide the final effective reference for enterprise decision-making.
Non-physical and non-consumable
Data assets, similar to intangible assets, do not exist in physical form. At the same time, the data itself has a lower intrinsic value and is more externally endowed with value, so it is not consumable and will not be depleted by repeated use. On the contrary, as the number of times it is used increases, the value of the data asset will increase, which will bring more benefits and generate more value for the enterprise.
Dependency
The non-physical nature of data assets leads to the existence of a certain dependency. That is, data cannot exist independently and needs to be stored on certain physical media, such as hard drives, SD cards, U-disks, and so on. The same data can be stored in different physical storage devices at the same time, relying on the medium to generate value.
Diversity of realisation methods
The ways of realizing the value of data assets are diverse, and the way of doing so differs depending on the different usage scenarios. In the four different phases of the data value chain, namely data generation, data collection, data analysis, and data trading, data asset value is realized in different ways. Enterprises can use data assets in various ways, and the value generated varies.
Value volatility
The value of data assets is mostly assigned externally and is susceptible to the influence of external factors, and the specific scenarios in which the data assets are used, the timeliness of the data, the level of data management, the market environment, and the supply and demand of data need to be taken into account in conducting the value assessment. As a result, the value of data assets is highly variable and different from other types of assets.
In this paper, the influence factors of data assets are divided into four aspects: data quality, data capacity, data application, and data risk.
Data Quality The quality of data can first reflect the value of data assets. With the continuous improvement of data quality, the enterprise’s data assets will become more valuable. High-quality data can help enterprises make better decisions and enhance their confidence in the market. When an organization embarks on market research, the quality of the data will significantly increase the reliability of its research. This not only ensures that the conclusions are more scientific and logical but also increases the strength and influence of the organization in the marketplace. Data Capacity Data capacity is comprised of data size, data type, and data density. With the increase in data capacity, the information that can be mined and utilised increases. An increase in data capacity isn’t just a change in numbers. It means an increase in the richness of information, and more valuable information can be collected, analysed, and utilised, and then transformed into real assets. Data Application Data assets in various industries will show differences in value with their application methods and scenarios, and differences in the degree of data mining will also lead to differences in data utilisation, which in turn affects the value of data assets. Data assets can be applied in many scenarios, indicating that they have diversity, and the dimension of the value generated will be expanded. With the expansion of the role of the extension, its scope will become larger and larger, and its value will increase. Data Risk The realisation of the value of data assets will also have certain risks, mainly legal risks and moral risks. The legal risk of data assets is the change of relevant laws and regulations, and the legal disputes caused by unclear data ownership will bring about the problem of data asset rights, which may lead to the difficult situation of enterprise operation. Moral risk exists in all the main parties involved in data asset securitisation, with the originator’s moral risk mainly including the risk of depreciation of data assets due to the arbitrary disposal of data assets by the enterprise after obtaining the financing and the intermediary’s moral risk mainly exists in the appraisal agency, rating agency and management agency.
The excess earnings method is a method of determining the value of an appraised object by discounting the cumulative expected excess earnings attributable to the target intangible asset for each period. Suppose the contribution of the intangible asset and related assets to the overall earnings of the enterprise is such that there is a residual after the sum of the divisible contributions is compared with the overall earnings of the enterprise. In that scenario, the residual earnings are the excess earnings and can show the impact of the intangible asset on the earnings. In the use of the excess earnings method, you need to measure the intangible assets and other related assets to contribute to the joint creation of revenue. Based on the other related assets that contribute to the creation of revenue deductions, the remaining revenue is the target intangible assets to create revenue, and the discount for the above revenue can be [23]. The formula is as follows:
Where
Δ
As this paper assesses the value of data assets, data assets, as part of intangible assets, need to be further analysed and stripped of intangible assets, and intangible assets need to be divided into data assets and intangible assets other than data assets in order to measure the value of data assets.
This paper adopts the excess earnings method for appraisal and introduces the data asset value adjustment coefficient to correct the value of data assets, which is also susceptible to non-financial factors. The model in this paper is based on the multi-period excess earnings method, using the difference method to measure the multi-period excess earnings, analysing the characteristics of the data assets, clarify the capital composition of the enterprise under appraisal, the source of the value of the data assets and other matters, and calculating the value of the data assets using the multi-period excess earnings method. The specific idea is to process and analyse the company’s financial information based on the annual report issued by the enterprise every year and to estimate and forecast the company’s free cash flow, on the basis of which the difference method is used to calculate the excess return on data assets.
Because intangible assets will generally generate economic benefits over a long time horizon, it is more reasonable to require the calculation of excess returns over multiple periods. The multi-period excess earnings method’s foundation lies in the projection of excess earnings. Since the measurement is the excess earnings due to the data assets, it is necessary to divest the data assets from the value brought by assets other than the data assets. The value of data assets is obtained by excluding fixed assets, current assets, and intangible assets other than data assets from the overall earnings of the enterprise using the difference method. The formula is shown below:
Among them:
This model uses the free cash flow of the enterprise to determine the overall earnings of the enterprise, because the cash flow data is more stable and less susceptible to the influence of other data. In order to make the valuation results more objective and reasonable, this paper considers the impact of both financial and non-financial factors on the value of data assets. Based on the above formula, this paper constructs a multi-period excess earnings method valuation model for enterprise data assets, with the formula shown below:
Among them:
Based on the parameters such as the free cash flow of the company, the contribution of fixed assets, the contribution of current assets, the contribution of other intangible assets, etc., as mentioned in the above formula, the specific calculation process can be determined as follows.
Corporate free cash flow is the cash flow that a company can freely distribute to all contributors while maintaining normal operations.
Corporate free cash flow = EBITDA - capital expenditure - working capital additions + depreciation and amortisation Since the prediction of business revenue is the basis of the whole calculation process, in order to predict the accuracy of the results, this paper adopts the grey
The grey prediction model is mainly divided into five steps, which are the testing and processing of raw data, model building, model reduction, model testing, and finally, model prediction [24].
Inspection and processing of raw data First of all, the collection of historical data, in order to ensure the feasibility of the use of the grey model, to carry out the necessary testing and processing of these raw data. List the original series:
A rank ratio test was performed on the raw data to calculate the rank ratio of the original series:
When all the level ratios fall within the tolerable coverage range Then the rank ratio of the affine transformed series is:
Establish GM(1, 1) model After the translation of the transformed series through the level ratio test can be added to obtain the cumulative sequence, and then generate the immediate neighbourhood mean sequence, the construction of the digital matrix B and vector matrix Y, the establishment of the whitening equation, that is, the GM (1, 1) model, the use of constructed matrices to find the solution of the whitening equation. Generate the cumulative sequence:
Let Construct the numeric matrix B and the vector matrix Y:
Create the whitening equation, the shadow equation:
The solution to the whitening equation is:
Substitute the data to solve:
Model Reduction After solving the whitening equation, the corresponding time response series of the whitening equation can be listed: Reduction of the model:
Model testing After the model has been reduced, a residual test is required. Calculate the mean squared error ratio and the probability of small error, compared with the accuracy scale, the model is feasible to establish, if it fails to meet the standards, then the model needs to be corrected. Residual test: the absolute error is:
The relative error is:
Therefore, the absolute residual sequence is:
The relative residual sequence is:
Posterior residual test: the mean of the original series is:
The standard deviation of the original series is:
The residual mean is:
The standard deviation of the residuals is:
The a posteriori differential ratio C:
Small error probability P:
Usually, it is necessary to ensure that the a posteriori difference ratio C is small enough, the larger the probability of small error P. After checking against the accuracy validation level table, it is possible to determine the model accuracy level and decide whether the model can be used for accurate prediction or not. Model Prediction After passing the residual test and post residual test, the grey prediction model with qualified accuracy is used to predict future data.
Contribution of current assets
Current assets are property held by a company for a period of less than one year and have a high potential for realisation. During the operating cycle of a company, current assets generally remain unchanged in value, although they may change in form, and investors can generally recover them in full at the end of a full operating cycle. Wear and tear or depreciation of current assets generally does not occur, so only the return on investment needs to be considered. The annual contribution value of current assets can be estimated as the product of the return on investment of current assets and the average annual value of current assets. In addition, the turnover cycle of current assets is generally one fiscal year, so the return on investment in current assets used in this paper is the one-year bank lending rate.
Contribution value of fixed assets
Unlike current assets, fixed assets have three depreciation losses included in the cost method, which is the most important characteristic. Therefore, from the time of purchase until the investor can no longer recover them in full, there is no residual value left in the book. Therefore, from the investor’s point of view, the contribution value of fixed assets consists of the sum of the depreciation compensation for fixed assets and investment income. While the depreciation compensation of fixed assets is equal to the sum of the annual depreciation of existing fixed assets and the depreciation of newly acquired fixed assets, the return on fixed assets can be considered as the product of the rate of return and the average annual amount of assets.
In addition, Internet financial enterprises with data assets its fixed assets are generally required to work for office buildings, all kinds of indoor and transport equipment, etc., and the main purpose of profit fixed assets for office equipment is mostly its depreciation life is usually about 5 years, the depreciation of the buildings owned by the enterprise is much longer, usually ranging from 20-50 years. Therefore, the return on investment in fixed assets used in this paper is expressed in terms of the interest rate of bank loans with a maturity of more than five years.
Other intangible assets contribution value
This paper divides other intangible assets into two categories. One category refers to the identifiable intangible assets in the annual report of the enterprise. Similar to fixed assets, the contribution value of this part of the asset is equal to the sum of amortisation compensation and investment return. Whereas the amortisation compensation of intangible assets is equal to the sum of the annual amortisation of existing intangible assets and the amortisation of newly acquired intangible assets, the return on investment of intangible assets is equal to the product of the rate of return on investment of the intangible assets and the average annual intangible asset amount. The average annual amount of intangible assets can be represented by the average of the beginning and ending balances of intangible assets. In addition, because the intangible assets held by enterprises have non-current attributes, the turnover period is long, and the amortisation period generally ranges from 3 to 10 years. Therefore, the return on investment of intangible assets used in this paper is the same as the return on investment of fixed assets, which is more than five-year bank loan interest rates.
The other category refers to intangible assets that are not covered in the financial annual report of the enterprise, including customer relationships, portfolio labor, brand, trademark, etc. Since it is very difficult to quantify the value of enterprise brands and trademarks, this paper only considers portfolio labour. Portfolio labour is an important production factor in the asset group, and the asset group cannot function properly without labour. It is usually believed that the combined labor force can reflect the value of goodwill, so the combined labor force can be approximated as equivalent to goodwill. In this paper, the value of portfolio labour contribution is expressed as the product of the annual input of labor and the labour contribution rate. In this case, it is appropriate to represent the annual expenditure on labour in the financial statements of the enterprise as “compensation payable to employees”, while the actual labour contribution rate is determined according to the average level of the contribution rate of human resources.
The discount rate is a critical parameter in the model used in this paper. The maximum profit created by the data assets for the enterprise is completed in the future during a certain income period, so when the price of the data assets is to make a judgement, it is also necessary to fully take into account the impact of the time value in the evaluation must be in accordance with the time value of the maximum profit of the data assets for the company’s creation of the maximum excess profit are calculated in accordance with the discounted method of the rate in the evaluation of the base date. The company’s discount rate does not represent the discount rate of the data assets. If the enterprise discount rate is used, it will make the data assets less risky. Currently, the more advanced discount rates to calculate the average value of the calculation method are mainly the risk accumulation method, weighted balanced cost of liabilities method, and the rate of return split method.
The weighted average cost of capital (WACC) is to multiply the cost of equity capital and the cost of debt capital of an Internet enterprise with their respective shares of total capital and then add them together to obtain the return on investment of the data assets of the Internet enterprise. This method underestimates the investment risk of the subject of assessment and may result in a low discount rate. Its calculation formula is expressed as:
Where WACC represents the weighted cost of capital, Ke represents the return on equity capital, E represents the value of equity, D represents the value of debt,
In the above equation,
Where,
Where,
As the risk accumulation method needs to consider various types of risks such as market risk, operational risk, financial risk and other risks to be borne by Internet financial enterprises, it is more difficult in practice. Therefore, this paper adopts a combination of two methods, the weighted average capital method and the rate of return split method, to determine the rate of return of the data assets of Internet financial enterprises, by calculating the average of the rate of return of the intangible assets of the similar enterprises in the market of the Internet financial industry and the company under appraisal as the rate of return of the data assets of the enterprise.
The benefit period for a data asset is the amount of time it takes to gain a benefit. Timeliness is an important factor affecting the value of data assets, which is subject to changes over time, and the value of most data assets gradually decreases. At the same time, data assets have low reproduction costs and high regeneration rates, and the management and application of the same data assets can also make the timeliness of data assets unstable. In addition, data assets are updated more frequently and have the possibility of being eliminated at any time. It can be seen that the income period of data assets cannot be stipulated in a uniform standard, so it is necessary to determine the income period of the data assets under appraisal by taking into account the enterprise’s conditions and specifically considering the situation of the data assets under appraisal.
Beijing Kingsoft Office Software Company Limited (hereinafter referred to as Kingsoft Office) is a software and information enterprise with office software and office cloud services as its main business, which was listed on the Shanghai Stock Exchange in 2019 and is the world’s leading Internet office service enterprise. Its main products and services include WPS Office Office software, Kingsoft Documents and Kingsoft Digital Office Platform. Among them, WPS Office was developed in 1988, and after more than 20 years of accumulation and, research and development, it can now be used on many mainstream operating platforms such as Android, iOS, Windows, etc., and has a business presence in more than 200 countries. Kingsoft Documents is a shared document service that supports real-time online collaboration among multiple people, providing users with an efficient and convenient cloud office experience with rich scenarios. Kingsoft Digital Office Platform is a comprehensive digital collaborative creation platform based on the cloud and mid-stage transformation and secondary development of Kingsoft’s digital office matrix. The platform combines the advantages of the enterprise to provide subscription services for the government or enterprises and provides a full set of services according to the use of the demand, forming a product matrix with three empowering scenarios and four delivery modes.
Kingsoft Office’s main products have more than 570 million monthly active devices, of which the WPS Office PC version has 245 million monthly active devices, and the WPS Office mobile version has 358 million monthly active devices. The data service platform developed by Kingsoft Office - the rice husk platform has 5 major template libraries, 10 major material libraries, 10 million library resources and over 100 million content resources. As of the end of December 2023, the number of cloud documents uploaded by users through the public cloud has exceeded 187.8 billion, a year-on-year increase of 40.02%, corresponding to the total amount of storage has reached 270PB, a year-on-year increase of 69.12%, the enterprise data and personal data is very large. With the continuous increase in the number of users participating in cloud office and cloud collaboration, it is expected that more and more data will be delivered to the cloud in the future. Kingsoft Office focuses on the needs of customers for full lifecycle management of data assets, gradually improves the user experience of data assets in the cloud, and optimizes the resource management capabilities of cloud documents. In addition, Kingsoft Office actively conducts independent research and development for the management and development of data assets, such as data cloud storage, secure cloud documents, data intelligent collaborative sharing, and other key technologies. With the accumulated advantages in the professional field, subdivided user needs and application scenarios, Kingsoft Office conducts in-depth excavation of data resources, and the data assets can provide good feedback for enterprise development.
Analysis of Kingsoft Office’s profitability
Kingsoft Office’s operating income and net profit have steadily increased in recent years, and the net profit margin has been maintained at around 30%, and the key financial data of Kingsoft Office’s profitability in 2019-2023 is shown in Table 1. The business structure is continuously optimised, with subscription and service revenue accounting for nearly 60%.
After studying the statements and related research reports, the main reasons for the sustained growth in earnings in recent years are as follows: First, due to the impact of the epidemic, the home office has become the norm, favouring the development of office-based Internet companies. The individual user base continues to expand, with 601 million monthly active users in 2023, up 15.01% year-on-year. Secondly, quality content platforms promote the conversion of users to long-term payments, with a cumulative annual number of 25.91 million paid users in 2023, up 31.22% year-on-year. Third, office terminals are transforming from traditional independent operations to cloud and collaborative offices, and the number of registered enterprises in the company’s public cloud market has exceeded one million. Fourthly, the demand for organisational users such as governments and enterprises increased significantly, the development of China’s Xinchuang industry accelerated, and party and government Xinchuang and industry Xinchuang continued to advance. Considering the revenue growth brought by the company’s cloud promotion and Xinchuang’s strength, coupled with the development of the Internet and computer technology, it is expected that earnings will grow steadily, and the future market space will be vast.
Analysis of the operating ability of Kingsoft Office
In recent years, the inventory turnover rate of Kingsoft Office has been improving, the number of inventory turnover days has been decreasing, and the number of accounts receivable turnover days has been further shortened. The capital turnover is guaranteed, and the operational efficiency is gradually improving. The main financial data of Kingsoft Office’s operation ability is shown in Table 2. This change is mainly due to Kingsoft Office’s Internet transformation, actively building a cloud-based platform, and a relatively large change in the business model. Kingsoft Office successfully shifted its main products from a stand-alone platform to a cross-platform multi-terminal office, product positioning from application tools to Internet services, and the business model has also achieved a shift from traditional industrialised products to cloud services and collaborative creation changes.
Analysis of the solvency of Jinshan Office
In recent years, the assets and liabilities ratio has increased, the current ratio and quick ratio have declined, and the equity ratio has increased. Jinshan Office’s solvency of the main financial data is shown in Table 3. This indicates that the company’s solvency has declined, and there has been comparatively large-scale financing in recent years. After analysis, the main reasons are as follows: First, the company has been vigorously expanding the cloud market and the government and enterprise market in recent years in preparation for the future comprehensive empowerment of large organization’s office digital transformation. Second, the company has increased its research on data science and product innovation and invested more in the transformation from a “tool and product” to an “application service” type of enterprise. The gearing increase within a healthy range is a reflection of the company’s confidence in the market, its determination to expand aggressively in the future, and its optimistic outlook on future development.
Main financial data of profitability of Kingsoft Office in 2019-2023
| Year | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|
| Net profit (100 million yuan) | 4.007 | 8.775 | 10.411 | 11.181 | 13.185 |
| Total revenue (million yuan) | 15.808 | 22.609 | 32.798 | 38.852 | 45.559 |
| Basic earnings per share (yuan) | 1.092 | 1.901 | 2.253 | 2.418 | 2.860 |
| Net assets per share (yuan) | 13.159 | 14.866 | 16.751 | 18.918 | 21.552 |
| Gross profit margin (%) | 85.582 | 87.701 | 86.909 | 85.000 | 85.300 |
| Return on equity (%) | 22.597 | 13.635 | 14.343 | 13.641 | 14.179 |
Main financial data of Kingsoft office operation capacity in 2019-2023
| Year | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|
| Business cycle (day) | 67.04 | 60.94 | 45.97 | 43.21 | 40.65 |
| Inventory turnover (times) | 187.23 | 200.39 | 235.59 | 251.66 | 286.32 |
| Inventory turnover days (days) | 1.84 | 1.86 | 1.55 | 1.38 | 1.15 |
| Accounts receivable turnover days (days) | 65.18 | 59.17 | 44.44 | 38.62 | 33.91 |
Main financial data of Kingsoft Office’s solvency in 2019-2023
| Year | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|
| Current ratio | 9.19 | 5.63 | 3.83 | 2.97 | 2.21 |
| Quick ratio | 9.16 | 5.61 | 3.81 | 2.95 | 2.18 |
| Equity ratio | 13.12% | 24.03% | 34.11% | 38.25% | 40.06% |
| Asset-liability ratio | 11.33% | 19.04% | 25.37% | 27.03% | 28.30% |
The valuation of Kingsoft Office requires the definition of key elements such as its valuation base date, income period, discount rate, and excess earnings. The article selects 31 December 2023 as the valuation reference date, and in order to ensure the timeliness and accuracy of the valuation process, the income period of Kingsoft Office’s data assets is set to 2024-2028 to reflect the expected situation of its future income. In determining the excess earnings, it is necessary to forecast the free cash flow of the Kingsoft Office enterprise, the contribution value of current assets, the contribution value of fixed assets and the contribution value of intangible assets, and the portion of the free cash flow of the enterprise after deducting the contribution value of other assets, such as fixed assets, will be regarded as the excess earnings of the Kingsoft Office data assets.
Enterprise free cash flow = EBIT + depreciation and amortisation - increase in working capital - capital expenditure. Based on the financial statement data of Kings Office for 2019-2023, we have compiled and calculated the free cash flow of Kings Office enterprise for the last five years, as shown in Table 4. All units involved are in billions of dollars.
Free cash flow of Kingsoft Office enterprises in 2019-2023 (Hundred million Yuan)
| Item / year | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|
| Operating income | 15.8 | 22.61 | 32.8 | 38.85 | 45.56 |
| Operating cost | 2.28 | 2.78 | 4.29 | 5.83 | 6.7 |
| Taxes and additional | 0.16 | 0.23 | 0.35 | 0.35 | 0.37 |
| Sales cost | 3.45 | 4.83 | 6.95 | 8.18 | 9.67 |
| Management fee | 1.36 | 2.13 | 3.26 | 3.92 | 4.44 |
| Financial cost | -0.03 | -0.09 | -0.17 | -0.13 | -0.95 |
| R&d cost | 5.99 | 7.11 | 10.82 | 13.31 | 14.72 |
| Gross profit | 4.06 | 9.36 | 11.19 | 11.98 | 13.9 |
| Minus: income tax | 0.06 | 0.49 | 0.5 | 0.67 | 0.75 |
| Net profit | 4.01 | 8.87 | 10.69 | 11.31 | 13.14 |
| Add: depreciation and amortization | 0.32 | 0.47 | 0.68 | 0.83 | 0.8 |
| Minus: capital expenditure | 0.63 | 0.55 | 1.49 | 1.76 | 1.66 |
| Reduction: net operating capital net increase | 48.88 | 6.97 | -2.24 | 5.99 | -0.32 |
| Free cash flow | -45.18 | 1.82 | 12.12 | 4.4 | 12.61 |
The article uses the GM (1,1) grey prediction model to predict the operating income of Kingsoft Office in the next five years from 2024 to 2028, and the GM (1,1) grey prediction model constructed based on the equation in the previous section is:
Using the constructed model to fit the free cash flow of Kingsoft Office in the past 20 years, Figure 2 shows the comparison of the actual value of the free cash flow of Kingsoft Office with the predicted value, and the fitted curve is basically consistent with the actual value.
The actual value of free cash flow in kingsoft is compared with the predicted value
On the basis of establishing a good prediction model, it is still necessary to carry out further tests on the data. The tests for the grey model GM(1,1) are the residual test and the post hoc test. The next content will use the above 2 kinds to carry out the test.
The residual test is a point-by-point test of the residuals between the model predicted reduced values and the actual values. According to the prediction formula of the above sequence,
Absolute error series:
Relative error series:
Average absolute error:
Average relative error:
The results of the post-test difference test are shown in Table 5. The mean relative error is barely passable, not very satisfactory but within the passable range, the standard deviation ratio is also passable, and the small error probability rating is good and therefore predictable.
Results of chip test differential test
| Raw sequence standard deviation | S1 | 1.23 |
|---|---|---|
| Standard deviation of the absolute error sequence | S2 | 0.33 |
| Variance ratio | C | 0.45 |
| Small error probability | P | 1.00 |
Similarly, the forecast data for the other data of Kingswood Office is collated to produce the forecast data for the free cash flow of the business of Kingswood Office for the years 2024-2028, as shown in Table 6.
2024-2028 Kingsoft office free cash flow forecast value (Hundred million Yuan)
| Item / year | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|
| Operating income | 55.19 | 64.91 | 75.56 | 87.25 | 100.05 |
| Operating cost | 8.55 | 10.37 | 12.41 | 14.7 | 17.26 |
| Taxes and additional | 0.43 | 0.48 | 0.53 | 0.59 | 0.65 |
| Sales cost | 11.67 | 13.72 | 15.96 | 18.42 | 21.11 |
| Management fee | 5.48 | 6.47 | 7.56 | 8.76 | 10.07 |
| Financial cost | -1.08 | -1.46 | -1.89 | -2.36 | -2.9 |
| R&d cost | 18.3 | 21.58 | 25.19 | 29.16 | 33.53 |
| Gross profit | 15.41 | 17.12 | 18.92 | 20.83 | 22.85 |
| Minus: income tax | 0.86 | 0.97 | 1.08 | 1.2 | 1.33 |
| Net profit | 14.55 | 16.14 | 17.81 | 19.59 | 21.46 |
| Add: depreciation and amortization | 1 | 1.13 | 1.28 | 1.44 | 1.62 |
| Minus: capital expenditure | 2.28 | 2.71 | 3.16 | 3.65 | 4.17 |
| Reduction: net operating capital net increase | -0.79 | -2.12 | -3.44 | -4.75 | -6.04 |
| Free cash flow | 14.05 | 16.69 | 19.38 | 22.13 | 24.95 |
The article takes the relevant items in the 2019-2023 financial report of Kingsoft Office as the original data and combines the GM (1,1) grey prediction model to predict the financial information for the next five years and calculate the contribution value of other assets. The article reasonably splits depreciation and amortisation according to the proportion of fixed assets and intangible assets, and gets the predicted financial data as shown in Table 7.
Other data forecasts for 2019-2024-jinshan (Hundred million Yuan) Forecast of current assets contribution The overall current assets of Kingsoft Office during the five years from 2019-2023 show a year-on-year growth trend, and based on the above table’s forecast of current assets for the next five years, it is calculated by substituting the formula for calculating the value of the contribution of current assets to calculate the value of the contribution of current assets of Kingsoft Office for the period from 2024 to 2028. Among them, the one-year bank loan interest rate of 3.448% in 2023 is adopted as the average return rate of current assets, and the calculation process is shown in Table 8. Projection of the value of contribution from fixed assets Based on the forecast results in the table above, the contribution value of fixed assets of Kingsway Office for 2024-2028 is calculated according to the formula for the contribution value of fixed assets. In this case, the bank loan interest rate of more than five years is used as the average return rate of fixed assets, which is 4.205%. The calculation process is shown in Table 9. Forecast of intangible asset contribution value The calculation process of intangible assets is similar to that of fixed assets, using the bank loan interest rate of 4.205% for a period of five years or more as the average rate of return on intangible assets and calculating the value of the table intangible asset contribution of Kingsford Office from 2024 to 2028, the calculation process is shown in Table 10.
Item / year
2024
2025
2026
2027
2028
Operating income
55.19
64.91
75.56
87.25
100.05
Current assets
111.19
120.28
130.11
140.75
152.25
Fixed assets
0.79
0.81
0.82
0.84
0.86
Intangible assets
1.08
1.15
1.22
1.3
1.37
Depreciation
0.42
0.47
0.52
0.57
0.62
Amortization
0.57
0.67
0.77
0.88
1.00
Kingsoft office current assets contribution value forecast
| Item / year | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|
| Initial current assets | 101.47 | 111.19 | 120.28 | 130.11 | 140.75 |
| Final current assets | 111.19 | 120.28 | 130.11 | 140.75 | 152.25 |
| Annual mean | 106.33 | 115.74 | 125.2 | 135.43 | 146.5 |
| Returns | 3.448% | ||||
| Contribution value | 3.67 | 3.99 | 4.32 | 4.67 | 5.05 |
Kingsoft office fixed assets contribution value forecast
| Item / year | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|
| Initial fixed assets | 0.69 | 0.79 | 0.81 | 0.82 | 0.84 |
| Final fixed assets | 0.79 | 0.81 | 0.82 | 0.84 | 0.86 |
| Annual mean | 0.74 | 0.8 | 0.82 | 0.83 | 0.85 |
| Depreciation | 0.42 | 0.47 | 0.52 | 0.57 | 0.62 |
| Returns | 4.205% | ||||
| Return on fixed assets | 0.03 | 0.03 | 0.03 | 0.03 | 0.04 |
| Contribution value | 0.45 | 0.5 | 0.55 | 0.6 | 0.66 |
Kingsoft office fixed assets contribution value forecast
| Item / year | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|
| Initial intangible assets | 0.87 | 1.08 | 1.15 | 1.22 | 1.3 |
| Final intangible assets | 1.08 | 1.15 | 1.22 | 1.3 | 1.37 |
| Annual mean | 0.98 | 1.11 | 1.19 | 1.26 | 1.33 |
| Amortization | 0.57 | 0.67 | 0.77 | 0.88 | 1 |
| Returns | 4.205% | ||||
| Returns on intangible assets | 0.04 | 0.05 | 0.05 | 0.05 | 0.06 |
| Contribution value | 0.62 | 0.71 | 0.82 | 0.93 | 1.05 |
The cost of capital pricing model can determine the return on the cost of equity capital of Kingswood Office, calculated the return on equity capital of Kingswood Office is 7.608%, the calculation of the weighted average cost of capital of Kingswood Office WACC, calculated from the above cost of equity capital of 7.608%, the selection of more than 5 years LPR for the return on bond capital of 4.205% as the cost of debt capital, the Kingswood Office equity and debt as a percentage of the ratio is calculated as shown in Table 11. The weighted average cost of capital for Kingsoft Office is 6.618%.
Kingsoft office equity and bond financing ratio
| Mean | 2024 | 2025 | 2026 | 2027 | 2028 | |
|---|---|---|---|---|---|---|
| Total liabilities | 24.51 | 7.75 | 16.21 | 26.45 | 32.59 | 39.54 |
| Gross equity | 79.12 | 60.69 | 68.91 | 77.8 | 87.98 | 100.2 |
| Total assets | 103.63 | 68.44 | 85.12 | 104.26 | 120.58 | 139.74 |
| Liability ratio (%) | 24.53 | 7.74 | 16.22 | 26.41 | 32.55 | 39.53 |
| Proportion of ownership interest (%) | 79.11 | 60.61 | 68.88 | 77.82 | 87.95 | 100.21 |
The value of Kingsford Office data assets can be calculated by the above calculation of the free cash forecast value of Kingsford Office business, the contribution value of other assets and the discount rate. The article uses the multi-period excess earnings method with a discount rate of 6.618 per cent to derive the value of the excess earnings of the Kingsford Office data assets, and the results of the calculation are shown in Table 12. The value of Kingsoft Office data assets is calculated to be 5.566 billion yuan through the multi-period excess earnings method, and the financial statements of Kingsoft Office on the valuation reference date of 31 December 2023 show that the total assets are 13.981 billion yuan, and the proportion of data assets to total assets is 39.81%. After analysis, although data assets have not yet been included in the accounting scope of the balance sheet, based on the in-depth assessment of this paper, data assets occupy a rather significant proportion of the total assets, fully demonstrating its status as a key new type of asset in the software development industry. Therefore, enterprises should pay full attention to data assets and promote the implementation of standardized data asset management measures to ensure their effective use and maximize their value.
Kingsoft office data asset value
| 2024 | 2025 | 2026 | 2027 | 2028 | |
|---|---|---|---|---|---|
| Free cash flow | 14.05 | 16.69 | 19.38 | 22.13 | 24.95 |
| Current asset contribution | 3.67 | 3.99 | 4.32 | 4.67 | 5.05 |
| Fixed asset contribution | 0.45 | 0.5 | 0.55 | 0.6 | 0.66 |
| Intangible contribution | 0.62 | 0.71 | 0.82 | 0.93 | 1.05 |
| Excess income of data assets | 9.32 | 11.48 | 13.69 | 15.93 | 18.19 |
| Discount rate | 6.618% | ||||
| Discount factor | 0.94 | 0.88 | 0.83 | 0.75 | 0.73 |
| Present value | 8.74 | 10.1 | 11.3 | 12.33 | 13.2 |
| Kingsoft office data asset value | 55.66 | ||||
| The proportion of data assets in the total assets | 39.81% | ||||
Since the twenty-first century, research on the value of data assets has been vibrant, with various theories and methods emerging, and big data has been commonly used in various fields. From astronomy and geography to food, clothing, housing, and transportation, it is intertwined with all walks of life. The study of data assets is or will be a topic that scholars are keen to explore.
Based on the value composition and characteristics of data assets, this paper analyzes the main influencing factors of enterprise data asset assessment and introduces the theoretical model of the multi-period excess return method, as well as the sources and values of each parameter. Then, a specific case is introduced: the value assessment of Kingsoft Office data assets. The calculation leads to the conclusion that the value of Kingsoft Office data assets is 5.566 billion yuan. This figure is significantly higher than the proportion of traditional assets in total, which fully illustrates the importance of data assets in Kingsoft Office’s corporate operations.
Based on the above conclusions, this study puts forward the following recommendations: First, Kingsoft Office should continue to strengthen data asset management, improve the data asset management system and process, and ensure data security and compliance. Second, strengthen data analysis and mining capabilities, make full use of data assets to provide support for business decisionmaking, and promote business innovation and service optimization. Lastly, continuously improve the structure of data assets, enhance their value and utilization, and generate more business value for enterprises.
In summary, Kingsoft Office’s data assets have become its important strategic resources, and in the future, it should continue to strengthen the management and utilization of data assets to cope with the fierce market competition and achieve sustainable development.