The NPV calculation is an essential tool for dynamic investment economics calculations and considers the time value of money. Therefore, the NPV is also excellent for analysing security investments (Brotby, 2009). To calculate the value of the expected expenses and returns before starting the investment, one must apply the following formula, in which CF_t is the annual cash flow, while r is the interest rate:

Unlike the NPV, the Net Future Value (NFV) calculates the value of a sum of the cash flows at some point in the future, giving the represented value:

In the above equations, it is assumed that cash flows occurred at the end of each year or, at least, they are discounted to the end of a given year by the effective interest rate, e^ri, where i is the number of days remaining until the end of the given year.

Although NPV and NFV calculations are essential tools in investment calculation, they are sensitive to determining the proper cash flows and choosing appropriate interest rates unless they represent valid present or future values (Beccarini, 2007).

When examining corporate and shareholder values, the basis for calculating interest is usually the cost of capital, where corporate (A), shareholder (E), and lending (D) capital costs differ. There are several options for calculating the shareholders’ cost of capital, from which the Capital Asset Pricing Model (CAPM) (Sharpe, 1964) is a widely applied formula:

In the above equation, r_E represents the return on an individual stock, r_f is the risk-free interest rate, and r_m is the market interest rate. β measures the volatility of an individual stock compared to the systematic risk of the entire market, representing a particular stock’s returns against those of the whole market. For an unleveraged company, the corporate interest rate (r_A) equals the shareholder interest rate (r_E); however, if a company is leveraged, the weighted-average cost of capital (r_wacc) must be taken into account. Furthermore, considering that particular case of r_wacc when D = 0, r_wacc, equals r_A (r_A = (r_wacc|D=0)). Therefore, we consequently apply r_wacc for the calculations.

When planning security controls, there are uncertainties in valuing expenditures and revenues (benefits) that each organisation must tackle. Over time, incidents may happen intentionally, according to the risk-proprietary approach. In this case, the expected total cost of security comprises the cost of the security-enhancing or reservation mechanism and the expected total cost of violations (Olovsson, 1992; Ruan, 2017), as displayed in Figure 2. This is a cost-based approach finding the optimal security level denoted by S* in Figure 1.

The base of estimation methods is very often the annualised loss expectancy (ALE) which is the product of the single loss expectancy (SLE) multiplied by the annualised rate of occurrence (ARO):

ALE = SLE × ARO

ARO is the estimated frequency of the given risk’s occurrence within one year. SLE is the amount of the aggregated expected monetary loss of a security incident’s impact on an entity’s operations, data, and IT assets. The SLE thus summarises direct, indirect, legal, operational, and human-like damage values which depend on the affected asset’s value (AV) and exposure factor (EF), which is the percentage of the damage or loss compared to the AV:

SLE = AV × EF

Finally, as the ALE is a multiplication of the AV, EF, and the annualised rate of occurrence (ARO) representing a one-year interval loss value, assuming the loss expectancy is constant for an n-year-long time interval, one can calculate it like this:

Several models or metrics use the ALE to analyse risks choosing security controls in the planning phase, and evolving the S* optimal control mixes, such as the value at risk (VAR) and the net present value (NPV) methodologies. We should be aware that security incidents might have national or global impacts on the market, such as the Colonial Pipeline shutdown on gasoline prices (Tsvetanov and Slaria, 2021). Due to its special nature, we cannot count on that in the ALE calculation.

Regarding the given security control mix that affects the operation, i.e., the controls can prevent incidents or reduce their impact from its activation with a planned ALE, the aggregated cost of commissioning and maintenance is the solution cost (SC). Therefore, if one needs to calculate the cash flow comprising possible expenditures, then the NPV is calculated as follows:

For the periodic reviews of security controls, the substantial economic impacts of incidents serve as a crucial exact input about the nature of the non-compliance with confidentiality, integrity, or availability requirements determined by business needs. In contrast to the design of security controls, one must examine the effects of incidents afterwards, i.e., the NFV of the damage caused by an I incident.

However, an incident’s effects may last for years so the value concerned can be strictly determined by the summation of each year’s effect, I_t, applying the following formula, in which the subscript A represents that the examination’s subject is a company:

However, in performing an ex-post analysis, it is necessary to avoid confusion between the uncertain planning values and the past factual budgeting and incidents’ values. Accordingly, a distinction must be made between the date of design and repeated analysis when discounting values. Therefore, when comparing the planning value to the value modified after the incident, one must analyse the same time interval, and cash flows must, of course, be discounted to the same date.

In the ex-ante analyses, ALE and SC_i are determined from the risk analysis that more or less represents the security budget for the given control mix, assuming an optimal cost-benefit balance. However, in the course of ex-post analysis, one has the exact yearly SC_i values from its yearly planning security budget. Nevertheless, there may be a clear difference between the planning budget at the beginning of the year (BOTY) and the realised budget at the end of that year (EOTY). So, incident types and impacts are maybe different than the planned value. The question is the magnitude and direction of deviation in which the risk-based planning, the planned (BOTY), and the realised (EOTY) budgets may differ. In the following calculation, their NPVs are calculated and displayed; however, NFVs could also be checked in the same way:

Furthermore, the realised interest rate can be applied for a more precise evaluation in an ex-post analysis. However, a negative deviation may result in an increased total cost for violations, having a chain-like effect that can ultimately reduce the security level of the entire system, as depicted in Figure 3.

In the interest of examining the mechanism’s on-the-fly effect on the budget, the following formula defines the Effect of incidents for an [1,n] examined interval based on NFV values of the security budget and incidents:

However, considering the shareholders’ behavioural biases and the differences in the perception of positive and negative events (Tversky and Kahneman, 1981), there may be a deviation between the shareholders’ perception and the magnitude of the incidents affecting the organisation. Matthew Rabin (1998) points out that shareholders overwhelmingly dislike losses. With regard to cybersecurity incidents, the following formula compares the change in stock prices (I_P) and the change in shareholders’ value (I_E) caused by the examined set of events connected with an incident that must be discounted by the shareholder interest rate (r_E):

We use an event study (Armitage, 1995) to quantify an events’ economic impact with abnormal returns (ARs), applying the market model (MM) to calculate the expected return:

AR_t = R_t – (α + βR_M,t)

The MM builds on the actual returns of a reference market and the correlation of the given firm’s stock with the reference market, for which this model uses the ordinary least squares (OLS). The AR_t signifies the difference between the actual stock return (R_t) on a particular day within the event window and the normal return, depicted by the relationship between the firm’s stock and its reference index (expressed by the α and β parameters). The model assumes that the residuals are normally distributed with a zero mean, have constant variance (homoscedasticity), are not serially correlated, and are not correlated with the explanatory variables. To test if heteroscedasticity negatively affects the estimation, we apply the Breusch-Pagan test (Breusch and Pagan, 1979).

To calculate the normal return, we use the S&P500 market’s return (R_M,t). The R_t and R_M,t are calculated by the natural log-normalised returns, i.e., in case of stock return, Rt=lnPtPt−1 where P_t is the closing price for a given day and P_t–1 is the closing price of the previous day.

We apply observation windows of [–150,–2] to analyse daily abnormal returns at a [–1,3] time interval as Figure 4 shows.

To measure the statistical significance of ARs, we apply the t-test (N = 149) for hypothesis testing, where the null hypothesis states that the mean of the ARs within the event window is zero (H₀:μ = 0) and the alternative hypothesis states the opposite (H₁:μ ≠ 0) for which test statistic is the standardised abnormal return given by:

tARt=ARtSARSAR is the standard deviation of the abnormal returns in the estimation window based on

where T₀ is the earliest day of the estimation window and T₁ is the latest day of the estimation window, and M denotes the number of non-missing (i.e., matched) returns.

In 2014, Cambridge Analytica collected Facebook user profiles in unethical and non-legal ways, affecting about 87 million users in the US (Business Insider, 2019). The publicity regarding the incident caused a drop in the company’s share price by approximately 7 per cent, on 19 March 2018 (CNBC, 2018).

According to revenue shortfalls, the share price fell 19 per cent on 26 July 2018 (MarketWatch, 2018). The closing price was $176.26, which means that compared to the previous day’s Wednesday market capitalisation, which was $630 billion, it fell to $510 billion by the end of Thursday’s trading day with a trading volume of 170 million. This change meant an impairment loss of approximately $120 billion.

On 28 September 2018, Meta revealed a data theft affecting about 2 million Facebook users’ date of birth, phone number, search history, and last login location. Even before the official announcement, on 27 September 2018, the share price fell by 3 per cent due to the publicity around the cyberattack (Business Insider, 2018).

By the end of the trading day on 18 March 2019, the shares were closing at 7.4 per cent lower than when the four-day long decrease began (International Business Times, 2019) due to the departure of product manager Chris Cox and vice president Chris Daniels of WhatsApp and the Needham downgrade. However, on 13 March, several hours of service outages affected all services due to an application error (The Verge, 2019).

On 24 March 2019, a security incident affecting the Instagram service was announced (Facebook, 2019c). On 18 April 2019, new information was revealed. When, on 12 June 2019, CEO Mark Zuckerberg’s sent a related email concerning problematic privacy practices, share prices fell 2.9 per cent (Markets Insider, 2019).

On 24 October 2018, the Information Commissioner’s Office (ICO) in the UK fined Meta £500,000 (approximately $643,000) for its role in the Cambridge Analytica scandal. However, Meta appealed on 21 November 2018, and on 14 June 2019, the General Court issued an interim decision ordering the ICO to disclose materials related to its decision-making process. On 2 September 2019, the ICO appealed against the interim decision, and finally, on 30 October 2019, the parties agreed, as a result of which Meta paid the penalty (Information Commissioner’s Office, 2019).

On 24 July 2019, the Federal Trade Commission (FTC) in the US imposed a $5 billion fine on the company (Federal Trade Commission, 2019). Furthermore, the Securities and Exchange Commission (SEC) charged an additional $100 million penalty (Facebook, 2019b) due to the investigation process.

Despite the fines and additional security incidents (e.g., in September 2019, Techcrunch (2019) reported data leaks due to several unencrypted databases with 419 million records), Meta’s 2019 Q3 results exceeded the expectations of analysts and investors (CNBC, 2019).

On 19 May 2020, the competent authority, the Competition Bureau Canada, imposed a CAD 9 million fine for improper data protection practices in Canada. The authority added a procedural fee of CAD 500,000 (approximately USD 13,221,150 in total) (Competition Bureau Canada, 2020).

Although several data protection authorities in the European Union have been active against the company on several issues, Meta was only fined €51,000 in Germany alone in 2019 for non-compliance with Article 37 of the General data protection regulation (GDPR), i.e., the failure to appoint a data protection officer (Hamburgischen Beauftragten für Datenschutz und Informationsfr., 2019).

Five distinguishable incidents affected Meta’s services (|I| = 5) – (1) Cambridge Analytica scandal, (2) Instagram vulnerability and possible data breach, (3) the leakage of 419 million data records, (4) data theft affecting 50 million users, and (5) downtime affecting all services. The fine of €51,000 imposed in Germany for non-compliance with Article 37 of the GDPR does not relate to the identified security incidents; therefore, we simply omit it.

Table 1 notes the identified events of the given incidents that modify the event date for which there are essential modifier factors discussed herewith. On 17 March 2018, Meta announced suspension of Cambridge Analytics access due to misuse of user data. The announcement was made on a Saturday. On 25 July 2018, the company announced a 19 per cent decrease based on a market report describing revenue shortfalls in the late afternoon. Unofficial sources revealed data theft affecting 50 million users on 27 Sep 2018; however, the official announcement was made during a call with reporters the following morning. On 13 March 2019, an application downtime for all services lasted for hours, which got publicity among shareholders the next day. However, on 18 March 2019, Facebook notified business and personal changes unrelated to the incidents but shortened the previous event’s observation period. Lastly, the FTC imposed a $5 billion penalty for Cambridge Analytica data leaks on 24 June 2019, which got publicity the following day.

In connection with the separate incidents, based on the modifier factors, we identified the first trading days determined by the incident-related events displayed in Table 2. Regarding further incidents, we use numbers in superscript to distinguish them consequently. The events of 12 June 2019 and 19 May 2020 occurred due to Meta’s previously conducted security behaviour; therefore, they cannot be clearly categorised as an incident. So, we divide their cash-flows among I¹, I², I³, and I⁴ incidents if there are any.

Based on the review we previously provided, we distinguish five security incidents: (1) End users suffered paramount and impactful privacy and information security incident in 2016 from Cambridge Analytica, (2) Instagram vulnerability and possible data breach, (3) leakage of 419 million data records, (4) data theft affecting 50 million users, and (5) downtime affecting all services. Table 3 displays the results of the publicly known corporate costs of the incidents. However, in our opinion, these values serve as the estimated minimum for the extra corporate costs of the incidents as there are potential publicly unknown extra negative cash flows.

The Cambridge Analytica scandal deeply affected Meta in the period following March 2018, resulting in the company’s revenue being reduced. For the 2018 Q2 period, revenue was $13.73 billion, which fell short of initial expectations (–$92.44 million). For the Q3 2018 period, the company had $13.23 billion in revenue, which also fell short of analysts’ expectations (–$115.24 million). These shortfalls are to be expected as an undesired impact of the incident. However, despite further incidents with high consequences, Meta was profitable in both years as it had annual revenues of $55,838 billion in 2018 and $70,697 billion in 2019, while its total operating expenses were $30,925 billion in 2018 and $46,711 billion in 2019, respectively (Facebook, 2020).

However, although the events displayed in Table 2 relating to the identified incidents started to occur in 2018, the root cause of the Cambridge Analytica scandal originated in 2016. Therefore, it is worth examining the overall company-related effects from 2016 to when the last event occurred in 2020. According to an announcement by the CEO of Meta (Roettgers, 2019), 2019’s security budget was worth $3.7 billion. However, to analyse the corporate effects, there is a further need to identify Meta’s yearly security budget. There is no additional exact information about budgeting, so we must assume other years.

Many organisations consider IT security and other security aspects as a subset of IT management functionally and fiscally despite the apparent difference, goals, and incompatible functions. Therefore, it is worth assuming the budget in the same way. According to Gartner (Hall et al., 2016), IT security spending ranged from approximately 1 per cent to 13 per cent of the IT budget in 2016. Meanwhile, in 2021, researchers measured IT spending of the software companies as 15 per cent of revenue on ICT (Flexera, 2021). With regard to the cybersecurity budget, the analysts estimated the cybersecurity budget from 6 to 14 per cent of their information technology budget according to the Deloitte and the Financial Services Information Sharing and Analysis Centre (FS-ISAC) report in 2019. On average, organisations allocated 10.1 per cent of the IT budget and 10.9 per cent for 2020 (Bernard et al., 2020). For the analysis, we assume higher budgeting percentiles. Assuming that the IT budget was 11.4 per cent of the revenue and the cybersecurity or IT security budget was 10.1 per cent of the IT budget, on average, we apply 0.011514 multipliers to the revenue for calculating the cybersecurity or IT security budget. Considering this multiplier is a rough estimate, we conduct a risk analysis later in the paper that examines budget changes for the given calculation.

Table 4 displays the NFV of the yearly estimated security budget of Meta and the identified incidents and other input data as revenues, expenses, shares, asset and shareholder value, and liabilities (Facebook, 2017, 2018, 2019a, 2020, 2021). The yearly cost of capital ex-post is based on the CAPM model using exact values for the given years. We applied the annual real returns on T Bond (Damodaran, 2021) as r_f and the MSCI ACWI Index (USD) (MSCI, 2021) for calculating r_M. We determined r_f,nom from the US real interest rate (Federal Reserve Bank of St. Louis, 2021) and the US inflation rate (Coin News, 2021), and lastly, we calculated β using Meta stock prices (Financial Content, 2021) and the S&P500 index (Yahoo! Finance, 2021). The publicly known corporate costs of the incidents displayed in Table 3 are discounted to the end of the given year by the effective interest rate (e^ri). Lastly, according to the annual reports, the company did not work with long-term debt; however, liabilities exist yearly, so we apply r_wacc to discount cash-flows, and we approximate r_D with r_f,nom assuming a perfect lending market (Ahn, 2016) without any spread, as Meta did not have any debt rating.

Table 5 displays the value of corporate changes related to incidents discounted to the end of the given year with the effective rate. Based on the highlighted extra cash-flows and the estimated security budgets, the calculated Effect of incidents metric shows that the corporate effects of the incidents increased the overall costs by approximately 87.95 per cent of the estimated security budget.

However, assuming the security budget’s estimation was probably inaccurate, we created what-if scenarios to analyse lower and higher yearly security budgets. Figure 4 displays the impact of incidents depending on the NFV of the annual budgets and the NFV of the incidents that altered the security budgets. Unsurprisingly, the higher the yearly budget, the lower the impact of incidents as the incidents’ values remain.

On the other hand, we created what-if scenarios regarding the valuation of the incidents’ corporate effects representing lower estimates. Figure 6 displays the impact of incidents depending on the NFV of the yearly budgets and the NFV of the incidents that altered the costs. Inevitably, the higher the corporate expenses of incidents (via the cost multiplier), the higher the impact of incidents. The security budget is constant for this time.

We used significant ARs to calculate the Incidence of incident recognition on the stock and asset changes on discounted values up to 2020, the corporate effects (I_A) of which we apply in Table 3, and the cost of capital and shares in Table 4. However, in this case, the corporate effects must be discounted with I_E to get the equity-related changes.

According to Table 6, there are six events for which significant ARs can be identified. Taking the identified ARs, we calculate the overall values they represent for a given year and their NFVs discounted to 2020. Comparing the calculated NFV of stock changes to the equity-related changes shows that the stock changes were -70.40 per cent of the effects on equity, meaning that the short term abnormal-return-related stock price changes and the equity-related changes had opposite effects. Because the estimation of the security budget creates uncertainty for the Incidence of incident recognition, we analyse what-if scenarios on the security budget. As Figure 7 depicts, the higher the costs of security incidents, the lower the Incidence of incident recognition.