final term risk management

However, by diversifying into portfolios, especially those incorporating negatively correlated assets like Stocks A and C, investors can potentially achieve higher expected returns with

Final-term Risk Management Full name: Le Gia Tien Vinh

Student code: 22070158

Question 1:

Financial Indicators of ABT: (target company)

Days accounts receivable

outstanding

31.26 1 20.46 5 55.07 2 82.33 9 107.9 58 Days accounts payable

outstanding

152.2 67 85.41 1 42.43 3 32.46 3 50.88 4

05 184.4 72 155.7 88 137.3 42 252.7 74 Required financing period 129.8

98 119.5 26 168.4 28 187.2 19 309.8 48

Solvency risk

4 176.9 18 44.82 9 94.78 9 172.6 95 Operating cash flow to capital

expenditures

-0.832 -0.072 -7.974 -1.751

-0.706 3

Profitability

% 69.92

% 21.98

% 10.92

% 10.41

%

Financial Indicators of CMX: (benchmarked company)

Days accounts receivable

outstanding

31.26 1 20.46 5 55.07 2 82.33 9 107.9 58 Days accounts payable

outstanding

152.2 67 85.41 1 42.43 3 32.46 3 50.88 4

05 184.4 72 155.7 88 137.3 42 252.7 74 Required financing period 129.8

98 119.5 26 168.4 28 187.2 19 309.8 48

Solvency risk

4 176.9 18 44.82 9 94.78 9 172.6 95 Operating cash flow to capital

expenditures

-0.832 -0.072 -7.974 -1.751

-0.706 3

Profitability

% 69.92

% 21.98

% 10.92

% 10.41

%

I Liquidity Risk

1 Current Ratio:

ABT's current ratio has remained relatively stable over the years, ranging from a high of 2.151 in 2019 to a low of 1.722 in 2021

The ability to pay short-term obligations is maintained at an acceptable level, although there was a slight dip in 2021

Compared to CMX, ABT's current ratio is approximately half, indicating weaker short-term solvency compared to CMX

2 Quick Ratio:

ABT's quick ratio has shown a significant increase, from 0.408 in 2019 to 0.994

in 2023

The ability to quickly pay off short-term debts has improved considerably, indicating better financial health

CMX also experienced some fluctuations but at a lower level, suggesting ABT's stronger improvement in this aspect

3 Days Accounts Receivable Outstanding:

ABT's days accounts receivable outstanding fluctuated but generally improved from 2019 to 2023

Despite some difficulties in 2021, ABT's ability to collect receivables improved significantly compared to CMX

4 Days Accounts Payable Outstanding:

ABT's days accounts payable outstanding increased slightly from 2019 to 2023

outperformed CMX in this aspect.

5 Days Inventory Held:

ABT's days inventory held showed a significant increase until 2021, followed by

a sharp decrease in 2022 and 2023

While inventory management efficiency improved, it still lags behind ideal levels However, ABT performs better than CMX in managing inventory

6 Required Financing Period:

ABT's required financing period decreased significantly from 2019 to 2023, indicating improved self-sustainability in business operations

In contrast, CMX's required financing period increased over the same period, indicating potential financial stability issues

II Solvency Risk

1 Interest Coverage Ratio:

ABT's interest coverage ratio fluctuated but showed an overall improvement from

2019 to 2023

ABT consistently outperformed CMX in its ability to cover interest expenses

2 Capital Expenditures:

ABT's capital expenditures increased significantly until 2022, followed by a slight decrease in 2023

ABT's capital expenditure level is lower compared to CMX, indicating more conservative investment strategies

3 Operating Cash Flow to Capital Expenditures:

ABT's operating cash flow improved notably from 2022 to 2023, indicating better profitability and sustainable growth

ABT generated more operating cash flow compared to CMX, indicating stronger financial performance

4 Debt Ratio:

ABT's debt ratio fluctuated moderately but remained relatively stable from 2019

to 2023

ABT relied less on debt compared to CMX, indicating a healthier financial

ABT's long-term debt ratio decreased slightly from 2019 to 2023, indicating reduced reliance on long-term debt

ABT maintained a lower long-term debt ratio compared to CMX, indicating better debt management

6 EBIT:

ABT's EBIT showed fluctuations but experienced overall growth from 2019 to 2023

ABT's profitability before interest and taxes was lower than CMX but demonstrated better growth trends

III Profitability

1 Return on Assets (ROA):

ABT's ROA fluctuated but showed improvement in recent years, outperforming CMX in terms of asset utilization for generating profits

2 Return on Capital (ROC):

ABT's ROC fluctuated but demonstrated a generally upward trend, indicating more efficient use of capital compared to CMX

3 Return on Equity (ROE):

ABT's ROE showed significant fluctuations but improved in recent years, reflecting better utilization of shareholder equity for generating profits compared

to CMX

Conclusion: ABT exhibits moderate liquidity risk, with improvements in short-term

solvency indicators It also demonstrates better solvency, profitability, and efficiency compared

to CMX However, ABT still faces challenges in inventory management and maintaining stable profitability Overall, ABT shows promising financial performance and stability, positioning it favorably in the market

Question 2:

a) We use Python Program to calculate the Expected Return & Standard Deviation: import numpy npas

# Given data

probabilities =np.array([0.3, 0.35, 0.2, 0.15])

returns_A =np.array([0.21, 0.12, 0.04, -0.07])

# Portfolio weights

weight_portfolio1 =np.array([0.55, 0.45]) # Portfolio 1: 55% Stock

A, 45% Stock B

weight_portfolio2 =np.array([0.55, 0.45]) # Portfolio 2: 55% Stock

A, 45% Stock C

# Calculate expected return

expected_return_portfolio1 =np.dot(probabilities returns_A, *

weight_portfolio1[0] +returns_B weight_portfolio1 * [ ])1

expected_return_portfolio2 =np.dot(probabilities returns_A, *

weight_portfolio2[0] +returns_C weight_portfolio2 * [ ])1

# Calculate standard deviation

portfolio_std_deviation1 = np.sqrt(

np.dot(probabilities, ((returns_A -expected_return_portfolio1)

* weight_portfolio1[0+

.(returns_B -expected_return_portfolio1)

* weight_portfolio1[1]) 2))

portfolio_std_deviation2 = np.sqrt(

np.dot(probabilities, ((returns_A -expected_return_portfolio2)

* weight_portfolio2[0+

.(returns_C -expected_return_portfolio2)

* weight_portfolio2[1]) 2))

# Output

print("Portfolio 1:")

print("Expected return:", expected_return_portfolio1)

print("Standard deviation:", portfolio_std_deviation1)

print("\nPortfolio 2:")

print("Expected return:", expected_return_portfolio2)

print("Standard deviation:", portfolio_std_deviation2)

This is the result:

Portfolio 1: Expected return: 8.54% Standard deviation: 9.09%

Portfolio 2: Expected return: 7.60% Standard deviation: 1.90%

Analyzing the outcomes from Question A, we notice that Portfolio 1, with an allocation

of 55% to Stock A and 45% to Stock B, demonstrates an expected return of 8.54% and a standard deviation of 9.09% Meanwhile, Portfolio 2, consisting of 55% Stock A and 45% Stock C, shows

an expected return of 7.60% and a significantly lower standard deviation of 1.90%

These results reflect the impact of correlation on risk reduction through diversification In Portfolio 2, where Stocks A and C exhibit a negative correlation, the combined standard deviation is notably lower compared to Portfolio 1, which includes positively correlated assets like Stocks A and B This highlights the effectiveness of diversification in reducing risk when incorporating assets with contrasting performance trends

In contrast, investing solely in individual assets amplifies the associated risks For instance, Stock A has a standard deviation of 9.09%, Stock B stands at 5.61%, and Stock C at 5.64% However, by diversifying into portfolios, especially those incorporating negatively correlated assets like Stocks A and C, investors can potentially achieve higher expected returns with lower risk, as demonstrated in Portfolio 2

In conclusion, diversifying investments across various asset types, particularly those

with opposing fluctuations, can increase expected profits and reduce risk

Question 3:

a) I have a Python code for calculating the NPV of old machines & new equipment:

# Given data

cash_flow_current_machine =150000 # Net cash inflow per year for

current machine

growth_rate_current_machine = 0.08 # Growth rate of net cash flow for

current machine

lifespan_current_machine = 25 # Lifespan of the current machine

cash_flow_new_equipment = 180000 # Net cash inflow per year for new

equipment

growth_rate_new_equipment =0.07 # Growth rate of net cash flow for new

equipment

initial_outflow = 20000 # Initial cash outflow for purchasing new

equipment

discount_rate =0.05 # Discount rate

# Calculate NPV for current machine

npv_current_machine = sum([cash_flow_current_machine ( *1

+ 1)])

# Calculate NPV for new equipment

npv_new_equipment = -initial_outflow +sum([cash_flow_new_equipment ( *1

+ growth_rate_new_equipment) ** year / ( + discount_rate) ** year

for year inrange(1,

lifespan_current_machine + 1)])

# Compare NPVs

if npv_new_equipment npv_current_machine > :

print("Leon should purchase the new equipment.")

print("NPV of new equipment:", npv_new_equipment)

print("NPV of current machine:", npv_current_machine)

else:

print("Leon should continue with the current machine.")

print("NPV of current machine:", npv_current_machine)

print("NPV of new equipment:", npv_new_equipment)

This is the result:

We can see that the NPV for a current machine is approximately $5.520.818,01, smaller than the NPV for new equipment ($5.784.346,73)

=> I think that Leon should purchase the new equipment

b)

There may be errors in NPV estimates and consequent investment decisions if all projects are treated equally and the Weighted Average Cost of Capital (WACC) is used as the discount rate Typically employed as a project discount rate, the WACC shows the average cost of financing for a business, taking into account both debt and equity It does, however, operate under the assumption that the project's risk is equal to the company's overall risk, which may not always be the case

The risk of either overestimating or underestimating a project's actual worth arises from depending just on WACC when discounting future cash flows This disparity results from the possibility that various projects within an organization have differing risk profiles, which are impacted by variables like industry dynamics, project-specific hazards, and market dynamics

inaccurate NPV estimates.

In the case of Leon and the decision to purchase new equipment, using the current WACC

of 5% as the discount rate for evaluating the new equipment investment might not accurately reflect the project's unique risk characteristics The new equipment project may have different risk factors compared to the company's overall operations, such as technological uncertainties, market demand variations, or regulatory considerations

Therefore, Leon should exercise caution when applying the WACC as a discount rate and should assess whether the risk of the new equipment project aligns with the company's overall risk profile If the new equipment project exhibits similar risk characteristics to the company's existing operations, using the current WACC could be appropriate However, if the project's risk differs significantly, Leon should consider using a discount rate that reflects the specific risk profile of the new equipment investment This approach ensures more accurate NPV calculations and facilitates informed investment decisions tailored to the project's unique circumstances c) I have a python code for recalculating the NPV for new equipment with 6% discount rate:

# Re-answer (a) using a 6% discount rate for the new equipment:

discount_rate_6_percent = 0.06 # Discount rate for new equipment

# Calculate NPV for new equipment with 6% discount rate

npv_new_equipment_6_percent = -initial_outflow +

sum([cash_flow_new_equipment ( *1 growth_rate_new_equipment) ** year /

(1 + discount_rate_6_percent) **year

for year in

range(1lifespan_current_machine + 1)])

# Compare NPVs

if npv_new_equipment_6_percent npv_current_machine > :

print("Leon should purchase the new equipment with a 6% discount

rate.")

print("NPV of new equipment (6% discount rate):",

npv_new_equipment_6_percent)

print("NPV of current machine:", npv_current_machine)

else:

print("Leon should continue with the current machine.")

print("NPV of current machine:", npv_current_machine)

print("NPV of new equipment (6% discount rate):",

npv_new_equipment_6_percent)

This is the result:

Question 4:

a) Operational Risks and Controls:

1 Attendance Recording Errors:

Risk: Staff may forget to tap their cards, leading to inaccuracies in attendance records

Control: Implement automated attendance tracking systems (e.g., biometric scanners) to reduce reliance on manual inputs

2 Data Extraction Errors:

Risk: Inaccurate extraction of timesheet data to Excel may result in incorrect payroll calculations

Control: Utilize automated data extraction tools to minimize human error and ensure data integrity

3 Manual Input Errors:

Risk: Payroll staff may make mistakes when inputting timesheets and salary data into Excel, leading to incorrect salary calculations

Control: Implement validation checks and dual authorization for data input to reduce the likelihood of errors

4 Review and Approval Delays:

Risk: Delays in HR review and approval may lead to late payment of salaries Control: Set clear timelines for review and approval processes, with escalation procedures for overdue tasks

5 Payment Processing Errors:

Risk: Errors in transmitting salary data to the bank may result in incorrect payments to employees

Control: Implement reconciliation procedures to ensure accurate transfer of salary

Key Risk: Attendance Recording Errors

Assumption: The attendance system relies solely on manual card tapping, with no

backup systems

Key Risk Indicator (KRI): Percentage of missing attendance records exceeding 5% Threshold:

Red: Missing attendance records exceed 10%

Amber: Missing attendance records between 5% and 10%

Green: Missing attendance records below 5%

Risk Profile:

Red: If more than 10% of attendance records are missing, there is a high risk of payroll

inaccuracies and potential employee dissatisfaction due to incorrect salary calculations

Amber: If missing attendance records range between 5% and 10%, there is a moderate

risk of payroll errors, requiring immediate attention to prevent escalation

Green: If missing attendance records are below 5%, the risk of payroll inaccuracies is

low, and operations are running smoothly

Analysis:

Regular monitoring of attendance records is crucial to identify any deviations from expected patterns

Implementation of automated attendance tracking systems can significantly mitigate this risk by reducing reliance on manual inputs

Continuous training and awareness programs for employees can help minimize errors in attendance recording

Question 5:

A Strategic Risk

To analyze and identify strategic risks related to Energizer's plan to expand its visibility

in the Vietnamese market and boost the sale of EVEREADY, we can use the SWOT analysis framework SWOT stands for Strengths, Weaknesses, Opportunities, and Threats Here's how we can apply it: