DAY 3 Problem Solving Techniques and Financial Risks .pdf
- 1. Problem Solving Techniques 1. SWOT Analysis: ▪ Strengths, Weaknesses, Opportunities, and Threats assessment for understanding the internal and external factors affecting financial risk. 2. Pareto Analysis (80/20 Rule): ▪ Identifying the 20% of causes responsible for 80% of the problems. 3. Brainstorming: ▪ Group discussions to generate a wide range of ideas for solutions.
- 2. Table of contents ▪ Define the problem ▪ Gather the data ▪ Identify potential causes ▪ Evaluate potential causes ▪ Confirm root cause ▪ Identify a solution ▪ Implement the solution
- 3. Problem Solving Techniques 4. The 5 Whys: ▪ Asking "Why?" five times to dig deeper into the cause of an issue. 5. Decision Tree Analysis: ▪ A visual representation of decisions, outcomes, and probabilities to guide complex financial decisions. 6. Failure Mode and Effects Analysis (FMEA): ▪ Identifying potential failure points and evaluating their impact on the system or process
- 4. Integrating RCA and Problem Solving in Financial Risk Management. 1.Proactive Risk Management: ▪ By combining RCA and problem-solving, businesses can anticipate financial risks and develop proactive strategies. 2. Continuous Improvement: ▪ These methods allow for ongoing monitoring and refinement of risk management strategies. Example: A company experiencing liquidity issues might use RCA to determine whether the root cause is poor forecasting, ineffective cash management practices, or external factors like market volatility. Problem-solving techniques will then help devise appropriate solutions.
- 5. Case Study: Financial Risk Management in Action Scenario: ▪ Problem: A company has been experiencing increasing credit losses due to defaults on loans. Step 1: Define the Problem: ▪ Credit risk is increasing, impacting profitability. Step 2: Collect Data ▪ Analyze loan portfolios, client payment histories, and market conditions.
- 6. Case Study: Financial Risk Management in Action Scenario: Step 3: Identify Possible Causes: ▪ Economic downturn, poor loan underwriting, ineffective collection practices. Step 4: RCA and Analyze Root Cause: ▪ Root cause identified: Inadequate credit risk assessment tools leading to poor lending decisions.
- 7. Case Study: Financial Risk Management in Action Scenario: Step 5: Develop Solutions ▪ Implement more robust credit scoring models and improve borrower screening. Step 6: Monitor and Evaluate ▪ Track loan repayment rates and adjust risk models based on performance.
- 8. Tools for Financial Risk Management 1. Risk Metrics and Models: ▪ Value at Risk (VaR), Conditional VaR, and stress testing. 2. Financial Derivatives: Hedging tools like options, futures, and swaps. 3. Risk-adjusted Performance Metrics: ▪ Sharpe ratio, Sortino ratio. 4. Scenario Analysis: ▪ Analyzing the impact of different economic scenarios on financial outcomes.
- 9. Conclusion Financial risk is an inherent part of business operations, but by understanding the types of risks, performing root cause analysis, and applying effective problem-solving techniques, companies can mitigate potential losses. Root Cause Analysis and Problem Solving empower organizations to address risks comprehensively and develop long-term solutions. Regular review and adaptation of risk management strategies will lead to more resilient financial systems.
- 10. Measurement of Financial Risks After completing this you should be able to: ▪ Describe the mean-variance framework and an efficient frontier. ▪ Compare the normal distribution with the typical distribution of returns of risky financial assets such as equities. ▪ Define the VaR measure of risk, describe assumptions about return distributions and holding period, and explain the limitations of VaR ▪ Explain and calculate Expected Shortfall (ES) and compare VaR and ES. ▪ Define the properties of a coherent risk measure and explain the meaning of each property. ▪ Explain why VaR is not a coherent risk measure.
- 11. Measurement of Financial Risks The Mean-Variance Framework The mean-variance framework uses the expected mean and standard deviation to measure the financial risk of portfolios. Under this framework, it is necessary to assume that returns follow a specified distribution, usually the normal distribution. The normal distribution is particularly common because it concentrates most of the data around the mean return. 66.7% of returns occur within plus or minus one standard deviations of the mean. A whopping 95% of the returns occur within plus or minus two standard deviations of the mean.
- 13. Measurement of Financial Risks The Mean-Variance Framework Investors are generally concerned with downside risk and are therefore interested in probabilities that lie to the left of the expected mean. Note the expected return does not imply the anticipated return but rather the average returns. On the other hand, the risk is measured using the standard deviation of returns.
- 14. Measurement of Financial Risks The Mean-Variance Framework Investors are generally concerned with downside risk and are therefore interested in probabilities that lie to the left of the expected mean. Note the expected return does not imply the anticipated return but rather the average returns. On the other hand, the risk is measured using the standard deviation of returns.
- 15. Efficient Frontier The efficient frontier represents the set of optimal portfolios that offers the highest expected return for a defined level of risk or the lowest risk for a given level of expected return. This concept can be represented on a graph by plotting the expected return (Y- axis) against the standard deviation (X-axis). For every point on the efficient frontier, at least one portfolio can be constructed from all available investments with the expected risk and return corresponding to that point. Portfolios that do not lie on the efficient frontier are suboptimal: those that lie below the line do not provide enough return for the level of risk. Those that lie on the right of the line have a higher level of risk for the defined rate of return.
- 17. Efficient Frontier The choice between optimal portfolios A, B, and D above will depend on an individual investor’s appetite for risk. A very risk-averse investor will choose portfolio A because it offers an optimal return at the lowest risk, whereas an investor with room for more risk might pick D. After all, it offers the highest optimal return at the highest risk. Note that, the efficient frontier above considers only the risky assets. Now, consider when we introduce a risk-free investment with a return of RF. It can be shown that the efficient frontier is a straight line. That is, there is a linear relationship between the expected return and the standard deviation of return.
- 19. Efficient Frontier F represents the risk-free return on the diagram above. Note that risk-free asset sits on the efficient frontier because you cannot get a higher return with no risk, and you cannot have less risk than zero. Consider the tangent line FM. By proportioning our investment between the risk-free asset F and the risky asset M (market portfolio), we can obtain a risk-return that lies on the line FM combination. In other words, the risk-return tradeoff is a linear function. Denote the risk-free return by RFRF (with a standard deviation of 0). Also, let the market portfolio return be RMRM, and its standard deviation is σMσM. Let the proportion of funds in a risky portfolio be ββ and that in risk-free assets, be 1−β1−β. Now using the formula
- 20. Efficient Frontier ▪ The efficient frontier involving a risk-free asset also shows that the investor should invest in risky assets (in this case, M) by borrowing and lending at a risk-free rate rFrF. For instance, we assume that an investor borrows at the rate rFrF so that now we are considering the efficient frontier beyond M. If this is the case, then β>1β>1 and the proportion of amount borrowed will be β−1β−1, and the total amount available is ββ multiplied by available funds. Assume now that we invest in risky asset M. Then the expected return is: ▪ βrM−(β−1)RF=(1−β)rF+βrMβrM−(β−1)RF=(1−β)rF+βrM ▪ The standard deviation can be shown to be βσMβσM, which is online arguments for the points below point M. ▪ Therefore, it is safe to say risk-averse investors will invest in points on line FM and close to F, and those investors that are risk-seeking will invest on points close to M or even points beyond M on line FM.
- 21. Values at Risk (VaR) ▪ VaR is a risk measure that is concerned with the occurrence of adverse events and their corresponding probability. VaR is built from two parameters: the time horizon and the confidence level. Therefore, we can say that VaR is the loss that we do not anticipate to be exceeded over a given period at a specified confidence level. ▪ For example, consider a time horizon of 30 days and a confidence interval of 98%. Therefore 98% VaR of USD 5 million implies that we are 98% confident that over the next 30 days, the loss will be less than USD 5 million. Similarly, we can say that we are 2% confident that over the next 30 days, the loss will be greater than USD 5 million. Consider the following loss distribution density function curve:
- 23. THANK YOU, TEAM.