<h3 id="heading-financial-analysis-lesson-3-financial-risk-and-return-analysis">Financial Analysis. Lesson 3. Financial Risk and Return Analysis</h3>
<ol>
<li>Systematic risk refers to market-wide risks that cannot be diversified.
</li>
<li>Unsystematic risk relates to risks specific to a single company.
</li>
<li>Standard deviation measures the variability of returns around the mean.
</li>
<li>Sharpe ratio evaluates risk-adjusted returns by comparing returns to volatility.
</li>
<li>Alpha indicates a portfolio’s excess returns compared to a benchmark index.
</li>
<li>Beta quantifies an asset's sensitivity to broader market movements.
</li>
<li>Value at risk (VaR) estimates potential loss at a given confidence level.
</li>
<li>Monte Carlo simulation predicts financial outcomes using random variable models.
</li>
<li>Covariance shows how two asset returns move relative to each other.
</li>
<li>Correlation coefficient measures the relationship strength between two variables' movements.
</li>
</ol>
<hr />
Technical Examples:
<ol>
<li>Using Sharpe ratio, analysts assess if returns justify investment risks.
</li>
<li>VaR analysis helps estimate maximum potential loss over a specific timeframe.
</li>
<li>Monte Carlo simulation models a range of potential portfolio outcomes.
</li>
</ol>

### Financial Analysis. Lesson 3. Financial Risk and Return Analysis

1. **Systematic risk** refers to market-wide risks that cannot be diversified.
    
2. **Unsystematic risk** relates to risks specific to a single company.
    
3. **Standard deviation** measures the variability of returns around the mean.
    
4. **Sharpe ratio** evaluates risk-adjusted returns by comparing returns to volatility.
    
5. **Alpha** indicates a portfolio’s excess returns compared to a benchmark index.
    
6. **Beta** quantifies an asset's sensitivity to broader market movements.
    
7. **Value at risk (VaR)** estimates potential loss at a given confidence level.
    
8. **Monte Carlo simulation** predicts financial outcomes using random variable models.
    
9. **Covariance** shows how two asset returns move relative to each other.
    
10. **Correlation coefficient** measures the relationship strength between two variables' movements.
    

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**Technical Examples:**

1. Using **Sharpe ratio**, analysts assess if returns justify investment risks.
    
2. **VaR analysis** helps estimate maximum potential loss over a specific timeframe.
    
3. **Monte Carlo simulation** models a range of potential portfolio outcomes.