FS1 Flashcards
Accruals Ratio and Accruals Calc
(Operating Assets and Liabilities)
- Accruals Ratio = Accruals / Net Operating Assets
- Net Operating Assets:
- Operating Assets : Total Assets - Cash & equivalents - marketable securites
- Operating Liabilities: total Liabilities - total Debt
- Net Operating Assets:
- OR:
- (YoY increase in Net Operating Assets) / (Avg Net Operating Assets)
- Accruals = NI - CFO - CFI
Bracketing Rates
Periodic Pension Cost (IFRS and GAAP)
IFRS:
+ Service Cost
+/- NET interest expense (BGN Funded Status * Discount Rate, or BGN Plan Assets - BGN PBO)
+/- Past Service Costs
= Periodic Pension Expense (INCOME STATEMENT)
GAAP:
+Service Cost
+ Interest Cost
- expected return on plan assets
+/- Amort of Actuarial G/L
+/- Amort of Prior Service Costs
= PPC –> (INCOME STATEMENT)
(bottom three are smoothed events)
PBO
BGN PBO
+ Service Cost
+ Interest Cost
+/- Actuarial G/L
+/- PSC
- Benefits Paid
END PBO
TPPC (plus difference between GAAP and IFRS)
Contributions - CHANGE in funded status
- same calc for GAAP and IFRS, but for IFRS it’s in OCI, not IS
Interest + service cost + plan amendments + actuarial G/L - Actual Return on Plan Assets
Net Premium Written vs. Earned
- Written = premiums earned over coverage period (net of reinsurance)
- Earned = premiums earned over accounting period
Expense Ratio
Underwriting Expenses (including commissions)
_______________________________________
Net Premium WRITTEN
UW Loss Ratio
Incurred Losses + Loss Adjustment Expenses
___________________________________________
Net Premium EARNED
- Incurred Losses = Claims + change in loss resreves*
- Loss Adjustment Expenses = cost of investigating claims*
Combined Ratio
Total Incurred Losses + Expenses
___________________________________-
Net Premium EARNED
- sum of UW loss and expense ratios
- High Ratio = soft market
- Low Ratio = hard market
- >100% = underwriting loss
LIFO Liquidation
- When a firm slows the purchase of inventory items so that older, lower costs are used to calculate COGS
- Leads to:
- Lower COGS
- Lower Inventory
- Artificially increase Gross and Net Margins
Cash Conversion Cycle
- DSO + Days Inventory on Hand - Days of Payables
- DSO = 365 / Receivables turnover
- Receivables Turnover = Rev / AR
- Days Inventory on Hand = 365 / Inventory Turnover
- Inventory Turnover = COGS / Avg Inventory
- Days of Payables = 365 / Payables Turnover
- Payables turnover = Purchase / Avg Payables
- DSO = 365 / Receivables turnover
Cash Ratio
Cash + Marketable Securities
_____________________________________
Current Liabilities
Hyperinflation
If cumulative 3-yr inflation is >100%
Inflation =
1+ Nominal Rate
_________________
1+ real rate
- During Hyperinflation, you want to reduce net monetary assets or increase net monetary liabilities
- issue debt in the local currency, and buy fixed assets using the proceeds
- GAAP: adjusting for nonmonetary A&L is not allowed - functional currency needs to be parent Currency
- IFRS: restate financials for inflation, then use current rate method (translate)
Treatment of Bonds under Amortized Cost
- Interest is calculated using yield at hte purchase date
- Find the yield at purchase date using the calculator (solve for i)
- (NOTE: if semiannual, DIVIDE RATE BY 2, and payments happen twice a year)
- Bond value at each date (every six months if semiannual) = original value - coupon + amortized discount
Lidquidity Coverage Ratio and # days of Stress Cash
- Liquidity Coverage Ratio =
- High Quality Liquid Assets / Net Outflows
- # days of stress volume Cash=
- Liquidity Coverage Ratio x # of days
- # of days will be given on exam eg “x-day liquidity needs”
- Liquidity Coverage Ratio x # of days
Adjusted Operating Profit
Reporting Operating Profit
+Reported Pension Expense
-Service Cost
_______________
Adjusted Operating Profit
FVPA Build
BGN FVPA
+/- ACTUAL return on plan assets
+ Contributions
- Benefits Paid
__________________
END FVPA
WCInv
Change In (Current Assets - Cash & Investments) - Current L’s - ST Debt & Dividends payable)
Or BGN WC - END WC
FCInv
Ending Net PPE
-BGN Net PPE
+Depreciation
- gain (loss) on sale of PPE
Unlever and Relever Beta
Unlever:
[1 / (1+DE)] * b
Re-lever
1+D/E * b
QM Terms (SEE, SST, etc.)
Terms: know what they measure and how you use them to construct ANOVA table
- Standard Error of Estimate (SEE): measures degree of variability of the actual Y-values relative to estimated Y-values from a regression equation à gauges “fit” of regression line à it is the standard deviation of error terms in the regression, or standard error of the regression à it’s the StDev of the residualsà strong relationship = low SEE – you want low
- Total sum of squares (SST): measures the total variation in the dependent variable (different from variance) à equal to sum of the squared differences between the actual Y-values and mean of Y
- Regression sum of squares (RSS): measures variation of the dependent variable that is explained by the independent variable à sum of the squared distances between predicted Y-values and the mean of Y
- Sum of squared errors (SSE): measures the unexplained variation in the dependent variable à sum of squared vertical distances between the actual Y-values and the predicted Y-values on the regression line à it’s the sum of the squared residuals (sum of squares not explained)
- SSE + RSS = SST: total variation = unexplained variation + explained variation
-
R Squared: the percentage of total variation in the dependent variable that is explained by the independent variable
- = explained variation (RSS) / total variation (SST)
- ANOVA Table: all it does is show the difference between what is or isn’t explained by the model à the total variability that is or isn’t explained à partial table will be given on exam
Heteroskedasticity and Correcting It
Heteroskedacity: when error term variance is non-constant
- Two types:
- Unconditional—not related to independent variablesàcauses no problems
- Conditional—related to independent variables (see scatter plot)àIS a problemàt-stats (And f-stat) are unreliable
- Note: heteroskedacity does not impact the estimate, but standard error is messed up, and therefore so are the T and F stats
Detecting: Scatter (see right) or Breusch-Pagan Test
- Know what Breusch does, nothing else:
- Regress squared residuals on “X” variablesàtest significant of resulting R2 (do the independent variables explain a significant part of the variation in squared residuals?
Correcting:
- Use robust standard errors (just memorize that this is how to fix it—don’t need to know what it is)
- Or use generalized least squares (modifying original equation to eliminate heteroskedasticity)
Serial Correlation
Serial Correlation: Positive autocorrelationàeach error term tends I nthe same direction as previous term
- Common in financial data
- T-ststas are to ohigh (type I errors)
- Coefficients still consistent and unbiased
Detecting
- Scatter Plot
- Durbin-Watson statistic: formal test of error term correlation (don’t blow this off but no need to be an expert)
- DW Test: three cases: no correlation, positive correlation, and negative correlation: DW = 2 times correlation – so “how close to 2” does DW have to be to conclude no serial correlation?
Correcting:
- Use robust standard errors (just memorize that this is how to fix it—don’t need to know what it is)
Or use generalized least squares (modifying original equation to eliminate heteroskedasticity)
More detail in slide deck
