8 9 forecasting of financial statements

Lectures 8 and 9Lectures 8 and 9
Forecasting and credit risk analysisForecasting and credit risk analysis
Reading on forecasting and analyst forecasts:
Chapters 14, 15 & 16 from Penman (OR Chapter 6 from Palepu et al.)
AND:
•Clement M. (1999) Analyst forecast accuracy: do ability, resources, and portfolio complexity matter?
Journal of Accounting and Economics, Vol. 27, pp. 285-303.
•Clement M. and Tse S. (2003) Do investors respond to analysts’ forecast revisions and if forecast
accuracy is all that matters? The Accounting Review, Vol. 78, pp. 227-249.
•Brav A. and Lehavy R. (2003) An empirical analysis of analysts’ target prices: short-term
informativeness and long-term dynamics. Journal of Finance, Vol. 58, pp. 1933-1968.
•Asquith P., Mikhail M. and Au A. (2005). Information content of equity analyst reports. Journal of
Financial Economics, Vol. 75 (2), pp. 245–282.
•McNichols M. and O’Brien P. (1997) Self-selection and analyst coverage, Journal of Accounting
Research, Vol. 35, pp. 167-199.
Reading on credit risk analysis:
Chapter 20 from Penman
AND:
•Beaver W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research. Vol. 4.
(Supplement). p.77-111
•Altman E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.
Journal of Fiancne. Vol. 23, No. 4 (September), pp. 589-609.
•Altmant E., Haldeman R., and Narayanan P. (1977). ZETATM Analysis: A new model to identify
bankruptcy risk of corporations. Journal of Banking and Finance. Vol.1, pp.29-54.
•Ohlson J. (1980) Financial Ratios and the Probabalistic Prediction of Bankruptcy. Journal of Accounting
Research. Vol. 18, No. 1(Spring), pp. 109-131.

Forecasting - two general approaches:
1. Non-Econometric, Qualitative, non-mechanical methods
(see: Investor’s Guide to Analysing Companies & Valuing Shares, by
Michael Cahill)
2. Econometric, quantitative or mechanical methods (see p. 714 from
White et al.)
Non-Econometric Forecasting:
• Most common among sell-side analysts
• Involves judgements and assumptions
• Uses the analyst’s knowledge and understanding of the firm, industry
and economy and focuses on prediction of key value drivers (usually
sales and profits)
• Incorporates qualitative as well as quantitative inputs
• Requires a consistent disciplined approach, i.e. same steps and
structure of the forecasting process: Top-down or Bottom-up approach

Top-down approach to forecastingTop-down approach to forecasting
=> from international and national macroeconomic forecasts to industry
forecasts and then to individual company.
E.g., to forecast revenue for a car manufacturer could start from
real/nominal GDP forecast:
• Forecasted industry revenues = function of nominal GDP and GDP
growth
• Forecasted company revenues = forecasted industry revenues * the
company’s forecasted market share
Or
• Forecasted industry unit sales = function of real GDP and real GDP
growth
• Forecasted company unit sales = Forecasted industry unit sales * the
company’s forecasted market share
• Forecasted company revenues = Forecasted company unit sales * unit
sales price

The top-down approach requires:The top-down approach requires:
1 Analysis of the economy
- growth trends; phase of the cycle; factors of demand & supply
2 Analysis of the industry
- overall factors of demand & supply; market share of each peer; industry
growth prospects
3 Analysis of the firm
- historical market share in the industry and growth rates
- Factors of risk & opportunities that can change the firm’s market
share, the short- and long-term growth rates
- Consider firm’s strategy, efficiency, sustainability, competitive threats;
value drivers and profit centres.
4 Financial/accounting analysis
- Analysis of the quality of reported earnings, assets and liabilities
- Adjustments? Effects on future fin. statements & ratios?
- Generate pro-forma fin. statements and forecast value drivers, ratios,
e.g.: sales, earnings, profit margin, OCF, ROE. Are they sustainable?

Bottom-up approach to forecastingBottom-up approach to forecasting
Aggregates forecasts at divisional level to company level.
E.g., a clothing retailer may have 20 stores in operation with 5 new stores
about to open.
•Use info on sales per square meter of the existing stores to forecast sales
per square meter of the new stores
•Add the sales forecasts for all 25 stores.
•Forecast the profit margins
•Forecast earnings: e.g., Net Income forecast = revenue forecast * net
profit margin forecast
Similar to the top-down method, revenue forecasting is the starting point
and should reflect company-specific forward-looking knowledge.

Maintain consistency!!!Maintain consistency!!!
Does your firm-level forecast fit into the industry- and economy-level
outlooks?
•E.g. firm’s sales growth forecast of 10%, while that of industry and
economy is 3%. => increase in firm’s market share and reduction in
competitors’ market share is implied. Is this realistic?
A forecast can be no better than the business strategy analysis, accounting
analysis and financial analysis underlying it !!!
It is best to forecast comprehensively, i.e., earnings and balance sheet
and cash flows.
- This prevents unrealistic implicit assumptions and avoids internal
inconsistencies
E.g., forecasted sales and earnings growth without envisaging increase in
fixed assets, working capital and associated financing. => forecasts imbed
unreasonable assumptions about asset turnover.
For group project - only forecast items needed for your valuation.

Anchor on the known ‘long-term’ behaviour of ratios:Anchor on the known ‘long-term’ behaviour of ratios:
•Sales growth
•Changes in sales profit margins
•Earnings
•ROE
•Growth rate of Net Operating Assets
•Return on NOA
•Unusual Operating Income items/NOA
•Operating Asset Turnover (ATO)
•Changes in operating Asset Turnover
•Growth in book value of ordinary equity
•Financial Leverage
Source for graphs (below): Nissim D, Penman S., Ratio Analysis and
Equity Valuation: from research to practice, Review of Accounting Studies,
2001 (march), pp.109-154.

1. Sales growth rates over time1. Sales growth rates over time
•mean reverting - growth rates revert to ‘normal’ level (6-11%)
•full reversion time – about 5+ years
•most of reversion happens in the first two years
•the speed of reversion depends on various factors:
- highly competitive sectors with low entry barriers => quick reversion
- unique products, tough entry barriers, monopolists => slow reversion =>
prolonged abnormal sales growth

2. Core Sales Profit Margin2. Core Sales Profit Margin
•Operating Profit Margin= (operating income – unusual operating income
items) / Sales
•Largely remains constant

3. Changes in core Sales Profit Margin3. Changes in core Sales Profit Margin
•strongly mean reverting
•revert to ‘normal’ level of zero within 1 year

4. Return on Equity (ROE) over time4. Return on Equity (ROE) over time
• unlike earnings, abnormally high/low ROE do revert to normal range of
10 to 20%
– as growth in earnings does not keep pace with growth in the investment base
– high profitability attracts competition => firm’s ROE decreases
– low profitability moves capital to more profitable ventures
• High ROE may persist for firms with unique market/product position
• High ROE may persist due to accounting distortions (expensing R&D for
technology firms => understated investment base)

5. Growth rate of Net Operating Assets5. Growth rate of Net Operating Assets
•NOA = operating assets - operating liabilities
•Extreme NOA growth rates revert to a common level of 8-12% within
about 4 years

6. Return on Net Operating Assets6. Return on Net Operating Assets
•RNOA= operating income / net operating assets
•Tends to move towards a common level, but firms with highest (lowest)
RNOA tend to maintain higher (lower) RNOA in subsequent five years
•Normal long-term range is from 8 to 15%

7. Unusual Operating Income items/NOA7. Unusual Operating Income items/NOA
•Reverts to close-to-zero levels very quickly, within 3 years
- Unusual operating income items can be set to zero in long-term
predictions

8. Operating Asset Turnover (ATO)8. Operating Asset Turnover (ATO)
•Remains fairly constant with the exception of the highest asset turnover
group. Extremely high values tend to decline but very slowly (10+ years)
•Normal values remain normal throughout times
•It is reasonable to assume constant ATO for most ‘normal’ firms

9. Changes in Operating Asset Turnover9. Changes in Operating Asset Turnover
•Changes in ATO are strongly mean reverting, i.e., revert quickly to
common level of 0%
•Large increases or decreases are temporary

10. Growth in book value of ordinary equity10. Growth in book value of ordinary equity
Strong reversion to average growth rates

11. Financial Leverage11. Financial Leverage
•the ratio of net financial obligations to book value of ordinary equity
•Is fairly constant over time, except for firms with extraordinarily high
leverage: extreme high (low) leverage drifts to ‘normal’ level at a very
slow pace and substantial differences remain after even 10 years
•management typically follows a stable capital structure policy, => It is
reasonable to assume constant OAT for most firms

Forecasting example:Forecasting example: PorschePorsche (see Palepu et al, pp. 233-(see Palepu et al, pp. 233-
235, for Porsche’s fin. statements)235, for Porsche’s fin. statements)
Step 1: Know the company’s business
Forecasting requires a sense of where Porsche’s business is going
•long established cars manufacturer; major changes in operating and
financing policies are unlikely; sales come from 6 sources:
 sales of 4 principal models (Porsche 911, Boxter, Cayenne, Carrera)
 sales of spare parts
 sales of financial services
Step 2: Forecast sales for 2006
Historical fin. statements contain forward looking info. => review latest
annual reports for hints on expected sales per model and use industry data
to make ‘reasonable’ adjustments to market share:
 34,000 of Porsche 911 at €90,000 per unit => 22% growth rate
 20,000 of Boxter at €48,000 => 11% growth
 36,000 of Cayenne at €60,000 => 13% decline, late stage of life cycle
 500 of Carrera at €290,000, => 24% decline, production stops in 2006
 sales of spare parts and financial services should be in line with overall sales
growth (~ 4%)
=> Total sales = €6,882 mln. (or 4.7% increase relative to year 2005)

Step 3: Compute Net Profit Margin and assess it against feasible long-
term trend
•2005 net profit margin = 779/6574=11.8%
 Historical industry average margin is ~ 3-6 %
•2006 onwards: can assume a steady 0.3% annual decline in margin
=> 2006 net profit margin = 11.5%
=> 2006 net profit = (2006 sales forecast) x (2006 net profit margin) =
€6,882 * 0.115 = €791
Step 4: Forecast capital structure
•2004 long-term debt / total assets ratio = 4258/9014 = 47%
•2005 long-term debt / total assets ratio = 4553/9710=47%
=> can assume that management sticks to constant capital structure
=> can assume the ratio will remain constant for 2006 and beyond.

Step 5: Forecast for up to 5 or 10 years
•Follow the above logic to generate forecasts for 2007 and beyond. Start
from predicting sales and then forecast other items.
•Consider factors of sales seasonality as well as product or firm life cycle
Step 6: Sensitivity analysis “What If” questions
•How sensitive are your conclusions to your assumptions?
•Consider a more conservative (or optimistic) scenario for Porsche’s future
performance, e.g.:
 lower (higher) sales growth assumptions
 lower (higher) profit margins
 lower (higher) asset turnovers
 lower (higher) investments
 the effect of discount rates on PV of ER, RE, Div, FCF, etc.

Sensitivity analysis: Porsche’s estimated market value under different
combinations of forecasted growth in sales and ROE…

Econometric or quantitative methods of forecastingEconometric or quantitative methods of forecasting
•mainly statistical, econometric models
•no further judgement from forecaster
•often used to forecast earnings and stock prices
1. Mean-reverting process
=> Next period’s expected earnings is the average of all past earnings
E(Xt+1) = (Xt+ Xt-1+ Xt-2+ … + X2+ X1)/t
Some ratios are also mean-reverting (ROE, sales growth rates, etc.)

2. Random walk models
Next period’s expected earnings is determined solely by current earnings
pure random walk: Et = Et-1 + ut
random walk with drift: Et = a + Et-1 + ut
where Et is earnings at year t, a#1, and ut is an error term with zero mean
and constant variance. Earnings are often assumed to follow
‘random walk’ or ‘random walk with drift’:
•=> a useful number to start with is the last
year’s earnings
•the average level of earnings over several
prior years is not useful!
•long-term trend tends to sustain (the drift
component)
•analysts’ earnings forecasts are only
moderately more accurate than simple
random walk models!
Earnings over time...
3
5
7
9
11
13
15
17
19
21
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Random Walk with Drift
Random Walk

)(*)()( tCyclicaltTrendYt =
3. Cyclical models with or without trend

4. Multivariate regression models
Step 1: Specify and estimate an equation that has its dependent variable the
item we wish to forecast
E.g.: E(Qt) = Qt-4 + a*( Qt-1 - Qt-5) + d
Quarterly earnings (Qt) forecasts are modelled as a linear function of past
quarterly earnings data (Qt-4, Qt-1, Qt-5)
Step 2: Obtain values for each of the independent variables for the
observations for which we want forecast and substitute them into our
forecasting equation:
the model may work well within sample. But would it forecast accurately
out-of-sample?
tt ea +++= 2t21t10 XbXby
12t211t101 XbXby +++ ++= at

Regression models’ issues:
Unconditional forecast: all values of the independent variables are known
with certainty
Conditional forecast: forced to obtain forecasts for the independent
variables before we can use our equation to forecast the dependent
variable, forecast of y conditional on our forecast of the Xs
• Choose independent variables that are easy to forecast
Omitted variable: important explanatory variable that had been left out of
a regression equation. This can cause bias: it can force the expected value
of the estimated coefficient away from the true value of the population
coefficient. When an omitted variable is added:
• the model’s R2
is likely to increase
• the added variable is likely to have high t-value
• existing variables’ coefficients are likely to change substantially

Irrelevant variables: It doesn’t cause bias, but it does increase the variance
of the estimated coefficients of the included variables significance of other
variables. When an irrelevant variable is added:
• the model’s R2
is likely to decrease
• the added variable will have an insignificant t-value
• existing variables’ coefficients are NOT likely to be affected
Choosing a correct functional form:
• Linear form vs. non-linear
Perform the sensitivity analysis
Say NO to data mining
…‘if you torture the data long enough, they will confess’…

Bankruptcy prediction and Credit Risk analysisBankruptcy prediction and Credit Risk analysis
• Credit risk – risk of default/bankruptcy, loss of principal and interest
• Reflects the uncertainty about the firm’s ability to continue operations if
its financial conditions worsen
• Bankruptcy/default => often total loss of shareholders’ wealth; creditors
may loose part of principal and interest
Bankruptcy prediction is essential as costs for debt & equity investors may
be huge
Existing bankruptcy prediction models are not perfect. They produce
errors. Some prediction errors are ‘more costly’ than others:

Bankruptcy prediction and Credit Risk analysisBankruptcy prediction and Credit Risk analysis
Types of misclassification errors in bankruptcy prediction:
Predicted outcome Actual outcome
Bankruptcy Non bankruptcy
Bankruptcy
Correct Type II
Cost: Small
0-10%
Non bankruptcy
Type I
Cost: Large
Up to 100%
Correct
•If a model predicts bankruptcy, than the lender will not lend.
•Under Type II his loss will only be the unearned interest
•Under Type I his loss may be the entire principal & interest

Beaver (1966) univariate model of bankruptcy predictionBeaver (1966) univariate model of bankruptcy prediction
•Compared patterns of
29 ratios for failing
vs. non-failing firms
over 5 year period
preceding bankruptcy
•Identified ratios and
their critical values
that could forecast
bankruptcy
•cash flow/total
liabilities ratio was the
best predictor

Cut off points used in classification test
Year before failure
1 2 3 4 5
Cash flow/total debt 0.03 0.05 0.10 0.09 0.11
Net income/total assets 0.00 0.01 0.03 0.02 0.04
Total debt/total assets 0.57 0.51 0.53 0.58 0.57
Working capital/total assets 0.19 0.33 0.26 0.40 0.43
Current assets/current liabilities 1.6 2.3 2.3 2.6 2.8
Cash flow/ total debt
Years prior to
bankruptcy
Error rate
(Type I)
Error rate
(Type II)
1 22% 5%
2 34% 8%
3 37% 8%
4 47% 3%
5 42% 4%

Issues with Beaver’s model:
•Type I error frequency was much higher (than Type II)
•errors increased strongly with the length of forecast horizon
•different ratios could give different predictions
•investors could be ‘trapped’ if trusted the predictions

Multivariate models of bankruptcy prediction:Multivariate models of bankruptcy prediction:
1. Altman’s Z-score (1968)1. Altman’s Z-score (1968)
•Estimated for data from 1946 to 1965
•33 manufacturing companies that became bankrupt vs. 33 firms for the
same period that survived
•A list of 22 potential ratios based on previous studies and 5 selected
variables which are statistically different in the bankrupt and non-bankrupt
sub-samples
•created Z-score – a single number capturing firm’s bankruptcy risk
For manufacturing firms:
Z = 1.2x working capital/total
assets
+1.4x retained earnings/total
assets
+3.3x EBIT/total assets
+0.6x market value of
equity/total debt
+1.0x sales/total assets
For non-manufacturing firms:
Z = 6.56x working capital/total
assets
+3.26x retained earnings/total
assets
+6.72x EBIT/total assets
+1.05x book value of
equity/book value of debt

Multivariate models of bankruptcy predictionMultivariate models of bankruptcy prediction:
1. Altman’s Z-score (1968)
Manufacturing firms:
Z< 1.8  Bankruptcy
1.8<Z< 3  Grey area
Z<2.67  Risk for bankruptcy
Z>3  No bankruptcy risk
Non-manufacturing firms:
Z<1.1  Bankruptcy
1.1<Z<2.6  Grey area
Z>2.6 No bankruptcy risk

1. Altman’s Z-score (1968)1. Altman’s Z-score (1968)
Altman’s Z-score: Conclusions
• A set of financial ratios is combined to give a single-value prediction
parameter
• Z-score predicts bankruptcy fairly accurately 1 to 2 years prior to
bankruptcy
• Works poorer for longer periods
• Is worked out for few broad industries only (e.g., manufacturing & non-
manufacturing)
• From about 1985 onwards, the Z-scores gained wide acceptance by
auditors, management accountants, courts, and database systems used for
loan evaluation.

2. ZETA2. ZETATMTM
model by Altman et al. (1977)model by Altman et al. (1977)
The ZETA model uses 7 variables:
1. Current ratio
2. Equity market value / Capital
3. Times interest earned
4. ROA
5. Retained earnings/Assets
6. Size (total assets)
7. Standard deviation of ROA
Uses adjusted financial statement data:
1. Off-balance-sheet debt: all non-cancelable operating leases are added to
firm assets & liabilities. Finance & nonconsolidated subsidiaries are
consolidated.
2. Intangible assets: Capitalized items such as interest costs, goodwill, and
other intangible assets are expensed.
The model is a commercial product – parameters are not disclosed

2. ZETA2. ZETATMTM
model by Altman et al. (1977)model by Altman et al. (1977)
The ZETA model: Conclusions
•ZETA model significantly improved accuracy in relation to previous
models, particularly in years 2 through 5 preceding bankruptcy.
•Accuracy ranges from over 96% 1 period before the bankruptcy to 70% 5
years before.

3. The Ohlson (1980) probability of bankruptcy model3. The Ohlson (1980) probability of bankruptcy model
Instead of specifying a cut off point it estimates a probability of
bankruptcy
The user can decide how high a probability he or she is willing to tolerate
The original model was based on 1970 –1976 data, including 105 bankrupt
firms vs. many non-bankrupt firms
y= - 1.32
- 0.407x size
+6.03x total liabilities/total assets
- 1.43x working capital/total assets
+0.0757x current liabilities/current assets
- 2.37x net income/total assets
- 1.83x working capital from operations/total liabilities
+0.285 (1if net income negative for the last two years,0 if not)
- 1.72 (1if total liabilities exceed total assets, 0 otherwise)
- 0.521(change in net income/sum of absolute values of current and
prior years’ net incomes)
y
e
yprobabilit −
+
=
1
1

3. The Ohlson (1980) probability of bankruptcy model3. The Ohlson (1980) probability of bankruptcy model
Bankruptcy is a legal, not economic phenomenon!
Bankruptcy may result in a complete liquidation of a company. However,
it could also result in rehabilitation, and the company survives.
Ohlson’s model is more useful than other models as the predictive variable
is not the ultimate event of bankruptcy, but rather a probability of going
bankrupt.
A higher probability can be used to assess corporate performance, that is
how “healthy” or “sick” the company is.

Use in practice – Bond ratingsUse in practice – Bond ratings
•reflect creditworthiness of the firm or likelihood that the firm will default
on its debt. The higher the rating, the lower the probability of default.
•ratings are based on both quantitative (e.g., ratios) and qualitative
parameters (judgement about the quality of management and business
model)
•determine the cost of debt and impact cost of equity
Standard & Poor’s ratings and corresponding ratio values
AAA AA A BBB BB B CCC
EBIT interest coverage 21.4 10.1 6.1 3.7 2.1 0.8 0.1
EBITDA interest coverage 26.5 12.9 9.1 5.8 3.4 1.8 1.3
Operating cash flow/total debt 84.2 25.2 15 8.5 2.6 (3.2) (12.9)
FFO/total debt 128.8 55.4 43.2 30.8 18.8 7.8 1.6
Return on capital 34.9 21.7 19.4 13.6 11.6 6.6 1
Operating income/sales 27.0 22.1 18.6 15.4 15.9 11.9 11.9
Long-term debt/capital 13.3 28.2 33.9 42.5 57.2 69.7 68.8
Total debt/capital 22.9 37.7 42.5 48.2 62.6 74.8 87.7

Other qualitative factors of importance in distress analysis:
•Industrial, geographical and size characteristics
•Director’s equity shareholdings, resignation of directors, delay in
submitting accounts.
Relationship between bond ratings and Z-score
U.S. Bond
Rating
Z”
score
U.S. Bond
Rating
Z”
score
U.S. Bond
Rating
Z”
score
AAA 8.15 BBB+ 6.40 B+ 4.75
AA+ 7.60 BBB 6.25 B 4.50
AA 7.30 BBB- 5.85 B- 4.15
AA- 7.00 BB+ 5.65 CCC+ 3.20
A+ 6.85 BB 5.25 CCC 2.50
A- 6.65 BB- 4.95 CCC- 1.75
Very high
quality
High
quality
Speculative Very
poor
S & P AAA , AA A, BBB BB, B CCC, D
Moody’s Aaa, Aa A, Baa Ba, B Caa, C

8 9 forecasting of financial statements

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