Forecasting 630 - Notes
Finance 630 is a cocktail of:




Assumption(s): wrong/right, leading to wrong/right results
Finance
Macro Economics
Statistics
Assumption – something already taken as guaranteed
Finance – normal, rational, applied behavior of humans
Macro economics – everyday approach to economics
Statistics – numerical effects or facts from figures
Forecasting – predictions, forward looking
Precise – highly definite
Predictions of outcomes are rarely precise
Philosophy of a CFO:




C = crisis
F = fluctuation
O = optimism
U = uncertainty
Industry – refers to production of goods and services
Commerce – refers to distribution. When industry finishes job, commercialization begins.
Regular business. Any word in business except “production.”
Uptown – residential
Downtown – business
Trade – branch of commerce. Many trades make one commerce
Business – summation (∑) of industry, commerce, trade.
Micro – (i) individual item. 1 single item.
Macro – (a) all/aggregate. ∑ of all micros.
Business -> macro -> industry -> micro
Production – quantity. Number of goods or services you produced.
U.S. is currently #1 in production
Productivity – Quality. = cost of production (COP). Japan is #1
Germany – land of innovation
U.S. – land of articulation
Japan – land of imitation
Always ask why; leads to culprit.
Chapter 1
Purpose is to make judgment for business in the face of uncertainty.
Humans tend to be optimistic, underestimate uncertainty.
Best way to forecast are quantitative techniques and good judgement.
Steps of Forecasting Process:
1.
2.
3.
4.
5.
Problem formulation and data collection
Data manipulation and cleaning
Model building and evaluation
Model implementation (actual forecast)
Forecast evaluation
Human intervention is very important in forecasting
Chapter 2
Population – all items of my interest
Sample – subgroup of items of interest
Sampling – segmenting by interest
Mean () – all of values of population, divided by number in population
Median (Md) – ½ above, ½ below.
Mode (Mo) – a value happening most frequently.
Standard deviation () – how far, or how you’re deviating from the mean.
SD = +3 -> means you are 3 points above mean
Range – difference between highest and lowest/max & min
Smaller ranges are good. Ex: 52pts – 92pts. R = 40
Quartile = ¼. Divide data into four groups
Cross section data – same time. Today. Same day. Price of milk across multiple stores on same
day.
Time series data – over time. Look at price of milk everyday over month. You can look at
products, just means over time.
Dot plot – data in points.
Histogram – data in boxes
Scatter diagram – visualize between two variables. For showing low/medium/high achievements
quickly.
Random variable – looking for one outcome by chance
Random – outcome by chance. No math, no science. Lotto ball numbers
Discrete random variable – specified value/figure/number (aka – discrete variable, or just
discrete)
Continuous random variable – within same range
Normal distribution – nothing unusual
Sampling distribution – something small that represents something larger. When micro
represents macro.
Sampling can be very wrong, but must start somewhere.
Estimation – sample gives you inference, it is called estimation. Aka – forecasting
Forecasting – estimation
Estimation in statistics – forecasting in finance, or in business subjects.
Ideas change from wisdom out of experience.
Hypothesis – assumption(s) that make me happy
Hypothesis testing – get an assumption and test it. Can be wrong, or accept it.
Hypothesis testing:
1. Formulate the hypothesis being tested (null hypothesis) and state the alternative
hypothesis.
2. Collect a random sample of items from the population, measure them, and compute the
appropriate sample test statistic.
3. Assume the null hypothesis is true, and determine the sampling distribution of the test
statistic.
4. Compute the probability that a value of the sample statistic at least as large as the one
observed could have been drawn from this sampling distribution.
5. If this probability is high, do not reject the null hypothesis; if this probability is low, the
null hypothesis is discredited and can be rejected with small chance of error.
Hypothesis = 1
Hypotheses = multiple
Can give you confidence either way, but does not provide forecasting.
Null hypothesis – (H0) – hypothesis being tested. Can be accepted or rejected.
H0 - Original hypothesis.
After rejection name it – H1
H1 means alternative hypothesis
Type I error – () – alpha - true Ho is rejected. Truth denied. First error in hypothesis testing.
Type II error – () - beta - false Ho is accepted. Second error in hypothesis testing
Correlation – how two elements relate. Relationships. Never questions causation.
Price up, quantity sold down = negative correlation
Price up, quantity sold down = positive correlation
Regression analysis – we estimate the equation that best fits sets of observations of dependent
variable and independent variables.
E(x) – expected value of x
P(x) – probability of x
- (x bar) sample mean
df – degrees of freedom
Population parameter – population quantity
S – sample standard deviation
r – correlation coefficient for sample data
The expected value of a random variable is the mean value of the variable over many trials or
observations.
The binomial distribution is a discrete probability distribution describing the likelihood of X
successes in n independent trials of binomial experiment.
The Z-score of any X value is the number of standard deviations from the central value of the
curve () to that value.
The normal distribution has a bell shape and is completely determined by its mean and standard
deviation.
A point estimate is a single-valued estimate of a population parameter.
An interval estimate is an interval within which the population parameter is likely to lie.
One-sided/one-tailed test – the alternative hypothesis specifies values of the population
parameter to one side of the value specified in the null hypothesis.
Two-sided/two-tailed test – values of the test statistic much larger or much smaller than the value
of the population parameter under H0 lead to rejection.
The p-value or significance probability is the probability of getting at least as extreme a sample
result as the one actually observed if the H0 is true. Equivalently, the p-value may be regarded as
the smallest  for which the observed test statistic lead to the rejection of H0.
Positive linear relationship – as X increases, so does Y
Perfect positive linear relationship – as X increases, Y increases also, and in a perfectly
predictable way. The X and Y data points appear to lie on a straight line.
Imperfect positive & negative linear relationship – as X increases in scatter diagrams, Y
increases or decreases but not in a perfectly predictable way. The X-Y points do not lie on a
straight line.
Linear relationships – the X- relationship, be it perfect or imperfect, can be summarized by a
straight line.
Slope of the line – the slope of any straight line is defined as the change in Y associate with a
one-unit increase in X
Correlation coefficient – measures the extent to which two variables are linearly related to each
other.
Chapter 3
First thing you must do is find pattern in data.
Data pattern tells you what technique to use.
GIGO – garbage in, garbage out
If data is wrong, no honest forecasting.
Data is used to find validity, reliability, and accuracy.
Data should be:
1.
2.
3.
4.
Reliable and accurate
Relevant
Consistent
Timely
Follow a system or technique for results
Time series data – collected over time period
Cross sectional data – over single time single point
Horizaontal data – fluctuate around a constant level. Virtually constant more or less. (time
series)
Trend – growth or decline in the time series over an extended period of time. (time series)
Cyclical – wavelike, goes up and down
Seasonal data – repeats itself year after year
Choosing forecast technique:
1. Why needed?
2. Who will use?
3. Continued on pg 77
Chapter 4
Chapter 5
Time is the glue of economic progress
Time series – observations of a variable that become available over time
Decomposition – separate into parts under some position
Index numbers – percentages that show changes over time
Business indicators – business related time series that are used to help assess the general state of
the economy. (East Germany – time series proof that communism is not a good theory)
Chapter 6
Regression line tells you two things:
1. Two variables, 1 independent & then 1 dependant
2. Dispersion
Standard error of the estimate –
Variance – standard deviation squared
ANOVA – analysis of variance
Coefficient of determination – extent of relationship
Analysis of residuals – 239
Residual – difference between forecast result and actual
Growth curves – 246
4 things needed for good writing
1.
2.
3.
4.
Theme
Logic
Language
Consistence
Imperfection – they know less than I know