101 Financial Forecasting Essay

1746 words - 7 pages

Financial Modelling and Forecasting
Lecture 1 Introduction and Descriptive Statistics

The need for forecasts

A forecast helps deal with an uncertain future by making decisions today No single forecasting method will lead to an accurate forecast. Forecasts can be wrong! “What’s the point of forecasting?”

A business requires predictions as inputs

E.g., Inventory, Personnel, Ordering, Production planning.

Governments require forecasts to guide monetary and fiscal policy
Lecture 1 2

Lecture 1


Forecasting Considerations

Application to Finance

A sensible forecast allows proactive decisions to be made today

Without it, management ...view middle of the document...

0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, 9.0

Lecture 1



Qualitative Forecasting

Qualitative Forecasting

Qualitative forecasts are suitable when no relevant historical data exists.

Sales-force composites

Individual forecasts of salespeople Combines the anonymous forecasts of experts, and then redistributes the forecasts for revision until a consensus is reached.

Used for deciding strategy, new product development and long-range plans

Delphi Method

 

These techniques incorporate judgment/intuition and subjective factors Common techniques include: Survey methods

Seeking opinions from potential users/customers

Lecture 1 7

These methods suffer from bias and require many years of experience to gain sufficient understanding of a particular market. Intelligent forecasting should incorporate both qualitative and quantitative methods
Lecture 1


Explanatory and Time-Series Models

Forecasting Methods Summary
Forecasting Method Qualitative Techniques Causal Techniques Data Requirement Useful when historical data are scarce or non-existent Useful when historical data are available for both the dependent (forecast) and the independent variable(s) Useful when historical data exists for forecast variable and the data exhibits a pattern Technique Examples Delphi Technique Scenario Writing Regression Models Leading Indicators

Explanatory, or causal, models assume the predicted variable is related to one or more independent variables

Some variables are controllable, some are not

Time-series models assume that the forecasted variable is a function of historical values of the same variable
 

Time-Series Techniques

Moving Average Exponential Smoothing Autoregressive Models

Relies on projecting past relationships into the future Requires less data
Lecture 1 9 Lecture 1 10

Application to company valuation
 


A fundamental analysis using the following data: Quantitative Data
   

GDP $ Advertising Interest rates Inventory levels SWOT analysis Management Quality Industry Outlook
Lecture 1 11

Qualitative Data
  

 

Building a model helps us forecast how much a variable might change However, events outside the firm’s control will lead to inaccurate forecasts regardless of the sophistication of the method Model misspecification, missing or inaccurate data, unexpected changes in variables and error build-ups are just some issues forecasters face. We can only hope to model ‘unknown knowns.’ Forecasting is difficult
Lecture 1



Descriptive statistics

Measures of Central Tendency

A histogram is one type of graphical display to get an initial overview of a set of data However, it is useful to summarise a large data set by using a few key numerical measures: Measures of Central Tendency

The mean, or average, is the most commonly used measure of central...

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