Introduction Fin250: Lecture 2 Fall 2010 Reading: Brooks, chapter 1

Outline  Software tools  Forecasting basics (key things) In sample bias Forecast objectives Changing relations  Financial forecasting basics Definitions Cautions

Software  Statistical packages Stata, Eviews, Rats SAS, TSP, SPSS  Computer languages C, C++, Java, V-basic, FORTRAN, GO Matlab, Gauss, S-plus, Octave, R  Other Excel Technical trading tools

Excel Advantages and Disadvantages  Advantages Every knows/has it Easy to use  Disadvantages Not powerful Hard to do sophisticated problems Few advanced tools

Matlab: Advantages and disadvantages  Advantages Powerful Relatively easy to use Great graphics Nice tools  Disadvantages Programming Expensive Not everyone uses

Key Things About Forecasting  In sample bias  Forecast objectives  Changing relations

In Sample Bias  Use data for two purposes Estimation Testing  Using the same series for both often makes testing look better  “Forecasting what you already know”  One answer “out of sample experiments”

Forecast Objectives  Many forecasting objectives  Statistical Mean squared error R-squared  Economic Trading rule profitability Risk measures

Changing Relations  Data features change over time  Model updating  Useful lifespan

Forecasting Definitions  Time series Predict prices and volatility over time  Cross section Comovements  Asset prices tend to move together CAPM/beta/correlations Pairs trading  Sometimes mixed

Forecasting Cautions  Forecasting (financial and otherwise) is messy  Predicting the stock market is difficult