The characteristic that all Stock Markets have in common is the uncertainty, which is related with their short and long-term future state. This feature is undesirable for the investor but it is also unavoidable whenever the Stock Market is selected as the investment tool, uncertainty is part and parcel of the Stock Market.

The best that one can do is to try to reduce this uncertainty. Stock Market Prediction or Forecasting is one of the instruments in this process.

Econometrics is derived from several disciplines, including mathematical economics, statistics, economic statistics, and economic theory. The goal of econometrics is twofold: It is a study that produces measurements, where qualitative data is turned into quantitative mathematical forms.

SSRN Econometric Modeling: Capital Markets - Forecasting eJournal

Once this is performed, these statements can then be empirically proven, disproven, measured, and compared.

The recent widespread availability of intraday tick-by-tick databases for stocks, options and currencies has had an important impact on research in applied financial econometrics and market microstructure. Traditional Time Series Prediction: The Traditional Time Series Prediction analyzes historic data and attempts to approximate future values of a time series as a linear combination of these historic data.

In econometrics there are two basic types of time series forecasting: These types of regression models are the most common tools used in econometrics to predict time series. The way they are applied in practice is that firstly a set of factors that influence or more specific is assumed that influence the series under prediction is formed. These factors are the explanatory variables xi of the prediction model.

These pairs are used to define the importance of each explanatory variable in the formulation of the to-be explained variable. In other words the linear combination of xi that approximates in an optimum way y is defined. Regression models have been used to predict stock market time series.

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A good example of the use of multivariate regression is the work of Pesaran and Timmermann The data they used was from Jan until Dec Initially they used the subset from Jan until Dec to adjust the coefficients of the explanatory variables of their models, and then applied the models to predict the returns for the next year, quarter and month respectively. Blog posts are not selected, edited or screened by Seeking Alpha editors.

Econometrics to Predict Stock Market Jan.