1 / 16

Time Series Analysis of Standard and Poors Price to Earnings Ratio from 1900 to 2008: Monthly Data

Time Series Analysis of Standard and Poors Price to Earnings Ratio from 1900 to 2008: Monthly Data. Presenter: Jane Doe. Price to Earnings Ratio (P/E). Benchmark statistic for single company Low P/E means either stock is undervalued or company on decline

yoshi
Download Presentation

Time Series Analysis of Standard and Poors Price to Earnings Ratio from 1900 to 2008: Monthly Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Time Series Analysis of Standard and Poors Price to Earnings Ratio from 1900 to 2008: Monthly Data Presenter: Jane Doe

  2. Price to Earnings Ratio (P/E) • Benchmark statistic for single company • Low P/E means either stock is undervalued or company on decline • High P/E means either stock is overvalued or company on the rise • Overall market confidence indicator • Low indicates confidence is low • High indicates confidence is High

  3. Plots of Data

  4. Model Fitting

  5. ARIMA(20,1,1)

  6. Diagnostics

  7. SARIMA(20,1,1)X(1,0,1)4

  8. Diagnostics

  9. ARIMA vs. SARIMA selection • Nature of Data set • Expected market statistic to have seasonal component at lag 4 • Residual Diagnostics show virtually no change between two models. • Coefficients of SARIMA model insignificant • Nature of the P/E ratio may be counteracting the seasonality • Final selection criteria: ability to forecast will be examined in a moment

  10. Forecasting • Final selection criteria for ARIMA vs. SARIMA standard error comparisons • ARIMA has lower standard error at every point greater than lag=5

  11. GARCH Model fitted to Residuals • GARCH(1,1)

  12. Selected Model • ARIMA(20,1,1) with a GARCH(1,1) fitted to the residuals

  13. Forecasting Forecast for Logged Data Forecast for Original Data

  14. Forecast comparisons of t+1 to actual data points

  15. Using first 1293 data points to predict last 12

  16. Price to Earnings/Long-Term Interest Rates Irrational Exuberance [Princeton University Press 2000, Broadway Books 2001]

More Related