1 / 60

Financial Analysis, Planning and Forecasting Theory and Application

This chapter explores the theory and application of time-series analysis for financial planning and forecasting. It covers topics such as classical time-series component models, moving average and seasonally adjusted time-series, linear and log-linear time trend regressions, exponential smoothing and forecasting, and autoregressive forecasting models.

judyestes
Download Presentation

Financial Analysis, Planning and Forecasting Theory and Application

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. Financial Analysis, Planning and ForecastingTheory and Application Chapter 25 Time-Series: Analysis, Model, and Forecasting By Cheng F. Lee Rutgers University, USA John Lee Center for PBBEF Research, USA

  2. Outline • 25.1 Introduction • 25.2 The Classical Time-Series Component Model • 25.3 Moving Average and Seasonally Adjusted Time-Series • 25.4 Linear and Log-Linear Time Trend Regressions • 25.5 Exponential Smoothing and Forecasting • 25.6 Autoregressive Forecasting Model • 25.7 Summary • Appendix 25A. The X-11 Model for Decomposing Time-Series Components • Appendix 25B. The Holt-Winters Forecasting Model for Seasonal Series

  3. 25.2 The Classical Time-Series Component Model

  4. 25.2 The Classical Time-Series Component Model Figure 25.1 Earnings per share of Johnson & Johnson

  5. 25.2 The Classical Time-Series Component Model

  6. 25.2 The Classical Time-Series Component Model

  7. 25.2 The Classical Time-Series Component Model

  8. 25.2 The Classical Time-Series Component Model

  9. 25.2 The Classical Time-Series Component Model （25.1） （25.2） where Tt = trend component Ct = cyclical component St = seasonal component It = irregular component

  10. 25.2 The Classical Time-Series Component Model Figure 25.5 Time-Series Decomposition

  11. 25.3 Moving Average and Seasonally Adjusted Time-Series （25.3） （25.4） （25.5）

  12. 25.3 Moving Average and Seasonally Adjusted Time-Series Table 25.3 Weighted average

  13. 24.3 Moving Average and Seasonally Adjusted Time-Series （25.6）

  14. 25.3 Moving Average and Seasonally Adjusted Time-Series

  15. 25.3 Moving Average and Seasonally Adjusted Time-Series

  16. 25.3 Moving Average and Seasonally Adjusted Time-Series （25.7） （25.7a） （25.8）

  17. 25.3 Moving Average and Seasonally Adjusted Time-Series （25.9）

  18. 25.3 Moving Average and Seasonally Adjusted Time-Series

  19. 25.3 Moving Average and Seasonally Adjusted Time-Series

  20. 25.3 Moving Average and Seasonally Adjusted Time-Series Figure 25.7 Trend of Ratio for Johnson & Johnson

  21. 25.3 Moving Average and Seasonally Adjusted Time-Series （25.10）

  22. 25.3 Moving Average and Seasonally Adjusted Time-Series Figure 25.8 Adjusted Earnings per Share (EPS) of Johnson & Johnson

  23. 25.4 Linear and Log-Linear Time Trend Regressions （25.11） （25.12） （25.13）

  24. 25.4 Linear and Log-Linear Time Trend Regressions

  25. 25.4 Linear and Log-Linear Time Trend Regressions

  26. 25.4 Linear and Log-Linear Time Trend Regressions

  27. 25.4 Linear and Log-Linear Time Trend Regressions

  28. 25.4 Linear and Log-Linear Time Trend Regressions

  29. 25.5 Exponential Smoothing and Forecasting （25.14）

  30. 25.5 Exponential Smoothing and Forecasting （25.15） （25.16）

  31. 25.5 Exponential Smoothing and Forecasting

  32. 25.5 Exponential Smoothing and Forecasting

  33. 25.5 Exponential Smoothing and Forecasting

  34. 25.5 Exponential Smoothing and Forecasting

  35. 25.5 Exponential Smoothing and Forecasting （25.18） （25.19a） （25.19b）

  36. 25.5 Exponential Smoothing and Forecasting

  37. 25.5 Exponential Smoothing and Forecasting （25.20）

  38. 25.5 Exponential Smoothing and Forecasting

  39. 25.5 Exponential Smoothing and Forecasting

  40. 25.6 Autoregressive Forecasting Model （25.21） （25.22） （25.23）

  41. 25.6 Autoregressive Forecasting Model （25.24） （25.25） （25.26）

  42. 25.6 Autoregressive Forecasting Model

  43. 25.6 Autoregressive Forecasting Model

  44. 25.6 Autoregressive Forecasting Model

  45. 25.6 Autoregressive Forecasting Model (25.27)

  46. 25.7 Summary In this chapter, we examined time-series component analysis and several methods of forecasting. The major components of a time series are the trend, cyclical, seasonal, and irregular components. To analyze these time-series components, we used the moving-average method to obtain seasonally adjusted time series. After investigating the analysis of time-series components, we discussed several forecasting models in detail. These forecasting models are linear time trend regression, simple exponential smoothing, the Holt-Winters forecasting model without seasonality, the Holt-Winters forecasting model with seasonality, and autoregressive forecasting. Many factors determine the power of any forecasting model. They include the time horizon of the forecast, the stability of variance of data, and the presence of a trend, seasonal, or cyclical component.

  47. Table 25A.1 Appendix 25A. The X-11 Model for Decomposing Time- Series Components （25A.1）

  48. Appendix 25A. The X-11 Model for Decomposing Time- Series Components Figure 25A.1 Original Sales and the X-11 Final Component Series of Caterpillar, 1969-1980 Source: J. A. Gentry and C. F. Lee, “Measuring and Interpreting Time, Firm and Ledger Effect,” in Cheng F. Lee(1983), Financial Analysis and Planning: Theory and Application, A book of Readings

  49. Table 25A.2 Appendix 25A. The X-11 Model for Decomposing Time- Series Components

  50. Appendix 25A. The X-11 Model for Decomposing Time- Series Components （25A.2）

More Related