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Time-Series Analysis and Forecasting in Finance: CPI Trends and Predictions

Explore time-series analysis and forecasting techniques in finance focusing on Consumer Price Index (CPI) trends. Learn about regression models, seasonal effects, smoothing techniques, and forecasting methods for predicting CPI trends.

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Time-Series Analysis and Forecasting in Finance: CPI Trends and Predictions

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  1. What will be the next year U/E, CPI..??? MinisterofFinance

  2. Time-series analysis and Forecasting

  3. Outline

  4. Overview • Recall Regression Model X: independent variable Y: dependent variable • Time-series: • Definition: Variable measured over time in sequential order • Independent variable: Time

  5. Overview Example:

  6. Overview

  7. Components

  8. Components + Long-term trend: Smooth pattern with duration > 1 year

  9. Components + Cyclical effect: wavelike pattern about a long-term trend, duration > 1 year, usually irregular Cyclesaresequencesofpointsabove & belowthe trendline Volume Time

  10. Components + Seasonal effect: like cycles but short repetitive periods, duration < 1 year (days, weeks, months…) Sales peak in Dec.

  11. Components + Random variation: irregular changes that we want to remove to detect other components Random variation that does not repeat Volume Time

  12. Smoothing techniques • Purpose: Remove random fluctuation to detect seasonal pattern • 2 types: • Moving average (MA) • Exponential smoothing

  13. Smoothing techniques Example of Moving average:

  14. Trend analysis

  15. Measuring seasonal effect =

  16. Time-series forecasting with regression • Forecast of trend & seasonality: Ft = [ β0 + β1t ] SIt where: Ft = forecast for period t SIt = seasonal index for period t

  17. Using the following data about CPI of Viet Nam from 2005 to 2008 for forecasting CPI in 2010: Application

  18. Application

  19. Application • Reasons: • CPI is measured over time (monthly) • 3 components exist  Technique: Time-series forecasting with regression

  20. Application Random variation in 2008 CPI peaks in Feb

  21. Application • Trend analysis Using Excel, the trend line is: yt = 100.551 + 0.016 t y = 100.551 + 0.016 t

  22. Measuring seasonal effect =

  23. Application • Seasonal index • Apply the formula: Ft = [ β0 + β1t ] SIt

  24. Application • Forecast CPI in 2008 Forecast CPI of 2008 did not match actual CPI due to unexpected events (recession)

  25. Application • Forecast CPI in 2010

  26. Forecasting results y = 100.551 + 0.016 t Forecasted CPI - Long term trend:slightincreaseinCPI -Seasonal effect: peak inFeb.

  27. References • ‘Time Series Analysis’, Citing or referencing electronic sources of information, viewed 15 May 2010, http://www.statsoft.com/textbook/time-series-analysis/?button=3 • Australian Bureau of Statistics, ‘Time Series Analysis: The Basics’, viewed 15 May 2010, http://www.abs.gov.au/websitedbs/d3310114.nsf/4a256353001af3ed4b2562bb00121564/b81ecff00cd36415ca256ce10017de2f!OpenDocument#WHAT%20IS%20A%20TIME%20SERIES%3F • ‘Introduction to Time Series Analysis’, Citing or referencing electronic sources of information, viewed 15 May 2010, http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm • Berenson, M. & Levine, D. 1998, Business Statistics - A first course, Prentice Hall Press. • Anderson, D., Sweeney, D. & Williams, T. 1999, Statistics for business and economics, South-Western College Publishing, Ohio. • Selvanathan, A., Selvanathan, S., Keller, G. & Warrack, B. 2004, Australian business statistics, Nelson Australia Pty Limited.

  28. Thank you for listening

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