[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/111556.html
   My bibliography  Save this paper

Long Run Returns Predictability and Volatility with Moving Averages

Author

Listed:
  • Chang, C-L.
  • Ilomäki, J.
  • Laurila, H.
  • McAleer, M.J.
Abstract
The paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affect financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.

Suggested Citation

  • Chang, C-L. & Ilomäki, J. & Laurila, H. & McAleer, M.J., 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Econometric Institute Research Papers EI2018-39, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:111556
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/111556/EI2018-39.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Chia-Lin Chang & Michael Mcaleer, 2012. "Aggregation, Heterogeneous Autoregression And Volatility Of Daily International Tourist Arrivals And Exchange Rates," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 397-419, September.
    2. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
    3. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    4. Yamamoto, Ryuichi, 2012. "Intraday technical analysis of individual stocks on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3033-3047.
    5. repec:bla:jfinan:v:55:y:2000:i:4:p:1705-1770 is not listed on IDEAS
    6. Paulo Maio, 2014. "Don’t Fight the Fed!," Review of Finance, European Finance Association, vol. 18(2), pages 623-679.
    7. Jukka Ilomäki, 2018. "Risk and return of a trend-chasing application in financial markets: an empirical test," Risk Management, Palgrave Macmillan, vol. 20(3), pages 258-272, August.
    8. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    9. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    10. John H. Cochrane, 1999. "New facts in finance," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 23(Q III), pages 36-58.
    11. Yun Xue & Zhiwen Liu & Jie Luo & Zhihao Ma & Meizhen Zhang & Xiaohui Hu & Qiuhua Kuang, 2015. "Stock Market Trading Rules Discovery Based on Biclustering Method," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, March.
    12. John Y. Campbell & John Cochrane, 1999. "Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 205-251, April.
    13. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 25(2), pages 65-86.
    14. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    15. CHIA-LIN CHANG & MICHAEL McALEER & ROENGCHAI TANSUCHAT, 2012. "Modelling Long Memory Volatility In Agricultural Commodity Futures Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-27.
    16. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    17. Michael McAleer, 2014. "Asymmetry and Leverage in Conditional Volatility Models," Econometrics, MDPI, vol. 2(3), pages 1-6, September.
    18. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    19. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    20. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    21. Ilomäki, J. & Laurila, H. & McAleer, M.J., 2018. "Simple Market Timing with Moving Averages," Econometric Institute Research Papers EI2018-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    22. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    23. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    24. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    25. Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages," Sustainability, MDPI, vol. 10(7), pages 1-25, June.
    26. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    27. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    28. John Y. Campbell & Luis M. Viceira, 1999. "Consumption and Portfolio Decisions when Expected Returns are Time Varying," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 433-495.
    29. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    30. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    31. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    32. Hjalmarsson, Erik, 2010. "Predicting Global Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(1), pages 49-80, February.
    33. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    34. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    35. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    36. Hudson, Robert & McGroarty, Frank & Urquhart, Andrew, 2017. "Sampling frequency and the performance of different types of technical trading rules," Finance Research Letters, Elsevier, vol. 22(C), pages 136-139.
    37. repec:pri:cepsud:91malkiel is not listed on IDEAS
    38. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    39. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    40. Jacob Boudoukh & Matthew Richardson & Robert F. Whitelaw, 2008. "The Myth of Long-Horizon Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1577-1605, July.
    41. Zhu, Yingzi & Zhou, Guofu, 2009. "Technical analysis: An asset allocation perspective on the use of moving averages," Journal of Financial Economics, Elsevier, vol. 92(3), pages 519-544, June.
    42. Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2017. "Time series momentum and moving average trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 405-421, March.
    43. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
    44. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.
    45. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    46. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.
    47. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    48. LeRoy, Stephen F, 1973. "Risk Aversion and the Martingale Property of Stock Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 436-446, June.
    49. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Lord, Montague & Chang, Susan, 2019. "Pre-Feasibility Study of Sarawak-West Kalimantan Cross-Border Value Chains," MPRA Paper 94732, University Library of Munich, Germany.
    3. Ken Chung & Anthony Bellotti, 2021. "Evidence and Behaviour of Support and Resistance Levels in Financial Time Series," Papers 2101.07410, arXiv.org.
    4. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Moving Average Market Timing in European Energy Markets: Production Versus Emissions," Energies, MDPI, vol. 11(12), pages 1-24, November.
    5. Parastoo Mousavi, 2021. "Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns," Mathematics, MDPI, vol. 9(13), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Asymmetric Risk Impacts of Chinese Tourists to Taiwan," Documentos de Trabajo del ICAE 2018-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages," Sustainability, MDPI, vol. 10(7), pages 1-25, June.
    3. Jukka Ilomaki & Hannu Laurila & Michael McAleer, 2018. "Simple Market Timing with Moving Averages," Tinbergen Institute Discussion Papers 18-048/III, Tinbergen Institute.
    4. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Moving Average Market Timing in European Energy Markets: Production Versus Emissions," Energies, MDPI, vol. 11(12), pages 1-24, November.
    5. Chang, C-L. & Ilomäki, J. & Laurila, H. & McAleer, M.J., 2018. "Market Timing with Moving Averages for Fossil Fuel and Renewable Energy Stocks," Econometric Institute Research Papers EI2018-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.
    7. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    8. Jukka Ilomäki, 2018. "Risk and return of a trend-chasing application in financial markets: an empirical test," Risk Management, Palgrave Macmillan, vol. 20(3), pages 258-272, August.
    9. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    10. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    11. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
    12. Chen, Long, 2009. "On the reversal of return and dividend growth predictability: A tale of two periods," Journal of Financial Economics, Elsevier, vol. 92(1), pages 128-151, April.
    13. Ang, Andrew & Liu, Jun, 2007. "Risk, return, and dividends," Journal of Financial Economics, Elsevier, vol. 85(1), pages 1-38, July.
    14. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    15. repec:grz:wpaper:2012-02 is not listed on IDEAS
    16. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    17. Maio, Paulo & Xu, Danielle, 2020. "Cash-flow or return predictability at long horizons? The case of earnings yield," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 172-192.
    18. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    19. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
    20. Atanasov, Victoria, 2018. "World output gap and global stock returns," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 181-197.
    21. Paskalis Glabadanidis, 2014. "The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes," International Review of Finance, International Review of Finance Ltd., vol. 14(2), pages 161-202, June.

    More about this item

    Keywords

    Trading strategies; Risk; Moving average; Market timing; Returns predictability; Volatility; Rolling window; Data frequency;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ems:eureir:111556. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.