Long Run Returns Predictability and Volatility with Moving Averages

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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.
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References Allen, D., McAleer, M., & Scharth, M. (2014). Asymmetric realized volatility risk. Journal of Risk and Financial Management, 7, 80-109. Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51, 245-271. Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20, 651-707. Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmark as limits to arbitrage: Understanding the low-volatility anomaly. Financial Analysts Journal, 67, 1-15. Black, F. (1986). Noise. Journal of Finance, 41, 528-543. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327. Boudoukh, J., Richardson, M., & Whitelaw, R. (2008). The myth of long-horizon predictability. Review of Financial Studies, 21, 1577-1603. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47, 1731-1764. Brown, D., & Jennings, R. (1989). On technical analysis. Review of Financial Studies, 2, 527-551. Campbell, J., & Cochrane, J. (1999). By force of habit: Consumption-based explanation of aggregate stock market behavior. Journal of Political Economy, 107, 205-251. Campbell, J., & Shiller, R. (1988). The dividend-price ratio and expectations of future dividends and discount factors. Review of Financial Studies, 1, 195-228. Campbell, J., & Thompson, S. (2008). Predicting excess returns out of sample: can anything beat the historical average? Review of Financial Studies, 21, 1509-1532. Campbell, J., & Viceira, I. (1999). Consumption and portfolio decisions when expected returns are time varying. Quarterly Journal of Economics, 114, 433-495. Campbell, J. & Yogo, M. (2006). Efficient test of stock return predictability. Journal of Financial Economics, 81, 27-60. Chang, C. & McAleer, M. (2012). Aggregation, heterogenous autoregression and volatility of daily international tourist arrivals and exchange rates. Japanese Economic Review, 63, 397-419. Chang, C., McAleer, M. & Tansuchat, R. (2012). Modelling long memory volatility in agricultural commodity futures returns. Annals of Financial Economics, 2, 1-27. Cochrane, J. (1999). New facts in finance. Economic Perspectives, 23, 36-58. Cochrane, J. (2008). The dog that did not bark: A defense of return predictability. Review of Financial Studies, 21, 1533-1573. Cochrane, J. (2011). Discount rates. Journal of Finance, 66, 1047-1108. Corsi, F. (2009) A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 174-196. Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987-1007. Engle, R., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5, 1-30. Fama, E. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49, 283-306. Fama, E. & French, K., (1988). Dividend yields and expected returns. Journal of Financial Economics, 22, 3-25. Gartley, H. (1935). Profits in the Stock Markets. Washington: Lambert-Gann Publishing. Hjalmarsson, E. (2010). Predicting global stock returns. Journal of Financial and Quantitative Analysis, 45, 49-80. Hudson, R., McGroarty, F., & Urquhart, A. (2017). Sampling frequency and the performance of different types of technical trading rules. Finance Research Letters, 22, 136-139. Ilomäki, J. (2018). Risk and return of a trend-chasing application in financial markets: an empirical test. Risk Management 20, 258-271. Ilomäki, J., Laurila, H., McAleer, M., (2018). Market timing with moving averages. Sustainability, 10 (7:2125), 2018, 1-25. LeRoy, S. (1973). Risk aversion and the martingale property of stock prices. International Economic Review, 14, 436-446. Lintner, J. (1965). The valuation of risky assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47, 13-37. Lo, A., Mamaysky, H., Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 54, 1705-1770. Lucas, R. (1978), Asset prices in an exchange economy. Econometrica, 46, 1429-1445. Maio, P. (2014). Don’t fight the Fed! Review of Finance, 18, 623–679. Markowitz, H. (1952), Portfolio selection. Journal of Finance 7, 77-91. Malkiel, B. (2003), The efficient market hypothesis and its critics. Journal of Economic Perspectives 17, 59-82. Marshall, B., Nguyen, N., Visaltanachoti, N. (2017). Time series momentum and moving average trading rules. Quantitative Finance 17, 405-421. McAleer, M. (2014). Asymmetry and leverage in conditional volatility models. Econometrics 2(3), 145-150. Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking and Finance, 34, 2573-2586. Menzly, L., Santos, T., & Veronesi, P. (1999). Understanding predictability. Journal of Political Economy, 112, 1-47. Merton, R. (1973). An intertemporal capital asset pricing model. Econometrica, 41, 867-887. Merton, R. (1981), On market timing and investment performance. I. An equilibrium theory of value for market forecast, Journal of Business 54: 363-406. Neely, C., Rapach, D., Tu, J., Zhou, G. (2014). Forecasting equity risk premium: The role of technical indicators. Management Science 66, 1772-1791. Ni, Y., Liao, Y., Huang, P. (2015). MA trading rules, and stock market overreaction. International Review of Economics and Finance, 39, 253-265. Sharpe, W. (1964), Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance 19, 425-442. Shiller, R. (1981). Do stock prices move too much to be justified by subsequent changes in dividends? American Economic Review, 71, 421-436. Sullivan, R., Timmermann, A., White, H. (1999), Data-snooping, technical trading rule performance and the bootstrap. Journal of Finance 53, 1647-1691. Tobin, J. (1958), Liquidity preference as behavior towards risk, Review of Economic Studies, 67. 65-86. Valkanov, R. (2003). Long-horizon regressions: Theoretical results and applications. Journal of Financial Economics, 68, 201-232. Zhu, Y., Zhou, G. (2009). Technical analysis: An asset allocation perspective on the use of moving averages. Journal of Financial Economics, 91, 519-544. Yamamoto, R. (2012). Intraday technical analysis of individual stocks on the Tokyo Stock Exchange. Journal of Banking and Finance, 36, 3033-3047.