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Determination of the Best Simple Moving Average By Stochastic Processes

Year 2017, Volume: 9 Issue: 16, 59 - 67, 01.01.2017
https://doi.org/10.14784/marufacd.305567

Abstract

In this study, we consider one of the most popular technical indicators and try to determine the best fitting
simple moving average to a given data. Here we utilize from a general mean reverting stochastic process
where the mean is time dependent. We propose an identification algorithm which mainly concentrates
on the normality of the residual terms after the data is demeaned from simple moving average and also provide
evidence that our algorithm works quite well for determination of the “best” simple moving average.

References

  • ALIA, Mohammad, BABAIB, Mohamed, BOYLAN John, SYNTETOSD, Aris (2015), “On the use of Simple Moving Averages for supply chains where information is not shared”, IFAC-PapersOnLine, 48(3), pp. 1756-1761.
  • ANDREW, Lo, MACKINLAY, Archie C. (2002), A Non-Random Walk Down Wall Street. Princeton University Press.
  • BROCK, William, LAKONISHOK, Josef, LeBARON, Blake (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Return”, The Journal of Finance, 47(5), pp. 1731–1764.
  • CARLSON, Charles B. (2004), Winning with the Dow's Losers: Beat the Market with Underdog Stocks, HarperCollins.
  • CHEN, Chien-Hua, SU Xuan-Qi, LIN Jun-Baio (2016), “The role of information uncertainty in moving-average technical analysis: A study of individual stock-option issuance in Taiwan”, Finance Research Letters, 18, pp. 263-272.
  • EDWARDS, Robert, MCGEE, John, BESSETTI, Charles (2007), Technical Analysis of Stock Trends, CRC Press.
  • FAMA, Eugene (1965), “The Behavior of Stock Market Prices”, Journal of Business, 38, pp. 34–105.
  • GENÇAY, Ramazan (1998), “The predictability of security returns with simple technical trading rules”, Journal of Empirical Finance, 5 pp. 347–359.
  • HAN, Yufeng, YANG, Ke, ZHOU, Guofu (2013), “A new anomaly: the cross-sectional profitability of technical analysis”, Journal of Financial and Quantitative Analysis, 48 (5), pp. 1433–1461.
  • HUANG, Paoyu, NI, Yensen (2016), “Board structure and stock price informativeness in terms of moving average rules”, The Quarterly Review of Economics and Finance, In Press.
  • HULL, John, WHITE, Alan (1990), “Pricing Interest Rate Derivative Securities”, The Review of Financial Studies, 3(4), pp. 573–592.
  • KIRKPATRICK, Charles D., DAHLQUIST, Julie R. (2006), Technical Analysis: The Complete Resource for Financial Market Technicians, Financial Times Press.
  • KUM, Sangho, LEE, Hosoo, LİM, Yongdo (2015), “Moving averages on convex metric spaces”, Journal of Mathematical Analysis and Applications, 421, pp. 1131-1150.
  • JARQUE, Carlos M., BERA, Anil K. (1980), “Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals”, Economics Letters, 6 (3), pp. 255–259.
  • JARQUE, Carlos M., BERA, Anil K. (1981), “Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals: Monte Carlo Evidence”, Economics Letters, 7 (4), pp. 313–318.
  • JARQUE, Carlos M., BERA, Anil K. (1987), “A Test for Normality of Observations and Regression Residuals”, International Statistical Review, 55 (2), pp. 163–172.
  • MALKIEL, Burton (1973), A Random Walk Down Wall Street. Scala.
  • MARSHALL, Ben R., CAHAN Rochester C., CAHAN Jared M. (2008), “Does intraday technical analysis in the U.S. equity market have value?”, Journal of Empirical Finance, 15 (2), pp. 199-210.
  • ORNSTEIN, Leonard, UHLENBECK, George (1930), “On the Theory of the Brownian Motion”. Physical Review, 36, pp. 823–841.
  • SCHLÜTER, Stephan (2009), “Constructing a Quasilinear Moving Average Using the Scaling Function”, IWQW Discussion Paper Series, No. 12/2009, pp. 1-21.
  • VASICEK, Oldrich (1977), “An equilibrium characterization of the term structure”, Journal of Financial Economics, 5, pp. 177–188.
  • ZHU, Yingzi, ZHOU, Guofu (2009), “Technical analysis: an asset allocation perspective on the use of moving averages”, Journal of Financial Economics, 92 (3), pp. 519-544.

Stokastik Süreçlerle En İyi Basit Hareketli Ortalamanın Belirlenmesi

Year 2017, Volume: 9 Issue: 16, 59 - 67, 01.01.2017
https://doi.org/10.14784/marufacd.305567

Abstract

Bu çalışmada en gözde teknik analiz indikatörlerinden birisi incelenmiş ve veriye en iyi uyan basit hareketli
ortalama belirlenmeye çalışılmıştır. Burada, ortalamanın zamana bağlı olduğu genel bir ortalamaya
dönen stokastik süreçten faydalanılmıştır. Veri basit hareketli ortalamadan arındırıldıktan sonra kalan terimlerin
normal dağılımına odaklanan bir algoritma sunulmuştur. En iyi hareketli ortalamayı belirleyen algoritmamızın
çalıştığı bir örnek verilmiştir.

References

  • ALIA, Mohammad, BABAIB, Mohamed, BOYLAN John, SYNTETOSD, Aris (2015), “On the use of Simple Moving Averages for supply chains where information is not shared”, IFAC-PapersOnLine, 48(3), pp. 1756-1761.
  • ANDREW, Lo, MACKINLAY, Archie C. (2002), A Non-Random Walk Down Wall Street. Princeton University Press.
  • BROCK, William, LAKONISHOK, Josef, LeBARON, Blake (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Return”, The Journal of Finance, 47(5), pp. 1731–1764.
  • CARLSON, Charles B. (2004), Winning with the Dow's Losers: Beat the Market with Underdog Stocks, HarperCollins.
  • CHEN, Chien-Hua, SU Xuan-Qi, LIN Jun-Baio (2016), “The role of information uncertainty in moving-average technical analysis: A study of individual stock-option issuance in Taiwan”, Finance Research Letters, 18, pp. 263-272.
  • EDWARDS, Robert, MCGEE, John, BESSETTI, Charles (2007), Technical Analysis of Stock Trends, CRC Press.
  • FAMA, Eugene (1965), “The Behavior of Stock Market Prices”, Journal of Business, 38, pp. 34–105.
  • GENÇAY, Ramazan (1998), “The predictability of security returns with simple technical trading rules”, Journal of Empirical Finance, 5 pp. 347–359.
  • HAN, Yufeng, YANG, Ke, ZHOU, Guofu (2013), “A new anomaly: the cross-sectional profitability of technical analysis”, Journal of Financial and Quantitative Analysis, 48 (5), pp. 1433–1461.
  • HUANG, Paoyu, NI, Yensen (2016), “Board structure and stock price informativeness in terms of moving average rules”, The Quarterly Review of Economics and Finance, In Press.
  • HULL, John, WHITE, Alan (1990), “Pricing Interest Rate Derivative Securities”, The Review of Financial Studies, 3(4), pp. 573–592.
  • KIRKPATRICK, Charles D., DAHLQUIST, Julie R. (2006), Technical Analysis: The Complete Resource for Financial Market Technicians, Financial Times Press.
  • KUM, Sangho, LEE, Hosoo, LİM, Yongdo (2015), “Moving averages on convex metric spaces”, Journal of Mathematical Analysis and Applications, 421, pp. 1131-1150.
  • JARQUE, Carlos M., BERA, Anil K. (1980), “Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals”, Economics Letters, 6 (3), pp. 255–259.
  • JARQUE, Carlos M., BERA, Anil K. (1981), “Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals: Monte Carlo Evidence”, Economics Letters, 7 (4), pp. 313–318.
  • JARQUE, Carlos M., BERA, Anil K. (1987), “A Test for Normality of Observations and Regression Residuals”, International Statistical Review, 55 (2), pp. 163–172.
  • MALKIEL, Burton (1973), A Random Walk Down Wall Street. Scala.
  • MARSHALL, Ben R., CAHAN Rochester C., CAHAN Jared M. (2008), “Does intraday technical analysis in the U.S. equity market have value?”, Journal of Empirical Finance, 15 (2), pp. 199-210.
  • ORNSTEIN, Leonard, UHLENBECK, George (1930), “On the Theory of the Brownian Motion”. Physical Review, 36, pp. 823–841.
  • SCHLÜTER, Stephan (2009), “Constructing a Quasilinear Moving Average Using the Scaling Function”, IWQW Discussion Paper Series, No. 12/2009, pp. 1-21.
  • VASICEK, Oldrich (1977), “An equilibrium characterization of the term structure”, Journal of Financial Economics, 5, pp. 177–188.
  • ZHU, Yingzi, ZHOU, Guofu (2009), “Technical analysis: an asset allocation perspective on the use of moving averages”, Journal of Financial Economics, 92 (3), pp. 519-544.
There are 22 citations in total.

Details

Journal Section Makaleler
Authors

Deniz İlalan This is me

Publication Date January 1, 2017
Submission Date April 11, 2017
Published in Issue Year 2017 Volume: 9 Issue: 16

Cite

APA İlalan, D. (2017). Stokastik Süreçlerle En İyi Basit Hareketli Ortalamanın Belirlenmesi. Finansal Araştırmalar Ve Çalışmalar Dergisi, 9(16), 59-67. https://doi.org/10.14784/marufacd.305567