A New Approach to Technical Analysis of Oil Prices

Year 2023, Volume: 15 Issue: 1, 145 - 156, 30.06.2023

Mücahit Akbıyık , Seda Yamaç Akbıyık , Ümit Tura , Elif Erer , Mehtap Çalış , Ferudun Kaya

https://doi.org/10.47000/tjmcs.1117784

Abstract

The aim of this study is to investigate the oil prices, which have crucial impact of an economy, using new ratios called Nickel ratios instead of the golden ratios on technical analysis. The Nickel ratios are developed considering Nickel Fibonacci sequence. This study is the first to use Nickel ratios in technical analysis in economics and finance. In this study, graphs comprising of weekly, daily, $4-$hour and $30-$minute periods are analyzed using Nickel ratios in Fibonacci retracement, fan, arcs and time zones applications, and the results are compared with the golden ratio obtained from the Fibonacci number sequence. In addition, the support and resistance points obtained from Nickel ratios have more significant levels than the golden ratio. The retracement, fan, arcs and time zones graphs with weekly, daily, four hourly and half-hourly data based on the golden and Nickel ratios show that the levels regarding the Nickel ratios confirm more significant points in comparison with the levels regarding the golden ratios. Finally, more efficient results are observed when the ratios of golden and Nickel are considered together.

Keywords

Technical analysis, Fibonacci retracement, Nickel ratios, oil prices

References

• Alquist, R., Kilian, L., Vigfusson, R.J., Forecasting the price of oil, In Handbook of Economic Forecasting, Elsevier, 2(2013), 427–507.
• Al-Musaylh, M.S., Deo, R.C., Adamowski, J.F., Li, Y., Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia, Advanced Engineering Informatics, 35(2018), 1–16.
• Basher, S.A., Sadorsky, P., Oil price risk and emerging stock markets, Global Finance Journal, 17(2)(2006), 224–251.
• Baumeister, C., Kilian, L., Forecasting the real price of oil in a changing world: a forecast combination approach, Journal of Business Economic Statistics, 33(3)(2015), 338–351.
• Baumeister, C., Guerin, P., Kilian, L., Do high-frequency financial data help forecast oil prices? The MIDAS touch at work, International Journal of Forecasting, 31(2)(2015), 238–252.
• Baumeister, C., Kilian, L., Real-time forecasts of the real price of oil, Journal of Business Economic Statistics, 30(2)(2012), 326–336.
• Bristone, M., Prasad, R., Abubakar, A.A., CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms, Petroleum, 6(4)(2020), 353–361.
• Cabedo, J.D., Moya, I., Estimating oil price ‘Value at Risk’using the historical simulation approach, Energy Economics, 25(3)(2003), 239–253.
• Chiang, Y.C., Ke, M.C., Liao, T.L., Wang, C.D., Are technical trading strategies still profitable?, Evidence from the Taiwan Stock Index Futures Market, Applied Financial Economics, 22(12)(2012), 955–965.
• Chiroma, H., Abdulkareem, S., Herawan, T., Evolutionary neural network model for West Texas intermediate crude oil price prediction, Applied Energy, 142(2015), 266–273.
• Degiannakis, S., Filis, G., Forecasting oil prices: High-frequency financial data are indeed useful, Energy Economics, 76(2018), 388–402.
• Fan, L., Pan, S., Li, Z., Li, H., An ICA-based support vector regression scheme for forecasting crude oil prices, Technological Forecasting and Social Change, 112(2016), 245–253.
• Ferderer, J.P., Oil price volatility and the macroeconomy, Journal of Macroeconomics, 18(1)(1996), 1–26.
• Gehrig, T., Menkhoff, L., Extended evidence on the use of technical analysis in foreign exchange, International Journal of Finance Economics, 11(4)(2006), 327–338.
• Gori, F., Ludovisi, D., Cerritelli, P.F., Forecast of oil price and consumption in the short term under three scenarios: parabolic, linear and chaotic behaviour, Energy, 32(7)(2007), 1291–1296.
• Kilian, L., Vega, C., Do energy prices respond to US macroeconomic news? A test of the hypothesis of predetermined energy prices, Review of Economics and Statistics, 93(2)(2011), 660–671.
• Korhonen, I., Ledyaeva, S., Trade linkages and macroeconomic effects of the price of oil, Energy Economics, 32(4)(2010), 848–856.
• Lee, Y., Brorsen, B.W., Permanent breaks and temporary shocks in a time series, Computational Economics, 49(2)(2017), 255–270.
• Luo, C., Tan, C., Zheng, Y., Long-term prediction of time series based on stepwise linear division algorithm and time-variant zonary fuzzy information granules, International Journal of Approximate Reasoning, 108(2019), 38–61.
• Mello, C.E., Carvalho, A.S., Lyra, A., Pedreira, C.E., Time series classification via divergence measures between probability density functions, Pattern Recognition Letters, 125(2019), 42–48.
• Narayan, P.K., Gupta, R., Has oil price predicted stock returns for over a century?, Energy Economics, 48(2015), 18–23.
• Neely, C.J., Rapach, D.E., Tu, J., Zhou, G., Forecasting the equity risk premium: the role of technical indicators, Management Science, 60(7)(2014), 1772–1791.
• Ni, Y., Huang, P., Chen, Y., Board structure, considerable capital, and stock price overreaction informativeness in terms of technical indicators, The North American Journal of Economics and Finance, 48(2019), 514–528.
• Noguera, J., Oil prices: Breaks and trends, Energy Economics, 37(2013), 60–67.
• Park, C.H., Irwin, S.H., What do we know about the profitability of technical analysis?, Journal of Economic Surveys, 21(4)(2007), 786–826.
• Sadorsky, P., Oil price shocks and stock market activity, Energy Economics, 21(5)(1999), 449–469.
• Shao, Y.H., Yang, Y.H., Shao, H.L., Stanley, H.E., Time-varying lead–lag structure between the crude oil spot and futures markets, Physica A: Statistical Mechanics and Its Applications, 523(2019), 723–733.
• Sloane, N.J.A., The on-line encyclopedia integer sequences, http://oeis.org/. Access date: 10.03.2021.
• Snudden, S., Targeted growth rates for long-horizon crude oil price forecasts, International Journal of Forecasting, 34(1)(2018), 1-16.
• Sobreiro, V.A., da Costa, T.R.C.C., Naz´ario, R.T.F., e Silva, J.L., Moreira, E.A. et al., The profitability of moving average trading rules in BRICS and emerging stock markets, The North American Journal of Economics and Finance, 38(2016), 86–101.
• Soeini, R.A., Niroomand, A. , Parizi, A.K., Using Fibonacci numbers to forecast the stock market, International Journal of Management Science and Engineering Management, 7(4)(2012), 268–279.
• Spinadel, V.W., The family of metallic means, Visual Mathematics, 1(3)(1999).
• Tura,U., Akbıyık, M., Yamaç Akbıyık, S., Kaya, F., Erer, E. et al., Technical analysis of oil prices using Nickel Fibonacci ratios, PressAcademia Procedia (PAP), 14(2021), 126–127.
• Wang, T., Sun, Q.,Why investors use technical analysis?, Information Discovery Versus Herding Behavior, China Finance Review International., (2015)
• Welch, I., Goyal, A., A comprehensive look at the empirical performance of equity premium prediction, The Review of Financial Studies, 21(4)(2008), 1455–1508.
• Wu, B., Wang, L., Wang, S., Zeng, Y.R., Forecasting the US oil markets based on social media information during the COVID-19 pandemic, Energy, 226(2021), 120403.
• Yin, L., Yang, Q., Predicting the oil prices: do technical indicators help?, Energy Economics, 56(2016), 338–350.
• Zhang, Y. J., Zhang, L., Interpreting the crude oil price movements: Evidence from the Markov regime switching model, Applied Energy, 143(2015), 96-109.
• Zhang, Y.J., Wu, Y.B., The time-varying spillover effect between WTI crude oil futures returns and hedge funds, International Review of Economics Finance, 61(2019), 156–169.