Performance of the Average Directional Index as a market timing tool for the most actively traded USD based currency pairs
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Author(s)Ikhlaas GurribLink to ORCID Index: https://orcid.org/0000-0001-8393-9790
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DOIhttp://dx.doi.org/10.21511/bbs.13(3).2018.06
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Article InfoVolume 13 2018, Issue #3, pp. 58-70
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Creative Commons Attribution-NonCommercial 4.0 International License
The aim of this study is to test a trading system based on the average directional index, which is complemented with the parabolic stop and reverse indicator. The trend-based system is tested onto the most actively traded USD based foreign currency pairs, using both monthly and weekly data set over 2000–2018. Sharpe and Sortino measures are used to track the performance of the currency pairs, based on total risk and downside risk assumptions. Results are robust tested by decomposing the data into pre and post 2008 financial crisis. Using an investment horizon over 18 years, the reliance upon the monthly model produced lower maximum drawdowns and lesser trades than the weekly model. While Swiss Franc had the best (worse) performance in the monthly (weekly) based model, the Chinese Renminbi witnessed the worse (best) performance in the monthly (weekly) based model. Pre and post financial crisis decompositions suggest the weekly-based system is more reliable than the monthly one with relatively more trades and positive performance, where the Chinese Renminbi and Japanese Yen posted the highest Sharpe and Sortino values of 0.996 and 4.452 respectively in the post crisis period. Proportionately high level of negative returns coupled with relatively low positive Sharpe and Sortino values, however, suggest that a trading system relying on the average directional index and parabolic stop and reverse indicator to be further tested and analyzed at higher frequencies.
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JEL Classification (Paper profile tab)G11, G15, G17
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References42
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Tables3
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Figures1
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- Figure 1. Foreign exchange market turnover of USD based currency pairs
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- Table 1. Performance of trading strategy (2000–2018)
- Table 2. Pre 2008 financial crisis performance
- Table 3. Post 2008 financial crisis performance
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- Aragon, G. O., & Ferson, W. E. (2006). Portfolio Performance Evaluation. Foundations and Trends in Finance, 2(2), 83-190.
- Ball, R. (1978). Filter Rules: Interpretation of Market Efficiency, Experimental Problem and Australian Experience. Accounting Education, 18(2), 1-7.
- BIS (2016). Triennial Central Bank Survey Foreign exchange turnover in April 2016. Monetary and Economic Department. Accessed on 28th September 2017.
- Chaudhry, A., & Johnson, H. L. (2008). The Efficacy of the Sortino Ratio and Other Benchmarked Performance Measures Under Skewed Return Distributions. Australian Journal of Management, 32(3), 485-502.
- Chen, S., & Lee, C. (1981). The Sampling Relationship Between Sharpe’s Performance Measure and its Risk Proxy: Sample Size, Investment Horizon and Market Conditions. Management Science, 27(6), 607-618.
- Coppes, R. C. (1995). Are exchange rate changes normally distributed? Economics Letters, 47(2), 117-121.
- Cornell, B., & Dietrich, J. K. (1978). Mean-Absolute-Deviation Versus Least-Squares Regression Estimation of Beta Coefficients. Journal of Financial and Quantitative Analysis, 13(1), 123-131.
- Dooley, M. P., & Shafer J. R. (1976). Analysis of Short run exchange rate behavior: March, 1973 to September 1975 (International Finance Discussion Papers No. 7). Washington: Federal Reserve System.
- Faber, M. (2013). A Quantitative Approach to Tactical Asset Allocation. The Journal of Wealth Management, 9(4), 69-79.
- Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
- Fama, E. F. (1972). Components of Investment Performance. Journal of Finance, 27(3), 551-567.
- Gunthorpe, D., & Levy, H. (1994). Portfolio Composition and the Investment Horizon. Financial Analysts Journal, 50(1), 51-56.
- Gurrib, I. (2016). Optimization of the Double Crossover Strategy for the S&P500 Market Index. Global Review of Accounting and Finance, 7(1), 92-107.
- Gurrib, I. (2018). Are Key Market Players In Currency Derivatives Markets Affected By Financial Conditions? Investment Management and Financial Innovations, 15(2), 183-193.
- Gurrib, I., & Kamalov, F. (2018). The Implementation of an Adjusted Relative Strength Index Model in the Foreign Currency and Energy Markets of Emerging and Developed Economies.
- Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. Journal of Finance, 23(2), 389-416.
- Leland, H. (1999). Beyond mean-variance: Performance measurement in a nonsymmetric world. Financial Analysts Journal, 55(1), 27-36.
- Levy, H., & Samuelson, P. A. (1992). The Capital Asset Pricing Model with Diverse Holding Periods. Management Science, 38(11), 1529-1542.
- Logue, D. E., & Sweeney R. J. (1977). White Noise in Imperfect Markets: The Case of the Franc/Dollar Exchange Rate. Journal of Finance, 32(3), 761-768.
- Logue, D. E., Sweeney, R. J., & Willett T. D. (1978). Speculative behavior of foreign exchange rates during the current float. Journal of Business Research, 6(2), 159-174.
- Markowitz, H. (1959). Portfolio Selection. New Haven, Connecticut: Yale University Press.
- Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance, 34(11), 2573-2586.
- Menkhoff, L., & Taylor M. P. (2007). The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis. Journal of Economic Literature, 45(4), 936-972.
- MSCI (2017a). MSCI Emerging and Frontier Markets Indexes. Accessed on 5th October 2017.
- MSCI (2017b). MSCI Emerging Markets Index (USD). Accessed on 5th October 2017.
- Neely, C. J., Weller, P. A., & Ulrich, J. M. (2009). The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market. Journal of Financial and Quantitative Analysis, 44(2), 467-488.
- Park, C. H., & Irwin, S. H. (2010). A Reality Check on Technical Trading Rule Profits in the US Futures Markets. Journal of Futures Markets, 30(7), 633-659.
- Pruitt, S. W., & White, R. E. (1988). The CRISMA trading system: Who says technical analysis cannot beat the market? The Journal of Portfolio Management, 14(3), 55-58.
- Schulmeister, S. (2010). An Essay on Exchange Rate Dynamics (WZB Discussion Paper No. 0722-673X).
- Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
- Sharpe, W. F. (1966). Mutual Fund Performance. Journal of Business, 39(1), 119-138.
- Siegel, J. (2013). Stocks for the Long Run: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies (pp. 151-448). New York: McGraw-Hill.
- Sortino, F., & Price, L. N. (1994). Performance Measurement in a Downside Risk Framework. The Journal of Investing, 3(3), 59-65.
- Sortino, F., & Van der Meer, R. (1991). Downside risk. The Journal of Portfolio Management, 17(4), 27-31.
- Sweeney, R. (1986). Beating the Foreign Exchange Market. Journal of Finance, 41(1), 163-182.
- Szakmary, A. C., Shen, Q., & Sharma, C. S. (2010). Trend-following trading strategies in commodity futures: A re-examination. Journal of Banking & Finance, 34(2), 409-426.
- Treynor, J. (1965). How to rate management of investment funds? Harvard Business Review, 43(1), 63-75.
- Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market? Harvard Business Review, 44(4), 131-136.
- Tsaih, R., Hsu, Y., & Lai, C. C. (1998). Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems, 23(2), 161-174.
- Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
- Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
- Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. Research Foundation of CFA Institute.
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Horizon of cryptocurrency before vs during COVID-19
Ikaputera Waspada ,
Dwi Fitrizal Salim 
,
Astrie Krisnawati
doi: http://dx.doi.org/10.21511/imfi.20(1).2023.02
Investment Management and Financial Innovations Volume 20, 2023 Issue #1 pp. 14-25 Views: 880 Downloads: 432 TO CITE АНОТАЦІЯInvestment cannot be separated from the level of return and risk inherent in assets. Today, investment instruments are not only stocks, currencies, bonds, deposits, savings and others. The beginning of Bitcoin’s emergence as a pioneer of Cryptocurrency was in 2009. Crypto assets are emerging rapidly and are accompanied by an increase in the number of transactions each period. The growth in the market capitalization value of crypto assets has also grown significantly. During COVID-19, many investments, such as stocks, experienced a decline due to market uncertainty. The results of this study prove that with the existence of COVID-19, the crypto market is not affected. Crypto is an attraction characterized by a high degree of fluctuation, and there is no limit to transactions in the open market 24 hours to trade. The Cryptocurrency market is currently a market that can provide short-term benefits to risk-taking investors, while the market in other investment instruments is declining. 78% of the value capitalization of the top 200 cryptocurrencies is represented by the top 9 cryptos used as samples in this study. So that if there is a decrease in these 9 cryptos, it will also have an impact on the overall capitalization value of crypto in the market. The future development of Cryptocurrencies will no longer be digital assets traded with many speculators who can control prices, it can even be digital money that can be used worldwide without any transaction fees and is controlled on a blockchain system.
