The application of technical trading rules developed from spot market prices on futures market prices using CAPM

Abstract

Futures markets have seen a phenomenal success since their inception both in developed and developing countries during the last four decades. This success is attributable to the tremendous leverage the futures provide to market participants. This study contributes to the literature by analyzing a trading strategy which benefits from this leverage by using the Capital Asset Pricing Model (CAPM) and cost-of-carry relationship. We apply the technical trading rules developed from spot market prices, on futures market prices using a CAPM based hedge ratio. Historical daily prices of twenty stocks from each of the ten markets (five developed markets and five emerging markets) are used for the analysis. Popular technical indicators, along with artificial intelligence techniques like Neural Networks and Genetic Algorithms, are used to generate buy and sell signals for each stock and for portfolios of stocks. The performance of the trading strategies is then calculated and compared. The results show that, although equal amounts invested in both spot and futures markets, the profit from the strategies applied on futures is considerably higher than that from the spot market in both developed and emerging markets. Moreover, the overall performance of the artificial intelligence strategies is far better than the traditional ones.

This is a preview of subscription content, log in to check access.

Fig. 1

Notes

  1. 1.

    Figlewski (1984, 1985), Holmes (1996), Butterworth and Holmes (2001), Lafuente and Novales (2003) and Yang and Allen (2005) are among the many studies on the hedging effectiveness of the SIFs. Cornell and French (1983), MacKinlay and Ramaswamy (1988), Yadav and Pope (1990, 1994), Butterworth and Holmes (2001) and Richie et al. (2008) are examples of the vast literature on SIF arbitrage.

  2. 2.

    The markets selected are US, UK, Australia, Germany, and Hong Kong, China, South Korea, India, Mexico and Turkey. They are among the most important financial markets in the developed and emerging economies.

  3. 3.

    EMH (Fama 1970) has been one of building blocks of finance theory over the past four decades. It describes an efficient market as a market where all the prices reflect their investment value. Market efficiency was later classified into three forms: weak-form efficiency, semi-strong-form efficiency and strong-form efficiency. In a market where information is cheaply and widely available to all the investors, the prices should reflect all the information available as investors are assumed to respond instantaneously to any new information arriving to the market.

  4. 4.

    In “Appendix 1”, the trading volumes of the spot and futures that are included in our sample are presented in a table. In 2014, in eight countries included in our sample, trading volumes in futures markets are higher than spot market volumes. Only in Turkey and Australia, spot market volumes exceed those of futures markets.

  5. 5.

    The countries are classified on the basis of MSCI Inc. classification.

  6. 6.

    Based on the Market Capitalization Monthly Report of World Federation of Exchanges-October 2015.

  7. 7.

    Based on the statistics issued by World Federation of Exchanges on January 31, 2015.

  8. 8.

    The samples of stocks selected from other markets also include a similar mixture of stocks. They are not shown here due to space constraints.

  9. 9.

    We assume that we invest in local risk-free securities when we are out of market.

  10. 10.

    Transaction cost in spot market is assumed to be 0.1 % per transaction for all the exchanges.

  11. 11.

    To avoid spurious regression, all the return series are tested for unit root using ADF (Augmented Dickey Fuller) unit root test. All the series meet stationarity condition. The unit root test results and regression results are not shown here because of space constraints; and can be provided if required.

  12. 12.

    Margin requirement in futures is assumed to be 10 % of the transaction amount for all the markets.

  13. 13.

    Even though validity of using CAPM to examine whether forecasters are successful in their market-timing activity is theoretically questionable, CAPM is also used to evaluate portfolio performance in active portfolio management and market timing studies.

  14. 14.

    The detailed results for all the stocks are not presented here due to space constraints.

  15. 15.

    Again, the detailed results for all the stocks are not presented here due to space constraints.

  16. 16.

    The results are not shown here due to space constraints and can be provided upon request.

References

  1. Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245–271.

    Article  Google Scholar 

  2. Bessembinder, H., & Chan, K. (1995). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal., 7, 257–284.

    Article  Google Scholar 

  3. Bhattacharya, U., & Galpin, N. (2011). The global rise of the value-weighted portfolio. Journal of Financial and Quantitative Analysis, 46(03), 737–756.

    Article  Google Scholar 

  4. Blennerhassett, M., & Bowman, R. G. (1998). A change in market microstructure: the switch to electronic screen trading on the New Zealand stock exchange. Journal of International Financial Markets, Institutions and Money, 8(3), 261–276.

    Article  Google Scholar 

  5. Block, S. B., & French, D. W. (2002). The effect of portfolio weighting on investment performance evaluation: The case of actively managed mutual funds. Journal of economics and finance, 26(1), 16–30.

    Article  Google Scholar 

  6. Brown, P., & Walter, T. (2013). The CAPM: theoretical validity, empirical intractability and practical applications. Abacus, 49(S1), 44–50.

    Article  Google Scholar 

  7. Butterworth, D., & Holmes, P. (2001). The hedging effectiveness of stock index futures: evidence for the FTSE-100 and FTSE-Mid250 indexes traded in the UK. Applied Financial Economics, 11(1), 57–68.

    Article  Google Scholar 

  8. Cecchetti, S. G., Cumby, R. E., Figlewski, S. (1988). Estimation of the optimal futures hedge. The Review of Economics and Statistics, 70(4), 623–630.

    Article  Google Scholar 

  9. Chance, D., & Brooks, R. (2010). Introduction to derivatives and risk management. Cengage Learning

  10. Cheung, Yin-Wong, Chinn Menzie, D., & Marsh, Ian W. (2004). How do UK-based foreign exchange dealers think their market operates? International Journal of Finance and Economics., 9(4), 289–306.

    Article  Google Scholar 

  11. Cheung, Yin-Wong, & Wong, C. Y. P. (2000). A survey of market practitioners’ views on exchange rate dynamics. Journal of International Economics, 51(2), 401–419.

    Article  Google Scholar 

  12. Cornell, B., & French, K. R. (1983). The pricing of stock index futures. Journal of Futures Markets, 3(1), 1–14.

    Article  Google Scholar 

  13. Cumming, D., Johan, S., & Li, D. (2011). Exchange trading rules and stock market liquidity. Journal of Financial Economics, 99(3), 651–671.

    Article  Google Scholar 

  14. Dempster, M. A. H., & Jones, C. M. (2001). A real-time adaptive trading system using genetic programming. Quantitative Finance., 1(2001), 397–413.

    Article  Google Scholar 

  15. Er, H., & Hushmat, A. (2012). The impact of the leverage provided by the futures on the performance of technical indicators: Evidence from Turkey. International Journal of Economics and Finance Studies., 4(2), 2012.

    Google Scholar 

  16. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.

    Article  Google Scholar 

  17. Figlewski, S. (1984). Hedging performance and basis risk in stock index futures. The Journal of Finance, 39(3), 657–669.

    Article  Google Scholar 

  18. Figlewski, S. (1985). Hedging with stock index futures: Theory and application in a new market. Journal of Futures Markets, 5(2), 183–199.

    Article  Google Scholar 

  19. Frino, A., McInish, T. H., & Toner, M. (1998). The liquidity of automated exchanges: New evidence from German Bund futures. Journal of International Financial Markets, Institutions and Money, 8(3), 225–241.

    Article  Google Scholar 

  20. Gehrig, Thomas, & Menkhoff, Lukas. (2006). Extended evidence on the use of technical analysis in foreign exchange. International Journal of Finance and Economics., 11, 327–338.

    Article  Google Scholar 

  21. Holland, J. H. (1962). Outline for a logical theory of adaptive systems. Journal of the ACM (JACM), 9(3), 297–314.

    Article  Google Scholar 

  22. Holmes, P. (1996). Stock index futures hedging: hedge ratio estimation, duration effects, expiration effects and hedge ratio stability. Journal of Business Finance and Accounting, 23(1), 63–77.

    Article  Google Scholar 

  23. Hull, J. C. (2009). Options, futures, and other derivatives. NJ: Pearson Education.

    Google Scholar 

  24. Ko, K. C., Lin, S. J., Su, H. J., & Chang, H. H. (2014). Value investing and technical analysis in Taiwan stock market. Pacific-Basin Finance Journal, 26, 14–36.

    Article  Google Scholar 

  25. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT press.

  26. Koza, J. R, Goldberg D., Fogel D., & Riolo R. (1996). Proceedings, first annual conference on genetic programming, MIT Press, MA, USA.

  27. Krausz, J., Lee, S. Y., & Nam, K. (2009). Profitability of nonlinear dynamics under technical trading rules: Evidence from Pacific Basin Stock markets. Emerging Markets Finance and Trade, 45(4), 13–35.

    Article  Google Scholar 

  28. Lafuente, J. A., & Novales, A. (2003). Optimal hedging under departures from the cost-of-carry valuation: Evidence from the Spanish stock index futures market. Journal of Banking & Finance, 27(6), 1053–1078.

    Article  Google Scholar 

  29. Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The review of economics and statistics, 47(1), 13–37.

    Article  Google Scholar 

  30. MacKinlay, A. C., & Ramaswamy, K. (1988). Index-futures arbitrage and the behavior of stock index futures prices. Review of Financial Studies, 1(2), 137–158.

    Article  Google Scholar 

  31. Menkhoff, L., & Schmidt U. (2005). The use of trading strategies by fund managers: Some first survey evidence. Applied Economics, 37(15), 1719–1730.

  32. Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance, 34(11), 2573–2586.

    Article  Google Scholar 

  33. Moosa, I., & Li, L. (2011). Technical and fundamental trading in the Chinese stock market: Evidence based on time-series and panel data. Emerging markets finance and trade, 47(sup1), 23–31.

    Article  Google Scholar 

  34. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.

    Article  Google Scholar 

  35. Mullins, D. W. (1982). Does the capital asset pricing model work? Harvard Business Review, 60(1), 105–114.

    Google Scholar 

  36. Mulvey, J. M., & Kim, W. C. (2008). Active equity managers in the US: Do the best follow momentum strategies? The Journal of Portfolio Management, 34(2), 126–134.

    Article  Google Scholar 

  37. Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), 786–826.

    Article  Google Scholar 

  38. Pirrong, C. (1996). Market liquidity and depth on computerized and open outcry trading systems: A comparison of DTB and LIFFE bund contracts. Journal of Futures Markets, 16(5), 519–543.

    Article  Google Scholar 

  39. Qu, H., & Li, X. (2014). Building technical trading system with genetic programming: A new method to test the efficiency of Chinese stock markets. Computational Economics, 43(3), 301–311.

    Article  Google Scholar 

  40. Richie, N., Daigler, R. T., & Gleason, K. C. (2008). The limits to stock index arbitrage: Examining S&P 500 futures and SPDRs. Journal of Futures Markets, 28(12), 1182–1205.

    Article  Google Scholar 

  41. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance., 19, 425–442.

    Google Scholar 

  42. Taylor, M. (2000). The Use of Technical Analysis in the London Stock Market. INQUIRE (Institute for Quantitative Investment Research) September 2000 conference, Gleneagles Hotel, Scotland, UK

  43. Tian, G. G., Wan, G. H., & Guo, M. (2002). Market efficiency and the returns to simple technical trading rules: new evidence from US equity market and Chinese equity markets. Asia-Pacific Financial Markets, 9(3–4), 241–258.

    Article  Google Scholar 

  44. Tse, Y., & Zabotina, T. V. (2001). Transaction costs and market quality: Open outcry versus electronic trading. Journal of Futures Markets, 21(8), 713–735.

    Article  Google Scholar 

  45. Vickers, D. (1994). Economics and the antagonism of time: Time, uncertainty, and choice in economic theory. University of Michigan Press.

  46. Wang, J. J., Wang, J. Z., Zhang, Z. G., & Guo, S. P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758–766.

    Article  Google Scholar 

  47. White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097–1126.

    Article  Google Scholar 

  48. Yadav, P. K., & Pope, P. F. (1990). Stock index futures arbitrage: International evidence. Journal of Futures Markets, 10(6), 573–603.

    Article  Google Scholar 

  49. Yadav, P. K., & Pope, P. F. (1994). Stock index futures mispricing: Profit opportunities or risk premia? Journal of Banking & Finance, 18(5), 921–953.

    Article  Google Scholar 

  50. Yang, W., & Allen, D. E. (2005). Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets. Accounting and Finance, 45(2), 301–321.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Adnan Hushmat.

Appendices

Appendix 1

See Table 7.

Table 7 Trading Volume in Spot Market and Derivatives Markets

Appendix 2

See Table 8.

Table 8 Formulae and signal generation rules of the technical indicators

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Er, H., Hushmat, A. The application of technical trading rules developed from spot market prices on futures market prices using CAPM. Eurasian Bus Rev 7, 313–353 (2017). https://doi.org/10.1007/s40821-016-0056-2

Download citation

Keywords

  • Futures
  • CAPM
  • Technical indicators
  • Artificial intelligence
  • Emerging markets
  • Neural networks
  • Genetic programming

JEL Classification

  • G14
  • G17
  • G19