Abstract
The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading.
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References
Andersen TG, Bollerslev T (1998) Deutsche mark-dollar volatility: intraday activity patterns, macroeconomic announcements, and longer run dependencies. J Financ 53(1):219–265
Ang A, Bekaert G (2006) Stock return predictability: is it there? Rev Financ Stud 20(3):651–707
Avdoulas C, Bekiros S, Boubaker S (2018) Evolutionary-based return forecasting with nonlinear star models: evidence from the eurozone peripheral stock markets. Ann Oper Res 262(2):307–333. https://doi.org/10.1007/s10479-015-2078-z
Aydogan K, Gursoy G (2000) P/e and price-to-book ratios as predictors of stock returns in emerging equity markets. Emerg Mark Q 4(4):60–67
Banzhaf W, Francone FD, Keller RE, Nordin P (1998) Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
Bates DS (1996) Jumps and stochastic volatility: exchange rate processes implicit in Deutsche mark options. Rev Financ Stud 9(1):69–107
Batten JA, Lucey BM, McGroarty F, Peat M, Urquhart A (2018) Does intraday technical trading have predictive power in precious metal markets? J Int Financ Mark Insti Money 52:102–113. https://doi.org/10.1016/j.intfin.2017.06.005
Bhola J, Soni S, Cheema GK (2019) Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. J Ambient Intell Human Comput 20:1–8
Bollerslev T, Todorov V, Xu L (2015) Tail risk premia and return predictability. J Financ Econ 118(1):113–134. https://doi.org/10.1016/j.jfineco.2015.02.010
Bollerslev T, Patton AJ, Quaedvlieg R (2016) Exploiting the errors: a simple approach for improved volatility forecasting. J Econom 192(1):1–18
Cai Z, Wang Y, Wang Y (2015) Testing instability in a predictive regression model with nonstationary regressors. Econom Theory 31(5):953–980. https://doi.org/10.1017/S0266466614000590
Campbell JY, Shiller RJ (1988) The dividend-price ratio and expectations of future dividends and discount factors. Rev Financ Stud 1(3):195–228
Campbell JY, Yogo M (2006) Efficient tests of stock return predictability. J Financ Econ 81(1):27–60. https://doi.org/10.1016/j.jfineco.2005.05.008
Chan WH, Maheu JM (2002) Conditional jump dynamics in stock market returns. J Bus Econ Stat 20(3):377–389. https://doi.org/10.1198/073500102288618513
Chen X, Gao P (2019) Path planning and control of soccer robot based on genetic algorithm. J Ambient Intell Human Comput 20:1–10
Chen WH, Shih JY, Wu S (2006) Comparison of support-vector machines and back propagation neural networks in forecasting the six major asian stock markets. Int J Electron Financ 1(1):49–67
Choudhry R, Garg K (2008) A hybrid machine learning system for stock market forecasting. World Acad Sci Eng Technol 39(3):315–318
Cremers M, Weinbaum D (2010) Deviations from put-call parity and stock return predictability. J Financ Quant Anal 45(2):335–367. https://doi.org/10.1017/S002210901000013X
Cremers M, Halling M, Weinbaum D (2015) Aggregate jump and volatility risk in the cross-section of stock returns. J Financ 70(2):577–614
Ding Z, Granger CW, Engle RF (1993) A long memory property of stock market returns and a new model. J Empir Financ 1(1):83–106. https://doi.org/10.1016/0927-5398(93)90006-D
Facchini F, Digiesi S, Mossa G (2020) Optimal dry port configuration for container terminals: a non-linear model for sustainable decision making. Int J Prod Econ 219:164–178
Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25(2):383–417
Fama EF, French KR (1988) Dividend yields and expected stock returns. J Financ Econ 22(1):3–25. https://doi.org/10.1016/0304-405X(88)90020-7
Ferrara L, Marcellino M, Mogliani M (2015) Macroeconomic forecasting during the great recession: the return of non-linearity? Int J Forecast 31(3):664–679. https://doi.org/10.1016/j.ijforecast.2014.11.005
Fong WM, Yong LH (2005) Chasing trends: recursive moving average trading rules and internet stocks. J Empir Financ 12(1):43–76. https://doi.org/10.1016/j.jempfin.2003.07.002
Gencay R, Selcuk F (2004) Extreme value theory and value-at-risk: relative performance in emerging markets. Int J Forecast 20(2):287–303. https://doi.org/10.1016/j.ijforecast.2003.09.005
Golez B, Koudijs P (2018) Four centuries of return predictability. J Financ Econ 127(2):248–263. https://doi.org/10.1016/j.jfineco.2017.12.007
Golosnoy V, Hamid A, Okhrin Y (2014) The empirical similarity approach for volatility prediction. J Bank Financ 40(Supplement C):321–329. https://doi.org/10.1016/j.jbankfin.2013.12.009
Goyal A, Welch I (2003) Predicting the equity premium with dividend ratios. Manag Sci 49(5):639–654. https://doi.org/10.1287/mnsc.49.5.639.15149
Hansen BE (1997) Inference in tar models. Stud Nonlinear Dyn Econom 2:1
Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603
Hirsh H, Banzhaf W, Koza JR, Ryan C, Spector L, Jacob C (2000) Genetic programming. IEEE Intell Syst 15(3):74–84
Hurn AS, Silvennoinen A, Teräsvirta T (2016) A smooth transition logit model of the effects of deregulation in the electricity market. J Appl Econom 31(4):707–733
Jiang X, Lee BS (2007) Stock returns, dividend yield, and book-to-market ratio. J Bank Financ 31(2):455–475. https://doi.org/10.1016/j.jbankfin.2006.07.012
Jiang F, Tong G, Song G (2019) Technical analysis profitability without data snooping bias: evidence from Chinese stock market. Int Rev Financ 19(1):191–206
Jondeau E, Zhang Q, Zhu X (2019) Average skewness matters. J Financ Econ 134(1):29–47
Kelly B, Jiang H (2014) Tail risk and asset prices. Rev Financ Stud 27(10):2841–2871
Kim HY, Mei JP (2001) What makes the stock market jump? An analysis of political risk on hong kong stock returns. J Int Money Financ 20(7):1003–1016. https://doi.org/10.1016/S0261-5606(01)00035-3
Kim JH, Shamsuddin A (2008) Are asian stock markets efficient? Evidence from new multiple variance ratio tests. J Empir Financ 15(3):518–532. https://doi.org/10.1016/j.jempfin.2007.07.001
Kou SG (2002) A jump-diffusion model for option pricing. Manag Sci 48(8):1086–1101
Kozhan R, Neuberger A, Schneider P (2013) The skew risk premium in the equity index market. Rev Financ Stud 26(9):2174–2203
Lamont O (1998) Earnings and expected returns. J Financ 53(5):1563–1587
Lee BS, Paek M, Ha Y, Ko K (2015) The dynamics of market volatility, market return, and equity fund flow: international evidence. Int Rev Econ Financ 35:214–227. https://doi.org/10.1016/j.iref.2014.10.001
Lettau M, Ludvigson S (2001) Consumption, aggregate wealth, and expected stock returns. J Financ 56(3):815–849
Liu R, Yang J, Ruan C (2019) Expected stock return and mixed frequency variance risk premium data. J Ambient Intell Human Comput 1:1–12
Manahov V, Hudson R, Hoque H (2015) Return predictability and the ‘wisdom of crowds’: genetic programming trading algorithms, the marginal trader hypothesis and the hayek hypothesis. J Int Financ Mark Inst Money 37(Supplement C):85–98. https://doi.org/10.1016/j.intfin.2015.02.009
Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep ar methods for forecasting macroeconomic time series. J Econom 135(1):499–526. https://doi.org/10.1016/j.jeconom.2005.07.020
Menkhoff L (2010) The use of technical analysis by fund managers: international evidence. J Bank Financ 34(11):2573–2586. https://doi.org/10.1016/j.jbankfin.2010.04.014
Nelson CR, Kim MJ (1993) Predictable stock returns: the role of small sample bias. J Financ 48(2):641–661
Park CH, Irwin SH (2007) What do we know about the profitability of technical analysis? J Econ Surv 21(4):786–826
Paye BS, Timmermann A (2006) Instability of return prediction models. J Empir Financ 13(3):274–315. https://doi.org/10.1016/j.jempfin.2005.11.001
Pimenta A, Nametala CAL, Guimarães FG, Carrano EG (2017) An automated investing method for stock market based on multiobjective genetic programming. Comput Econ. https://doi.org/10.1007/s10614-017-9665-9
Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Lulu Enterprises, Morrisville
Pong S, Shackleton MB, Taylor SJ, Xu X (2004) Forecasting currency volatility: a comparison of implied volatilities and ar(fi)ma models. J Bank Financ 28(10):2541–2563. https://doi.org/10.1016/j.jbankfin.2003.10.015
Scheinkman JA, LeBaron B (1989) Nonlinear dynamics and stock returns. J Bus 62(3):311–337
Shoba S, Rajavel R (2020) A new genetic algorithm based fusion scheme in monaural casa system to improve the performance of the speech. J Ambient Intell Humaniz Comput 11:433–446
Terasvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Am Stat Assoc 89(425):208–218. https://doi.org/10.1080/01621459.1994.10476462
Timmermann A, Granger CW (2004) Efficient market hypothesis and forecasting. Int J Forecast 20(1):15–27. https://doi.org/10.1016/S0169-2070(03)00012-8
Tong H (1978) On a threshold model
Tong H (1990) Non-linear time series: a dynamical system approach. Oxford University Press, Oxford
Welch I, Goyal A (2007) A comprehensive look at the empirical performance of equity premium prediction. Rev Financ Stud 21(4):1455–1508
Zhao J, Xiang Y, Yang S, Zhang D, Yu P (2019) The control of the movement of intelligent car based on self-adaptive non-linear algorithm. J Ambient Intell Humaniz Comput 10(11):4595–4602
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Ding, S., Cui, T., Xiong, X. et al. Forecasting stock market return with nonlinearity: a genetic programming approach. J Ambient Intell Human Comput 11, 4927–4939 (2020). https://doi.org/10.1007/s12652-020-01762-0
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DOI: https://doi.org/10.1007/s12652-020-01762-0