1. Home
  2. Volume 8 - Number 1
  3. High-frequency CSI300 futures trading volume predicting through the neural network
New submission
Issues
Journal Information
Aims and ScopeEditorial BoardEditorial Advisory BoardJournal Policies
Resources
For Readers
For Authors
For Editors
For Reviewers
Publication EthicsCall for Special Issue
ISSN
ISSN 2615-9821
ISSN(Previous) 2588-1396

Volume 8 - Number 1 | March 2024

High-frequency CSI300 futures trading volume predicting through the neural network

Xiaojie Xu, and Yun Zhang

25/03/2024 10 0 0
Like Buy now 20.000 đ
CSI300 futures Trading volume Forecasting Neural network

Abstract:

Purpose
For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

Design/methodology/approach
In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

Findings
The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

Originality/value
The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

References:

  1. Akaike, H. (1974), “A new look at the statistical model identification”, IEEE Transactions on Automatic Control, Vol. 19 No. 6, pp. 716-723.
  2. Al Bataineh, A. and Kaur, D. (2018), “A comparative study of different curve fitting algorithms in artificial neural network using housing dataset”, NAECON 2018-IEEE National Aerospace and Electronics Conference, pp. 174-178, IEEE, doi: 10.1109/NAECON.2018.8556738.
  3. Alvim, L., dos Santos, C.N. and Milidiu, R.L. (2010), “Daily volume forecasting using high frequency predictors”, Proceedings of the 10th IASTED International Conference, p. 248.
  4. Ausloos, M., Zhang, Y. and Dhesi, G. (2020), “Stock index futures trading impact on spot price volatility. the CSI 300 studied with a Tgarch model”, Expert Systems with Applications, Vol. 160 No. 1, 113688, doi: 10.1016/j.eswa.2020.113688.
  5. Baghirli, O. (2015), “Comparison of Lavenberg-Marquardt, scaled conjugate gradient and Bayesian regularization backpropagation algorithms for multistep ahead wind speed forecasting using multilayer perceptron feedforward neural network”.
  6. Blake, A. and Kapetanios, G. (1999), “Forecast combination and leading indicators: combining artificial neural network and autoregressive forecasts”, Manuscript, National Institute of Economic and Social Research.
  7. Bordino, I., Kourtellis, N., Laptev, N. and Billawala, Y. (2014), “Stock trade volume prediction with yahoo finance user browsing behavior”, 2014 IEEE 30th International Conference on Data Engineering, pp. 1168-1173, IEEE, doi: 10.1109/ICDE.2014.6816733.
  8. Brownlees, C.T., Cipollini, F. and Gallo, G.M. (2011), “Intra-daily volume modeling and prediction for algorithmic trading”, Journal of Financial Econometrics, Vol. 9 No. 3, pp. 489-518, doi: 10.1093/jjfinec/nbq024.
  9. Cabrera, J., Wang, T. and Yang, J. (2011), “Linear and nonlinear predictablity of international securitized real estate returns: a reality check”, Journal of Real Estate Research, Vol. 33 No. 4, pp. 565-594, doi: 10.1080/10835547.2011.12091317.
  10. Chacón, H.D., Kesici, E. and Najafirad, P. (2020), “Improving financial time series prediction accuracy using ensemble empirical mode decomposition and recurrent neural networks”, IEEE Access, Vol. 8, pp. 117133-117145, doi: 10.1109/ACCESS.2020.2996981.
  11. Chen, S., Chen, R., Ardell, G. and Lin, B. (2011), “End-of-day stock trading volume prediction with a two-component hierarchical model”, The Journal of Trading, Vol. 6 No. 3, pp. 61-68, doi: 10.3905/jot.2011.6.3.061.
  12. Chen, R., Feng, Y. and Palomar, D. (2016), “Forecasting intraday trading volume: a Kalman filter approach”, available at: SSRN 3101695.
  13. Diebold, F.X. and Mariano, R.S. (2002), “Comparing predictive accuracy”, Journal of Business and Economic Statistics, Vol. 20 No. 3, pp. 134-144, doi: 10.2307/1392185.
  14. Doan, C.D. and Liong, S.y. (2004), “Generalization for multilayer neural network bayesian regularization or early stopping”, Proceedings of Asia Pacific Association of Hydrology and Water Resources 2nd Conference, pp. 5-8.
  15. Foresee, F.D. and Hagan, M.T. (1997), “Gauss-newton approximation to bayesian learning”, Proceedings of International Conference on Neural Networks (ICNN’97), pp. 1930-1935, IEEE, doi: 10.1109/ICNN.1997.614194.
  16. Gharehchopogh, F.S., Bonab, T.H. and Khaze, S.R. (2013), “A linear regression approach to prediction of stock market trading volume: a case study”, International Journal of Managing Value and Supply Chains, Vol. 4 No. 3, p. 25, doi: 10.5121/ijmvsc.2013.4303.
  17. Hagan, M.T. and Menhaj, M.B. (1994), “Training feedforward networks with the marquardt algorithm”, IEEE Transactions on Neural Networks, Vol. 5 No. 6, pp. 989-993, doi: 10.1109/72.329697.
  18. Hansen, P.R. (2005), “A test for superior predictive ability”, Journal of Business and Economic Statistics, Vol. 23 No. 4, pp. 365-380, doi: 10.1198/073500105000000063.
  19. Hansen, P.R., Lunde, A. and Nason, J.M. (2011), “The model confidence set”, Econometrica, Vol. 79 No. 2, pp. 453-497, doi: 10.3982/ECTA5771.
  20. Harvey, D., Leybourne, S. and Newbold, P. (1997), “Testing the equality of prediction mean squared errors”, International Journal of Forecasting, Vol. 13 No. 2, pp. 281-291 No. 2, doi: 10.1016/S0169-2070(96)00719-4.
  21. Hou, Y. and Li, S. (2013), “Price discovery in Chinese stock index futures market: new evidence based on intraday data”, Asia-Pacific Financial Markets, Vol. 20 No. 1, pp. 49-70, doi: 10.1007/s10690-012-9158-8.
  22. Hou, Y. and Li, S. (2014), “The impact of the csi 300 stock index futures: positive feedback trading and autocorrelation of stock returns”, International Review of Economics and Finance, Vol. 33, September 2014, pp. 319-337, doi: 10.1016/j.iref.2014.03.001.
  23. Huang, W., Lai, P.C. and Bessler, D.A. (2018), “On the changing structure among Chinese equity markets: Hong Kong, Shanghai, and Shenzhen”, European Journal of Operational Research, Vol. 264 No. 3, pp. 1020-1032, doi: 10.1016/j.ejor.2017.01.019.
  24. Joseph, K., Wintoki, M.B. and Zhang, Z. (2011), “Forecasting abnormal stock returns and trading volume using investor sentiment: evidence from online search”, International Journal of Forecasting, Vol. 27 No. 4, pp. 1116-1127, doi: 10.1016/j.ijforecast.2010.11.001.
  25. Kaastra, I. and Boyd, M.S. (1995), “Forecasting futures trading volume using neural networks”, The Journal of Futures Markets, Vol. 15 No. 8, p. 953, doi: 10.1002/fut.3990150806.
  26. Kao, Y.S., Chuang, H.L. and Ku, Y.C. (2020), “The empirical linkages among market returns, return volatility, and trading volume: evidence from the S&P 500 VIX futures”, The North American Journal of Economics and Finance, Vol. 54, November 2020, 100871, doi: 10.1016/j.najef.2018.10.019.
  27. Kayri, M. (2016), “Predictive abilities of Bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data”, Mathematical and Computational Applications, Vol. 21 No. 2, p. 20, doi: 10.3390/mca21020020.
  28. Khan, T.A., Alam, M., Shahid, Z. and Mazliham, M. (2019), “Comparative performance analysis of Levenberg-Marquardt, Bayesian regularization and scaled conjugate gradient for the prediction of flash floods”, Journal of Information Communication Technologies and Robotic Applications, Vol. 10 No. 2, pp. 52-58.
  29. Kong, A. and Zhu, H. (2018), “Predicting trend of high frequency csi 300 index using adaptive input selection and machine learning techniques”, Journal of Systems Science and Information, Vol. 6 No. 2, pp. 120-133, doi: 10.21078/JSSI-2018-120-14.
  30. Levenberg, K. (1944), “A method for the solution of certain non-linear problems in least squares”, Quarterly of Applied Mathematics, Vol. 2 No. 2, pp. 164-168.
  31. Liu, M., Choo, W.C., Lee, C.C. and Lee, C.C. (2022), “Trading volume and realized volatility forecasting: evidence from the China stock market”, Journal of Forecasting, Vol. 42 No. 1, doi: 10.1002/for.2897.
  32. Long, W., Lu, Z. and Cui, L. (2019), “Deep learning-based feature engineering for stock price movement prediction”, Knowledge-Based Systems, Vol. 164 No. 15, pp. 163-173, doi: 10.1016/j.knosys.2018.10.034.
  33. Lu, T. and Li, Z. (2017), “Forecasting csi 300 index using a hybrid functional link artificial neural network and particle swarm optimization with improved wavelet mutation”, 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA), pp. 241-246, IEEE, doi: 10.1109/ICCNEA.2017.55.
  34. Lu, H., Ma, X., Huang, K. and Azimi, M. (2020a), “Carbon trading volume and price forecasting in China using multiple machine learning models”, Journal of Cleaner Production, Vol. 249 No. 10, 119386, doi: 10.1016/j.jclepro.2019.119386.
  35. Lu, W., Li, J., Li, Y., Sun, A. and Wang, J. (2020b), “A CNN-LSTM-based model to forecast stock prices”, Complexity, Vol. 2020, 6622927, doi: 10.1155/2020/6622927.
  36. Ma, S. and Li, P. (2021), “Predicting daily trading volume via various hidden states”, arXiv preprint arXiv:2107.07678.
  37. MacKay, D.J. (1992), “Bayesian interpolation”, Neural Computation, Vol. 4 No. 3, pp. 415-447, doi: 10.1162/neco.1992.4.3.415.
  38. Marquardt, D.W. (1963), “An algorithm for least-squares estimation of nonlinear parameters”, Journal of the Society for Industrial and Applied Mathematics, Vol. 11 No. 2, pp. 431-441, doi: 10.1137/0111030.
  39. Møller, M.F. (1993), “A scaled conjugate gradient algorithm for fast supervised learning”, Neural Networks, Vol. 6 No. 4, pp. 525-533, doi: 10.1016/S0893-6080(05)80056-5.
  40. Nasir, M.A., Huynh, T.L.D., Nguyen, S.P. and Duong, D. (2019), “Forecasting cryptocurrency returns and volume using search engines”, Financial Innovation, Vol. 5 No. 2, pp. 1-13, doi: 10.1186/s40854-018-0119-8.
  41. Ng, L. and Wu, F. (2007), “The trading behavior of institutions and individuals in Chinese equity markets”, Journal of Banking and Finance, Vol. 31 No. 9, pp. 2695-2710, doi: 10.1016/j.jbankfin.2006.10.029.
  42. Ning, S. (2020), “Short-term prediction of the csi 300 based on the bp neural network model”, Journal of Physics: Conference Series, 012054, IOP Publishing, doi: 10.1088/1742-6596/1437/1/012054.
  43. Oliveira, N., Cortez, P. and Areal, N. (2017), “The impact of microblogging data for stock market prediction: using twitter to predict returns, volatility, trading volume and survey sentiment indices”, Expert Systems with Applications, Vol. 73 No. 1, pp. 125-144, doi: 10.1016/j.eswa.2016.12.036.
  44. Paluszek, M. and Thomas, S. (2020), Practical MATLAB Deep Learning: A Project-Based Approach, Apress, New York.
  45. Satish, V., Saxena, A. and Palmer, M. (2014), “Predicting intraday trading volume and volume percentages”, The Journal of Trading, Vol. 9 No. 3, pp. 15-25, doi: 10.3905/jot.2014.9.3.015.
  46. Schwarz, G. (1978), “Estimating the dimension of a model”, The Annals of Statistics, Vol. 6 No. 2, pp. 461-464, doi: 10.1214/aos/1176344136.
  47. Selvamuthu, D., Kumar, V. and Mishra, A. (2019), “Indian stock market prediction using artificial neural networks on tick data”, Financial Innovation, Vol. 5 No. 16, p. 16, doi: 10.1186/s40854-019-0131-7.
  48. Shen, M.L., Lee, C.F., Liu, H.H., Chang, P.Y. and Yang, C.H. (2021), “Effective multinational trade forecasting using lstm recurrent neural network”, Expert Systems with Applications, Vol. 182 No. 15, 115199, doi: 10.1016/j.eswa.2021.115199.
  49. Sohn, S. and Zhang, X. (2017), “Could the extended trading of csi 300 index futures facilitate its role of price discovery?”, Journal of Futures Markets, Vol. 37 No. 7, pp. 717-740, doi: 10.1002/fut.21804.
  50. Stock, J.H. and Watson, M.W. (1998), “A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series”. doi: 10.3386/w6607.
  51. Sun, B., Guo, H., Karimi, H.R., Ge, Y. and Xiong, S. (2015), “Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series”, Neurocomputing, Vol. 151, Part 3, pp. 1528-1536, doi: 10.1016/j.neucom.2014.09.018.
  52. Susheng, W. and Zhen, Y. (2014), “The dynamic relationship between volatility, volume and open interest in csi 300 futures market”, WSEAS Transactions on Systems, Vol. 13, pp. 1-11.
  53. Wang, C. and Chen, R. (2013), “Forecasting csi 300 volatility: the role of persistence, asymmetry, and distributional assumption in Garch models”, 2013 Sixth International Conference on Business Intelligence and Financial Engineering, IEEE, pp. 355-358, doi: 10.1109/BIFE.2013.74.
  54. Wang, D.H., Suo, Y.Y., Yu, X.W. and Lei, M. (2013), “Price–volume cross-correlation analysis of csi300 index futures”, Physica A: Statistical Mechanics and Its Applications, Vol. 392 No. 5, pp. 1172-1179, doi: 10.1016/j.physa.2012.11.031.
  55. Wang, S., Li, G. and Wang, J. (2019), “Dynamic interactions between intraday returns and trading volume on the CSI 300 index futures: an application of an SVAR model”, Mathematical Problems in Engineering, Vol. 2019, 8676531, doi: 10.1155/2019/8676531.
  56. Wang, T. and Yang, J. (2010), “Nonlinearity and intraday efficiency tests on energy futures markets”, Energy Economics, Vol. 32 No. 2, pp. 496-503, doi: 10.1016/j.eneco.2009.08.001.
  57. Wang, J., Hou, R., Wang, C. and Shen, L. (2016), “Improved v-support vector regression model based on variable selection and brain storm optimization for stock price forecasting”, Applied Soft Computing, Vol. 49, December 2016, pp. 164-178, doi: 10.1016/j.asoc.2016.07.024.
  58. Xie, M., Zhang, M., Liu, X., Ma, G. and He, P. (2020), “Decomposition model framework of trading volume of cascade hydropower stations under the linking mode of medium-long term and spot market”, Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control, pp. 897-905, Springer, doi: 10.1007/978-981-13-9779-0_73.
  59. Xu, X. (2017), “The rolling causal structure between the Chinese stock index and futures”, Financial Markets and Portfolio Management, Vol. 31 No. 4, pp. 491-509, doi: 10.1007/s11408-017-0299-7.
  60. Xu, X. (2018), “Intraday price information flows between the csi300 and futures market: an application of wavelet analysis”, Empirical Economics, Vol. 54 No. 3, pp. 1267-1295, doi: 10.1007/s00181-017-1245-2.
  61. Xu, X. (2019a), “Contemporaneous and granger causality among us corn cash and futures prices”, European Review of Agricultural Economics, Vol. 46 No. 4, pp. 663-695, doi: 10.1093/erae/jby036.
  62. Xu, X. (2019b), “Contemporaneous causal orderings of csi300 and futures prices through directed acyclic graphs”, Economics Bulletin, Vol. 39 No. 3, pp. 2052-2077.
  63. Xu, X. (2020), “Corn cash price forecasting”, American Journal of Agricultural Economics, Vol. 102 No. 4, pp. 1297-1320, doi: 10.1002/ajae.12041.
  64. Xu, X. and Zhang, Y. (2021a), “Corn cash price forecasting with neural networks”, Computers and Electronics in Agriculture, Vol. 184, May 2021, 106120, doi: 10.1016/j.compag.2021.106120.
  65. Xu, X. and Zhang, Y. (2021b), “House price forecasting with neural networks”, Intelligent Systems with Applications, Vol. 12, November 2021, 200052, doi: 10.1016/j.iswa.2021.200052.
  66. Xu, X. and Zhang, Y. (2021c), “Individual time series and composite forecasting of the Chinese stock index”, Machine Learning with Applications, Vol. 5 No. 15, 100035, doi: 10.1016/j.mlwa.2021.100035.
  67. Xu, X. and Zhang, Y. (2021d), “Network analysis of corn cash price comovements”, Machine Learning with Applications, Vol. 6 No. 15, 100140, doi: 10.1016/j.mlwa.2021.100140.
  68. Xu, X. and Zhang, Y. (2022a), “Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat”, Intelligent Systems in Accounting, Finance and Management, Vol. 29 No. 3, pp. 169-181, doi: 10.1002/isaf.1519.
  69. Xu, X. and Zhang, Y. (2022b), “Neural network predictions of the high-frequency CSI300 first distant futures trading volume”, Financial Markets and Portfolio Management. doi: 10.1007/s11408-022-00421-y.
  70. Xu, X. and Zhang, Y. (2022c), “Residential housing price index forecasting via neural networks”, Neural Computing and Applications, Vol. 34 No. 17, pp. 14763-14776, doi: 10.1007/s00521-022-07309-y.
  71. Xu, X. and Zhang, Y. (2022d), “Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products”, Mineral Economics. doi: 10.1007/s13563-022-00357-9.
  72. Xu, X. and Zhang, Y. (2023a), “A high-frequency trading volume prediction model using neural networks”, Decision Analytics Journal, Vol. 7, June 2023, 100235, doi: 10.1016/j.dajour.2023.100235.
  73. Xu, X. and Zhang, Y. (2023b), “Regional steel price index forecasts with neural networks: evidence from East, South, North, Central South, Northeast, Southwest, and Northwest China”, The Journal of Supercomputing. doi: 10.1007/s11227-023-05207-1.
  74. Yan, Y. and Hongbing, O. (2018), “Dynamic probability of informed trading and price movements: evidence from the csi300 index futures market”, Applied Economics Letters, Vol. 25 No. 14, pp. 998-1003, doi: 10.1080/13504851.2017.1391990.
  75. Yan, Y. and Yang, D. (2021), “A stock trend forecast algorithm based on deep neural networks”, Scientific Programming, Vol. 2021 No. 4, 7510641, doi: 10.1155/2021/7510641.
  76. Yang, L. and Cheng, X. (2015), “Predictive analytics on CSI 300 index based on ARIMA and RBF-ANN combined model”, Journal of Mathematical Finance, Vol. 5 No. 4, p. 393, doi: 10.4236/jmf.2015.54033.
  77. Yang, J., Cabrera, J. and Wang, T. (2010), “Nonlinearity, data-snooping, and stock index ETF return predictability”, European Journal of Operational Research, Vol. 200 No. 2, pp. 498-507, doi: 10.1016/j.ejor.2009.01.009.
  78. Yang, J., Su, X. and Kolari, J.W. (2008), “Do euro exchange rates follow a martingale? Some out-of-sample evidence”, Journal of Banking and Finance, Vol. 32 No. 5, pp. 729-740, doi: 10.1016/j.jbankfin.2007.05.009.
  79. Yang, J., Yang, Z. and Zhou, Y. (2012), “Intraday price discovery and volatility transmission in stock index and stock index futures markets: evidence from China”, Journal of Futures Markets, Vol. 32 No. 2, pp. 99-121, doi: 10.1002/fut.20514.
  80. Yao, S., Luo, L. and Peng, H. (2018), “High-frequency stock trend forecast using LSTM model”, 2018 13th International Conference on Computer Science and Education (ICCSE), pp. 1-4, IEEE, doi: 10.1109/ICCSE.2018.8468703.
  81. Ye, X., Yan, R. and Li, H. (2014), “Forecasting trading volume in the Chinese stock market based on the dynamic VWAP”, Studies in Nonlinear Dynamics and Econometrics, Vol. 18 No. 2, pp. 125-144, doi: 10.1515/snde-2013-0023.
  82. Zhang, Z. (2020), “Bp neural network trade volume prediction and enterprises HRM optimization model based on ES-LM training”, Journal of Intelligent and Fuzzy Systems, Vol. 39 No. 5, pp. 5883-5894, doi: 10.3233/JIFS-219218.
  83. Zhang, C. and Pan, H. (2014), “Experimenting with 3 different input-output mapping structures of ANN models for predicting CSI 300 index”, Management Science and Engineering, Vol. 8 No. 1, pp. 22-34, doi: 10.3968/j.mse.1913035X20140801.4274.
  84. Zhang, Y.T. and Sun, B. (2017), “Analysis of CSI 300 stock index futures price trend based on ARIMA model”, DEStech Transactions on Social Science, Education and Human Science. doi: 10.12783/dtssehs/seme2017/18022.
  85. Zhao, L., Li, W., Bao, R., Harimoto, K. and Sun, X. (2021), “Long-term, short-term and sudden event: trading volume movement prediction with graph-based multi-view modeling”, arXiv preprint arXiv:2108.11318.
  86. Zhou, W., Pan, J. and Wu, X. (2019a), “Forecasting the realized volatility of csi 300”, Physica A: Statistical Mechanics and Its Applications, Vol. 531 No. 1, 121799, doi: 10.1016/j.physa.2019.121799.
  87. Zhou, Y.L., Han, R.J., Xu, Q., Jiang, Q.J. and Zhang, W.K. (2019b), “Long short-term memory networks for CSI300 volatility prediction with baidu search volume”, Concurrency and Computation: Practice and Experience, Vol. 31 No. 10, e4721, doi: 10.1002/cpe.4721.

JEL classification: C45,C53,G12,G15,G17

Related articles