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Financial Markets and Portfolio Management

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04-11-2022

Neural network predictions of the high-frequency CSI300 first distant futures trading volume

Authors: Xiaojie Xu, Yun Zhang

Published in: Financial Markets and Portfolio Management | Issue 2/2023

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Abstract

Predictions of financial index trading volumes represent an essential issue to market participants and policy makers. We investigate this problem for the high-frequency one-minute CSI300 first distant futures trading volume from the launch of the futures index to two years after all its constituent stocks becoming shortable, a period with generally continuously expanding trading magnitude. We utilize the neural network to model this complex and irregular trading volume series and attempt to answer the following questions: can the trading volume be predicted by its own lags, and if so, how far to predict and how well; can the nearby futures or spot trading volume series help predictions, and if so, by how much; how complex does the model need to be and how robust can it be? We find that the trading volume could be predicted using one to thirty minutes ahead data through a relatively low complex model with five hidden neurons based on its own lags, resulting in generally accurate and stable performance based on the relative root mean square error. Incorporating the nearby futures or spot trading volume series generally does not help improve predictions. Results here should be of interest and use to design trading platforms, monitor system risk, and form index price predictions.

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Al Bataineh, A., Kaur, D.: A comparative study of different curve fitting algorithms in artificial neural network using housing dataset. In: NAECON 2018-IEEE National Aerospace and Electronics Conference, IEEE, pp. 174–178 (2018). https://doi.org/10.1109/NAECON.2018.8556738

Alvim, L., dos Santos, C.N., Milidiu, R.L.: Daily volume forecasting using high frequency predictors. In: Proceedings of the 10th IASTED International Conference, vol. 674, pp. 248 (2010)

Ausloos, M., Zhang, Y., Dhesi, G.: Stock index futures trading impact on spot price volatility. The CSI 300 studied with a TGARCH model. Expert Syst. Appl. (2020). https://doi.org/10.1016/j.eswa.2020.113688CrossRef

Baghirli, O.: Comparison of Lavenberg-marquardt, scaled conjugate gradient and Bayesian regularization backpropagation algorithms for multistep ahead wind speed forecasting using multilayer perceptron feedforward neural network (2015)

Bessler, D.A., Wang, Z.: D-separation, forecasting, and economic science: a conjecture. Theory Decis. 73, 295–314 (2012). https://doi.org/10.1007/s11238-012-9305-8CrossRef

Bordino, I., Kourtellis, N., Laptev, N., Billawala, Y.: Stock trade volume prediction with yahoo finance user browsing behavior. In: 2014 IEEE 30th International Conference on Data Engineering, IEEE, pp. 1168–1173 (2014). https://doi.org/10.1109/ICDE.2014.6816733

Brownlees, C.T., Cipollini, F., Gallo, G.M.: Intra-daily volume modeling and prediction for algorithmic trading. J. Financ. Econom. 9, 489–518 (2011). https://doi.org/10.1093/jjfinec/nbq024CrossRef

Cabrera, J., Wang, T., Yang, J.: Linear and nonlinear predictablity of international securitized real estate returns: a reality check. J. Real Estate Res. 33, 565–594 (2011). https://doi.org/10.1080/10835547.2011.12091317CrossRef

Chen, R., Feng, Y., Palomar, D.: Forecasting intraday trading volume: a kalman filter approach, Available at SSRN 3101695 (2016)

Chen, S., Chen, R., Ardell, G., Lin, B.: End-of-day stock trading volume prediction with a two-component hierarchical model. J. Trading 6, 61–68 (2011). https://doi.org/10.3905/jot.2011.6.3.061CrossRef

Chiang, T.C., Qiao, Z., Wong, W.-K.: New evidence on the relation between return volatility and trading volume. J. Forecast. 29, 502–515 (2010). https://doi.org/10.1002/for.1151CrossRef

Doan, C.D., Liong, S.-Y.: Generalization for multilayer neural network bayesian regularization or early stopping. In: Proceedings of Asia Pacific Association of Hydrology and Water Resources 2nd Conference, pp. 5–8 (2004)

Fujihara, R.A., Mougoué, M.: An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets. J. Futures Mark. Futures Options Other Deriv. Prod. 17, 385–416 (1997). https://doi.org/10.1002/(SICI)1096-9934(199706)17:4<385::AID-FUT2>3.0.CO;2-DCrossRef

Gharehchopogh, F.S., Bonab, T.H., Khaze, S.R.: A linear regression approach to prediction of stock market trading volume: a case study. Int. J. Manag. Value Supply Chains 4, 25 (2013). https://doi.org/10.5121/ijmvsc.2013.4303CrossRef

Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5, 989–993 (1994). https://doi.org/10.1109/72.329697CrossRef

Hiemstra, C., Jones, J.D.: Testing for linear and nonlinear granger causality in the stock price-volume relation. J. Finance 49, 1639–1664 (1994). https://doi.org/10.1111/j.1540-6261.1994.tb04776.xCrossRef

Hou, Y., Li, S.: Price discovery in Chinese stock index futures market: new evidence based on intraday data. Asia-Pac. Financ. Mark. 20, 49–70 (2013). https://doi.org/10.1007/s10690-012-9158-8CrossRef

Hou, Y., Li, S.: The impact of the CSI 300 stock index futures: positive feedback trading and autocorrelation of stock returns. Int. Rev. Econ. Finance 33, 319–337 (2014). https://doi.org/10.1016/j.iref.2014.03.001CrossRef

Huang, W., Lai, P.-C., Bessler, D.A.: On the changing structure among Chinese equity markets: Hong Kong, Shanghai, and Shenzhen. Eur. J. Oper. Res. 264, 1020–1032 (2018). https://doi.org/10.1016/j.ejor.2017.01.019CrossRef

Joseph, K., Wintoki, M.B., Zhang, Z.: Forecasting abnormal stock returns and trading volume using investor sentiment: evidence from online search. Int. J. Forecast. 27, 1116–1127 (2011). https://doi.org/10.1016/j.ijforecast.2010.11.001CrossRef

Kaastra, I., Boyd, M.S.: Forecasting futures trading volume using neural networks. J. Futures Mark. 15, 953 (1995). https://doi.org/10.1002/fut.3990150806CrossRef

Kano, Y., Shimizu, S. et al.: Causal inference using nonnormality. In: Proceedings of the International Symposium on Science of Modeling, The 30th Anniversary of the Information Criterion, pp. 261–270 (2003)

Kayri, M.: Predictive abilities of Bayesian regularization and Levenberg-marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math. Comput. Appl. 21, 20 (2016). https://doi.org/10.3390/mca21020020CrossRef

Khan, T.A., Alam, M., Shahid, Z., Mazliham, M.: Comparative performance analysis of levenberg-marquardt, bayesian regularization and scaled conjugate gradient for the prediction of flash floods. J. Inf. Commun. Technol. Robot. Appl. 10, 52–58 (2019)

Kong, A.: Predicting trend of high frequency CSI 300 index using adaptive input selection and machine learning techniques. J. Syst. Sci. Inf. 6, 120–133 (2018). https://doi.org/10.21078/JSSI-2018-120-14CrossRef

Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)CrossRef

Long, W., Lu, Z., Cui, L.: Deep learning-based feature engineering for stock price movement prediction. Knowl.-Based Syst. 164, 163–173 (2019). https://doi.org/10.1016/j.knosys.2018.10.034CrossRef

Lu, T., Li, Z.: Forecasting csi 300 index using a hybrid functional link artificial neural network and particle swarm optimization with improved wavelet mutation. In: 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA), IEEE, pp. 241–246 (2017). https://doi.org/10.1109/ICCNEA.2017.55

Ma, S., Li, P.: Predicting daily trading volume via various hidden states, arXiv preprint arXiv:2107.07678 (2021)

Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963). https://doi.org/10.1137/0111030CrossRef

Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6, 525–533 (1993). https://doi.org/10.1016/S0893-6080(05)80056-5CrossRef

Nasir, M.A., Huynh, T.L.D., Nguyen, S.P., Duong, D.: Forecasting cryptocurrency returns and volume using search engines. Financ. Innov. 5, 1–13 (2019). https://doi.org/10.1186/s40854-018-0119-8CrossRef

Ng, L., Wu, F.: The trading behavior of institutions and individuals in Chinese equity markets. J. Bank. Finance 31, 2695–2710 (2007). https://doi.org/10.1016/j.jbankfin.2006.10.029CrossRef

Ning, S.: Short-term prediction of the CSI300 based on the bp neural network model. In: Journal of Physics: Conference Series. IOP Publishing, vol. 1437, p. 012054 (2020). https://doi.org/10.1088/1742-6596/1437/1/012054

Oliveira, N., Cortez, P., Areal, N.: The impact of microblogging data for stock market prediction: using twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst. Appl. 73, 125–144 (2017). https://doi.org/10.1016/j.eswa.2016.12.036CrossRef

Paluszek, M., Thomas, S.: Practical MATLAB Deep Learning: A Project-Based Approach. Apress (2020)

Rashid, A.: Stock prices and trading volume: an assessment for linear and nonlinear granger causality. J. Asian Econ. 18, 595–612 (2007). https://doi.org/10.1016/j.asieco.2007.03.003CrossRef

Satish, V., Saxena, A., Palmer, M.: Predicting intraday trading volume and volume percentages. J. Trading 9, 15–25 (2014). https://doi.org/10.3905/jot.2014.9.3.015CrossRef

Selvamuthu, D., Kumar, V., Mishra, A.: Indian stock market prediction using artificial neural networks on tick data. Financ. Innov. 5, 16 (2019). https://doi.org/10.1186/s40854-019-0131-7CrossRef

Shimizu, S., Kano, Y.: Use of non-normality in structural equation modeling: application to direction of causation. J. Stat. Plan. Inference 138, 3483–3491 (2008). https://doi.org/10.1016/j.jspi.2006.01.017CrossRef

Shimizu, S., Hoyer, P.O., Hyvärinen, A., Kerminen, A., Jordan, M.: A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7, 2003–2030 (2006)

Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P.O., Bollen, K.: Directlingam: a direct method for learning a linear non-Gaussian structural equation model. J. Mach. Learn. Res. 12, 1225–1248 (2011)

Silvapulle, P., Choi, J.-S.: Testing for linear and nonlinear granger causality in the stock price-volume relation: Korean evidence. Q. Rev. Econ. Finance 39, 59–76 (1999). https://doi.org/10.1016/S1062-9769(99)80004-0CrossRef

Sohn, S., Zhang, X.: Could the extended trading of CSI 300 index futures facilitate its role of price discovery? J. Futures Mark. 37, 717–740 (2017). https://doi.org/10.1002/fut.21804CrossRef

Sun, B., Guo, H., Karimi, H.R., Ge, Y., Xiong, S.: Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing 151, 1528–1536 (2015). https://doi.org/10.1016/j.neucom.2014.09.018CrossRef

Susheng, W., Zhen, Y.: The dynamic relationship between volatility, volume and open interest in CSI 300 futures market. WSEAS Trans. Syst. 13, 1–11 (2014)

Wang, C., Chen, R.: Forecasting csi 300 volatility: the role of persistence, asymmetry, and distributional assumption in garch models. In: 2013 Sixth International Conference on Business Intelligence and Financial Engineering, IEEE, pp. 355–358 (2013). https://doi.org/10.1109/BIFE.2013.74

Wang, T., Yang, J.: Nonlinearity and intraday efficiency tests on energy futures markets. Energy Econ. 32, 496–503 (2010). https://doi.org/10.1016/j.eneco.2009.08.001CrossRef

Wang, D.-H., Suo, Y.-Y., Yu, X.-W., Lei, M.: Price-volume cross-correlation analysis of csi300 index futures. Phys. A Stat. Mech. Appl. 392, 1172–1179 (2013). https://doi.org/10.1016/j.physa.2012.11.031CrossRef

Wang, J., Hou, R., Wang, C., Shen, L.: Improved v-support vector regression model based on variable selection and brain storm optimization for stock price forecasting. Appl. Soft Comput. 49, 164–178 (2016). https://doi.org/10.1016/j.asoc.2016.07.024CrossRef

Wang, S., Li, G., Wang, J.: Dynamic interactions between intraday returns and trading volume on the CSI 300 index futures: an application of an svar model. Math. Probl. Eng. (2019). https://doi.org/10.1155/2019/8676531CrossRef

Xu, X., Thurman, W.N.: Using local information to improve short-run corn cash price forecasts (2015a) NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management. https://doi.org/10.22004/ag.econ.285845

Xu, X., Zhang, Y.: Forecasting the total market value of a shares traded in the shenzhen stock exchange via the neural network, Econ. Bull. (2022)

Xu, X., Zhang, Y.: High-frequency csi300 futures trading volume predicting through the neural network (2021) working paper

Xu, X.: Causality and price discovery in US corn markets: an application of error correction modeling and directed acyclic graphs, Technical Report, (2014b). https://doi.org/10.22004/ag.econ.169806

Xu, X.: Cointegration and price discovery in us corn markets. In: Agricultural and Resource Economics Seminar Series, North Carolina State University (2014c). https://doi.org/10.13140/RG.2.2.30153.49768

Xu, X.: Price discovery in us corn cash and futures markets: the role of cash market selection. In: Selected Paper Prepared for Presentation at the Agricultural & Applied Economics Association’s 2014 AAEA Annual Meeting, Minneapolis, (2014a). https://doi.org/10.22004/ag.econ.169809

Xu, X.: Cointegration among regional corn cash prices. Econ. Bull. 35, 2581–2594 (2015)

Xu, X.: The rolling causal structure between the chinese stock index and futures. Financ. Mark. Portf. Manag. 31, 491–509 (2017). https://doi.org/10.1007/s11408-017-0299-7CrossRef

Xu, X.: Short-run price forecast performance of individual and composite models for 496 corn cash markets. J. Appl. Stat. 44, 2593–2620 (2017). https://doi.org/10.1080/02664763.2016.1259399CrossRef

Xu, X.: Contemporaneous causal orderings of us corn cash prices through directed acyclic graphs. Empir. Econ. 52, 731–758 (2017). https://doi.org/10.1007/s00181-016-1094-4CrossRef

Xu, X.: Causal structure among us corn futures and regional cash prices in the time and frequency domain. J. Appl. Stat. 45, 2455–2480 (2018). https://doi.org/10.1080/02664763.2017.1423044CrossRef

Xu, X.: Cointegration and price discovery in us corn cash and futures markets. Empir. Econ. 55, 1889–1923 (2018). https://doi.org/10.1007/s00181-017-1322-6CrossRef

Xu, X.: Intraday price information flows between the CSI300 and futures market: an application of wavelet analysis. Empir. Econ. 54, 1267–1295 (2018). https://doi.org/10.1007/s00181-017-1245-2CrossRef

Xu, X.: Linear and nonlinear causality between corn cash and futures prices. J. Agric. Food Ind. Organ. 16, 20160006 (2018). https://doi.org/10.1515/jafio-2016-0006CrossRef

Xu, X.: Using local information to improve short-run corn price forecasts. J. Agric. Food Ind. Organ. (2018). https://doi.org/10.1515/jafio-2017-0018CrossRef

Xu, X.: Contemporaneous and granger causality among us corn cash and futures prices. Eur. Rev. Agric. Econ. 46, 663–695 (2019). https://doi.org/10.1093/erae/jby036CrossRef

Xu, X.: Contemporaneous causal orderings of CSI300 and futures prices through directed acyclic graphs. Econ. Bull. 39, 2052–2077 (2019)

Xu, X.: Price dynamics in corn cash and futures markets: cointegration, causality, and forecasting through a rolling window approach. Financ. Mark. Portf. Manag. 33, 155–181 (2019). https://doi.org/10.1007/s11408-019-00330-7CrossRef

Xu, X.: Corn cash price forecasting. Am. J. Agric. Econ. 102, 1297–1320 (2020). https://doi.org/10.1002/ajae.12041CrossRef

Xu, X., Thurman, W.: Forecasting local grain prices: an evaluation of composite models in 500 corn cash markets. Tech. Rep. (2015). https://doi.org/10.22004/ag.econ.205332CrossRef

Xu, X., Zhang, Y.: Individual time series and composite forecasting of the Chinese stock index. Mach. Learn. Appl. 5, 100035 (2021). https://doi.org/10.1016/j.mlwa.2021.100035CrossRef

Xu, X., Zhang, Y.: House price forecasting with neural networks. Intell. Syst. Appl. 12, 200052 (2021). https://doi.org/10.1016/j.iswa.2021.200052CrossRef

Xu, X., Zhang, Y.: Rent index forecasting through neural networks. J. Econ. Stud. (2021). https://doi.org/10.1108/JES-06-2021-0316CrossRef

Xu, X., Zhang, Y.: Network analysis of corn cash price comovements. Mach. Learn. Appl. 6, 100140 (2021). https://doi.org/10.1016/j.mlwa.2021.100140CrossRef

Xu, X., Zhang, Y.: Corn cash price forecasting with neural networks. Comput. Electron. Agric. 184, 106120 (2021). https://doi.org/10.1016/j.compag.2021.106120CrossRef

Xu, X., Zhang, Y.: Second-hand house price index forecasting with neural networks. J. Prop. Res. 39, 215–236 (2022). https://doi.org/10.1080/09599916.2021.1996446CrossRef

Xu, X., Zhang, Y.: Thermal coal price forecasting via the neural network. Intell. Syst. Appl. 14, 200084 (2022). https://doi.org/10.1016/j.iswa.2022.200084CrossRef

Xu, X., Zhang, Y.: Coking coal futures price index forecasting with the neural network. Miner. Econ. (2022). https://doi.org/10.1007/s13563-022-00311-9CrossRef

Xu, X., Zhang, Y.: Residential housing price index forecasting via neural networks. Neural Comput. Appl. 34, 14763–14776 (2022). https://doi.org/10.1007/s00521-022-07309-yCrossRef

Xu, X., Zhang, Y.: Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat. Intell. Syst. Account. Finance Manag. 29, 169–181 (2022). https://doi.org/10.1002/isaf.1519CrossRef

Xu, X., Zhang, Y.: Network analysis of housing price comovements of a hundred Chinese cities. Natl. Inst. Econ. Rev. (2022). https://doi.org/10.1017/nie.2021.34CrossRef

Xu, X., Zhang, Y.: Contemporaneous causality among one hundred Chinese cities. Empir. Econ. 63, 2315–2329 (2022). https://doi.org/10.1007/s00181-021-02190-5CrossRef

Xu, X., Zhang, Y.: Cointegration between housing prices: evidence from one hundred chinese cities. J. Prop. Res. (2022). https://doi.org/10.1080/09599916.2022.2114926CrossRef

Xu, X., Zhang, Y.: Network analysis of price comovements among corn futures and cash prices. J. Agric. Food Ind. Organ. (2022). https://doi.org/10.1515/jafio-2022-0009CrossRef

Xu, X., Zhang, Y.: Retail property price index forecasting through neural networks. J. Real Estate Portf. Manag. (2022). https://doi.org/10.1080/10835547.2022.2110668CrossRef

Xu, X., Zhang, Y.: Canola and soybean oil price forecasts via neural networks. Adv. Comput. Intell. 2, 32 (2022). https://doi.org/10.1007/s43674-022-00045-9CrossRef

Xu, X., Zhang, Y.: Soybean and soybean oil price forecasting through the nonlinear autoregressive neural network (narnn) and narnn with exogenous inputs (narnn-x). Intell. Syst. Appl. 13, 200061 (2022). https://doi.org/10.1016/j.iswa.2022.200061CrossRef

Xu, X., Zhang, Y.: Contemporaneous causality among residential housing prices of ten major Chinese cities. Int. J. Hous. Mark. Anal. (2022). https://doi.org/10.1108/IJHMA-03-2022-0039CrossRef

Xu, X., Zhang, Y.: House price information flows among some major Chinese cities: linear and nonlinear causality in time and frequency domains. Int. J. Hous. Mark. Anal. (2022). https://doi.org/10.1108/IJHMA-07-2022-0098CrossRef

Yan, Y., Hongbing, O.: Dynamic probability of informed trading and price movements: evidence from the CSI300 index futures market. Appl. Econ. Lett. 25, 998–1003 (2018). https://doi.org/10.1080/13504851.2017.1391990CrossRef

Yang, L., Cheng, X.: Predictive analytics on csi 300 index based on arima and rbf-ann combined model. J. Math. Finance 5, 393 (2015). https://doi.org/10.4236/jmf.2015.54033CrossRef

Yang, J., Su, X., Kolari, J.W.: Do euro exchange rates follow a martingale? some out-of-sample evidence. J. Bank. Finance 32, 729–740 (2008). https://doi.org/10.1016/j.jbankfin.2007.05.009CrossRef

Yang, J., Cabrera, J., Wang, T.: Nonlinearity, data-snooping, and stock index ETF return predictability. Eur. J. Op. Res. 200, 498–507 (2010). https://doi.org/10.1016/j.ejor.2009.01.009CrossRef

Yang, J., Yang, Z., Zhou, Y.: Intraday price discovery and volatility transmission in stock index and stock index futures markets: evidence from china. J. Futures Mark. 32, 99–121 (2012). https://doi.org/10.1002/fut.20514CrossRef

Yao, S., Luo, L., Peng, H.: High-frequency stock trend forecast using lstm model. In: 2018 13th International Conference on Computer Science & Education (ICCSE), IEEE, pp. 1–4 (2018). https://doi.org/10.1109/ICCSE.2018.8468703

Ye, X., Yan, R., Li, H.: Forecasting trading volume in the chinese stock market based on the dynamic vwap. Stud. Nonlinear Dyn. Econom. 18, 125–144 (2014). https://doi.org/10.1515/snde-2013-0023CrossRef

Zhang, C., Pan, H.: Experimenting with 3 different input-output mapping structures of ANN models for predicting CSI 300 index. Manag. Sci. Eng. 8, 22–34 (2014). https://doi.org/10.3968/j.mse.1913035X20140801.4274CrossRef

Zhang, Y.-T., Sun, B.: Analysis of CSI 300 stock index futures price trend based on arima model. DEStech Trans. Soc. Sci. Educ. Hum. Sci. (2017). https://doi.org/10.12783/dtssehs/seme2017/18022CrossRef

Zhou, W., Pan, J., Wu, X.: Forecasting the realized volatility of CSI 300. Phys. A Stat. Mech. Appl. 531, 121799 (2019). https://doi.org/10.1016/j.physa.2019.121799CrossRef

Title: Neural network predictions of the high-frequency CSI300 first distant futures trading volume
Authors: Xiaojie Xu
Yun Zhang
Publication date: 04-11-2022
Publisher: Springer US
Published in: Financial Markets and Portfolio Management / Issue 2/2023
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-022-00421-y

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