Optimal Daily scalping trading portfolio based on interval-valued prediction with ANN approach

Document Type : Original Article

Authors

1 PhD Candidate in Industrial Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.

2 Assistant Prof.. Department of Management ,Dehaghan Branch, Islamic Azad University, Dehaghan, Iran

3 Assistant Prof.. Department of Accounting, Sarvestan Branch, Islamic Azad University, Sarvestan, Iran

Abstract


Interval-valued forecasting is related to predicting an interval that is determined by two random variables. In the present study, using the neural networks method, the interval related to the lowest and highest daily prices is predicted and then based on it, a daily scalping trading system is formed, including buying and selling in the forecasted amounts. To reduce the risk of the trading system and increase the number of trading positions, the optimal daily scalping trading portfolio is developed in the mean-variance framework. The sample portfolio includes five shares of the Tehran Stock Exchange in a 190-day period, taking into account trading costs, shows that the average daily return is 0.0028 and the Sharpe ratio is 0.6379, which is better than the Sharpe ratio of individual daily scalping trading of portfolio assets. The daily average of the total index in the research period is 0.001 and the Sharp ratio is 0.0835, which shows that the trading system has a much better performance than the buy and hold strategy.

Keywords

References

  1. Buansing, T., Golan. A., & Ullah,A. (2020), An information-theoretic approach for forecasting interval-valued SP500 daily returns. International Journal of Forecasting. 12(1), 1-14.
  2. Davoodi, S., Rabiei, M. (2022). Interval Forecasting of Stock Price Changes using the Hybrid of Holt’s Exponential Smoothing and Multi-Output Support Vector Regression. Advances in Mathematical Finance and Applications, 7(2), 405-421.
  3. He, A. W., Kwok, J. T., & Wan, A. T. (2017). An empirical model of daily highs and lows of West Texas Intermediate crude oil prices. Energy Economics, 32(6), 1499–1506.
  4. Huang, B., Sun, Y., & Wang, S. (2021). A new two-stage approach with boosting and model averaging for interval-valued crude oil prices forecasting in uncertainty environments. Frontiers in Energy Research, 402.
  5. Lim, C. (2016). Interval-valued data regression using nonparametric additive models. Journal of the Korean Statistical Society, 45(3), 358-370.
  6. Maciel, L., & Ballini, R. (2021). Functional fuzzy rule-based modeling for interval-valued data: An empirical application for exchange rates forecasting. Computational Economics, 57(2), 743-771.
  7. Neto, E. D. A. L., & De Carvalho, F. D. A. (2008). Centre and range method for fitting a linear regression model to symbolic interval data. Computational Statistics & Data Analysis, 52(3), 1500-1515.
  8. Rodrigues, P. M., & Salish, N. (2018). Modeling and forecasting interval time series with threshold models. Advances in Data Analysis and Classification, 9(1), 41–57.
  9. Xiong, T., Bao, Y., & Hu, Z. (2014). Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge-Based Systems, 55, 87-100.
  10. Xiong .T., Chongguang, L., & Yukun, B. (2018). Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model, In Economic Modelling, 60, 11-23.
  11. Yang, Z ., Lin, D., & Zhang, A. (2019). Interval-valued Data Prediction via Regularized Artificial Neural Network. Neurocomputing, 71(14),4102–4032.
  12. Zhang, J., & Zhang, K. (2022). Portfolio selection models based on interval-valued conditional value at risk (ICVaR) and empirical analysis. arXiv preprint arXiv:2201.02987.
Financial Management Perspective
Volume 12, Issue 39
September 2022
Pages 103-120

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