This document has not been cited yet.

1. Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205–213.
   Paper not yet in RePEc: Add citation now
   

2. Alhashel, B. S., Almudhaf, F. W., & Hansz, J. A. (2018). Can technical analysis generate superior returns in securitized property markets? Evidence from east Asia markets. Pacific-Basin Finance Journal, 47, 92–108.

3. Appel, G. (1979). The moving average convergence divergence trading method (150 Great Neck Rd, Great Neck, NY 11021: Signalert Corp).
   Paper not yet in RePEc: Add citation now
   
4. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Universal language model fine-tuning for text classification, pp. 30–44.
   Paper not yet in RePEc: Add citation now
   
5. Barbaglia, L., Manzan, S., & Tosetti, E. (2021). Forecasting loan default in europe with machine learning. Journal of Financial Econometrics pp. 1–28.
   Paper not yet in RePEc: Add citation now
   

6. Bucci, A. (2020). Realized volatility forecasting with neural networks. Journal of Financial Econometrics, 18(3), 502–531.

7. Cho, K., Van Merriënboer, B., & Bahdanau, D. (2014). On the properties of neural machine translation: Encoder-decoder approaches. Proceedings of SSST 2014—8th workshop on syntax, semantics and structure in statistical translation, pp. 103–111.
   Paper not yet in RePEc: Add citation now
   
8. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 .
   Paper not yet in RePEc: Add citation now
   

9. Creamer, G. (2012). Model calibration and automated trading agent for euro futures. Quantitative Finance, 12(4), 531–545.

10. Edwards, R. D., Magee, J., & Bassetti, W. (2018). Technical analysis of stock trends. CRC Press.
    Paper not yet in RePEc: Add citation now
    
11. Elend, L., Tideman, S. A., Lopatta, K., & Kramer, O. (2020). Earnings prediction with deep leaning. KI 2020: Advances in artificial intelligence, pp. 267–274. Cham: Springer International Publishing.
    Paper not yet in RePEc: Add citation now
    

12. Erel, I., Stern, L. H., Tan, C., & Weisbach, M. S. (2021). Selecting directors using machine learning. The Review of Financial Studies, 34(7), 3226–3264.

13. Goel, K., Vohra, R., & Sahoo, J. K. (2014). Polyphonic music generation by modeling temporal dependencies using a rnn-dbn. In International conference on artificial neural networks, pp. 217–224.
    Paper not yet in RePEc: Add citation now
    
14. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.
    Paper not yet in RePEc: Add citation now
    
15. Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: Deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3–12.
    Paper not yet in RePEc: Add citation now
    
16. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
    Paper not yet in RePEc: Add citation now
    
17. Huang, C. L., & Tsai, C. Y. (2009). A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert Systems with Applications, 36(2), 1529–1539.
    Paper not yet in RePEc: Add citation now
    
18. Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange. Expert systems with Applications, 38(5), 5311–5319.
    Paper not yet in RePEc: Add citation now
    

19. Koo, E., & Kim, G. (2023). A new neural network approach for predicting the volatility of stock market. Computational Economics, 61, 1665–1679.

20. Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017). Temporal convolutional networks for action segmentation and detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 156–165.
    Paper not yet in RePEc: Add citation now
    
21. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
    Paper not yet in RePEc: Add citation now
    

22. Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64–82.

23. Liu, Y., Dong, H., Wang, X., & Han, S. (2019). Time series prediction based on temporal convolutional network. 2019 IEEE/ACIS 18th International conference on computer and information science (ICIS), pp. 300–305.
    Paper not yet in RePEc: Add citation now
    

24. Novak, M. G., & Velušček, D. (2016). Prediction of stock price movement based on daily high prices. Quantitative Finance, 16(5), 793–826.

25. Persio, L. D., & Honchar, O. (2017). Recurrent neural networks approach to the financial forecast of google assets. International Journal of Mathematics and Computers in Simulation, 11, 7–13.
    Paper not yet in RePEc: Add citation now
    
26. Sen, J., & Mehtab, S. (2021). Accurate stock price forecasting using robust and optimized deep learning models. International Conference on Intelligent Technologies (CONIT), 2021, 1–9.
    Paper not yet in RePEc: Add citation now
    
27. Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., & Zhang, C. (2018). Disan: Directional self-attention network for RNN/CNN-free language understanding. Proceedings of the AAAI conference on artificial intelligence vol 32, no. 1.
    Paper not yet in RePEc: Add citation now
    
28. Tan, T. Z., Quek, C., & Ng, G. S. (2007). Biological brain-inspired genetic complementary learning for stock market and bank failure prediction. Computational Intelligence, 23(2), 236–261.
    Paper not yet in RePEc: Add citation now
    
29. Thawornwong, S., Enke, D., & Dagli, C. (2003). Neural networks as a decision maker for stock trading: A technical analysis approach. International Journal of Smart Engineering System Design, 5(4), 313–325.
    Paper not yet in RePEc: Add citation now
    
30. Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14), 5501–5506.
    Paper not yet in RePEc: Add citation now
    
31. Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599–606.
    Paper not yet in RePEc: Add citation now
    
32. Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153–163.
    Paper not yet in RePEc: Add citation now
    

33. Wiese, M., Knobloch, R., Korn, R., & Kretschmer, P. (2020). Quant gans: Deep generation of financial time series. Quantitative Finance, 20(9), 1419–1440.

34. Wilder, J. (1978). New concepts in technical trading systems.
    Paper not yet in RePEc: Add citation now
    

35. Yao, H., Xia, S., & Liu, H. (2022). Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market. Pacific-Basin Finance Journal, 76, 101886.

36. Yu, F., & Koltun, V. (2016). Multi-scale context aggregation by dilated convolutions. International Conference on Learning Representations (ICLR), 2016, 1–13.
    Paper not yet in RePEc: Add citation now
    
37. Yule, G. U. (1909). The applications of the method of correlation to social and economic statistics. Journal of the Royal Statistical Society, 72(4), 721–730.
    Paper not yet in RePEc: Add citation now
    

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    Full description at Econpapers || Download paper

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    Full description at Econpapers || Download paper

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    Full description at Econpapers || Download paper

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    Full description at Econpapers || Download paper

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    RePEc:arx:papers:2302.08002.

    Full description at Econpapers || Download paper

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    RePEc:arx:papers:2202.08962.

    Full description at Econpapers || Download paper

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    In: Intelligent Systems in Accounting, Finance and Management.
    RePEc:wly:isacfm:v:29:y:2022:i:2:p:103-117.

    Full description at Econpapers || Download paper

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    In: Computational Economics.
    RePEc:kap:compec:v:60:y:2022:i:2:d:10.1007_s10614-022-10283-1.

    Full description at Econpapers || Download paper

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    In: Finance Research Letters.
    RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322002653.

    Full description at Econpapers || Download paper

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    In: Papers.
    RePEc:arx:papers:2205.07101.

    Full description at Econpapers || Download paper

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    In: Pacific-Basin Finance Journal.
    RePEc:eee:pacfin:v:69:y:2021:i:c:s0927538x21001402.

    Full description at Econpapers || Download paper

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    In: CREATES Research Papers.
    RePEc:aah:create:2021-03.

    Full description at Econpapers || Download paper

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    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:51:y:2020:i:c:s1062940818302250.

    Full description at Econpapers || Download paper

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    In: Pacific-Basin Finance Journal.
    RePEc:eee:pacfin:v:57:y:2019:i:c:s0927538x18300775.

    Full description at Econpapers || Download paper

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