Iranian Journal of Accounting, Auditing and Finance

Article 9, Volume 9, Issue 3 - Serial Number 32, August 2025, Pages 151-169   

Document Type: Original Article

DOI: 10.22067/ijaaf.2025.46039.1466

References

1. Ahmad, I., Ahmad, M. O., Alqarni, M. A., Almazroi, A. A. and Khalil, M. I. K. (2021). Using algorithmic trading to analyze short term profitability of Bitcoin. PeerJ Computer Science, 7(1), A. e337. https://doi.org/10.7717/peerj-cs.337
2. Ariyo, A. A., Adewumi, A. O. and Ayo, C. K. (2014). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, (pp. 106-112). IEEE. Cambridge, UK
3. Aycel, Ü. and Santur, Y. (2022). A new moving average approach to predict the direction of stock movements in algorithmic trading. Journal of New Results in Science, 11(1), pp. 13-25. https://doi.org/10.54187/jnrs.979836
4. Bertsimas, D. and Sim, M. (2004). The price of robustness. Operations Research, 52(1), pp. 35-53. https://doi.org/10.1287/opre.1030.0065
5. Brock, W., Lakonishok, J. and LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), pp. 1731-1764. https://doi.org/10.1111/j.1540-6261.1992.tb04681.x
6. Chaboud, A. P., Chiquoine, B., Hjalmarsson, E. and Vega, C. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), pp. 2045-2084. https://doi.org/10.1111/jofi.12186
7. Chang, Y. H., Jong, C. C. and Wang, S. C. (2017). Size, trading volume, and the profitability of technical trading. International Journal of Managerial Finance, 13(4), pp. 475-494. https://doi.org/10.1108/IJMF-09-2016-0179
8. Chen, W., Zhang, H., Mehlawat, M. K. and Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100(1), A. 106943. https://doi.org/10.1016/j.asoc.2020.106943
9. Cipiloglu Yildiz, Z. and Yildiz, S. B. (2022). A portfolio construction framework using LSTM‐based stock markets forecasting. International Journal of Finance & Economics, 27(2), pp. 2356-2366. https://doi.org/10.1002/ijfe.2277
10. Dai, Z. and Zhu, H. (2020). Stock return predictability from a mixed model perspective. Pacific-Basin Finance Journal, 60, A. 101267. https://doi.org/10.1016/j.pacfin.2020.101267
11. Eskorouchi, A., Ghanbari, H., & Mohammadi, E. (2024). Exploring the evolution of robust portfolio optimization: a scientometric analysis. Iranian Journal of Accounting, Auditing and Finance, 8(3), pp. 75-92. https://doi.org/22067/ijaaf.2024.44518.1402
12. Eskorouchi, A., Mohammadi, E. and Sadjadi, S. J. (2022). Robust portfolio optimization based on evidence theory. Scientia Iranica. Articles in Press (In Persian). https://doi.org/10.24200/sci.2022.59575.6330
13. Ferdiansyah, F., Othman, S. H., Radzi, R. Z. R. M., Stiawan, D., Sazaki, Y. and Ependi, U. (2019). A lstm-method for bitcoin price prediction: A case study yahoo finance stock market. In 2019 international conference on electrical engineering and computer science (ICECOS) (pp. 206-210). IEEE. Cambridge, UK
14. Frattini, A., Bianchini, I., Garzonio, A. and Mercuri, L. (2022). Financial technical indicator and algorithmic trading strategy based on machine learning and alternative data. Risks, 10(12), p. 225. https://www.mdpi.com/2227-9091/10/12/225#
15. Fu, A. and Wang, B. (2020). Portfolio optimization based on LSTM neural network prediction. In 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-5). IEEE. Cambridge, UK
16. Garcia, D. and Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin. Royal Society open science, 2(9), A. 150288. https://doi.org/10.1098/rsos.150288
17. Goldfarb, D. and Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of Operations Research, 28(1), pp. 1-38. https://doi.org/10.1287/moor.28.1.1.14260
18. Graves, A. and Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5-6), pp. 602-610. https://doi.org/10.1016/j.neunet.2005.06.042
19. Hendershott, T., Jones, C. M. and Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), pp. 1-33. https://doi.org/10.1111/j.1540-6261.2010.01624.x
20. Imajo, K., Minami, K., Ito, K. and Nakagawa, K. (2021). Deep portfolio optimization via distributional prediction of residual factors. In Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), pp. 213-222. https://doi.org/10.1609/aaai.v35i1.16095
21. Iqbal, J., Ahmad, I. and Shah, A. (2019). Competitive algorithms for online conversion problem with interrelated prices. International Journal of Advanced Computer Science and Applications, 10(6), pp. 1-9.
22. Kalariya, V., Parmar, P., Jay, P., Tanwar, S., Raboaca, M. S., Alqahtani, F., ... and Neagu, B. C. (2022). Stochastic neural networks-based algorithmic trading for the cryptocurrency market. Mathematics, 10(9), A. 1456. https://doi.org/10.3390/math10091456
23. Kim, J. H., Kim, W. C. and Fabozzi, F. J. (2018). Recent advancements in robust optimization for investment management. Annals of Operations Research, 266, pp. 183-198. https://doi.org/10.1007/s10479-017-2573-5
24. Lassance, N. (2022). Reconciling mean-variance portfolio theory with non-Gaussian returns. European Journal of Operational Research, 297(2), pp. 729-740. https://doi.org/10.1016/j.ejor.2021.06.016
25. Lee, J. W. (2001). Stock price prediction using reinforcement learning. In ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570), 1, pp. 690-695. IEEE. Cambridge, UK
26. Lei, Y., Peng, Q. and Shen, Y. (2020). Deep learning for algorithmic trading: enhancing MACD strategy. In Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence, University of Tsukuba, Tsukuba, Japan (pp. 51-57). https://doi.org/10.1145/3404555.3404604
27. Lim, A. E. and Zhou, X. Y. (2002). Mean-variance portfolio selection with random parameters in a complete market. Mathematics of Operations Research, 27(1), pp. 101-120. https://doi.org/10.1287/moor.27.1.101.337
28. Lin, Q., Luo, Y. and Sun, X. (2022). Robust investment strategies with two risky assets. Journal of Economic Dynamics and Control, 134, A. 104275. https://doi.org/10.1016/j.jedc.2021.104275
29. Low, R. K. Y., Faff, R. and Aas, K. (2016). Enhancing mean–variance portfolio selection by modeling distributional asymmetries. Journal of Economics and Business, 85, pp. 49-72. https://doi.org/10.1016/j.jeconbus.2016.01.003
30. Ma, Y., Han, R. and Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, A. 113973. https://doi.org/10.1016/j.eswa.2020.113973
31. Markowitz, H. (1952). The utility of wealth. Journal of political Economy, 60(2), pp. 151-158. https://doi.org/10.1086/257177
32. Ming-Ming, L. and Siok-Hwa, L. (2006). The profitability of the simple moving averages and trading range breakout in the Asian stock markets. Journal of Asian Economics, 17(1), pp. 144-170. https://doi.org/10.1016/j.asieco.2005.12.001
33. Mohr, E., Ahmad, I. and Schmidt, G. (2014). Online algorithms for conversion problems: a survey. Surveys in Operations Research and Management Science, 19(2), pp. 87-104. https://doi.org/10.1016/j.sorms.2014.08.001
34. Moldovan, D., Moca, M. and Nitchi, S. (2011). A stock trading algorithm model proposal, based on technical indicators signals. Informatica Economica, 15(1), p. 183.
35. Mondal, P., Shit, L. and Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2), p. 13.
36. Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W. and Wills, G. (2020). A survey on machine learning for stock price prediction: Algorithms and techniques. 2nd International Conference on Finance, Economics, Management and IT Business, Vienna House Diplomat Prague, Prague, Czech Republic, 63-71. http://dx.doi.org/10.5220/0009340700630071
37. Raffinetti, E. (2023). A rank graduation accuracy measure to mitigate artificial intelligence risks. Quality & Quantity, 57(Suppl 2), 131-150. https://doi.org/10.1007/s11135-023-01613-y
38. Rather, A. M. (2021). Lstm-based deep learning model for stock prediction and predictive optimization model. EURO Journal on Decision Processes, 9(1), A. 100001. https://doi.org/10.1016/j.ejdp.2021.100001
39. Salkar, T., Shinde, A., Tamhankar, N. and Bhagat, N. (2021). Algorithmic trading using technical indicators. In 2021 International Conference on Communication information and Computing Technology (ICCICT) (pp. 1-6). IEEE. Cambridge, UK
40. Schmidt, G., Mohr, E. and Kersch, M. (2010). Experimental analysis of an online trading algorithm. Electronic Notes in Discrete Mathematics, 36, pp. 519-526. https://doi.org/10.1016/j.endm.2010.05.066
41. Scholtus, M., Van Dijk, D. and Frijns, B. (2014). Speed, algorithmic trading, and market quality around macroeconomic news announcements. Journal of Banking & Finance, 38, pp. 89-105. https://doi.org/10.1016/j.jbankfin.2013.09.016
42. Schöneburg, E. (1990). Stock price prediction using neural networks: A project report. Neurocomputing, 2(1), pp. 17-27. https://doi.org/10.1016/0925-2312(90)90013-H
43. Sen, J., Mondal, S. and Nath, G. (2021). Robust portfolio design and stock price prediction using an optimized LSTM model. In 2021 IEEE 18th India Council International Conference (INDICON) (pp. 1-6). IEEE. Cambridge, UK
44. Shahvaroughi Farahani, M. (2021). Prediction of interest rate using artificial neural network and novel meta-heuristic algorithms. Iranian Journal of Accounting, Auditing and Finance, 5(1), pp. 1-30 (In Persian). https://doi.org/10.22067/ijaaf.2021.67957.1017
45. Shavandi, A. and Khedmati, M. (2022). A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Systems with Applications, 208, A. 118124. https://doi.org/10.1016/j.eswa.2022.118124
46. Siami-Namini, S., Tavakoli, N. and Namin, A. S. (2019). The performance of LSTM and BiLSTM in forecasting time series. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 3285-3292). IEEE. New York
47. Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21(5), pp. 1154-1157. https://doi.org/10.1287/opre.21.5.1154
48. Spearman, C. (1961). The proof and measurement of association between two things. Appleton-Century-Crofts. (pp. 45–58). https://psycnet.apa.org/doi/10.1037/11491-005
49. Ta, V. D., Liu, C. M. and Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2), p. 437. https://doi.org/10.3390/app10020437
50. Vo, A. and Yost-Bremm, C. (2020). A high-frequency algorithmic trading strategy for cryptocurrency. Journal of Computer Information Systems, 60(6), pp. 555-568. https://doi.org/10.1080/08874417.2018.1552090
51. Weller, B. M. (2018). Does algorithmic trading reduce information acquisition? The Review of Financial Studies, 31(6), 2184-2226. https://doi.org/10.1093/rfs/hhx137
52. Yu, J. R., Chiou, W. J. P., Lee, W. Y. and Lin, S. J. (2020). Portfolio models with return forecasting and transaction costs. International Review of Economics & Finance, 66, pp. 118-130. https://doi.org/10.1016/j.iref.2019.11.002
53. Zhu, H., Jiang, Z. Q., Li, S. P. and Zhou, W. X. (2015). Profitability of simple technical trading rules of Chinese stock exchange indexes. Physica A: Statistical Mechanics and its Applications, 439, pp. 75-84. https://doi.org/10.1016/j.physa.2015.07.032