Acta Informatica Pragensia 2025, 14(3), 506-534 | DOI: 10.18267/j.aip.2761309

Systematic Review on Algorithmic Trading

David Jukl, Jan Lansky ORCID...
Faculty of Economics, University of Finance and Administration, Prague, Czech Republic

Background: Algorithmic trading systems (ATS) are defined by the use of computational algorithms for automating financial transactions. They have become a critical part of modern financial markets because of their efficiency and ability to carry out complex strategies.

Objective: This research involves a systematic review that assesses the market impact, technological advancements, strategic approaches and regulatory challenges related to algorithmic trading.

Methods: Following PRISMA 2020 guidelines, this study conducts a systematic literature review by screening 1,567 articles across five academic databases, namely IEEE Xplore, ACM Digital Library, SpringerLink, Web of Science and SSRN. After applying predefined inclusion and exclusion criteria, 208 peer-reviewed journal and conference papers published between 2015 and 2024 are selected. The PICOC framework is used to define the review scope. Data are extracted using structured templates capturing study details, research objectives, artificial intelligence (AI) integration, profitability analysis and limitations. Tools such as Rayyan, NVivo, MS Excel and Zotero support the screening, coding and qualitative synthesis of findings.

Results: AI methods, especially machine learning (used in 50% of the studies) and sentiment analysis (20%), significantly improve predictive accuracy and profitability. Most studies focus on equities (35%) and forex (30%), with high-frequency trading being the most examined strategy (30%). Challenges include latency (30%), scalability (25%) and regulatory issues (25%).

Conclusion: Future research should prioritize ethical frameworks, regulatory clarity and wider access to AI-driven ATS components. This review provides a robust foundation for academics and practitioners to innovate and optimize algorithmic trading strategies.

Keywords: Profitability; Algorithmic trading systems; Artificial intelligence; Meta trading; Sentiment analysis; Systematic literature review; SLR; High-frequency trading.

Received: January 24, 2025; Revised: May 28, 2025; Accepted: June 11, 2025; Prepublished online: August 9, 2025; Published: August 19, 2025  Show citation

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Jukl, D., & Lansky, J. (2025). Systematic Review on Algorithmic Trading. Acta Informatica Pragensia, 14(3), 506-534. doi: 10.18267/j.aip.276
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    References

    1. Abdul-Rahim, R.-R., Abd Wahab, A., & Hudaib, M. (2022). The effects of foreign currency exposure and Shari'ah-compliant status on financial hedging strategy. International Journal of Islamic and Middle Eastern Finance and Management, 16(2), 323-342. https://doi.org/10.1108/IMEFM-08-2021-0352 Go to original source...
    2. Adegboye, A., Kampouridis, M., & Otero, F. (2022). Algorithmic trading with directional changes. Artificial Intelligence Review, 56(1), 5619-5644. https://doi.org/10.1007/s10462-022-10307-0 Go to original source...
    3. Addy, N. W. A., Ajayi-Nifise, N. a. O., Bello, N. B. G., Tula, N. S. T., Odeyemi, N. O., & Falaiye, N. T. (2024). Algorithmic Trading and AI: A Review of Strategies and Market impact. World Journal of Advanced Engineering Technology and Sciences, 11(1), 258-267. https://doi.org/10.30574/wjaets.2024.11.1.0054 Go to original source...
    4. Aggarwal, N., Panchapagesan, V., & Thomas, S. (2023). When is the order-to-trade ratio fee effective? Journal of Financial Markets, 62, 100762. https://doi.org/10.1016/j.finmar.2022.100762 Go to original source...
    5. Ahmed, S., Akhtaruzzaman, M., Le, V., Nath, T., & Rahman, M. (2024). Interconnectedness in the FOREX market during the high inflation regime: A network analysis. Research in International Business and Finance, 71, 102467. https://doi.org/10.1016/j.ribaf.2024.102467 Go to original source...
    6. Aitkazinov, A. (2023). The Role of Artificial Intelligence in Auditing: Opportunities and Challenges. Research Journal of Finance and Accounting, 6(6), 117-119.
    7. Aitken, M., Cumming, D., & Zhan, F. (2023). Algorithmic trading and market quality: International evidence of the impact of errors in colocation dates. Journal of Banking & Finance, 151, 106843. https://doi.org/10.1016/j.jbankfin.2023.106843 Go to original source...
    8. Aitken, M., Harris, D., & Harris, F. (2022). The joint impact of fragmentation into the dark and algorithmic trading on implicit trading costs and market manipulation. Journal of Investing, 31(1), 25-46. https://doi.org/10.3905/joi.2021.1.211 Go to original source...
    9. Ajao, I., Olayinka, H., Olugbode, M., Yaya, O., & Shittu, O. (2023). Long memory cointegration and dynamic connectedness of volatility in US dollar exchange rates, with FOREX portfolio investment strategy. Quantitative Finance and Economics, 7(4), 646-664. https://doi.org/10.3934/QFE.2023031 Go to original source...
    10. Alaminos, D., Salas, M., & Callejón-Gil, A. G. (2024). Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks. Quantitative Finance and Economics, 8(1), 153-209. https://doi.org/10.3934/QFE.2024007 Go to original source...
    11. Ali, H., Aftab, M., Aslam, F., & Ferreira, P. (2024). Inner multifractal dynamics in the jumps of cryptocurrency and Forex markets. Fractal and Fractional, 8(10), 571. https://doi.org/10.3390/fractalfract8100571 Go to original source...
    12. Ali, M. U., & Amad Zafar, S. H.-J. (2019). Towards a smarter battery management system for electric vehicle applications: A critical review of lithium-ion battery state of charge estimation. Energies, 12(3), 446. https://doi.org/10.3390/en12030446 Go to original source...
    13. Anand, V., Gupta, S., Koundal, D., Nayak, S., Barsocchi, P., & Bhoi, A. (2022). Modified U-NET architecture for segmentation of skin lesion. Sensors, 22(3), 867. https://doi.org/10.3390/s22030867 Go to original source...
    14. Angerer, M., Neugebauer, T., & Shachat, J. (2022). Arbitrage bots in experimental asset markets. Journal of Economic Behavior & Organization, 206, 262-278. https://doi.org/10.1016/j.jebo.2022.12.004 Go to original source...
    15. Arifovic, J., He, X.-Z., & Wei, L. (2022). Machine learning and speed in high-frequency trading. Journal of Economic Dynamics & Control, 139, 104438. https://doi.org/10.1016/j.jedc.2022.104438 Go to original source...
    16. Arratia, A. (2023). What to do with your sentiments in finance. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, (pp. 25-37). Springer. https://doi.org/10.1007/978-3-031-23633-4_2 Go to original source...
    17. Arumugam, D. (2024). Algorithmic trading: Intraday profitability and trading behavior. Economic Modelling, 128, 106521. https://doi.org/10.1016/j.econmod.2023.106521 Go to original source...
    18. Asodekar, E., Nookala, A., Ayre, S., & Nimkar, A. (2022). Deep reinforcement learning for automated stock trading: Inclusion of short selling. In Foundations of Intelligent Systems (ISMIS 2022) (pp. 382-390). IOS Press. https://doi.org/10.3233/ATDE220738 Go to original source...
    19. Auh, J., & Cho, W. (2023). Factor-based portfolio optimization. Economics Letters, 228, 111137. https://doi.org/10.1016/j.econlet.2023.111137 Go to original source...
    20. Ayitey, M., Jr., Appiahene, P., Appiah, O., & Bombie, C. N. (2023). Forex market forecasting using machine learning: Systematic Literature Review and meta-analysis. Journal of Big Data, 10(1), Article 9. https://doi.org/10.1186/s40537-022-00676-2 Go to original source...
    21. Bagci, M., & Soylu, P. (2024). Optimal portfolio selection with volatility information for a high frequency rebalancing algorithm. Financial Innovation, 10(1), 107. https://doi.org/10.1186/s40854-023-00590-3 Go to original source...
    22. Baldacci, B., Bergault, P., & Possamaï, D. (2023). A mean-field game of market-making against strategic traders. SIAM Journal on Financial Mathematics, 14(4), 1080-1112. https://doi.org/10.1137/22M1486492 Go to original source...
    23. Behera, R., Bala, P., & Rana, N. (2023). Creation of sustainable growth with explainable artificial intelligence: An empirical insight from consumer packaged goods retailers. Journal of Cleaner Production, 399, 136605. https://doi.org/10.1016/j.jclepro.2023.136605 Go to original source...
    24. Bao, T., Nekrasova, E., Neugebauer, T., & Riyanto, Y. E. (2022). Algorithmic trading in experimental markets with human traders: A literature survey. In Handbook of Experimental Finance (pp. 302-322). https://doi.org/10.4337/9781800372337.00030 Go to original source...
    25. Ben Ammar, I., & Hellara, S. (2022). High-frequency trading, stock volatility, and intraday crashes. Quarterly Review of Economics and Finance, 84, 337-344. https://doi.org/10.1016/j.qref.2022.03.004 Go to original source...
    26. Bhat, A. (2022). Construction of a volatility index from exchange-traded dollar-rupee options. Journal of Indian Business Research, 14(4), 403-425. https://doi.org/10.1108/JIBR-10-2020-0344 Go to original source...
    27. Blanco, C., & Raurich, X. (2022). Agricultural composition and labor productivity. Journal of Development Economics, 158, 102934. https://doi.org/10.1016/j.jdeveco.2022.102934 Go to original source...
    28. Breedon, F., Chen, L., Ranaldo, A., & Vause, N. (2023). Judgment day: Algorithmic trading around the Swiss franc cap removal. Journal of International Economics, 140, 103713. https://doi.org/10.1016/j.jinteco.2022.103713 Go to original source...
    29. Brogaard, J., Ringgenberg, M., & Roesch, D. (2024). Does floor trading matter? Journal of Finance, 80(1), 375-414. https://doi.org/10.1111/jofi.13401 Go to original source...
    30. Budhwar, P., & Malik, A., MT., & De Silva, T. (2022). Artificial intelligence - challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097. https://doi.org/10.1080/09585192.2022.2035161 Go to original source...
    31. Carè, R., & Cumming, D. (2024). Technology and automation in financial trading: A bibliometric review. Research in International Business and Finance, 71, 102471. https://doi.org/10.1016/j.ribaf.2024.102471 Go to original source...
    32. Cartea, A., & Sánchez-Betancourt, L. B. (2023). Optimal execution with stochastic delay. Finance and Stochastics, 27(1), 1-47. https://doi.org/10.1007/s00780-022-00491-w Go to original source...
    33. Cartea, Á., Drissi, F., & Monga, M. (2024). Decentralized finance and automated market making: Predictable loss and optimal liquidity provision. SIAM Journal on Financial Mathematics, 15(3), 931-959. https://doi.org/10.1137/23m1602103 Go to original source...
    34. Cartea, A., & Jaimungal, S. (2016). Algorithmic trading of co-integrated assets. International Journal of Theoretical and Applied Finance, 19(6), 1650040. https://doi.org/10.1142/S0219024916500382 Go to original source...
    35. Carvajal-Patiño, D. P., & Ramos-Pollán, R. (2022). Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies. Research in International Business and Finance, 62, 101747. https://doi.org/10.1016/j.ribaf.2022.101747 Go to original source...
    36. Chakrabarty, B., & Pascual, R. (2023). Stock liquidity and algorithmic market making during the COVID-19 crisis. Journal of Banking & Finance, 147, 106415. https://doi.org/10.1016/j.jbankfin.2022.106415 Go to original source...
    37. Chakravarty, R. R., & Pani, S. (2022). Investigating Intertrade Durations using Copulas: An Experiment with NASDAQ Data. Algorithmic Finance, 9(3-4), 81-102. https://doi.org/10.3233/af-200362 Go to original source...
    38. Chamma, E., McGee, A., & Gillmann, A. (2023). Feasible applications of quantum computing in varying fields. In 2023 International Conference on Computational Science and Computational Intelligence (CSCI 2023), (pp. 454-459). IEEE. https://doi.org/10.1109/CSCI62032.2023.00080 Go to original source...
    39. Chan, J.-L., Phoong, S., Cheng, W., & Chen, Y.-L. (2022). Support resistance levels towards profitability in intelligent algorithmic trading models. Mathematics, 10(20), 3888. https://doi.org/10.3390/math10203888 Go to original source...
    40. Chang, Y. K., & Chou, R. (2022). Algorithmic trading and market quality: Evidence from the Taiwan index futures market. Journal of Futures Markets, 42(10), 1837-1855. https://doi.org/10.1002/fut.22362 Go to original source...
    41. Chen, R., Li, W., Zhang, Z., Bao, R., Harimoto, K., & Sun, X. (2023). Stock trading volume prediction with dual-process meta-learning. In Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, (pp. 137-153). Springer. https://doi.org/10.1007/978-3-031-26422-1_9 Go to original source...
    42. Chen, T. (2023). Algorithmic trading and post-earnings-announcement drift: A cross-country study. International Journal of Accounting, 58(1), 2250003. https://doi.org/10.1142/S1094406023500038 Go to original source...
    43. Chen, T., Chan, K., & Chang, H. (2022). Periodicity of trading activity in foreign exchange markets. Journal of Financial Research, 45(2), 445-465. https://doi.org/10.1111/jfir.12280 Go to original source...
    44. Cheng, X., Zhang, J., Zeng, Y., & Xue, W. (2024). MOT: A mixture-of-actors reinforcement learning method by optimal transport for algorithmic trading. In Advances in Knowledge Discovery and Data Mining, PAKDD 2024, (pp. 30-42). https://doi.org/10.1007/978-981-97-2238-9_3 Go to original source...
    45. Christodoulaki, E., & Kampouridis, M. (2022). Using strongly typed genetic programming to combine technical and sentiment analysis for algorithmic trading. In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. https://doi.org/10.1109/CEC55065.2022.9870240 Go to original source...
    46. Christodoulaki, E., & Kampouridis, M. (2024). Combining technical and sentiment analysis under a genetic programming algorithm. In Advances in Computational Intelligence Systems, UKCI 2022 (pp. 502-513). https://doi.org/10.1007/978-3-031-55568-8_42 Go to original source...
    47. Christodoulaki, E., Kampouridis, M., & Kyropoulou, M. (2023). Enhanced strongly typed genetic programming for algorithmic trading. In Proceedings of the 2023 Genetic and Evolutionary Computation Conference (GECCO 2023) (pp. 1055-1063). https://doi.org/10.1145/3583131.3590359 Go to original source...
    48. Cofre, N., & Mosionek-Schweda, M. S. (2024). A simulated electronic market with speculative behaviour and bubble formation. Finance Research Letters, 67, 105745. https://doi.org/10.1016/j.frl.2024.105745 Go to original source...
    49. Cohen, G. (2022). Algorithmic trading and financial forecasting using advanced artificial intelligence methodologies. Mathematics, 10(18), 3302. https://doi.org/10.3390/math10183302 Go to original source...
    50. Cohen, G. (2023). Technical analysis in investing. Review of Pacific Basin Financial Markets and Policies, 26(2), 2350013. https://doi.org/10.1142/S0219091523500133 Go to original source...
    51. Cooper, R., Currie, W., Seddon, J., & Van Vliet, B. (2023). Competitive advantage in algorithmic trading: A behavioral innovation economics approach. Review of Behavioral Finance, 15(3), 371-395. https://doi.org/10.1108/RBF-06-2021-0119 Go to original source...
    52. Corazza, M., Pizzi, C., & Marchioni, A. (2024). A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through particle swarm optimization. Computational Management Science, 21(1), Article number 26. https://doi.org/10.1007/s10287-024-00506-1 Go to original source...
    53. Courdent, A., & McClelland, D. (2022). The impact of algorithmic trading on market quality: Evidence from the Johannesburg Stock Exchange. Investment Analysts Journal, 51(3), 157-171. https://doi.org/10.1080/10293523.2022.2090056 Go to original source...
    54. Cox, J., & Woods, D. (2023). COVID-19 and market structure dynamics. Journal of Banking & Finance, 147, 106362. https://doi.org/10.1016/j.jbankfin.2021.106362 Go to original source...
    55. Cox, J., Van Ness, B., & Van Ness, R. (2022). The dark side of IPOs: Examining where and who trades in the IPO secondary market. Financial Management, 51(4), 1091-1126. https://doi.org/10.1111/fima.12394 Go to original source...
    56. Culley, A. (2023a). Does the deployment of algorithms combined with direct electronic access increase conduct risk? Evidence from the LME. Journal of Financial Regulation and Compliance, 31(2), 220-236. https://doi.org/10.1108/JFRC-04-2022-0046 Go to original source...
    57. Culley, A. (2023b). Insights into UK investment firms' efforts to comply with MiFID II RTS 6 that governs the conduct of algorithmic trading. Journal of Financial Regulation and Compliance, 31(5), 607-629. https://doi.org/10.1108/JFRC-12-2022-0144 Go to original source...
    58. Daluiso, R., Pinciroli, M., Trapletti, M., & Vittori, E. (2023). CVA hedging with reinforcement learning. In Proceedings of the 4th ACM International Conference on AI in Finance (ICAIF 2023) (pp. 261-269). ACM. https://doi.org/10.1145/3604237.3626852 Go to original source...
    59. Dang, V. H., & Lindsay, V. (2022). Determinants of hedging strategy in foreign exchange risk management by exporting small and medium-sized enterprises: The mediating role of resources. Journal of General Management, 48(1), 3-13. https://doi.org/10.1177/03063070211063310 Go to original source...
    60. Das, A. K., Mishra, D., Das, K., Mohanty, A. K., Mohammed, M. A., Al-Waisy, A. S., Kadry, S., & Kim, J. (2022). A deep Network-Based trade and trend analysis system to observe entry and exit points in the forex market. Mathematics, 10(19), 3632. https://doi.org/10.3390/math10193632 Go to original source...
    61. Day, M., Yang, C., & Ni, Y. (2024). Portfolio dynamic trading strategies using deep reinforcement learning. Soft Computing, 28(15-16), 8715-8730. https://doi.org/10.1007/s00500-023-08973-5 Go to original source...
    62. Deng, Y., Kong, Y., Bao, F., & Dai, Q. (2015). Sparse Coding-Inspired optimal trading system for HFT industry. IEEE Transactions on Industrial Informatics, 11(2), 467-475. https://doi.org/10.1109/tii.2015.2404299 Go to original source...
    63. De La Torre-Torres, O. V., Álvarez-García, J., & De La Cruz Del Río-Rama, M. (2024). An EM/MCMC Markov-Switching GARCH behavioral algorithm for Random-Length lumber Futures trading. Mathematics, 12(3), 485. https://doi.org/10.3390/math12030485 Go to original source...
    64. Di, S. (2022). The application of Lasso in stock selection. In Proceedings of the 2022 7th International Conference on Machine Learning Technologies (ICMLT '22), (pp. 24-29). ACM. https://doi.org/10.1145/3529399.3529404 Go to original source...
    65. Duan, Z., Chen, C., Cheng, D., Liang, Y., & Qian, W. (2022). Optimal Action Space search. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management, (pp. 406-415). ACM. https://doi.org/10.1145/3511808.3557412 Go to original source...
    66. Dubey, R. K. (2022). Algorithmic Trading: The intelligent trading systems and its impact on trade size. Expert Systems With Applications, 202, 117279. https://doi.org/10.1016/j.eswa.2022.117279 Go to original source...
    67. Dutta, C., Karpman, K., Basu, S., & Ravishanker, N. (2022). Review of Statistical Approaches for Modeling High-Frequency Trading Data. Sankhya B, 85(S1), 1-48. https://doi.org/10.1007/s13571-022-00280-7 Go to original source...
    68. Felizardo, L. K., Paiva, F. C. L., De Vita Graves, C., Matsumoto, E. Y., Costa, A. H. R., Del-Moral-Hernandez, E., & Brandimarte, P. (2022). Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market. Expert Systems With Applications, 202, 117259. https://doi.org/10.1016/j.eswa.2022.117259 Go to original source...
    69. Feng, W., & Zhang, Z. (2023). Currency exchange rate predictability: The new power of Bitcoin prices. Journal of International Money and Finance, 132, 102811. https://doi.org/10.1016/j.jimonfin.2023.102811 Go to original source...
    70. Fereydooni, A., & Mahootchi, M. (2023). An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market. Global Finance Journal, 56, 100825. https://doi.org/10.1016/j.gfj.2023.100825 Go to original source...
    71. Fikri, N., Moussaid, K., Rida, M., Omri, A. E., & Abghour, N. (2022). A channeled multilayer perceptron as Multi-Modal approach for two Time-Frames Algo-Trading strategy. International Journal of Advanced Computer Science and Applications, 13(2). https://doi.org/10.14569/ijacsa.2022.0130259 Go to original source...
    72. Fior, J., Cagliero, L., & Garza, P. (2022). Leveraging explainable AI to support cryptocurrency investors. Future Internet, 14(9), 251. https://doi.org/10.3390/fi14090251 Go to original source...
    73. Fouque, J., Jaimungal, S., & Saporito, Y. F. (2022). Optimal Trading with Signals and Stochastic Price Impact. SIAM Journal on Financial Mathematics, 13(3), 944-968. https://doi.org/10.1137/21m1394473 Go to original source...
    74. Frattini, A., Bianchini, I., Garzonio, A., & Mercuri, L. (2022). Financial Technical Indicator and algorithmic trading strategy based on machine learning and alternative data. Risks, 10(12), 225. https://doi.org/10.3390/risks10120225 Go to original source...
    75. Gál, H., & Lovas, A. (2022). Ergodic behavior of returns in a buy low and sell high type trading strategy. In Mathematical and Statistical Methods for Actuarial Sciences and Finance, (pp. 278-283). https://doi.org/10.1007/978-3-030-99638-3_45 Go to original source...
    76. Garcia, D., & Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin. Royal Society Open Science, 2(9), 150288. https://doi.org/10.1098/rsos.150288 Go to original source...
    77. Garza, J. (2023). Reinforcement learning for stock option trading. In 2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2023) (pp. 49-60). https://doi.org/10.1109/AICS60730.2023.10470596 Go to original source...
    78. Ge, H., Yang, H., & Doukas, J. A. (2024). The optimal strategies of competitive high-frequency traders and effects on market liquidity. International Review of Economics & Finance, 91, 653-679. https://doi.org/10.1016/j.iref.2024.01.064 Go to original source...
    79. Ghimire, A., Thapa, S., Jha, A. K., Adhikari, S., & Kumar, A. (2020). Accelerating Business Growth with Big Data and Artificial Intelligence. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), (pp. 441-448). IEEE. https://doi.org/10.1109/i-smac49090.2020.9243318 Go to original source...
    80. Ghotbi, M., & Zahedi, M. (2024). Predicting price trends combining kinetic energy and deep reinforcement learning. Expert Systems With Applications, 244, 122994. https://doi.org/10.1016/j.eswa.2023.122994 Go to original source...
    81. Gil, C. (2022). Intraday trading of precious metals futures using algorithmic systems. Chaos Solitons & Fractals, 154, 111676. https://doi.org/10.1016/j.chaos.2021.111676 Go to original source...
    82. Goetzmann, W. N. (2022). Shareholder democracy, meet memocracy. Financial Analysts Journal, 78(3), 5-8. https://doi.org/10.1080/0015198x.2022.2074773 Go to original source...
    83. Gómez-Martínez, R., Orden-Cruz, C., & Martínez-Navalón, J. G. (2022). Wikipedia pageviews as investors' attention indicator for Nasdaq. Intelligent Systems in Accounting Finance & Management, 29(1), 41-49. https://doi.org/10.1002/isaf.1508 Go to original source...
    84. Gorshenin, A. K., & Vilyaev, A. L. (2022). Finite Normal Mixture Models for the Ensemble Learning of Recurrent Neural Networks with Applications to Currency Pairs. Pattern Recognition and Image Analysis, 32(4), 780-792. https://doi.org/10.1134/s1054661822040058 Go to original source...
    85. Gradzki, P., & Wójcik, P. (2023). Is attention all you need for intraday Forex trading? Expert Systems, 41(2), e13317. https://doi.org/10.1111/exsy.13317 Go to original source...
    86. Hansen, P. R., Kim, C., & Kimbrough, W. (2021). Periodicity in cryptocurrency volatility and liquidity. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2109.12142 Go to original source...
    87. Harnpadungkij, T., Chaisangmongkon, W., & Phunchongharn, P. (2022). Risk-Sensitive portfolio management by using C51 algorithm. Chiang Mai Journal of Science, 49(5), 1458-1482. https://doi.org/10.12982/cmjs.2022.094 Go to original source...
    88. Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2023). Practical machine learning: Forecasting daily financial markets directions. Expert Systems With Applications, 233, 120840. https://doi.org/10.1016/j.eswa.2023.120840 Go to original source...
    89. Hernes, M., Korczak, J., Krol, D., Pondel, M., & Becker, J. (2024). Multi-agent platform to support trading decisions in the FOREX market. Applied Intelligence, 54(22), 11690-11708. https://doi.org/10.1007/s10489-024-05770-x Go to original source...
    90. Horobet, A., Boubaker, S., Belascu, L., Negreanu, C. C., & Dinca, Z. (2024). Technology-driven advancements: Mapping the landscape of algorithmic trading literature. Technological Forecasting and Social Change, 209, 123746. https://doi.org/10.1016/j.techfore.2024.123746 Go to original source...
    91. Hu, W., & Zhou, J. (2024). Trading Signal Survival Analysis: A framework for enhancing technical analysis strategies in stock markets. Computational Economics. https://doi.org/10.1007/s10614-024-10567-8 Go to original source...
    92. Huang, Y., & Song, Y. (2023). A new hybrid method of recurrent reinforcement learning and BiLSTM for algorithmic trading. Journal of Intelligent & Fuzzy Systems, 45(2), 1939-1951. https://doi.org/10.3233/jifs-223101 Go to original source...
    93. Huang, Y., Lu, X., Zhou, C., & Song, Y. (2023a). DADE-DQN: Dual Action and Dual Environment Deep Q-Network for enhancing stock trading strategy. Mathematics, 11(17), 3626. https://doi.org/10.3390/math11173626 Go to original source...
    94. Huang, Y., Wan, X., Zhang, L., & Lu, X. (2023b). A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading. Expert Systems With Applications, 240, 122581. https://doi.org/10.1016/j.eswa.2023.122581 Go to original source...
    95. Huang, Y., Zhou, C., Cui, K., & Lu, X. (2023c). A multi-agent reinforcement learning framework for optimizing financial trading strategies based on TimesNet. Expert Systems With Applications, 237, 121502. https://doi.org/10.1016/j.eswa.2023.121502 Go to original source...
    96. Huang, Z., Li, N., Mei, W., & Gong, W. (2023d). Algorithmic trading using combinational rule vector and deep reinforcement learning. Applied Soft Computing, 147, 110802. https://doi.org/10.1016/j.asoc.2023.110802 Go to original source...
    97. Huang, Z., Gong, W., & Duan, J. (2023e). TBDQN: A novel two-branch deep Q-network for crude oil and natural gas futures trading. Applied Energy, 347, 121321. https://doi.org/10.1016/j.apenergy.2023.121321 Go to original source...
    98. Jacob-Leal, S., & Hanaki, N. (2024). Algorithmic trading, what if it is just an illusion? Evidence from experimental asset markets. Journal of Behavioral and Experimental Economics, 112, 102240. https://doi.org/10.1016/j.socec.2024.102240 Go to original source...
    99. Kabaca, S., Maas, R., Mavromatis, K., & Priftis, R. (2023). Optimal quantitative easing in a monetary union. European Economic Review, 152, 104342. https://doi.org/10.1016/j.euroecorev.2022.104342 Go to original source...
    100. Karpoff, J. M., Litan, R., Schrand, C., & Weil, R. L. (2022). What ESG-Related disclosures should the SEC mandate? Financial Analysts Journal, 78(2), 9-18. https://doi.org/10.1080/0015198x.2022.2044718 Go to original source...
    101. Kashera, V., Jain, S., Banerjee, A., & Purini, S. (2023). Building low-latency order books with hybrid binary-linear search data structures on FPGAs. In 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL) (pp. 299-304). IEEE. https://doi.org/10.1109/FPL60245.2023.00051 Go to original source...
    102. Kaur, M., Kumar, A., & Mittal, A. K. (2025). Mapping the knowledge structure of artificial neural network research in the stock market: A bibliometric analysis and future research pathways. Benchmarking: An International Journal, 32(3), 1069-1098. https://doi.org/10.1108/BIJ-06-2023-0373 Go to original source...
    103. Kausar, R., Iqbal, F., Raziq, A., & Sheikh, N. (2023). A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model. Sains Malaysiana, 52(11), 3293-3306. https://doi.org/10.17576/jsm-2023-5211-20 Go to original source...
    104. Khames, W., Hadjali, A., & Lagha, M. (2024). Parallel continuous skyline query over high-dimensional data stream windows. Distributed and Parallel Databases, 42(4), 469-524. https://doi.org/10.1007/s10619-024-07443-7 Go to original source...
    105. Khurana, S. S., Singh, P., & Garg, N. K. (2024). OG-CAT: a novel algorithmic trading alternative to investment in crypto market. Computational Economics, 63(5), 1735-1756. https://doi.org/10.1007/s10614-023-10380-9 Go to original source...
    106. King, J. C., Dale, R., & Amigó, J. M. (2024). Blockchain metrics and indicators in cryptocurrency trading. Chaos Solitons & Fractals, 178, 114305. https://doi.org/10.1016/j.chaos.2023.114305 Go to original source...
    107. Koegelenberg, C., & Van Vuuren, J. (2023). Heterogeneous Trading Strategy Ensembling for Intraday Trading Algorithms. The South African Journal of Industrial Engineering, 34(3), 156-169. https://doi.org/10.7166/34-3-2951 Go to original source...
    108. Kwak, D., Choi, S., & Chang, W. (2022). Self-attention based deep direct recurrent reinforcement learning with hybrid loss for trading signal generation. Information Sciences, 623, 592-606. https://doi.org/10.1016/j.ins.2022.12.042 Go to original source...
    109. Lee, A. J., & Chung, K. H. (2022). Hidden liquidity, market quality, and order submission strategies. Journal of Financial Markets, 61, 100739. https://doi.org/10.1016/j.finmar.2022.100739 Go to original source...
    110. Lei, Z., Gong, G., Wang, T., & Li, W. (2021). Accounting information quality, financing constraints, and company innovation investment efficiency by big data analysis. Journal of Organizational and End User Computing, 34(3), 1-21. https://doi.org/10.4018/joeuc.292525 Go to original source...
    111. Li, J., Wang, X., Ahmad, S., Huang, X., & Khan, Y. A. (2023). Optimization of investment strategies through machine learning. Heliyon, 9(5), e16155. https://doi.org/10.1016/j.heliyon.2023.e16155 Go to original source...
    112. Li, X., Wu, P., Zou, C., & Li, Q. (2024). Hierarchical Deep Reinforcement learning for VWAP Strategy optimization. IEEE Transactions on Big Data, 10(3), 288-300. https://doi.org/10.1109/tbdata.2023.3338011 Go to original source...
    113. Li, Y., Jiang, S., Li, X., & Wang, S. (2022). Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading. Financial Innovation, 8(1), Article 31. https://doi.org/10.1186/s40854-022-00336-7 Go to original source...
    114. Liu, P., Zhang, Y., Bao, F., Yao, X., & Zhang, C. (2022). Multi-type data fusion framework based on deep reinforcement learning for algorithmic trading. Applied Intelligence, 53(2), 1683-1706. https://doi.org/10.1007/s10489-022-03321-w Go to original source...
    115. Liu, Y. (2023a). Trading strategy optimisation with a multi-objective genetic algorithm. In 2023 11th International Conference on Information Technology: IoT and Smart City (ITIoTSC) (pp. 186-191). IEEE. https://doi.org/10.1109/ITIoTSC60379.2023.00040 Go to original source...
    116. Liu, P. (2023b). Seeking Better Sharpe Ratio via Bayesian Optimization. The Journal of Portfolio Management, 49(7), 35-43. https://doi.org/10.3905/jpm.2023.1.497 Go to original source...
    117. Liu, Y., & Huang, J. (2024). Cryptocurrency trend forecast using technical analysis and trading with randomness-preserving. Computers & Electrical Engineering, 118, 109368. https://doi.org/10.1016/j.compeleceng.2024.109368 Go to original source...
    118. Liu, Z., Luo, H., Chen, P., Xia, Q., Gan, Z., & Shan, W. (2022). An efficient isomorphic CNN-based prediction and decision framework for financial time series. Intelligent Data Analysis, 26(4), 893-909. https://doi.org/10.3233/ida-216142 Go to original source...
    119. Long, X., Kampouridis, M., & Kanellopoulos, P. (2022). Genetic programming for combining directional changes indicators in international stock markets. In Lecture notes in computer science (pp. 33-47). Spinger. https://doi.org/10.1007/978-3-031-14721-0_3 Go to original source...
    120. Loon, H. S., Dewi, D. A., Thinakaran, R., Kurniawan, T. B., & Batumalay, M. (2023). Algorithmic trading strategy development using machine learning. Journal of Engineering Science and Technology, 18(6), 22-31.
    121. Lukiæ, ®., & Milo¹eviæ, B. (2024). A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance. Annals of the Institute of Statistical Mathematics, 76(5), 797-820. https://doi.org/10.1007/s10463-024-00902-z Go to original source...
    122. Luo, Q., Song, S., & Li, H. (2022). Research on the effects of liquidation strategies in the multi-asset artificial market. Computational Economics, 62(4), 1721-1750. https://doi.org/10.1007/s10614-022-10316-9 Go to original source...
    123. Lyle, M. R., & Naughton, J. P. (2015). How does algorithmic trading improve market quality? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2587730 Go to original source...
    124. Mabrouk, N., Chihab, M., Hachkar, Z., & Chihab, Y. (2022). Intraday Trading Strategy based on Gated Recurrent Unit and Convolutional Neural Network: Forecasting Daily Price Direction. International Journal of Advanced Computer Science and Applications, 13(3), 585-592. https://doi.org/10.14569/ijacsa.2022.0130369 Go to original source...
    125. Mahayana, D., Shan, E., & Fadhl'Abbas, M. (2022). Deep reinforcement learning to automate cryptocurrency trading. In 2022 12th International Conference on System Engineering and Technology (ICSET) (pp. 36-41). IEEE. https://doi.org/10.1109/ICSET57543.2022.10010940 Go to original source...
    126. Mahmoodzadeh, S., & Tseng, M. C. (2023). Spot arbitrage in FX market and algorithmic trading: Speed is not of the essence. Market Microstructure and Liquidity, 6(1), 2050011. https://doi.org/10.1142/s2382626620500112 Go to original source...
    127. Majidi, N., Shamsi, M., & Marvasti, F. (2023). Algorithmic trading using continuous action space deep reinforcement learning. Expert Systems With Applications, 235, 121245. https://doi.org/10.1016/j.eswa.2023.121245 Go to original source...
    128. Maknickienė, N., Martinkutė-Kaulienė, R., & Stasytytė, V. (2023). Investigation of algorithmic trading models for shares of the drug manufacturing industry. Transformations in Business & Economics, 22(2), 64-81.
    129. Malik, A. (2023). A comparison of machine learning and econometric models for pricing perpetual Bitcoin futures and their application to algorithmic trading. Expert Systems, 40(10), e13414. https://doi.org/10.1111/exsy.13414 Go to original source...
    130. Martínez, R. G., García, M. L. M., & Mateos, J. V. (2024). Investment strategies based on investors' mood: Better for crypto. Revista Perspectiva Empresarial, 10(2), 6-16. https://doi.org/10.16967/23898186.843 Go to original source...
    131. Martínez, R. G., Román, M. P., & Casado, P. P. (2018). Big data algorithmic trading systems based on investors' mood. Journal of Behavioral Finance, 20(2), 227-238. https://doi.org/10.1080/15427560.2018.1506786 Go to original source...
    132. Martins, C. J. L. (2020). The role of automation in financial trading companies. Journal of Management and Financial Sciences, 39, 29-42. https://doi.org/10.33119/jmfs.2019.39.3 Go to original source...
    133. Massahi, M., & Mahootchi, M. (2024). A deep Q-learning based algorithmic trading system for commodity futures markets. Expert Systems With Applications, 237, 121711. https://doi.org/10.1016/j.eswa.2023.121711 Go to original source...
    134. McFadden, D. (2022). Instability in mixed logit demand models. Journal of Choice Modelling, 43, 100353. https://doi.org/10.1016/j.jocm.2022.100353 Go to original source...
    135. Merkin, L. A., & Rezin, R. M. (2022). On approximation of transition densities in calibration of 1-Dimensional stochastic models of asset prices. Lobachevskii Journal of Mathematics, 43(2), 429-442. https://doi.org/10.1134/s1995080222050183 Go to original source...
    136. Mestel, R., Steffen, V., & Theissen, E. (2024). Algorithmic trading and mini flash crashes: Evidence from Austria. Economics Letters, 111982. https://doi.org/10.1016/j.econlet.2024.111982 Go to original source...
    137. Milke, V., Luca, C., & Wilson, G. B. (2024). Reduction of financial tick big data for intraday trading. Expert Systems, 41(7), e13537. https://doi.org/10.1111/exsy.13537 Go to original source...
    138. Mohamed, I., & Otero, F. E. B. (2022). A performance study of multiobjective particle swarm optimization algorithms for market timing. In 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) (pp. 1-10). IEEE. https://doi.org/10.1109/CIFEr52523.2022.9776019 Go to original source...
    139. Mukerji, P., Chung, C., Walsh, T., & Xiong, B. (2019). The impact of algorithmic trading in a simulated asset market. Journal of Risk and Financial Management, 12(2), 68. https://doi.org/10.3390/jrfm12020068 Go to original source...
    140. Muravyev, D., & Picard, J. (2022). Does trade clustering reduce trading costs? Evidence from periodicity in algorithmic trading. Financial Management, 51(4), 1201-1229. https://doi.org/10.1111/fima.12405 Go to original source...
    141. Murtza, I., Saadia, A., Basri, R., Imran, A., Almuhaimeed, A., & Alzahrani, A. (2022). Forex investment optimization using instantaneous stochastic gradient ascent-Formulation of an adaptive machine learning approach. Sustainability, 14(22), 15328. https://doi.org/10.3390/su142215328 Go to original source...
    142. Nan, A., Perumal, A., & Zaiane, O. R. (2022). Sentiment and knowledge based algorithmic trading with deep reinforcement learning. In C. Strauss, A. Cuzzocrea, G. Kotsis, A. M. Tjoa, & I. Khalil (Eds.), Database and expert systems applications. DEXA 2022, (pp. 176-190). Springer. https://doi.org/10.1007/978-3-031-12423-5_13 Go to original source...
    143. Nawn, S., & Raizada, G. (2022). Trade informativeness in modern markets. Financial Analysts Journal, 79(128), 1-22. https://doi.org/10.1080/0015198X.2022.2126590 Go to original source...
    144. Nayanar, N. (2023). Interpreting institutional investment activity as a Markov process: A stock recommender. Intelligent Decision Technologies, 17(3), 673-685. https://doi.org/10.3233/IDT-220264 Go to original source...
    145. Ndlovu, T., & Chikobvu, D. (2023). A wavelet-decomposed WD-ARMA-GARCH-EVT model approach to comparing the riskiness of the BitCoin and South African Rand exchange rates. Data, 8(7), 122. https://doi.org/10.3390/data8070122 Go to original source...
    146. Omran, S. M., El-Behaidy, W. H., & Youssif, A. A. A. (2024). Optimization of cryptocurrency algorithmic trading strategies using the decomposition approach. Big Data and Cognitive Computing, 7(4), 174. https://doi.org/10.3390/bdcc7040174 Go to original source...
    147. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 Go to original source...
    148. Parente, M., Rizzuti, L., & Trerotola, M. (2024). A profitable trading algorithm for cryptocurrencies using a neural network model. Expert Systems with Applications, 238, 121806. https://doi.org/10.1016/j.eswa.2023.121806 Go to original source...
    149. Paule-Vianez, J., Orden-Cruz, C., Gómez-Martínez, R., & Escamilla-Solano, S. (2023). Fear of COVID-19 effect on stock markets: A proposal for an algorithmic trading system based on fear. Journal of Theoretical and Applied Electronic Commerce Research, 18(2), 1142-1156. https://doi.org/10.3390/jtaer18020058 Go to original source...
    150. Pemy, M., & Zhang, N. (2023). Optimal liquidation of a basket of stocks using reinforcement learning. In 2023 Proceedings of the Conference on Control and its Applications (CT) (pp. 41-47). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977745.6 Go to original source...
    151. Peng, Y., & de Souza, J. G. M. (2024). Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia-Ukraine war. Revista de Gestão, 31(2), 152-165. https://doi.org/10.1108/REGE-05-2022-0079 Go to original source...
    152. Ponomarev, E. S., Oseledets, I. V., & Cichocki, A. S. (2019). Using reinforcement learning in the algorithmic trading problem. Journal of Communications Technology and Electronics, 64(12), 1450-1457. https://doi.org/10.1134/S1064226919120131 Go to original source...
    153. Raheman, A., Kolonin, A., & Ansari, I. (2022). Adaptive multi-strategy market making agent. In Lecture notes in computer science (pp. 204-209). Springer. https://doi.org/10.1007/978-3-030-93758-4_21 Go to original source...
    154. Rahimpour, S. M., Goudarzi, R., Shahparifard, V., & Mirpoorian, S. N. (2024). Algorithmic trading using technical indicators and eXtreme gradient boosting. In 2024 11th IEEE Swiss Conference on Data Science (SDS) (pp. 92-98). IEEE. https://doi.org/10.1109/SDS60720.2024.00021 Go to original source...
    155. Salkar, T., Shinde, A., Tamhankar, N., & Bhagat, N. (2021). Algorithmic trading using technical indicators. In 2021 International Conference on Communication Information and Computing Technology (ICCICT) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCICT50803.2021.9510135 Go to original source...
    156. Sanati, G., & Bhandari, A. K. (2024). Operational efficiency in the presence of undesirable byproducts: An analysis of Indian banking sector under traditional and market-based banking framework. Indian Growth and Development Review, 17(2), 140-166. https://doi.org/10.1108/IGDR-07-2023-0093 Go to original source...
    157. Santuci, A., Sbruzzi, E., Araújo-Filho, L., & Leles, M. (2022). Evaluation of FOREX trading strategies based on random forest and support vector machines. IEEE Latin America Transactions, 20(9), 2146-2152. https://doi.org/10.1109/TLA.2022.9878170 Go to original source...
    158. Sarin, S., Singh, S. K., Kumar, S., Goyal, S., Gupta, B. B., Alhalabi, W., & Arya, V. (2024). Unleashing the power of multi-agent reinforcement learning for algorithmic trading in the digital financial frontier and enterprise information systems. Computers, Materials & Continua, 80(2), 3123-3138. https://doi.org/10.32604/cmc.2024.051599 Go to original source...
    159. Sevastjanov, P., Kaczmarek, K., & Rutkowski, L. (2024). A multi-model approach to the development of algorithmic trading systems for the Forex market. Expert Systems with Applications, 213, 121310. https://doi.org/10.1016/j.eswa.2023.121310 Go to original source...
    160. Seyfert, R. (2018). Automation and affect. In Affect in Relation (pp. 197-217). Routledge. https://doi.org/10.4324/9781315163864-10 Go to original source...
    161. Shafiq, S., Qureshi, S. S., & Akbar, M. (2023). Dynamic relationship of volatility of returns across different markets: evidence from selected next 11 countries. Journal of Economic and Administrative Sciences, (in press). https://doi.org/10.1108/jeas-09-2022-0216 Go to original source...
    162. Shang, J., & Hamori, S. (2023). Differential Tail Dependence between Crude Oil and Forex Markets in Oil-Importing and Oil-Exporting Countries during Recent Crisis Periods. Sustainability, 15(19), 14445. https://doi.org/10.3390/su151914445 Go to original source...
    163. Shanmugam, R. (2023). Algorithmic trading and quantitative strategies. Journal of Statistical Computation and Simulation, 93(9), 1478. https://doi.org/10.1080/00949655.2023.2168168 Go to original source...
    164. Shavandi, A., & Khedmati, M. (2022). A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Systems With Applications, 208, 118124. https://doi.org/10.1016/j.eswa.2022.118124 Go to original source...
    165. Singh, J., Bowala, S., Thavaneswaran, A., Thulasiram, R., & Mandal, S. (2022a). Data-Driven and Neuro-Volatility fuzzy forecasts for cryptocurrencies. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (pp. 1-8). IEEE. https://doi.org/10.1109/fuzz-ieee55066.2022.9882812 Go to original source...
    166. Singh, J., Thulasiram, R., & Thavaneswaran, A. (2022b). LSTM based algorithmic trading model for Bitcoin. In 2022 IEEE Symposium Series on Computational Intelligence, (pp. 1591-1598). IEEE. https://doi.org/10.1109/SSCI51031.2022.10022021 Go to original source...
    167. Sohail, M. K., Raheman, A., Iqbal, J., Sindhu, M. I., Staar, A., Mushafiq, M., & Afzal, H. (2022). Are pair trading strategies profitable during COVID-19 period? Journal of Information & Knowledge Management, 21(Supp01). https://doi.org/10.1142/s021964922240010x Go to original source...
    168. Sokolovsky, A., & Arnaboldi, L. (2023). A generic methodology for the statistically uniform & comparable evaluation of Automated Trading Platform components. Expert Systems With Applications, 223, 119836. https://doi.org/10.1016/j.eswa.2023.119836 Go to original source...
    169. Song, Y. H., Park, M., & Kim, J. (2024). Improving the Machine learning stock trading System: an N-Period volatility labeling and instance selection technique. Complexity, 2024(1), 5036389. https://doi.org/10.1155/2024/5036389 Go to original source...
    170. Stádník, B. (2022). Convexity arbitrage - the idea which does not work. Cogent Economics & Finance, 10(1). https://doi.org/10.1080/23322039.2021.2019361 Go to original source...
    171. Stasiak, M. D. (2022). Algoritmic trading system based on state model for moving average in a Binary-Temporal representation. Risks, 10(4), 69. https://doi.org/10.3390/risks10040069 Go to original source...
    172. Sukma, N., & Namahoot, C. S. (2025). Enhancing Trading Strategies: A multi-indicator analysis for profitable algorithmic trading. Computational Economics, 65, 3807-3840. https://doi.org/10.1007/s10614-024-10669-3 Go to original source...
    173. Suliman, U., van Zyl, T. L., & Paskaramoorthy, A. (2022). Cryptocurrency trading agent using deep reinforcement learning. In 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) (pp. 6-10). IEEE. https://doi.org/10.1109/ISCMI56532.2022.10068485 Go to original source...
    174. Sun, Q., & Si, Y. (2022). Supervised actor-critic reinforcement learning with action feedback for algorithmic trading. Applied Intelligence, 53(13), 16875-16892. https://doi.org/10.1007/s10489-022-04322-5 Go to original source...
    175. Sun, Q., Gong, X., & Si, Y. (2023). Transaction-aware inverse reinforcement learning for trading in stock markets. Applied Intelligence, 53(23), 28186-28206. https://doi.org/10.1007/s10489-023-04959-w Go to original source...
    176. Sun, S., Xue, W., Wang, R., He, X., Zhu, J., Li, J., & An, B. (2022). DeepScalper: A risk-aware reinforcement learning framework to capture fleeting intraday trading opportunities. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22) (pp. 1858-1867). ACM. https://doi.org/10.1145/3511808.3557283 Go to original source...
    177. Tabaro, L., Kinani, J. M. V., Rosales-Silva, A. J., Salgado-Ramírez, J. C., Mújica-Vargas, D., Escamilla-Ambrosio, P. J., & Ramos-Díaz, E. (2024). Algorithmic trading using double deep Q-Networks and sentiment analysis. Information, 15(8), 473. https://doi.org/10.3390/info15080473 Go to original source...
    178. Thomas, W. B., Wang, Y., & Zhang, L. (2024). Algorithmic Trading and Forward-Looking MD&A Disclosures. Journal of Accounting Research, 62(4), 1533-1569. https://doi.org/10.1111/1475-679x.12540 Go to original source...
    179. Tian, H., Wang, W., Yang, M., & Yilmaz, A. (2024). Investigating the profit performance of quantitative timing trading strategies in the Shanghai copper futures market, 2020-2022. International Studies of Economics, 19(4), 589-616. https://doi.org/10.1002/ise3.87 Go to original source...
    180. Tucci, G., Jain, S., Zhang, A., & Ge, W. (2022). Enhanced expected impact cost model under abnormally high volatility. Journal of Investment Strategies, 11(3), 43-59. https://doi.org/10.21314/JOIS.2022.010 Go to original source...
    181. Tudor, C., & Sova, R. (2022a). EU Net-Zero Policy Achievement Assessment in Selected Members through Automated Forecasting Algorithms. ISPRS International Journal of Geo-Information, 11(4), 232. https://doi.org/10.3390/ijgi11040232 Go to original source...
    182. Tudor, C., & Sova, R. (2022b). Infodemiological study on the impact of the COVID-19 pandemic on increased headache incidences at the world level. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-13663-7 Go to original source...
    183. Tuncer, T., Kaya, U., Sefer, E., Alacam, O., & Hoser, T. (2022). Asset price and direction prediction via deep 2D transformer and convolutional neural networks. In Proceedings of the Third ACM International Conference on AI in Finance (ICAIF '22) (pp. 79-86). ACM. https://doi.org/10.1145/3533271.3561738 Go to original source...
    184. Velu, R., Hardy, M., & Nehren, D. (2020). Algorithmic trading and quantitative strategies. Chapman and Hall/CRC. https://doi.org/10.1201/9780429183942 Go to original source...
    185. Vogel, J. U. N. (2024). Better things to do or doing nothing at all? - the implications of incomplete share repurchase programs. International Journal of Managerial Finance, 21(1), 46-66. https://doi.org/10.1108/ijmf-12-2023-0612 Go to original source...
    186. Wang, L. R., Henderson, T. C., Ong, Y. S., Ng, Y. Y., & Fan, X. (2024). An uncertainty estimation model for health signal prediction. SSRN. https://doi.org/10.2139/ssrn.4668687 Go to original source...
    187. Watorek, M., Skupieñ, M., Kwapieñ, J., & Dro¿d¿, S. (2023). Decomposing cryptocurrency high-frequency price dynamics into recurring and noisy components. Chaos an Interdisciplinary Journal of Nonlinear Science, 33(8), 083146 . https://doi.org/10.1063/5.0165635 Go to original source...
    188. Welekar, R., Ghosh, S., Kurve, A., Kumar, S., & Deshmukh, A. (2022). Options Trading using Artificial Neural Network and Algorithmic Trading. International Journal of Next-Generation Computing, 13(5), 924. https://doi.org/10.47164/ijngc.v13i5.924 Go to original source...
    189. Wong, A., Figini, J., Raheem, A., Hains, G., Khmelevsky, Y., & Chu, P. (2023). Forecasting of stock prices using machine learning models. In 2023 IEEE International Systems Conference (SysCon) (pp. 1-8). IEEE. https://doi.org/10.1109/SysCon53073.2023.10131091 Go to original source...
    190. Yadav, Y. (2015). How algorithmic trading undermines efficiency in capital markets. Vanderbilt Law Review, 68(6), 1607-1676.
    191. Yao, L., & Parthasarathy, R. (2023). Applying artificial intelligence technology in China's intelligent manufacturing industry. In Proceedings of the 2023 8th International Conference on Business and Industrial Research (ICBIR) (pp. 612-617). IEEE. https://doi.org/10.1109/ICBIR57571.2023.10147402 Go to original source...
    192. Ye, Z., & Schuller, B. (2024). Human-aligned trading by imitative multi-loss reinforcement learning. Expert Systems with Applications, 234, 120939. https://doi.org/10.1016/j.eswa.2023.120939 Go to original source...
    193. Yigitcanlar, T., & Senadheera, R. M. (2024). Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends. Cities, 152, 105151. https://doi.org/10.1016/j.cities.2024.105151 Go to original source...
    194. Yi-Le Chan, J.-L., Leow, S., Bea, K., Cheng, W., Phoong, S., Hong, Z.-W., … Chen, Y.-L. (2022). A correlation-embedded attention module to mitigate multicollinearity: An algorithmic trading application. Mathematics, 10(8), 1231. https://doi.org/10.3390/math10081231 Go to original source...
    195. Yip, A., Ng, W., Siu, K.-W., Cheung, A., & Ng, M. (2023). Graph embedded dynamic mode decomposition for stock price prediction. Algorithmic Finance, 10(1-2), 39-51. https://doi.org/10.3233/AF-220432 Go to original source...
    196. Yuferova, D. (2024). Algorithmic trading and market efficiency around the introduction of the NYSE hybrid market. Journal of Financial Markets, 65, 100909. https://doi.org/10.1016/j.finmar.2024.100909 Go to original source...
    197. Zafeiriou, T., & Kalles, D. (2024). Comparative analysis of neural network architectures for short-term FOREX forecasting. Intelligent Decision Technologies, 18(3), 2177-2190. https://doi.org/10.3233/idt-240713 Go to original source...
    198. Zhang, J., Peng, Z., Zeng, Y., & Yang, H. (2024). Do big data mutual funds outperform? Journal of International Financial Markets, Institutions & Money, 88, 101842. https://doi.org/10.1016/j.intfin.2023.101842 Go to original source...
    199. Zhang, K., & Aslan, A. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025 Go to original source...
    200. Zhang, L., Wu, T., Lahrichi, S., Salas-Flores, C.-F.-G., & Li, J. (2022). A data science pipeline for algorithmic trading: A comparative study of applications for finance and cryptoeconomics. In 2022 IEEE International Conference on Blockchain (Blockchain 2022) (pp. 298-303). IEEE. https://doi.org/10.1109/Blockchain55522.2022.00048 Go to original source...
    201. Zheng, J., & Zhu, Y. (2023). Algorithmic trading and block ownership initiation: An information perspective. British Accounting Review, 55(4), 101146. https://doi.org/10.1016/j.bar.2022.101146 Go to original source...
    202. Zheng, X., Gildea, E., Chai, S., Zhang, T., & Wang, S. (2024). Data science in finance: Challenges and opportunities. AI, 5(1), 55-71. https://doi.org/10.3390/ai5010004 Go to original source...
    203. Zhou, C., Huang, Y., Cui, K., & Lu, X. (2024). R-DDQN: Optimizing algorithmic trading strategies using a reward network in a double DQN. Mathematics, 12(11), 1621. https://doi.org/10.3390/math12111621 Go to original source...
    204. Zhu, Q., Zhou, X., & Liu, S. (2023). High return and low risk: Shaping composite financial investment decision in the new energy stock market. Energy Economics, 122, 106683. https://doi.org/10.1016/j.eneco.2023.106683 Go to original source...
    205. Zolfagharinia, H., Najafi, M., Rizvi, S., & Haghighi, A. (2024). Unleashing the power of tweets and news in stock-price prediction using machine-learning techniques. Algorithms, 17(6), 234. https://doi.org/10.3390/a17060234 Go to original source...
    206. Zou, W., & Xiong, Y. (2023). Does artificial intelligence promote industrial upgrading? Evidence from China. Economic Research - Ekonomska Istra¾ivanja, 36(1), 1666-1687. https://doi.org/10.1080/1331677X.2022.2092168 Go to original source...

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