This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

Aburto, L., Romero-Romero, R., Linfati, R., et al. (2023). An Approach for a Multi-Period Portfolio Selection Problem by considering Transaction Costs and Prediction on the Stock Market. Complexity, 2023, 1–15. https://doi.org/10.1155/2023/3056411

Ahmadi-Javid, A., & Fallah-Tafti, M. (2019). Portfolio optimization with entropic value-at-risk. European Journal of Operational Research, 279(1), 225–241. https://doi.org/10.1016/j.ejor.2019.02.007

Al Janabi, M. A. M. (2007). Risk analysis, reporting and control of equity trading exposure: Viable applications to the Mexican financial markets. Journal of Derivatives & Hedge Funds, 13(1), 33–58. https://doi.org/10.1057/palgrave.jdhf.1850059

Al-Gasawneh, J. A., AL-Hawamleh, A. M., Alorfi, A., et al. (2022). Moderating the role of the perceived security and endorsement on the relationship between per-ceived risk and intention to use the artificial intelligence in financial services. International Journal of Data and Network Science, 6(3), 743–752. https://doi.org/10.5267/j.ijdns.2022.3.007

Alves, T. W. (2020). Shift: A Highly Realistic Financial Market Simulation Platform. Available online: https://arxiv.org/abs/2002.11158 (accessed on 12 January 2024).

Ammann, M., & Schaub, N. (2021). Do Individual Investors Trade on Investment-Related Internet Postings? Management Science, 67(9), 5679–5702. https://doi.org/10.1287/mnsc.2020.3733

Amrouni, S., Moulin, A., Vann, J., et al. (2021). ABIDES-gym. Proceedings of the Second ACM International Conference on AI in Finance. https://doi.org/10.1145/3490354.3494433

Andraszewicz, S., Kaszás, D., Zeisberger, S., et al. (2022). The Influence of Upward Social Comparison on Retail Trading Behavior. https://doi.org/10.31219/osf.io/48deq

Ang, A., Papanikolaou, D., & Westerfield, M. M. (2014). Portfolio Choice with Illiquid Assets. Management Science, 60(11), 2737–2761. https://doi.org/10.1287/mnsc.2014.1986

Anginer, D., Piza, C., Ray, S., et al. (2020). Trading Simulations and Real Money Outcomes. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3634775

Annette, L., Nazareth, D. P., Wardwell, L.L.P. (2015). SEC Adopts Regulation SCI to Strengthen Securities Market Infrastructure. Available online: https://corpgov.law.harvard.edu/2015/01/07/sec-adopts-regulation-sci-to-strengthen-securities-market-infrastructure/ (accessed on 12 January 2024).

Aragon, G. O., & Ferson, W. E. (2006). Portfolio Performance Evaluation. Foundations and Trends® in Finance, 2(2), 83–190. https://doi.org/10.1561/0500000015

Babel, B., Buehler, K., Pivonka, A., et al. (2019). Derisking machine learning and artificial intelligence—The added risk brought on by the complexi-ty of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks. Available online: https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/derisking-machine-learning-and-artificial-intelligence#/ (accessed on 6 January 2024).

Back, C., Morana, S., & Spann, M. (2023). When do robo-advisors make us better investors? The impact of social design elements on investor behavior. Journal of Behavioral and Experimental Economics, 103, 101984. https://doi.org/10.1016/j.socec.2023.101984

Bakoush, M. (2022). Evaluating the role of simulation-based experiential learning in improving satisfaction of finance students. The International Journal of Management Education, 20(3), 100690. https://doi.org/10.1016/j.ijme.2022.100690

Balakrishnan, V., Khan, I. A., & Birkök, M. C. (2022). Atlantis Highlights in Social Sciences, Education and Humanities. In: Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-044-2

Bank of England. (2020). Evaluation of the senior managers and certification regime Available online: https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/report/evaluation-of-smcr-2020.pdf?la=en&hash=151E78315E5C50E70A6B8B08AE3D5E93563D0168 (accessed on 12 January 2024).

Bansal, S., Garg, I., & Sharma, G. (2019). Social Entrepreneurship as a Path for Social Change and Driver of Sustainable Development: A Systematic Review and Research Agenda. Sustainability, 11(4), 1091. https://doi.org/10.3390/su11041091

Barber, B. M., Huang, X., Odean, T., et al. (2022). Attention‐Induced Trading and Returns: Evidence from Robinhood Users. The Journal of Finance, 77(6), 3141–3190. Portico. https://doi.org/10.1111/jofi.13183

Belim, T., Soni, S., Sharma, S. (2023). A Study On Equity Derivatives And Challenges Faced By New Traders. International Research Journal of Modernization in Engineering Technology and Science, 5(2). https://doi.org/10.56726/irjmets33671

Biondo, A. E., Mazzarino, L., & Rossello, D. (2022). Portfolio Optimization and Trading Strategies: a simulation ap-proach. CEUR Workshop Proceedings, 3182. https://ceur-ws.org/Vol-3182/paper8.pdf(accessed on 12 January 2024).

Borsboom, C., & Zeisberger, S. (2020). What makes an investment risky? An analysis of price path characteristics. Journal of Economic Behavior & Organization, 169, 92–125. https://doi.org/10.1016/j.jebo.2019.11.002

Byrd, D., Hybinette, M., & Balch, T. H. (2020). ABIDES. Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. https://doi.org/10.1145/3384441.3395986

Cesari, R., & Cremonini, D. (2003). Benchmarking, portfolio insurance and technical analysis: a monte carlo compari-son of dynamic strategies of asset allocation. Journal of Economic Dynamics and Control, 27(6), 987–1011.

Checklist, S. P. (2018). Strategic Plan Checklist Strategic Plan Checklist. Available online: https://www.sec.gov/files/SEC_Strategic_Plan_FY18-FY22_FINAL.pdf (accessed on 12 January 2024).

Cueva, C., Roberts, R. E., Spencer, T., et al. (2015). Cortisol and testosterone increase financial risk taking and may destabilize markets. Scientific Reports, 5(1). https://doi.org/10.1038/srep11206

Daley, B., & Green, B. (2016). An Information‐Based Theory of Time‐Varying Liquidity. The Journal of Finance, 71(2), 809–870. https://doi.org/10.1111/jofi.12272

Doering, J., Kizys, R., Juan, A. A., et al. (2019). Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends. Operations Research Perspectives, 6, 100121. https://doi.org/10.1016/j.orp.2019.100121

ESMA. (2023). Opinion on the Trading Venue Perimeter. Available online: https://www.esma.europa.eu/sites/default/files/library/ESMA70-156-6383 Final Report on ESMA%27s Opinion on the trading venue perimeter.pdf (accessed on 12 January 2024).

European Central Bank. (2019). Algorithmic trading: trends and existing regulation. Available online: https://www.bankingsupervision.europa.eu/press/publications/newsletter/2019/html/ssm.nl190213_5.en.html. (accessed on 12 January 2024).

Fabozzi, F. J., & Markowitz, H. M. (2011). The Theory and Practice of Investment Management: Asset Allocation, Valuation, Portfolio Construction, and Strategies. Wiley. https://doi.org/10.1002/9781118267028.

FCA. (2020). The MiFID 2 Guide. Available online: https://www.handbook.fca.org.uk/ (accessed on 12 January 2024).

Federal Financial Supervisory Authority. (2018). Big data meets artificial intelligence. Federal Financial Supervisory Authority.

Financial Conduct Authority. (2018). Algorithmic Trading Compliance in Wholesale Markets. Available online: https://www.fca.org.uk/publication/multi-firm-reviews/algorithmic-trading-compliance-wholesale-markets.pdf (accessed on 12 January 2024).

Fong, K. Y. L., Holden, C. W., & Trzcinka, C. A. (2017). What Are the Best Liquidity Proxies for Global Research? Review of Finance, 21(4), 1355–1401. https://doi.org/10.1093/rof/rfx003

Gang, T. U., & Choi, J. H. (2023). Optimal Investment in an Illiquid Market with Search Frictions and Transaction Costs. Applied Mathematics & Optimization, 88(1). https://doi.org/10.1007/s00245-023-09971-7

Hartman, S. R., & Green, M. S. (2020). Forecasting the CFTC’s 2020 Agenda. Available online: https://www.steptoe.com/en/news-publications/forecasting-the-cftcs-2020-agenda.html. (accessed on 12 January 2024).

Hassanien, A. E., Tolba, M. F., Shaalan, K., & Azar, A. T. (2019). Advances in Intelligent Systems and Computing. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. Springer International Publishing. https://doi.org/10.1007/978-3-319-99010-1

He, G., & Litterman, R. (2002). The Intuition Behind Black-Litterman Model Portfolios. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.334304

Hu, R., & Watt, S. M. (2014). An Agent-Based Financial Market Simulator for Evaluation of Algorithmic Trading Strategies. In: 6th International Conference on Advances in System Simulation. Nice, France. pp. 221-227.

Huber, C. (2019). oTree: The bubble game. Journal of Behavioral and Experimental Finance, 22, 3–6. https://doi.org/10.1016/j.jbef.2018.12.001

IOSCO. (2011). Regulatory Issues Raised by the Impact of Technological Changes on Market Integrity and Efficiency Available online: https://www.iosco.org/library/pubdocs/pdf/IOSCOPD354.pdf (accessed on 12 January 2024).

Jankowski, J., & Shank, T. (2010). A Comparison of Online Stock Trading Simulators for Teaching Investments. Available online: https://www.jstor.org/stable/41948638 (accessed on 12 January 2024).

Kapsis, & Ilias. (2020). Artificial intelligence in financial services: systemic implications and regulatory responses. Available online: https://bradscholars.brad.ac.uk/handle/10454/17935 (accessed on 12 January 2024).

Keller, A. J. (2012). Robocops: Regulating High Frequency Trading After the Flash Crash of 2010. Available online: https://kb.osu.edu/bitstream/handle/1811/71570/OSLJ_V73N6_1457.pdf?sequence=1&isAllowed=y (accessed on 12 January 2024).

Kim, T. H., Choi, B., Lee, J.-N., et al. (2021). Portfolio effects of knowledge management strategies on firm performance: Complementarity or substitutability? Information & Management, 58(4), 103468. https://doi.org/10.1016/j.im.2021.103468

Kolm, P. N., Tütüncü, R., & Fabozzi, F. J. (2014). 60 Years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research, 234(2), 356–371. https://doi.org/10.1016/j.ejor.2013.10.060

Kwak, Y., Song, J., & Lee, H. (2021). Neural network with fixed noise for index-tracking portfolio optimization. Expert Systems with Applications, 183, 115298. https://doi.org/10.1016/j.eswa.2021.115298

Labonte, M. (2020). Who Regulates Whom? An Overview of the U.S. Financial Regulatory Framework. Congressional Research Service, 34. https://docplayer.pub/docs/16fa1_who-regulates-whom-an-overview-of-the-u-s-financial.html

Lee, C.F., & Lee, A. C. (2022). Encyclopedia of Finance. Springer. https://doi.org/10.1007/978-3-030-91231-4

Lee, J., & Schu, L. (2022). Regulation of Algorithmic Trading: Frameworks or Human Supervision and Direct Market Interventions. European Business Law Review, 33(2), 193–226. https://doi.org/10.54648/eulr2022006

Legislation, T. (2020). Onshoring and the Temporary Transitional Power. Available online: https://www.fca.org.uk/brexit/onshoring-temporary-transitional-power-ttp (accessed on 12 January 2024).

Lejarraga, T., Woike, J. K., & Hertwig, R. (2016). Description and experience: How experimental investors learn about booms and busts affects their financial risk taking. Cognition, 157, 365–383. https://doi.org/10.1016/j.cognition.2016.10.001

Liu, B., & Zhao, Q. (2022). Financial Derivative Price Forecasting and Trading for Multiple Time Horizons with Deep Long Short-Term Memory Networks. Scientific Programming, 2022, 1–9. https://doi.org/10.1155/2022/6526512

Lu, Y.-N., Li, S.-P., Zhong, L.-X., et al. (2018). A clustering-based portfolio strategy incorporating momentum effect and market trend prediction. Chaos, Solitons & Fractals, 117, 1–15. https://doi.org/10.1016/j.chaos.2018.10.012

Lwin, K. T., Qu, R., & MacCarthy, B. L. (2017). Mean-VaR portfolio optimization: A nonparametric approach. European Journal of Operational Research, 260(2), 751–766. https://doi.org/10.1016/j.ejor.2017.01.005

Ma, Y., Han, R., & Wang, W. (2020). Prediction-Based Portfolio Optimization Models Using Deep Neural Networks. IEEE Access, 8, 115393–115405. https://doi.org/10.1109/access.2020.3003819

Manrai, R., & Gupta, K. P. (2022). Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing, 28(1), 1–14. https://doi.org/10.1057/s41264-021-00134-9

Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77. https://doi.org/10.2307/2975974

Metaxiotis, K., & Liagkouras, K. (2012). Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review. Expert Systems with Applications, 39(14), 11685–11698. https://doi.org/10.1016/j.eswa.2012.04.053

Moazeni, S., Coleman, T. F., & Li, Y. (2010). Optimal Portfolio Execution Strategies and Sensitivity to Price Impact Parameters. SIAM Journal on Optimization, 20(3), 1620–1654. https://doi.org/10.1137/080715901

Moffit, T., Stull, C., & McKinney, H. (2010). Learning Through Equity Trading Simulation. American Journal of Business Education (AJBE), 3(2), 65–74. https://doi.org/10.19030/ajbe.v3i2.386

Montgomerie-Neilson, G. (2012). Selecting the Worst-Case Portfolio: A proposed pre-trade risk validation algorithm of SPAN. Available online: https://www.diva-portal.org/smash/get/diva2:555872/FULLTEXT01.pdf (accessed on 12 January 2024).

Nazareth A. L. (2015). SEC Adopts Regulation SCI to Strengthen Securities Market Infrastructure. Available online: https://corpgov.law.harvard.edu/2015/01/07/sec-adopts-regulation-sci-to-strengthen-securities-market-infrastructure/ (accessed on 12 January 2024).

Olorunnimbe, K., & Viktor, H. (2022). Deep learning in the stock market—a systematic survey of practice, backtesting, and applications. Artificial Intelligence Review, 56(3), 2057–2109. https://doi.org/10.1007/s10462-022-10226-0

Piehlmaier, D. M. (2022). Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice. Financial Innovation, 8(1). https://doi.org/10.1186/s40854-021-00324-3

Pramod, D., & Raman, R. (2022). Intention to use Artificial Intelligence services in Financial Investment Decisions. 2022 International Conference on Decision Aid Sciences and Applications (DASA). https://doi.org/10.1109/dasa54658.2022.9765183

Qin, Z. (2015). Mean-variance model for portfolio optimization problem in the simultaneous presence of random and uncertain returns. European Journal of Operational Research, 245(2), 480–488. https://doi.org/10.1016/j.ejor.2015.03.017

Qiu, R., Chan, W. K. V., Chen, W., et al. (2022). City, Society, and Digital Transformation. Springer International Publishing.

Raschner, P. (2021). Algorithms put to test: Control of algorithms in securities trading through mandatory market simulations? Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3807935 (accessed on 12 January 2024).

Ren, F., Lu, Y.-N., Li, S.-P., et al. (2017). Dynamic Portfolio Strategy Using Clustering Approach. PLOS ONE, 12(1), e0169299. https://doi.org/10.1371/journal.pone.0169299

Riemann., Charlotte, J. (2022). An examination of critical factors influencing the future usage intention of innovative digital financial solutions for investment activities: consumers’ attitude towards online trading services provided by Neobanks in Germany Available online: https://run.unl.pt/handle/10362/138767 (accessed on 12 January 2024).

Roll, R., & Ross, S. A. (1980). An Empirical Investigation of the Arbitrage Pricing Theory. The Journal of Finance, 35(5), 1073–1103. https://doi.org/10.1111/j.1540-6261.1980.tb02197.x

Sadoghi, A., & Vecer, J. (2022). Optimal liquidation problem in illiquid markets. European Journal of Operational Research, 296(3), 1050–1066. https://doi.org/10.1016/j.ejor.2021.05.020

Sankar, J.R., Bhaskar Udaya, N.U. (2022). A Study on Problems of Investors in Derivatives Trading W.R.T Select Cities Of Andhra Pradesh Available online: https://www.researchgate.net/publication/357701627_a_study_on_problems_of_investors_in_derivatives_trading_wrt_select_cities_of_andhra_pradesh/link/61dbff43d4500608169f52e6/download (accessed on 12 January 2024).

Sharpe, W. F. (1970). Portfolio Theory and Capital Markets. Available online: https://www.gsb.stanford.edu/faculty-research/books/portfolio-theory-capital-markets (accessed on 12 January 2024).

Ta, V.-D., Liu, C.-M., & Tadesse, D. A. (2020). Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading. Applied Sciences, 10(2), 437. https://doi.org/10.3390/app10020437

Tao, R., Su, C.-W., Xiao, Y., et al. (2021). Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets. Technological Forecasting and Social Change, 163, 120421. https://doi.org/10.1016/j.techfore.2020.120421

Tatewaki, K. (2012). Banking and Finance in Japan (RLE Banking & Finance), 98–114. https://doi.org/10.4324/9780203109298-14

The European Commission. (2017). Commission Delegated Regulation (EU) 2017/589 of 19 July 2016. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0589&from=EN (accessed on 12 January 2024).

The European Commission. (2017). Commission Delegated Regulation (EU) 2017/582 of 29 June 2016. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0582&rid=1 (accessed on 12 January 2024).

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence‐Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375

Tubert-Brohman, I., Sherman, W., Repasky, M., et al. (2013). Improved Docking of Polypeptides with Glide. Journal of Chemical Information and Modeling, 53(7), 1689–1699. https://doi.org/10.1021/ci400128m

United States Patent. (2014) United States Patent. 2. Available online: https://patentimages.storage.googleapis.com/1b/94/d7/6a8467f14eb2d2/US10296973.pdf (accessed on 12 January 2024).

Vijayalakshmi Pai, G. A. (2017). Metaheuristics for Portfolio Optimization: An Introduction using MATLAB. Available online: https://www.wiley.com/en-us/Metaheuristics+for+Portfolio+Optimization%3A+An+Introduction+using+MATLAB-p-9781119482796. (accessed on 12 January 2024).

Vyetrenko, S., Byrd, D., Petosa, N., et al. (2020). Get real. Proceedings of the First ACM International Conference on AI in Finance. https://doi.org/10.1145/3383455.3422561

Wellman, M. P., Amy, G., Peter, S., et al. (2003). The 2001 Trading Agent Competition. Electronic Markets, 13(1), 4–12. https://doi.org/10.1080/1019678032000062212

Wu, X, Chen, H., Wang, J., et al. (2020). Adaptive stock trading strategies with deep reinforcement learning methods. Information Sciences, 538, 142–158. https://doi.org/10.1016/j.ins.2020.05.066

Yang, H., Liu, X.-Y., Zhong, S., et al. (2020). Deep reinforcement learning for automated stock trading. Proceedings of the First ACM International Conference on AI in Finance. https://doi.org/10.1145/3383455.3422540

Zaineb, E. K., Sahar, S., & Zouhir, M. (2022). Pricing American Put Option using RBF-NN: New Simulation of Black-Scholes. Moroccan Journal of Pure and Applied Analysis, 8(1), 78–91. https://doi.org/10.2478/mjpaa-2022-0007