Welcome to Schwab—we’re glad you’re here.
TD Ameritrade, Inc. has been acquired by Charles Schwab, and all accounts have been moved. At Schwab, you get access to thinkorswim ® trading platforms and robust trading education, along with great service, a commitment to low costs, and a wide range of wealth management and investing solutions.
See what Schwab has to offer.
World-class trading and investing
Get intuitive platforms—including thinkorswim (desktop, web, mobile), Schwab.com, and Schwab Mobile—designed for traders and investors like you.
Transparent pricing
We clearly explain the fees we charge, and we’ll always be committed to giving you transparent pricing.
Exceptional service
We’ve combined our teams to provide even better support. Plus, you’ll have access to more than 400 physical branches.
Wide product range
Count on an even greater range of investment choices and wealth management solutions, tailored to your unique needs.
Ready to get started?
Common questions.
Expand All Collapse All
How do I access my account if I was a TD Ameritrade, Inc. client?
All clients of TD Ameritrade, Inc. are now Schwab clients. If you’re new to Schwab, you’ll need to set up a Schwab Login ID and password at schwab.com/login. If you’re already a Schwab client, you can use your existing Schwab login; there’s no need to create new credentials.
- If you have questions about creating your Schwab Login ID and password, call 800-435-4000 to speak with a representative. If you live outside the U.S., contact us at +1-415-667-8400 for support.
How do I access historical information, including past TD Ameritrade, Inc. statements and tax documents?
You are able to access:
- Up to 10 years of historical tax documents, brokerage statements, and trade confirmations on Schwab.com by navigating to Accounts > Statements & Tax Forms, and on the Schwab Mobile app by navigating to More > Documents.
- Up to four years of transaction history on Schwab.com by navigating to Accounts > History. Your TD Ameritrade, Inc. history will be shown under your Schwab account number, along with the recent transaction history for that account.
- Realized gain/loss data via a link on the Realized Gains/Loss page on Schwab.com. The information will be available for at least two years after your move to Schwab.
- Historical balance information via historical TD Ameritrade, Inc. statements on the Statements & Tax Forms tab. Your Personal Value Chart on the Accounts Summary page of Schwab.com and the Schwab Mobile app will only reflect your account balance history from the date your transition to Schwab is complete. It will start to populate about three days after your move.
What platforms do I have access to now that my account has moved to Schwab?
As a Schwab client, you’ll have continued access to the thinkorswim platform suite and can also use Schwab.com and the Schwab Mobile app. Use them all–or pick and choose–the choice is yours. You have several platform choices at Schwab:
Mobile apps
- thinkorswim mobile: Enjoy an optimized trading experience on the go. You’ll get Level 2 streaming quotes, customizable charting tools, and additional options trading features. Plus, just like on TD Ameritrade Mobile, you can quickly and easily view your balances, positions, and more by scrolling up and down.
- Schwab Mobile: Manage all of your accounts on the go with a convenient summary view. Just swipe left to right, rather than up and down, to access account information, market insights, trading, move money functionality, and account documents–all at your fingertips. We’re continuing to make enhancements based on what you love about TD Ameritrade Mobile–and will have even more great features on the way.
Web platforms
- thinkorswim web: Get the power of thinkorswim in a more streamlined web platform that puts important tools front and center. Enjoy charting and analysis tools, streaming Level 2 quotes, more customization, and robust options trading tools.
- Schwab.com: Get a comprehensive portfolio performance view, investing income tools, fundamental research, thematic investing, move money functionality, trading, and more. Here’s where you’ll also find account statements, confirmations, and tax documents.
Desktop platform
- thinkorswim desktop: Trade on a fully customizable software–based platform filled with elite tools that help you test strategies, develop ideas, and place complex trades.
What if I am a former Scottrade client?
In February 2018, Scottrade clients transitioned to TD Ameritrade, Inc. Now, Schwab and TD Ameritrade, Inc. are one combined company, dedicated to serving investors across every phase of their financial journey. If you’re new to Schwab, you’ll need to set up a Schwab Login ID and password to access your account. If you’re already a Schwab client, you can use your existing Schwab login; there’s no need to create new credentials.
- If you have questions about creating your Schwab Login ID and password, call 800-435-4000 to speak with a representative. If you live outside the U.S., contact us at +1-415-667-8400 for support.
Have more questions? We’re here to help.
Call
TD Ameritrade, Inc. account questions? Call 800-435-4000 to speak to a Schwab representative.
Visit
Looking for a Schwab branch near you? Talk to an experienced financial representative at one of our 400+ branches nationwide.
- thinkorswim mobile and Schwab Mobile requires a wireless signal or mobile connection. System availability and response times are subject to market conditions and your mobile connection limitations. Functionality may vary by operating system and/or device.
Investment and Insurance Products Are: Not FDIC Insured • Not Insured by Any Federal Government Agency • Not a Deposit or Other Obligation of, or Guaranteed by, the Bank or any of its Affiliates • Subject to Investment Risks, Including Possible Loss of Principal Amount Invested
The Charles Schwab Corporation provides a full range of brokerage, banking and financial advisory services through its operating subsidiaries. Its broker-dealer subsidiary, Charles Schwab & Co. Inc. (Member SIPC), and its affiliates offer investment services and products. Its banking subsidiary, Charles Schwab Bank, SSB (member FDIC and an Equal Housing Lender), provides deposit and lending services and products. This site is designed for U.S. residents. Non-U.S. residents are subject to country-specific restrictions. Learn more about our services for non-U.S. residents, Charles Schwab Hong Kong clients, Charles Schwab U.K. clients.
© 2025 Charles Schwab & Co., Inc. All rights reserved. Member SIPC . Unauthorized access is prohibited. Usage will be monitored.
- Accounts
- Brokerage
- 401(k) Rollover
- Individual Retirement Accounts (IRAs)
- Schwab Bank Checking
- Small Business Retirement
- See More Accounts
- Stocks
- Mutual Funds
- Exchange Traded Funds (ETFs)
- Annuities
- Bonds
- See More Investment Products
- Trading Platforms
- Execution Quality
- Options
- Futures
- Retirement Calculator
- Roth vs. Traditional IRA Calculator
- Research Tools
- Mobile Apps
- Insights & Education
- Trading
- Market Commentary
- Planning & Retirement
- Podcasts
- Schwab Network
- Why Schwab
- Compare Us
- Satisfaction Guarantee
- Forms & Applications
- Pricing
- Notify us of a Death
- SchwabSafe
- Privacy
- Additional Schwab Sites
- Site Map
- Business Continuity
- Financial Statement
- Accessibility Help
- Contact Us
- About Schwab
- Careers
- Investment Professionals’ Compensation
- Important Notices
- Account Protection
- SIPC®
- FDIC Insurance
- FINRA’s Broker Check
- Bank Client Complaints
- Client Relationship Summaries
Print this article
Indexing metadata
How to cite item
Review policy
- View
- Subscribe
- By Issue
- By Author
- By Title
- Other Journals
Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions
Meshal I. Alhusaynan, Majed Almashari
Article ID: 4463
Vol 8, Issue 7, 2024
VIEWS – 1184 (Abstract)Abstract
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.
Keywords
equity trading simulators; portfolio investment management; pre-trade compliance
Full Text:
References
- 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
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Meshal I. Alhusaynan, Majed Almashari
https://www.schwab.com/welcome-to-schwabhttps://systems.enpress-publisher.com/index.php/jipd/article/view/4463/0