Artificial Intelligence is a forever emerging force that is constantly impacting our daily lives. Some people experience this impact in their work life more than others do. One profession that witnesses the change that AI brings is stockbrokers. AI technologies have significantly transformed how financial professionals analyze data, make investment decisions, and manage portfolios. Stockbrokers utilize AI every single day when they perform trades, communicate with clients and other business partners, and when they test and perfect new trading strategies.
One of the biggest and most influential ways that stockbrokers use AI in their work is when they make trades on the stock market. They employ many different aspects of AI to help assist them with trading, one of them being algorithmic trading. Algorithmic trading executes orders at a “much faster pace than human traders can achieve. This speed is essential in markets where split-second decisions can make a significant difference. Algorithms can also react to market changes in real-time, reducing latency and ensuring that stockbrokers capture the best available prices” (Seth).
Along with the benefit of the speed that algorithmic trading has, it also can help by “simultaneously managing multiple trading strategies and assets, diversifying portfolios across various instruments and markets. This diversification can reduce overall risk and enhance potential returns” (Seth). Similar to the speed that algorithmic trading offers, this ability to manage multiple trading options at one time cannot be done by one single person. Therefore, the ability for algorithmic trading to complete not just one but both functions while only having to have one person oversee the trading.
Another thing that algorithmic trading does that humans cannot do is twenty-four seven market monitoring. These trading systems can “monitor the market every single day, all day long. This allows them to respond efficiently to global events that impact markets” (Boehmer, Ekkehart, et al). With the mix of these three things as well as other upsides of algorithmic trading, it is highly effective in assisting stockbrokers complete their duties in a productive and profitable way.
Algorithmic trading seems great, but there are still a few risks and downfalls of it. One major concern is the potential for extreme market volatility and crashes, as algorithms can react rapidly to market events, amplifying price swings. This, in turn, can “lead to “flash crashes” and unpredictable market behavior. Moreover, algorithmic trading can perpetuate market inefficiencies and bubbles as automated systems often follow trends, potentially causing overvaluation or undervaluation. The use of complex algorithms also introduces a risk of technical glitches, which can result in substantial losses” (Seth). Furthermore, there are concerns about algorithmic trading favoring large financial institutions with advanced technology, potentially undermining market fairness and equity. These challenges highlight the need for stringent regulations and safeguards in algorithmic trading.
Artificial Intelligence is also used by stockbrokers in communication. They use AI when speaking with clients about future business and when talking with coworkers about current and long-term trades. One way that communication with clients is assisted is by AI chatbots. Chatbots can provide “instant responses to client inquiries, ensuring that clients do not have to wait for a human broker’s availability. This immediate communication can be particularly valuable during market hours when timely information can impact investment decisions” (Sorokin). This immediate and personalized response can also help stockbrokers attract and retain clients.
Another benefit of chatbots for stockbrokers is the twenty-four seven availabilities that AI powered chatbots have. This constant and always available chatbot “allows for better customer service and gives clients a more convenient experience. This can lead to higher customer service satisfaction which can lead to more clients coming to stockbrokers” (Sorokin).
Aside from helping communication with clients, AI can also help stockbrokers communicate with coworkers. AI-driven collaboration tools can “streamline communication by offering features like document sharing, version control, and task management. These tools make it easier for stockbrokers to work together on research, trading strategies, and portfolio management” (Zirar, Araz, et al). These different communication tools make it possible for stockbrokers to communicate in real time and help each other with different trades and trends. Without these AI powered communication tools, it would be extremely difficult for stockbrokers to communicate with each other in an efficient and productive way. However, by harnessing the power of AI in different forms of communication platforms they can work together to make smarter trades and better-informed decisions.
Despite these great qualities of chatbots, they are not perfect. A significant disadvantage is the potential for frustrating and impersonal interactions. While chatbots have improved, they often struggle to “fully understand and address complex or nuanced customer inquiries, leading to customer dissatisfaction. This can erode trust and damage the customer-business relationship” (sciencedirect.com). Another concern is customer privacy. Chatbots can mishandle sensitive information and struggle to maintain data security, putting private information at risk. If clients are trying to chat about different account details, it is detrimental that the information about their account remains safe and private.
Besides all these benefits of AI for stockbrokers, AI also helps them test and perfect different trading strategies without taking on lots of risk. Instead of risking real money testing and experimenting with different strategies, different AI programs help stockbrokers do this without putting real money into the market. An example of a commonly used program that allows traders to do this is TrendSpider. “TrendSpider is a software platform designed for technical analysis and charting in the financial markets, primarily for trading stocks, forex, cryptocurrencies, and other assets. The platform leverages technology, including Artificial Intelligence and automation, to help traders and investors make more informed decisions and enhance their technical analysis process” (Horvath).
These programs provide stockbrokers with access to a wealth of data, market analysis, and expert opinions. By offering “up-to-date financial news, research reports, and historical data, research platforms empower stockbrokers to stay on top of market trends, evaluate potential investments, and assess the performance of various financial instruments. They also enable stockbrokers to provide their clients with valuable insights and recommendations, building trust and credibility in their advisory roles” (Chowdhury).
By back testing new strategies, AI programs allow stockbrokers to assess their viability by simulating how these strategies would have performed in the past. Moreover, AI-driven simulations provide “real-time testing in current market conditions, giving stockbrokers an opportunity to fine-tune their approaches and assess their effectiveness in near real-time. This data-driven approach not only saves time but also provides stockbrokers with a more systematic and informed way to develop and optimize their trading strategies, ultimately leading to more confident and successful decision-making” (Chowdhury).
The integration of Artificial Intelligence into the daily routines of stockbrokers has proven to be a game-changing development in the world of financial markets. AI provides traders with tools and insights that were previously unavailable, empowering them to make more informed and efficient trading decisions. With advanced algorithms and predictive analytics, AI can quickly process vast volumes of data, identify trends, and generate accurate predictions, giving day traders a competitive edge. Moreover, automation in trade execution allows for swift reactions to market conditions and the ability to execute complex strategies with precision.
AI also gives them different platforms to communicate and research different strategies with other stockbrokers. Through the integration of AI into financial markets there are now unlimited possibilities for traders with what they can do.
While the advantages of AI for day traders are evident, it’s important to acknowledge that this technology is not without its challenges. The potential for algorithmic errors, data biases, and the need for constant monitoring remains. However, as AI continues to evolve and adapt, it has become an indispensable ally for traders seeking to navigate the complexities of financial markets, reduce risks, and optimize their trading strategies for sustained success. The future of day trading undoubtedly lies in the balance between human expertise and AI-driven insights.
Boehmer, Ekkehart, et al. “Algorithmic Trading and Market Quality: International Evidence: Journal of Financial and Quantitative Analysis.” Cambridge Core, Cambridge University Press, 13 Oct. 2020, www.cambridge.org/core/journals/journal-of-financial-and-quantitative-analysis/article/abs/algorithmic-trading-and-market-quality-international-evidence/4B96E916E3E13AFF1DF9B5FCC188F4E0. Accessed 5 Nov. 2023.
Chowdhury, Emon Kalyan. “Use of Artiﬁcial Intelligence in Stock Trading.” MPRA, MPRA, 10 Mar. 2019, mpra.ub.uni-muenchen.de/118175/1/Use%20of%20AI%20in%20Stock%20Trading.pdf. Accessed 5 Nov. 2023.
Sorokin, Evgeny. “How to Use Chatbots in the Online Trading Industry.” Medium, Chatbots Magazine, 30 Mar. 2018, chatbotsmagazine.com/how-to-use-chatbots-in-the-online-trading-industry-4fa7870fb3ea. Accessed 5 Nov. 2023.
Horvath, Sarah. “Trendspider Review 2023: Pros, Cons & More .” Benzinga, Benzinga, 10 Oct. 2023, www.benzinga.com/money/trendspider-review. Accessed 5 Nov. 2023.
Seth, Shobhit. “Basics of Algorithmic Trading: Concepts and Examples.” Investopedia, Investopedia, www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp. Accessed 5 Nov. 2023.
Zirar, Araz, et al. “Worker and Workplace Artificial Intelligence (AI) Coexistence: Emerging Themes and Research Agenda.” Technovation, Elsevier, 15 Mar. 2023, www.sciencedirect.com/science/article/pii/S0166497223000585. Accessed 5 Nov. 2023.