AI Applications in Trading
Introduction
Artificial intelligence (AI) technologies have seen uses spanning a wide variety of industries from healthcare to trade. The financial sector has not been spared by the disruptive AI and one area that has had one of the longest histories of AI is trading (Dezhic, 2017). Commodity trade has benefited from AI technologies such as natural language processing (NLP) and artificial surveillance. Among the functions are supply chain visualization, inventory management, scheduling among other activities operational management activities. Blockchain technologies have also enhanced secure business transactions on digital currencies. This paper discusses the uses and opportunities of AI applications in trading, the challenges and possible ways of overcoming them.
Uses of AI in Trading
The ability of AI systems to predict stock price fluctuations is perhaps the greatest function of AI that people are looking for in trading especially for financial institutions and hedge fund organizations. Stock prices are very unpredictable and follow a random sequence that makes it difficult for human investors to correctly predict the winning bets (Waisi, 2020). Machine learning technologies are able to collect huge datasets and analyses them to come up with more accurate predictions. AI systems are able to analyses thousands of investor decisions and biases, as well as market changes (Eck et al., 2019). AI systems enable an analysis of weekly, monthly or yearly patterns and establish areas of stability. The awareness of stability enables is exploited to increase the profitability of trading. While manual trading may not benefit from such an in-depth analysis, AI thrives in establishing patterns. The analysis based on AI algorithms is apt to overfitting and investor sentiments which are major challenges in trading. Such agility to information improves the efficiency of AI systems for trading. AI trading is also effective for speed trading that creates bigger profits. Deep learning and natural language processing provide AI components with superior computation power that can be exploited for investment decisions (Ullah et al., 2021). AI is also able to the technical indicators and patterns, analyze multiple factors and assign to them weight according to learned information to support investment decisions. Such indicators include RSI and moving average. AI-based trading platforms provide essential information in the development of trading algorithms. Some of the companies that use machine learning for stock trading include Morgan Stanley which leverages robot advisers to assist investors in managing their wealth. Kavout is an example of an investment platform that is used by investors seeking information on market trends (“Applications of machine learning algorithms for trading,” 2020). The company uses machine learning capabilities to scan market information to provide a fair evaluation that some traders rely on in making investment decisions. Tickeron, trend spider and MetaStock are other examples of trading bots that show greater efficacy (Moore, 2021).
Strengths and opportunities of AI in Trading
The strengths of AI compared to manual trading include the ability to analyze a large amount of data. The ability to determine large sets of data and compute them in seconds enables AI systems to perform the desired functions. AI systems show superiority of manual trading through accurate forecasting, easier analysis of fluctuations and recognition of patterns. AI systems for trading are also able to research multiple sources to establish information on companies before the effect of investor sentiment on stock prices. Automated trading through AI systems reduces the amount of time spent on trading for employees. They can spend extra time on managerial activities that enhance organizational efficiency. Automated trading is also helpful for speed trading which is highly profitable but riskier than manual trading.
Some of the opportunities of AI in trading include analysis of past data, human-machine collaboration possibilities and increased AI attention in research. The analysis of past data could further improve the strengths of AI systems for trading. Waisi (2020) suggests that past trends could improve stock price prediction by 40% which is quite very difficult to achieve through manual trading. The strong emotions and high stakes involved ensuring that investors will, in most cases, want some level of control over the trading process. Thus, there are opportunities for human-machine collaboration which is considered the ideal situation in AI literature. Moreover, increased research attention on AI means many AI solutions for trading are likely to come up which is better for the future of AI-based trading.
Weaknesses and Threats of AI in Trading
Some of the weaknesses of AI systems in trading include concentration on segments which makes it difficult to determine whole processes or systems. This means that the chances of losing are still significant due lack of control over certain factors. Apart from this, AI implementation is often faced with certain challenges. First, acquiring AI systems and experts to execute the systems for trading is quite expensive (Ferreira et al., 2021). Secondly, AI analysis depends on the quality of data used for training or for the development of an algorithm. An increased lack of transparency means some AI systems may be trained on insufficient or inaccurate data. Further, it encourages investments in trading events where there is a higher risk of low returns on investments.
Increased adoption of AI may mean that everyone has a near-perfect knowledge of the market which is likely to reduce the margins for everyone. The benefits from trading could be channelled to wealthy persons which leave governments and the poor in a difficult financial situation (Ferreira et al., 2021). The possibility of changing roles in finance may mean that the roles of some expertise may be rendered inefficient. There is no clarity in the way the labour markets and organizations would respond. Past experiences with AI trading contributed to the economic slump of the early 2010s which led to a significant impact on stock prices of multiple companies.
Improving AI for Trading
Improve Data:
AI systems are as good as the data for training. First, addressing the challenge of the unavailability of data significantly undermines AI development for trading. An increasing lack of data privacy and commercialization of data makes it more expensive to acquire the necessary data for machine learning and the generation of algorithms (Bacoyannis et al., 2018). Incorrectly trained AI systems for trading can lead to significant investment capital. Thus, it is essential to improve the legislation around AI data to enhance its availability and transparency. Adequate and accurate datasets are essential for tapping into AI systems’ analysis strengths for trading.
Enhance human-machine collaboration:
Some AI applications in finance including trading may reduce human roles. Nevertheless, a combination of human labour and AI-based decision support rather than100% automation is essential for sustainability. One reason for encouraging human roles in trading include the ability for a holistic view of the systems rather than AI automated systems that are trained to emphasize certain factors and not others. The particularity of AI analyses and the holistic view of human labour can combine to improve the outcomes of trading. Enhancing human-machine collaboration for trading would also fit the desired situation with increased AI implementation in many industries: improve human labour rather than replace it. Increased human-machine collaboration could help avoid or mitigate the risk of loss if an algorithm is trained on a period of low volatility and used in a period of high market volatility (“Potential and pitfalls of artificial intelligence in the trading environment,” 2018).
Support AI research and education:
An effective AI policy spanning education, regulation on data standards among other things could improve AI applications in trading. The development of capacity for AI educations and research through policies that encourage high-quality science educations, adequate infrastructure and knowledge sharing would enhance AI application in trading and beyond. Supporting a holistic view of AI could also help the government deal with economic inequalities and disparities that continue to plague the human race in different territories and states.
Improve the regulatory framework:
AI applications for trading are high risk because it is not easy to audit automated transactions. These may give room for people with malicious intentions to determine the outcomes (Bacoyannis et al., 2018). The lack of a strong regulatory framework may hinder the interpretation of law around AI for trading. A more uniform interpretation of the law ensures that every stakeholder in trading is aware of the consequences of their actions and their legal repercussions. A clearer regulatory framework would also improve data availability and use for AI training which is essential for their performance in any setting including trading of securities.
Conclusions:
AI for trading is based on the ability of AI technologies like machine learning to analyze large amounts of data in seconds. The capabilities of AI enable the prediction of stock price fluctuation, stability, investments decisions support and pattern recognition. Some of the strengths of AI in trading include the ability to analyze big data in a short time, accurate forecasting, and easier analysis of fluctuations and recognition of patterns. Automated trading through AI also saves time. The challenges include lack of high-quality data, disjointed regulatory frameworks and widespread use would result in a reduction of gains. Some of the ways AI could be improved to enhance trading applications include enhanced support for AI education and infrastructure, augmenting human-machine collaboration opportunities. Further studies on the impacts of AI trading on market stability and the regulatory environment are encouraged to increase awareness of the possible implications of widespread AI use on the financial sector.
