Artificial Intelligence In Trading : Playing the Devil’s Advocate
Artificial Intelligence In Trading AI has immense potential in trading to leave behind human decision-makers. Such as obtaining data intelligently, making predictions and optimizations, and establishing better quantitative investment strategies.
For example, Schumaker and Chen (2009) developed a powerful quantitative financial prediction system with Natural Language Processing technologies.
As a result with such potential advantages, hedge funds have begun to deploy artificial intelligence. Rebellion Research, headquartered in New York, launched the first pure artificial intelligence investment fund (Guangfa Securities, 2017). Bridgewater, Goldman Sachs, Citadel, Renaissance, and Two Sigma, etc. have also joined the new arena of quantitative investment.
The following are some of the AI trading platforms.
https://www.tradingtechnologies.com/– “Algo trading: Easily build algorithms and get better execution of your automated trading strategies. “
https://www.greenkey.ai/ – “Domain-specific Natural Language Processing (NLP) to identify and understand inquiries, quotes, trades, market color, and more as they are discussed in voice, chat, and email conversations between financial market professionals.”
https://www.auquan.com/ – “Solutions account for lookahead bias, overfitting and other pitfalls uniquely encountered in financial daasets.”
https://equbot.com/ – “Helps global investment professionals deliver better outcomes through custom AI portfolios as a service (PaaS).”
https://www.trade-ideas.com/ – “The A.I. powered robo-advisement consists of several dozen different investment algorithms subjected to over a million trading scenarios overnight to arrive at a subset with the highest probability for alpha in the next market session.”
However, AI in trading also raises some critical arguments!
Can AI be emotional?
In fact, emotion and empathy are what AI researchers are working hard on.
Moreover, we know that emotion is just a neuron signal, and maybe it can be imitated in the future.
Somers (2019) gives an explanation of emotion AI, which starts at least 1995 (Picard, 2000), as AI learns to understand and give feedback to human emotion. Lastly, some industries have already introduced emotional AI with success.
Firstly, Entropik Tech (https://entropiktech.com/) helps brands to understand human emotions by facial expressions, voice, etc. wearehuman.io (https://www.linkedin.com/company/city-sail/about/) can capture human behavior and predict it.
Secondly, Affectiva is an emotion AI company that helps automotive AI and advertising. As shown in Affectiva (https://www.affectiva.com/who/about-us/), it is used by 25% of Fortune Global 500 and 1,400 + brands.
Thirdly, BeyondVerbal (https://il.linkedin.com/company/beyond-verbal-communication) provides personal healthcare monitoring services which can predict and monitor chronic disease and human emotions.
Lastly, for traders, emotion may not be a good thing.
We may want to be completely rational but not emotional. That is, traders can optimize all the opportunities and goals, achieve them through the principle of cost-benefit or seeking advantages and avoiding disadvantages. On the other hand,
Can we totally believe AI in investing?
Dhar (2016) emphasizes the trade-off between prediction and cost per error when it comes to when to trust robots’ decisions. Although ML is known for excellent performance in big-data tasks, such as face recognition, machine translation, etc., It remains unclear whether it’s generally cost-wise to build an ML-based trading system.
Written by Jimei Shen
Dhar, V. (2016). When to trust robots with decisions, and when not to. Harvard Business Review, 17.
Guangfa Securities Financial Engineering (2017). Overview of artificial intelligence investment strategies. https://www.sohu.com/a/194536678_465470.
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1-19.
Somers, M. (2019). Emotion AL, explained. https://mitsloan.mit.edu/ideas-made-to-matter/emotion-ai-explained
Picard, R. W. (2000). Affective computing. MIT press.