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AI/ML in trading currently focuses on analyzing historical prices, trading volumes, and financial data like balance sheets and quarterly earnings. While this captures trends and seasonality, it doesn’t account for external factors such as market sentiment, economic conditions, geopolitical tensions, and rumors or news—all of which significantly influence stock prices. To make predictions more reliable, these factors must be integrated into AI/ML systems.
Sentiment analysis, powered by AI, can interpret market mood by analyzing news, social media, and financial reports. Combined with Generative AI, it enables deeper insights and predictions, creating robust models that consider both historical data and real-time sentiment.
According to Andrew Ng (Ng, 2017), "AI is not replacing traders; it’s augmenting them by turning data into actionable insights." He also says just like the way almost every industries use electricity, every industry will use AI.
Risk Curiosity-Driven Learning
Risk Curiosity-Driven Learning
According to Hirchoua et al. (2021), “Risk, Curiosity-Driven Learning for Financial Rules-Based Policy proposes a novel RL approach where agents trade in a continuous virtual environment." Trained on 504 risky datasets, these agents use curiosity-driven learning to identify relationships between market actions and behaviors.
This reward system helps the agents learn rules on their own to make better decisions in uncertain situations. Tests with eight real-world stocks showed that these curiosity-driven agents made smarter trades with fewer transactions, beating traditional methods. The system shows great promise for automating financial trading with better results.
Hirchoua et al. (2021, p.6)
Reinforcement Learning
Reinforcement Learning
Reinforcement Learning for Trading: Reinforcement learning algorithms optimize trading strategies through reward-based systems. These models learn autonomously or with supervision, improving their performance over time. By simulating trades and adjusting based on outcomes, they enhance decision-making. This AI technique is key for creating self-learning trading systems.(Cuello, 2024, n.p.)
Tabaro et al. (2024) (p. 7 of 24)
Sentiment Analysis
Sentiment Analysis
Tabaro et al. (2023) bring in a unique perspective by discussing the role of sentiment analysis, using AI to interpret public opinion in real-time to make trading decisions. They believe this can make AI-driven trading more effective, especially in day trading, where short term market shifts are crucial. By adding sentiment analysis, AI can consider the human emotions that often influence markets, making decisions more accurate.
AI can now process vast amounts of unstructured data, such as news articles, tweets, and forum posts, to detect sentiment trends that might influence market movements. Machine Learning models can be trained to identify positive, negative, or neutral sentiments in text, allowing them to assess how the market feels about specific assets or economic conditions.
"For example, a sudden spike in negative sentiment around a particular stock might signal a potential price drop, while a surge in positive sentiment could indicate a buying opportunity. Sentiment analysis using AI provides traders with a more comprehensive view of market dynamics, as it captures real-time reactions from a wide range of sources." (Cuello, 2024, n.p.)
Tabaro et shows Sentimental analysis by predicting "sentiment analysis of Tesla’s annual and quarterly financial reports to the input of our proposed algorithm, using the ”buy-and-hold” trading strategy." Tabaro et al. (2024) (p. 2 of 24)
Portfolio Management
Portfolio Management
Portfolio management is another area where AI and ML have made a significant impact. Traditionally, portfolio management involved balancing risk and return based on historical performance, diversification strategies, and the investor’s risk tolerance. However, AI and ML have introduced a new level of sophistication to this process.
"These technologies can optimize asset allocation by continuously analyzing market conditions, asset correlations, and other financial metrics. Machine Learning models can assess the risk and return profiles of various assets, recommending optimal portfolio compositions that align with the investor’s goals." (Cuello, 2024, n.p.)
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