AI stands for artificial intelligence. AI is very smart computer software. AI can study huge amounts of information very quickly. Because of this, some people think AI can help pick stocks that will go up in price.
There are a few main ways people are trying to use AI for stock picking:
Training AI on Historical Data
One way to use AI is to "train" it on past stock data from many years ago. Researchers feed the AI software tons of old data about:
- Stock prices over time
- Company financial numbers like sales, profits, etc.
- News and social media posts about the companies
- Economic conditions at different times
- Many other kinds of data, too.
The AI software and
automated trading bots study all this old data. It tries to figure out what kinds of patterns and signs in the data meant a stock price was about to go up or down.
For example, stocks with rising profits, positive news coverage, and strong economic growth tended to go up in value afterward. Once the AI learns these patterns from studying old data, it can then look at new, current data and identify
stocks that might go up based on the patterns it learned.
Using AI for Data Mining
AI is also very useful for quickly mining and analyzing massive amounts of new data from all over. This can help spot potential stock opportunities.
For instance, an AI could scan through millions of news articles, social media posts, corporate filings, and other data sources every day. It can instantly detect negative or positive sentiment about a particular company or industry.
If a company has really positive sentiment data but its stock is still cheap, that could be a chance to buy the undervalued stock. On the other hand, if there is very negative sentiment data about an expensive stock, that could mean it is overpriced and needs to be sold.
The AI can correlate the sentiment data with all kinds of other data points like:
- Changes in company management teams
- Supply chain or regulatory issues
- Releases of new products or services
- Economic factors like jobs reports
By finding connections across many data sources, AI may spot stock mispricings that human investors could miss.
Using AI with Other Analysis
Some investors use AI together with other investment analysis methods like:
- Fundamental analysis of company finances and valuations
- Technical analysis studying past charts and price patterns
- Quantitative analysis crunching economic and statistical data
The AI can enhance these strategies by processing way more data inputs. It can uncover predictive data patterns that are hard for human analysts to see.
Limitations of Using AI
While AI has powerful potential for stock picking, there are still some big limitations:
Data Flaws
The quality of AI's stock picks depends entirely on the data quality fed into it. If there are flaws like:
- Incorrect company financial data
- Fake or misleading social media posts
- Lack of data about rare "black swan" events
- Old data that is now outdated
...then the AI's recommendations could be bad. Humans may still need to check the AI's data sources.
Real-World Changes
AI models are trained on past data, but the real world keeps changing in unexpected ways. New technologies, consumer trends, economic shocks and other surprises could make old data patterns less useful for predicting the future.
No Context
While AI is great at finding patterns across data, it lacks human context and real-world knowledge. It cannot apply judgement about things like a company's competitive advantages, supply chain risks, or the impacts of potential government policy changes.
Human Oversight Needed
For these reasons, most experts say AI for investing should not be used alone without human oversight - at least not yet. The best approach is using AI to rapidly process
huge amounts of data, combined with human expertise to:
- Verify the accuracy of data inputs
- Put AI's recommendations in context
- Check for real-world factors the AI may miss
- Apply investment strategy and risk management principles
Working together, AI can enhance human decision-making for stock picking rather than fully automating the process.
Future of AI Stock Picking
Although it is not perfect yet, experts expect AI's role in investing to grow rapidly. As AI and data quality improves over time, the technology could become more autonomous and require less human supervision.
In the future, AI stock picking systems may operate more like this:
- AI continuously monitors a vast universe of data streams across the web, finance, economics, and other sectors.
- Using advanced machine learning models, the AI identifies potential stock mispricing opportunities in real-time by combining all the data signals.
- The AI dynamically rebalances the investor's portfolio by executing automated trades to capitalize on its predictions.
- Human experts provide high-level governance by adjusting the AI's risk preferences and double checking outputs - but most day-to-day decisions are made by the AI
- The AI gets continually smarter over time by learning from its trading experience across different market environments.
Of course, getting to this level of AI sophistication will likely take many years. In the meantime, investors can start by using AI as a powerful tool to enhance their existing stock picking strategies through augmented intelligence capabilities.
Conclusion
In conclusion, AI is rapidly emerging as a potent tool for stock picking and investment management. While AI's ability to rapidly process massive amounts of data gives it strong potential, it also has significant limitations around data quality, lack of real-world context, and the inability to anticipate "black swan" events fully.
For now, the optimal approach is to use AI for augmented intelligence - leveraging AI's quantitative strength to surface potential investment opportunities while also applying human expertise, oversight, and qualitative judgment. As AI continues advancing and more experience is gained deploying it in finance, we may eventually see a future of highly autonomous AI investment systems requiring only high-level human governance.
However, that future is still years or decades away. In the near term, the investment industry should focus on responsibly harnessing AI's capabilities through a balanced augmented approach, continually monitoring the technology's strengths and weaknesses. With proper due diligence, AI can provide a powerful complementary tool for enhancing investing decisions without fully ceding control to the machines - at least not yet.
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