Investors have many tools at their disposal nowadays ranging from charting software to online brokerage platforms like those reviewed by Techopedia writer Kane Pepi.
In the review, Pepi lists 12 of the best brokers online who are regulated, have low fees, and offer a large range of stocks. However, the tool that has been making the biggest waves recently is AI which is being used to analyze and pick stocks by some traders. That’s why in this article, we’ll be exploring if using AI for stock selection is worth it or not.
The Promise of AI in Stock Selection
Artificial Intelligence (AI) has changed how various industries operate, and stock selection has not been spared. Here’s how AI is changing how we analyze and predict market trends.
Analyzing Vast Data Sets
Traditional stock analysis has always involved sifting through huge amounts of data, including news articles, financial reports, and historical stock prices. AI algorithms have excelled in handling these large datasets with efficiency.
That’s because AI models can process unstructured and structured data simultaneously to extract relevant information from social media posts, financial statements, and even satellite imagery. By analyzing these different data sources, AI can identify correlations and patterns that human analysts might miss.
Predicting Market Trends
AI algorithms can also use historical data to make predictions about future stock prices. They do this by learning from existing patterns and using new information to adjust their predictions.
Time-series models, like long short-term memory (LSTM) and recurrent neural networks (RNNs), are also used to capture temporal dependencies in stock prices. AI sentiment analysis tools have also been invaluable in gauging market sentiment by analyzing news pieces and social media posts.
Risk Management and Portfolio Optimization
AI programs can help with risk management by assessing portfolio diversification. They do this by optimizing asset allocation based on expected returns and risk tolerance.
There are also portfolio management tools that use AI to recalibrate portfolios to ensure they align with investment goals. Some investors even use AI-powered robo-advisors who give them personalized investment tips that align with their financial situations and risk preferences.
Case Study: Danelfin’s AI Platform
By launching its cutting-edge AI platform Danelfin, a leading fintech company has revolutionized how stock analysis is performed. That’s because contrary to traditional methods that rely on historical data and human intuition, Danelfin’s approach analyzes large sets of information in real-time using machine learning algorithms. Their platform weighs in factors like macroeconomic indicators, news sentiments, and market trends.
Low-Risk Scores Explained
The most notable feature of Danelfin’s AI platform is that it calculates Low-Risk Scores (LRS) for individual stocks. To determine LRS figures the platform assesses risks associated with particular stocks. Some of the risk factors it considers are company fundamentals, liquidity, and stock price volatility. Once the platform has determined stock LRSs, stocks with higher scores are recommended to risk-averse investors, whilst those that have lower scores are recommended to investors who are okay with the risks that come with potentially high returns.
Performance of Danelfin’s Stock Picks
Over the last three years, Danelfin’s stock picks have consistently outperformed market benchmarks. The recommendations made by the platform resulted in impressive gains for investors in various sectors. From predicting market trends to identifying undervalued tech stocks, Danelfin’s AI platform has proved itself an invaluable tool.
Benefits of Using AI for Investors
AI has changed the investment sphere by arming individual investors with tools that used to only lie in the hands of institutional players. Let’s see how these tools have benefited those who use them.
Access to Institutional-Level Analysis
Many retail investors have gained access to next-level analytics that sometimes outcompete those used by investment banks and hedge funds. They’ve managed to do this by leveraging AI-powered platforms that can spot investment opportunities with incredible precision by analyzing market activity and optimizing portfolios.
Potential for Higher Returns
AI algorithms can be implemented and used in several industries to see higher returns. For example, betting sites and anonymous casinos use this tech to understand player behavior and yield higher returns. In the finance sector, investors have been using this tech to adapt to changing market conditions. By learning from this data, AI programs have been able to keep investors agile by helping them execute trades at optimal times that their human eyes could miss. However, investors must keep in mind that AI programs are just tools they can use, and not every prediction will lead to guaranteed profits. Investors still need to consider factors that AI tools might disregard and do their due diligence before every trade.
Challenges and Considerations
With AI continuing to disrupt the fintech sector, it has brought both benefits and challenges to investors. Here are some key considerations that analysts and investors should have in mind when they use AI to select stocks.
AI’s Limitations
Even though AI programs can identify patterns in the market that humans can miss, these algorithms aren’t infallible. Some of the limitations they have include the quality of the data they receive, as these programs rely heavily on historical data. If the data they receive is biased, incomplete, or contains too many outliers, it can result in inaccurate predictions.
The Risk of Overreliance
A risk of relying too much on AI programs is that many of these operate as “black boxes,” which makes it hard to determine the processes behind their decision-making. This absence of transparency can be detrimental if investors rely on these algorithms for critical financial decisions without understanding how they work. AI models also tend to struggle with adapting quickly to unprecedented events that affect the market and sudden market shifts, which is why investors need to maintain a healthy level of skepticism when using these tools.
The Importance of Human Oversight
Human judgment is still very important in stock selection since AI models don’t have sufficient contextual understanding. That means AI programs can disregard qualitative factors such as management quality in companies that humans can notice. Here are some other areas where these tools can benefit from having human oversight.
Risk Assessment
Even though AI can quantify risk, assessing its significance still needs human judgment. For example, an AI program may deem a stock too risky and decide not to trade it, but a human could notice that it’s still attractive due to its potential rewards.
Ethical Considerations
AI programs might not also fully grasp the ethical implications of the stocks they trade, like how buying into a certain company could perpetuate issues such as climate change.
Comparing AI Recommendations with Traditional Analysis
Instead of replacing traditional analysis, AI stock recommendations should complement the work analysts do, here are some areas where this union could be very fruitful.
Quantitative vs. Qualitative Analysis
AI programs are good at doing quantitative analysis such as understanding statistical models, while humans are fantastic at qualitative analysis which includes understanding market dynamics.
Diversification
When it comes to diversification, AI is excellent at optimizing portfolios, but humans could help these programs by assessing sector-specific risks they could miss.
Conclusion
So, is using AI to select stocks worth it? As we’ve seen, AI programs are useful due to their ability to spot patterns accurately by analyzing large datasets and making data-driven decisions that can enhance portfolio performance. However, supplementing AI conclusions with human judgment remains crucial. That’s because AI models still have limitations, especially when they have to deal with unprecedented market events.