How Investors Use AI To Analyze Language And What You Can Learn From Them

Putting your heart and soul into a business is a nerve-wracking decision. Especially when it isn’t yours. There are some things you can’t see coming, and a company can look good at surface level, but it doesn’t mean it will be a good investment. 

Hidden risks, miscommunications, and outdated information can wreak havoc on an investor’s financial return. Avoiding these problems isn’t always possible, but having a clear understanding of a company, industry, and market can minimize them.

Investors are more likely to see the bigger picture when they leverage artificial intelligence (AI). The talents of this technology are far-reaching, but we’re going to focus on its ability to analyze language. Below is everything you need to know about how it’s done.

Applications of AI in the analysis of language

Where would we be without the human language? It’s a fascinating system that allows us to learn, share ideas, and build relationships. 

It can sometimes be overlooked by investors who prefer the dependability of financial and market data. But when these are combined with the analysis of language, you’ll be empowered to make the best decisions and unlock many benefits. You’ll have access to even more when you optimize the process with AI. 

Below are the most effective applications and use cases that are sure to drive the best results. Let’s explore how they work and why you need to try them out. 

Sentiment analysis 

Human bias is a powerful thing. Half of the time, we won’t even think twice about an assumption we’ve made. Texting is a perfect example of this. Has anyone ever reacted negatively to a message you’ve sent because you added a full stop to the end?

The first message asks if the responder is angry. They say "No." The initiator assumes that the full-stop is aggressive, so then responds with "Really?". The responder once again answers with "No."

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You probably had no intention behind the punctuation. Maybe it was a force of habit. But the respondent perceived it as passive-aggressive and assumed you were angry with them, despite your words being nothing but friendly. 

This prejudice can be trouble for investors. For example, you may fall victim to the regret theory, overestimate your ability to handle risks, or filter information based on your beliefs. As humans, we’re predisposed to making these mistakes.

But this doesn’t mean you can’t avoid them. Being aware is a good first step, but why not leverage technology designed to help you make sensible and informed investment decisions? It’s time you introduced sentiment analysis into your operations. 

So, how does it work? The technique uses a branch of machine learning called natural language processing (NLP) to analyze things like social media posts, online blogs, or reviews. The algorithms can classify the words as positive, negative, or neutral. 

The Venn diagram shows that NLP is in the middle of linguistics and computer science. The definition is in text at the top: "The interdisciplinary field of computer science and linguistics. NLP is the ability for computers to understand human language."

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Below are some of the ways this approach can be applied. 

  • Monitoring social media. Just because your friends like a product or the business boasts good reviews doesn’t mean you shouldn’t investigate. Find out for yourself what customers think. Also, find out what the complaints are about. Some will have a bigger impact on the company, for example, if the prices are too high. 
  • Getting insider knowledge from employees. Business owners might incentivize employees to speak positively even when they’re dissatisfied. You can find out what they think by looking through surveys and feedback. 
  • Finding out the truth. Business owners might sway you to think a certain way, which could influence your course of action. For example, they might exaggerate performance or financial statements to impress you. AI will help you uncover the emotional weight behind the words and help you decide whether or not to trust their numbers.
  • Quantitative trading strategies. The idea is that biases and emotions are removed because the algorithms make decisions based on data and numbers. These strategies can be developed by analyzing language patterns and sentiment. 

Risk management 

No matter how much you research and prepare, your success could be affected by emerging technologies, new market trends, inexperienced startups, or inflation. Investing will always involve risks, but there are ways you can manage them. 

The heading reads "Risk Management" and the definition underneath reads "The process of identification, analysis, and acceptance or mitigation of uncertainty in investment decisions."

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You guessed it! Risks can be managed with the use of AI. Models will analyze language datasets from various resources, including regulatory filings, annual reports, or investment policy statements (IPS). From this, they learn and identify potential risks that could jeopardize investment. 

Below are some examples of what they might flag. 

  • Liquidity concerns
  • Supply chain disruptions
  • Market volatility
  • Business problems
  • Products that have been recalled 
  • High debt levels
  • Compliance issues (fraud, money laundering, privacy breaches)

This information will form a crucial part of the decision-making process. While uncertainties will still loom over your head, you will have all the data you need to determine whether the risk is worth the reward for your next investment opportunity. 

Portfolio management

Portfolio management is where everything ties together. This process makes communicating with clients and stakeholders easier, clarifies any strategy you’ve developed, and keeps your actions aligned with your goals.

The heading heads "Benefits of Strategic Portfolio Management" and has four surrounding the text. These are "focusing operations", "improved efficiency", "cohesion and integration", and "agility".

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You can improve this framework in many ways when you commit to AI investment, but how do they work?

Specific AI models and technologies have been developed to tackle portfolio management. Primarily, they are built with a combination of predictive analytics, machine learning, and natural language processing. 

Language data gets continuously analyzed, so you will receive real-time updates about anything that can have ramifications on your portfolio holdings. Through AI, you gain actionable knowledge, helping you to take a more proactive approach to management. 

Let’s dive into some ways this becomes useful. 

  • Portfolio optimization. Diversifying your holdings can protect your money because only some will be affected by losses. AI models aim to provide insights into sectors with the most potential, so you can invest in companies across locations or industries even if you have less knowledge in certain areas. 
  • News monitoring. Portfolio management involves lots of monitoring. This can be time-consuming, and nearly impossible for even a big team to sift through every news article out there. Luckily, the AI models rapidly process information, so you always know where you stand. 
  • Automating documentation. Documenting your operations shows others you know what you’re doing and can help keep track of progress. AI language analysis can automate this task, for example, by generating summaries of meetings with clients.
  • Alternative data analysis. AI processes sources of information that aren’t traditional, such as online search patterns or consumer behavior data. These insights would otherwise be hidden. Therefore, they can provide you with a unique perspective of needs that haven’t been met yet, highlight innovation opportunities, and can detect trends quicker than if you were only leveraging conventional data. 
The image shows the overlap of big data, traditional data, and alternative data. The traditional includes examples like news, trading, and financial reports. The alternative shows geo-location, social media, payments, and satellite.

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Language-based predictive analysis

What’s the most effective way to determine whether a decision is going to be a good one? By looking into the future. Unfortunately, that isn’t possible yet, but it might be one day if technologies continue to develop. 

In the meantime, investors should use language-based predictive analysis, which is the next best thing. By integrating this method with other traditional approaches, you’ll gain a competitive edge, even in a constantly changing business landscape. 

But how does it work? Once the machine learning model is trained on historical data, it will use the learned language patterns to generate probabilities. It becomes possible to make predictions about the market, events, or a business’s financial situation. 

Here are some of the ways this technique can be employed.

  • Forecasting financial performance. A company’s success is made up of many factors, but future economic health needs to be looked at. Analyzing patterns in earnings calls and textual financial data will tell you everything you need to know. 
  • Event impact assessment. You can discover the potential outcome of a business’s events, such as mergers and product launches. If the model predicts this might have a hugely damaging impact, you might be better off taking your money elsewhere. 
  • Identifying emerging trends. There are patterns in language that models use for analysis. These findings can indicate a change in law, a technological advance, or a game-changing trend. You can use this to align your strategies and be quicker than other investors, helping you gain authority.
  • Evaluating growth opportunities. These predictions can also be used to determine a company’s potential for growth and estimate how high the investment returns will be. 

Text summarization 

Our world is overflowing with big data. It’s everywhere, and it’s becoming hard to manage. Traditional processing software cannot handle it, which is why data scientists and business analysts began to utilize AI processing algorithms. Now, even famous brands like Apple and Microsoft incorporate the technology to gain valuable insights from huge sources.

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It might sound strange, but too much information can be a problem for investors. Social media posts are constantly being generated, and reviews are being published from various sites every day. You also have lengthy documents from the business, such as research reports, business plans, and contracts. 

This might feel as though you’re being pulled in every direction possible. When this happens, key information can be missed. You could feel pressured to invest in a business that isn’t a good fit, make a decision based on inaccurate information, or become influenced by short-lived fanfare. 

So, how can you improve your investment strategy? By utilizing summarization algorithms, a method of AI that automatically creates summaries of any text. It will be easier to focus on what matters most when it is presented to you in a concise format. 

Let’s look at where this technology can be applied and why you need it. 

  • Trend analysis. A lot of noise surrounding a business can come from fleeting trends or hype from influencer marketing. These aren’t sustainable, and you shouldn’t base decisions on them. Text summarization identifies patterns on the internet. This makes it easier to capitalize on the most reliable opportunities. 
  • Competitor analysis. You can determine whether the competitors have a better market or financial position by analyzing documents related to their brand. This will tell you if an investment into a business is likely to be worthwhile.
  • Minimizing human error. There are many risks when it comes to analyzing documents. You may misread something, skip over crucial facts, or lose your place and have to start from the beginning. The algorithms remove anything you don’t need, so you’ll always be on the right track. 
  • Understanding values. Companies that consider sustainability are better positioned for the long term, making them a smarter investment. Looking into Environment, Social, and Governance (ESG) frameworks will help you measure how a business conforms to the set standards. 

Disclaimer

The capabilities of AI surpassed many of our wildest imaginations. The fact that our world could soon be filled with self-driving cars, virtual companions, and AI-powered rappers is an astonishing thought. However, it’s important not to put the technology on a pedestal. 

AI isn’t a fortune-teller with a crystal ball. These models can learn, but they do not have all the answers. There isn’t a promise of accuracy or a guarantee that the predictions will ring true. Although language analytics is a fascinating and impressive tool, it should be used alongside respected and well-practiced traditional methods. 

Your investment process should utilize these other factors before you make your decision:

  • Professional and expert opinions 
  • Fundamental analysis 
  • Market research 
  • Company research
  • Long-term perspective
  • Authoritative forums and communities

What will you achieve with AI?

AI has the potential to change the landscape of business investment completely. However, it is important to remember that human expertise, judgment, and intervention will continue to be necessary cogs in the machine. Investors who understand how to work alongside this technology will soon find themselves leaps and bounds ahead of their competition. 

Leveraging AI will bring you insights and data you didn’t have access to before. With data-driven insights, smart predictions, and masses of data at your fingertips, you won’t feel left in the dark ever again. There’s no better way to take charge of your investments.

AI is already doing amazing things, but developers and users are pushing the boundaries of what it can do. To find out what this technology is capable of, head over to Top Apps.