How AI Could Transform the Way You Listen to Earnings Calls | Global News Avenue

The income telephone is designed to provide a clarity, but sometimes, there are more questions they bring to investors than answers. The executives carefully chose their words to avoid difficult topics, and sometimes more in the neglecting content than the actual saying. This is where AI began to change the game.

Researchers have found that artificial intelligence can be achieved through subtle transformations of language and tone, and these signals may suggest that the company’s policy changes before officials. Investors can not only rely on headlines or prepared statements, but also may soon have AI -driven tools to screen noise, highlighting the key takeaway, and even found a model that humans may miss.

If AI can change how traders analyze data and news, can it reshape the way we receive the income phone? Let’s talk about this.

How to use AI in the income phone number

Artificial intelligence, especially tools such as ChatGPT, have proven to be a good resources that analyzes the company’s policy changes and opens up. This may not be clearly explained. Studies from Georgia State University and Chicago showed how AI extracted subtle opinions from these telephones. For example, even if it is not mentioned directly, the statement of executives, such as “we are investing in growth plan”, may mean a large amount of capital expenditure. Traditionally, it is necessary to determine that such subtle are proficient analysts, but now AI can detect such a delicate meaning.

The study analyzed nearly 75,000 income sales transactions between 3,900 companies between 2006 and 2020. Using ChatGPT, researchers allocated scores to predict the company’s investment policy changes according to the language used in the call. The scores generated by these AI are closely consistent with the actual changes of capital expenditure and CFO survey response, which shows high accuracy. In addition to investment policies, this method has also successfully determined changes in dividends and employment. The results of the study show that AI can be consistent and objectively handle a large number of texts, so that human analysts may ignore insights. The consensus is that the AI ​​tool is now essential for investors who want to better understand the income telephone.

AI can also analyze CEO’s human voice characteristics to identify signs of depression

Recent research also shows that artificial intelligence can now detect signs of depression in the CEO by analyzing the sound mode in the income telephone. A study published in the “Accounting Research Magazine” in January 2025 depicts how the machine learning model can identify depression in the executives by checking the subtle sound characteristics in the income call record.

Researchers analyzed more than 14,500 revenue records from 500 companies from Standard Purcells from 2010 to 2021. Using AI -driven speech analysis, they can classify more than 9,500 CEOs into potential depression according to their voice mode.

This AI -driven method surpasses the traditional voice analysis method, and it detects the subtle sound characteristics that human ears cannot detect. The machine learning model uses complex algorithms to analyze the numerical embedding of the audio band, thereby making a more complicated assessment of the speaker’s mental state.

The discovery of the study shows that chief executive depression may be related to greater business risks, such as raising litigation and stock volatility. Limited evidence shows that CEO of depression tends to obtain greater compensation packages, which is high and related to performance.

Bottom line

Investors explain that the way of income phone calls is changing, and AI is responsible for a lot. It can detect language changes, tone clues and hidden signals that traditional analysis may be ignored. The AI ​​-driven tool has become more and more complicated, so relying only on execution statements may become the past. Now, this is no longer saying, but that AI can detect the content missed by human analysts.