To start with the conclusion, many people who stumble when gathering information for stock or real estate investments are struggling with ‘organizing and comparing information’ rather than the selection of stocks or properties itself. AI is not a tool to make investment decisions for you, but is best used as a sounding board that takes on the burden of organizing information, understanding technical terms, and making comparisons.
In previous articles, I have shared how to use AI to shorten writing time, how to foster engagement with readers, how to use AI for gathering and comparing information for side businesses, and how to shape your first paid article with AI. This time, I will broaden the theme further to organize how AI can be used in the fields of stock and real estate investment, where mistakes directly impact your own money.
I would like to state one thing in advance. This article is not investment advice. I will not touch upon specific stock names, property names, specific yield levels, or investment decisions themselves, such as ‘whether now is the time to buy.’ I will focus strictly on how to share the division of labor with AI for the tasks that precede decision-making: organizing information, comparing it, and understanding technical terms. Given my own background as a former public servant and employee of an independent administrative institution, I have a personality that values steady information organization. I hope you will read this as a story about building habits for such steady organization rather than about flashy techniques.
Three common worries when organizing stock and real estate investment information
Even if you are interested in investing, the walls that people who stumble at the information-gathering stage often hit can generally be summarized into the following three points.
1. There is too much information to keep up with
Financial results summaries, IR materials, news, property documents, and opinions on social media. In both the stock and real estate worlds, the sources of information are by no means scarce. In fact, gathering information itself is not difficult; the problem is that there is so much of it that you cannot keep up, and you end up not knowing where to start.
2. You don’t understand the meaning of technical terms or numbers, and you stop before you can even compare
PER, yield, net yield, vacancy rate. Even if you look at documents while the meanings of these terms and numbers remain vague, there is no way to compare them. Many people run out of time while looking up the meanings of words one by one and get tired before they even reach the stage of comparison and consideration.
3. You cannot judge whether ‘this is a suspicious story’
Displays promising abnormally high yields, solicitations for private stocks, and overly sensational sales talk. When you encounter such information, it is difficult to judge instantly whether it is a legitimate story or something to be wary of.
What these three worries have in common is that the bottleneck is the stage before the investment decision itself—that is, the work of organizing and comparing information. In the next chapter, let’s look at how to incorporate AI into this work.
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Three situations where you can use AI as an ‘investment sounding board’
It is easier to organize how to use AI by dividing it into three situations: ‘primary organization of information,’ ‘creating comparison tables,’ and ‘identifying risk factors.’
・Primary information organization: Have AI summarize key points from financial statements, property documents, and news, and provide simple explanations of technical terms → This reduces the effort of reading from scratch and helps you grasp the big picture.
・Creating comparison tables: Have AI align multiple stocks or properties along the same axis using only “objective items” such as industry, area, building age, and estimated management costs → This allows for comparative review based on a consistent axis rather than intuition.
・Identifying risk factors: Have AI create a checklist of generally recognized risk factors such as market fluctuations, vacancy risks, and interest rate changes → This converts easily overlooked check items into concrete points.
There is one important caveat here. When handling numerical values like yields in comparison tables, the key is to limit AI usage to “organizing the numbers exactly as written in the documents,” rather than having the AI make predictions or evaluate whether a level is “attractive.” This is because the interpretation of numbers and the judgment of superiority or inferiority are areas that should remain entirely your own responsibility. In the next chapter, I will
introduce practical prompt examples.
