A human-centered approach to investing
What if investment research felt more like using Spotify?
Investing can be complicated, especially for those of us that don’t do it for a living. Increasingly opaque securities like derivative swaps and CDOs famously became so difficult to understand that they destabilized the world economy leading up to the 2008 financial crisis. This stuff is so confusing that the filmmakers of 2015’s, The Big Short made sure the audience understood what was going on by having actors constantly break the fourth wall to explain incomprehensible concepts. This type of complexity penetrates even the most mundane investments. Investing has insulated itself by inserting a lexicographic barrier between laypeople and experts that makes it difficult to be active in one’s own investment outcomes without a significant input of time and energy.
As designers, there’s a real, tangible problem to solve:
Simplifying the complicated financial terminology and concepts used to evaluate common stocks and securities so more people feel comfortable investing in their future.
Until you know all the rules, investing your own money into a system you don’t fully understand is too much of a risk, which is why people stop before they even start. According to a Bankrate survey, a staggering quarter of Americans stay away from investing because “they don’t understand stocks.” Why? For starters, most online investment tools don’t adapt to your needs in the same way that, say, Amazon does based on your past purchases and searches. The terminology and jargon in investment research platforms create a high barrier to entry that makes users less confident in their investment decisions. Things should be different.
There are countless services and products that already exist to help us make more educated decisions in other areas of our lives, so why not investing? For example, ESPN’s weekly wrap-up converts NFL data into simplified projections that help fledgling fantasy managers determine what players to start. Spotify helps users explore new music with curated playlists. Weather apps like Poncho convert temperatures into “what to wear” recommendations. These services are simple on the surface but are sophisticated in how they frame content. There’s a thing or two investing products can learn from products like these.
To begin to address these problems, we created a concept called Level that highlights areas where trade research platforms could use some improvement. Even though Level isn’t a real product, the opportunities and problems it addresses are genuine and persistent across any number of trading and research platforms.
Simplicity without cannibalizing complexity
When you want to invest your money, where do you begin? Hopefully you answered, “research.” There’s no denying that there’s a wealth of information out there, but finding it at your personal level of understanding is challenging. Most of today’s investment quote pages display tons of information the average person can’t comprehend. Statistics like Alpha, Beta, Sharpe Ratio, P/E, Bid, Ask, EPS, and others might be helpful but only if you’re able to glean any sort of understanding from them.
Today’s trading platforms are sold like VCR’s in the 1980’s: more buttons equals a better product. We know this isn’t true, and set out to envision a better and simpler way to research potential investments. Simplicity in this context doesn’t mean content should disappear, rather the complexity should be better managed.
Not much has changed in the world of stock searching in the last 20 years. While Google can seemingly predict what you’re going to type, to search for a stock, you still have to know the stock’s name or ticker symbol. If you want to look up Apple, you have to type “AAPL” or “Apple stock.” But what happens if you want to look up the companies that compete with Apple or that make components for Apple devices? You’d have to do your own research. Wouldn’t it be nice if a service knew what you meant and showed you possible matches? These types of features would be especially helpful if your personal interests guide your investing.
Instead of distinct companies, what if we had a flexible way to set search parameters? If you’re not exactly sure how to find companies that meet your investment criteria (or what your investment criteria are in the first place) these types of features come in handy, in the same way Spotify can recommend a new band based on your love for Nirvana.
A successful implementation should omit unhelpful criteria, while simplifying other criteria that are useful but often very complex, like asset classes such as stocks, bonds, and options. Mad libs are a useful metaphor for conversationally understanding what you might be looking for. Traditional search styles wouldn’t necessarily work in these situations, but a Mad Libs-style search or quick survey like the Care/of personalized vitamins survey or Ladder’s life insurance quote tool might be a better fit.
Results that relate to your investment criteria
Instead of showing you numbers that might not mean much to you—like stock price or volume—what if you could see how well a stock matches your personal investment criteria? Criteria as complex as your budget or values. Providing this high-level breakdown of various aspects of a tradeable asset in relation to your goals could help you decide if you want to dive deep into a quote page.
Curated content to learn about the market
Another large research component is learning about the context of the market and the terminology that goes along with it. For example, a Beta value is known as a measure of risk. But is a Beta value of 1 good or bad? You need to know the range, underlying math, and the index being referenced in order for a Beta to be useful. Investment research platforms assume users know the answers to these questions and don’t provide the additional context a beginner might need.
Again, taking a page from Spotify in the way they serve as a personal DJ, an investing platform could act as a personal fund manager. What if the discover page of an investment platform was educational and explanatory to the point where it could curate stock lists that explain different financial concepts, much like Spotify’s Discover Weekly playlists.
Why risk gambling real money?
Once you decide to “follow” a stock, what happens? Too often when you check in on your watch list using mainstream software, it just gives you an updated quote. But users don’t follow just to see updated quotes. They want to see what’s changed—the delta between what the investment was doing then and now. To do this, a solution should show trailing data from the last quarter of trading, so you can keep track of recent progress.
The design of a quote page today doesn’t change based on the number of shares you trade, the money you have, or your personal investment preferences. Quote pages should instead adapt to be more straightforward. When you see a stock chart, the title should help explain what you’re seeing. When you see key quantitative data about a stock, it should include integrated visualizations into sentence summaries. (In our concept, we did this using Spark, a font by After the flood, that uses OpenType ligatures to render visualization pretty much anywhere you write text.)
While investing your money is a task to be taken seriously, there should be no reason why it can’t be a more democratic process that helps you learn more about the decisions you make and the effects they have down the line. We hope one day something like Level exists in the real world so people can better teach themselves what matters when it comes to their investments and make the right decisions for their future. Right now though, it’s up for grabs.
This post is a collaboration between Andrew Gold, Julie Morycz, and Daniel Orbach.
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