Stock research made simple by mapping news articles to price fluctuations.

Yeyen Ong

Did you profit from the GameStop or AMC rallies? Or were you sitting on the sidelines, experiencing FOMO, as you watched your friends benefit from the largest short squeeze of the century? Since the pandemic, the amount of retail investors in the US increased by 250%, growing the stock research market to a $28B+ industry. Retail investors now account for 1/3 of all US stock market trading, with a demonstrated ability to move markets. 

Now that the concept of growing wealth, generating passive income, and the rise of meme stocks are at the forefront of mainstream media, millennials and subsequent generations are realizing that they need to invest now in order to retire later. With inflation rising faster than most savings accounts, betting on stocks proves to be the more efficient way to build wealth.

However, there’s always been a huge barrier to entry into the stock market, especially for beginner retail investors. With the large amount of stock information available, combined with the riskiness of investing, there exists a need to make stock research easier for beginner investors*.

Currently, when users look at a stock’s performance over time, they can hover over any point in time on a stock chart and see quantitative data (price, trade volume, etc.). However, there is no corresponding qualitative data (press releases, news, etc.) displayed on the page. Users must perform multiple searches in separate tabs in order to piece the puzzle together, which leads to a high drop-off rate throughout the research process.

We hypothesize that:

  • Beginner investors rely primarily on qualitative research (news articles, press releases, etc.) instead of quantitative research (financial statements, balance sheets, etc.) to drive investment decisions
  • Performing qualitative research is time consuming for beginner investors

Leading us to the following problem statement:

“How might we make stock research easier for beginner investors so that they can derive insights faster and feel more confident about their research?”

*We define beginner investors as individuals with less than 1-2 years of active trading experience and a lower confidence in stock picking. 

User pain point + feedback 

To confirm our hypotheses and gain deeper understanding of our users’ pains, needs and desires, we applied generative and evaluative research methodologies.

1. User interviews

  • 5 beginner investors
  • 1 professional trader (to gain deep industry insight)

2. Survey 

  • Deployed a Google Survey with 11 questions, received 1000+ responses

3. Key Insights

  • 80% of beginner investors rely heavily on qualitative information and analysis (rather than quantitative) when researching stocks
  • beginner investors choose qualitative data because they can’t read a chart or they don’t feel confident about their financial analyses
  • consolidating qualitative data is time consuming (90% of investors spend >1-2 hours performing qualitative research)
  • users felt “information overload” on stock research sites and want more digestible information 

1. Usability Tests and In-Depth Interviews (IDI)

  • Conducted 10 moderated (using IDI) and 10 unmoderated (using Maze) usability tests to gain feedback from users at each stage of our development process; leveraged insights to improve and iterate our product.

Our research enabled Yeyen to create:

User Persona

Empathy Map

User Flow

Customer Journey Map

Landing on the solution 

Through our research, we confirmed our initial hypotheses and established 3 key jobs to be done:

  • Functional: Reduce time spent performing qualitative stock research in order to derive insights faster
  • Emotional: Feel more confident performing stock research in order to make investment decisions
  • Social: Be informed about stock news in order to have engaging conversations about the topic with peers

Next, we ideated on Miro to arrive at some interesting solutions:

  • aggregate qualitative stock data from credible sources (Bloomberg, etc.) and display insights on a stock chart using annotations at major price fluctuations     
  • define and add financial/technical jargon on the page to a ‘dictionary’
  • data filtering by ‘news articles’, ‘SEC filings’, etc.
  • add stocks to portfolio/watchlist
  • save searches

Since we only had 5 weeks of development time, Joey grounded us on what was technically feasible to build. With Joey’s input, combined with what we thought would bring the most value for our users, we landed on 3 key features to prioritize for demo day:

  1. Search function + Stock chart elements
  2. Data filtering
  3. Annotations

After finalizing our key features, we defined our North Star metrics as: 

  • user confidence in stock research
  • # of stocks searched

For our product to be successful, we would have to see both metrics increase

Explanation of solution 

HelloStocks makes stock research quicker and more digestible for beginner investors. Our product maps news articles to price fluctuations on a stock chart by fetching data from Polygon and Finnhub API’s. With HelloStocks, users are able to see qualitative and quantitative data on a stock chart so that they can save time and stay on one webpage. Our goal is to empower users by providing a more holistic view of major price movements and serve as a starting point for their stock research.


Product Manager Learnings:

Muse Guo

Adaptive Communication: Communication is key to collaboration. Understanding how my team members communicate and adapting my communication style to match theirs leads to smoother collaboration and unlocks true empathy.

Prioritization & Negotiation: Every week consisted of narrowing the scope and prioritizing features while balancing user needs. I learned to negotiate with my teammates by providing alternatives and leveraging data to drive decisions. Working in Agile enabled me to iterate as needed in order to deliver incremental value to our users.

Designer Learnings:

Yeyen Ong

Data backed design decisions: Starting off with a research plan for the entire cycle and collecting data helped me become a more empathetic designer that continually advocates for users. When questioned on design decisions, I can always back up my designs based on real data, not intuition.

Agile Methodology & Open Communication: Working in Agile has taught me to be flexible and patient with my research and design processes, and helps me iterate and pivot faster with my team. By using this process with open communication, we quickly put out small fires before they became bigger problems.

Developer Learnings:


Collaborating with a PM & Designer: Everyone has their distinct roles but we all come together to bring the project to life. Cross communication between team members is very important. Being a team player is everything.

Agile Methodology: It’s important to stay on track, but sometimes things don’t go as planned so being adaptable is key. Given we had 5 weeks to complete this project, 2 week sprints became one week sprints so it was important to focus on main features and quickly make adjustments when needed.

Developers Learnings:


Full Team Learning

Making trade-offs is prevalent throughout the product development cycle! Whether it be design, development or product trade-offs, striking a balance between delivering maximum user value and meeting business needs is a tough feat. Our team was able to master this by empathizing with our users, understanding project scope, and ensuring alignment between each other.