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

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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.

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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.‍

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Generative
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

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Evaluative
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.

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Our research enabled Yeyen to create:

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:

Search function + Stock chart elements
Data filtering
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.

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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.

Learnings

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.

Designer Learnings:

Jo Sturdivant

Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.

Developer Learnings:

Joseph

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.

Developer Learnings:

Vanady Beard

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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.

Developer Learnings:

Stephen Asiedu

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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.

Developer Learnings:

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Developer Learnings:

Maurquise Williams

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Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.

Developer Learnings:

Jeremiah Williams

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All in all this experience was very awesome I learned that in coding with others being transparent is key

Developers Learnings:

Justin Farley

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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.

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.

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