Christina Qi
CEO of Databento, Board of MIT - Modernizing the market data industry.
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“Risk is randomness that you can parameterize. Uncertainty is randomness that you cannot. The best opportunities come from taking on uncertainty, not risk.” - Andrew Lo In any data-heavy industry, you'll hear the terms "risk" and "uncertainty." A classic example of uncertainty in finance is the occurrence of unexpected events or market shocks. COVID-19, wars, sudden regulatory changes, and other events can have a profound impact on financial markets and investment returns. Unlike risk, where probabilities can be assigned based on historical data, uncertainty makes it challenging to quantify the potential outcomes or the likelihood of their occurrence. Usually, if you make an earlier-stage investment, you face less data and more uncertainty. I'd argue that, in general: VC > PE > IB > HF in terms of uncertainty. That's why VCs, when they do score a win, typically win big. Some hedge funds face increased uncertainty too (e.g. emerging market funds), where limited historical data and unpredictable factors make decision-making more challenging.To navigate uncertainty, investors may employ strategies such as diversification, hedging, or incorporating scenario analysis into their decision-making process. These approaches allow for flexibility and adaptation in the face of unknown future outcomes. If you're considering a career as an investor, it's important to think about how much risk vs. uncertainty you're willing to take on.
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Andreas von Hirschhausen, CFA, CAIA
Passionate about Financial Education, Planning, and Technology
11mo
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reminds me of a story Toby Main told me on The Wall Street Lab: what is riskier: jumping of the 3rd or 30th floor of a building?
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Andy Freeman
I do product, technology, business, and help startups figure out and tell their story. What do you need?
11mo
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Not only is it important for an investor to understand how much uncertainty s\he is willing to take on, it is important for folks seeking investment to understand what "lane" a potential investor is in.Two extremes are Venture Capital and Main Street banking. The latter charges 2-10% while the former is looking for 10-100x However, the former insists on "sure things" while the latter's business model can make money with 70% failures.It's sort of obvious why MS doesn't do venture investing, but it is just as true that VCs aren't interested in properly priced "sure things".
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Elliot Haugen
Technical Solutions Engineer at Epic | Mathematics at Mount Holyoke College
11mo
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Have you read any of Douglas Hubbard's work? He argues that there is more historical precedent at least within the actuarial science/decision theory/math worlds to speak of measurable uncertainty. He believes its less confusing to refer to the "unknown unknowns" as areas of ignorance. For example, if I'm about to flip a coin, and I ask you if you are uncertain that I will land on tails, you would say yes. However we can clearly measure this uncertainty (50%). So by your definition, we can't call it uncertainty, and that doesn't really make sense to anyone outside of certain schools of economics and finance. I totally agree with the underlying message of your post, just wanted to share a different way of looking at these terms.
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Fred Viole
OVVO Financial Systems | ovvolabs.com
11mo
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Related:https://www.linkedin.com/pulse/expected-partial-moments-fred-viole
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Andy Freeman
I do product, technology, business, and help startups figure out and tell their story. What do you need?
11mo
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Risk can be (somewhat) accurately "priced in". Uncertainty can't.
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Shruti Mandaokar
MSCS @Purdue | Project Manager @Laboratory of Data Science | SDE@Vodafone | ML@SCAAI
11mo
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Very interesting 💯.
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Julie C.
11mo
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Nice one .
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This risk vs uncertainty post hits home Christina. Very nice breakdown.
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Matthew DeBow
Helping Customers Navigate the World of Copilots - AI - Cloud
11mo
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I love that quote!
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Christina Qi
CEO of Databento, Board of MIT - Modernizing the market data industry.
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This was the best book I've read all year, hands down. This book is helpful for anyone with a stake in the data business today, whether as an operator or as a VC. Chin and Davis validate the unique struggles that every data business faces today. From high barriers to entry, to common misconceptions, to real advice on sales and marketing, this book was both educational and validating!
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Christina Qi
CEO of Databento, Board of MIT - Modernizing the market data industry.
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What are some common misconceptions in your industry? Below are some irrelevant sentences that we hear often: 1. "I'm excited to apply my data science skills to Databento." - job candidate 2. "Market data is too small. I'll only invest if you gather the world's data, like satellite data, social media, and healthcare data." - investor 3. "Databento could use our AI services to enhance your offering." - startup 1 doesn't bother us, as it's exhausting applying to job after job, so candidates deserve leniency for not grasping what every company does. 2 indicates a lack of knowledge about market data vs. alternative data, the different audiences they appeal to, and the extreme differences in product, costs, and regulations between the two. 3 just no. The only AI startup I support is the one that eliminates all of the AI spam in my inbox every morning.
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Christina Qi
CEO of Databento, Board of MIT - Modernizing the market data industry.
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Shoutout to our team who worked late-nights to write this super detailed IEX Cloud <> Databento migration guide. We hope that this will bring some much-needed transparency as you compare all of the IEX alternatives out there. https://to.dbn.to/3XIgIs4
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Christina Qi
CEO of Databento, Board of MIT - Modernizing the market data industry.
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#bts We're growing, slowly but surely!
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Christina Qi
CEO of Databento, Board of MIT - Modernizing the market data industry.
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Sharing my thoughts on data startups since we're quoted in this front-page article today. - It sucks that startups are pitted as rivals when we try our best NOT to compete against each other. If we did, we would all die. Most users switch to Databento from a $10B incumbent. We rarely hear startup names during sales calls - it would suck (& be an omen for all) if we stole significant business away from any startup. - Believe it or not, Databento's enterprise customers don't switch over because of the price (despite having fair pricing: https://lnkd.in/gVMjvZvC). They switch over because our API and web experience are superior to the incumbent vendors. We consistently improve our uptime, latency, and technical support. - I cannot stress enough that targeting retail customers is a formula for failure, unless you get enough user data to sell it to HFT firms, as Robinhood does. That doesn't mean that we should shun retail folks, but rather that startups should think about how to scale from Day 1. Is the answer PLG? Scalable pricing? Better APIs? Better analytics? Cloud or on-prem delivery? - Whatever the innovation is, the answer should NEVER be "cheaper prices." "We're Bloomberg but cheaper," is a formula for failure UNLESS you offer a true technological innovation outside of price.
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