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Perceptions – Better investment results by avoiding behavioral pitfalls

Hacker News

Perceptions – Better investment results by avoiding behavioral pitfalls

Show HN: Perceptions - Better investment results by avoiding behavioral pitfalls Hi All, I’ve been working with a team of institutional investors to develop a product (called "Perceptions") that allows an investor to create a thesis (reasoning) for an investment and then add measurement criteria, confidence level as the thesis evolves. It ensures you stay true to the reason you bought the shares and avoid behavioural pitfalls. It gives you nudges when it looks like you are making a behavioural error. The goal is to allow investors to organise their reasoning behind an investment, what’s their expected outcome and their confidence level. Tracking any changes on their thesis and the development of the investment. After the fact, it allows us to report back to the investor how successful they have been and how to adjust their processes/thinking Although we’ve been working with institutional investors to date, I believe this could be useful for anyone who wants to get a bit more serious and wants to avoid the usual behavioural pitfalls, so… we’ll be opening beta access to retail investors as well with a forever-free tier! Please let me know what you think, suggestions on the idea and whether this could be helpful to you! All feedback welcome! https://www.perceptions.io/

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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