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Stock market analysis

Hacker News

Stock market analysis

https://www.forestpin.com/cse/ This is still experimental and is a different way of analyzing stocks in the long term. We consider Dividends, Rights Issues, Bonus Issues, and Splits to calculate the value of the share. This gives a more realistic view than simply looking at the share price variations. We reinvest cash from dividends in the same stock and sell existing shares to execute rights. It's a simple calculation but it gives a much better perspective of the stocks in the long term than simply looking at the share price. You can select a stock symbol or click on following links to see some of the largest companies. https://www.forestpin.com/cse/#JKH https://www.forestpin.com/cse/#DIAL https://www.forestpin.com/cse/#COMB https://www.forestpin.com/cse/#NEST The first graph shows the value if you bought a share at the IPO. The second graph shows the number of times your investment has grown. This is in log scale to give a better visualization of ROI with time. The third graph is the actual share price. The fourth graph is the number of shares you own. (You buy more shares with dividends, the number of shares increases with Splits, etc) The fifth graph is the current value if you invested in the stock at the given time. i.e. if the graph shows 10 in 2007, it means your investment of $1 in 2007 is now worth $10. The list of events below shows the calculation. Unfortunately, right now, we only have data from Colombo Stock Exchange now, and even that is until August 2014. We had to do a lot of data cleaning and still there seem to be some errors. We plan on adding up-to-date data as well as including other stock exchanges.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: visual · Missing: mac, agents, macos
21%21% predicted probability of success on Product Hunt, 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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