FinBrain’s DOW30 Prediction Performance – 03-Jun-2019 – 14-Jun-2019

FinBrain’s AI algorithms have yielded 100% signal prediction accuracy for Dow Jones Industrial Average stocks within 03-Jun – 14-Jun time period.

FinBrain’s algorithms collect and analyze gigabytes of financial data for more than 6000 Stocks, Currency Pairs, Cryptocurrencies and ETFs on a daily basis. The following predictions were provided to our customers the weekend before 03-Jun as TOP LONG STOCKS under DOW30 market. Our customers have enjoyed up to 10% returns within 10 trading days, by utilizing FinBrain’s Intelligent Algorithms.

FinBrain has correctly predicted the TOP 10 LONG STOCKS with 100% accuracy for the 3-day time period. The maximum return was yielded by NKE with a 7.23% change in the stock price for this period.

FinBrain’s Top Long Portfolio – 3-Day Period

FinBrain’s algorithms have also correctly predicted the TOP 10 LONG STOCKS for 5-day interval with a 100% accuracy. AAPL has returned 8.61% within the 5 day period, where our customers have collected 5.79% returns on average thanks to the top portfolio provided by FinBrain Technologies.

FinBrain’s Top Long Portfolio – 5-Day Period

Finally, AAPL has returned the highest gain within the 10-day period where FinBrain’s signal-wise prediction accuracy was 90%. Traders have collected decent and safe returns using our state-of-the-art technologies within a very short amount of time.

FinBrain’s Top Long Portfolio – 10-Day Period

Our algorithms have helped thousands of people from 133 countries all around the world, by maximizing their profits and minimizing their risks. The map below shows the locations of FinBrain’s registered users.

FinBrain’s registered users from 133 countries

Don’t be late to utilize AI Enabled Predictions on your trades. We are here to support you on yielding high returns on your trades with our cutting edge AI algorithms. Please remember that, you will have an extra 20% discount when you choose the “Subscription” option.

Best Regards,

FinBrain Technologies, 2019

[email protected]

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