Terra Classic (LUNC) price predictions seem to have changed. In our article published on September 6th, the price prediction for LUNC, which was estimated to trade at $0.000060 at the end of the month by machine algorithms, appears to have been updated. This time, algorithms presented a lower price prediction to investors.
Will LUNC Rise?
While Terra Classic network continues to lose value in the cryptocurrency market, according to new predictions from PricePredictions.com algorithms on September 21st, the price prediction for LUNC on the first day of October has been revised to $0.000057.
Especially with this new price prediction, it appeared to be 5% lower than the price prediction set for the last day of September (0.000060 dollars) about two weeks ago. This situation supports the negative sentiment of Terra Classic investors, the structure of technical analysis (TA) indicators, and other data used by these machine algorithms, which predict a bear market for the next 30 days.
In addition, LUNC, one of the most popular DeFi projects in the crypto market, can change rapidly again depending on future developments (or their absence), just as it has undergone significant changes in the past.
When the calendar showed April 5, 2022, Terra Classic (formerly known as Terra LUNA) had a market value of $41 billion. Considering a circulating supply of approximately 5.82 trillion tokens, LUNC could have a value close to $0.0070446 when it reaches its all-time high price.
LUNC Price Analysis
In addition to all these events, LUNC is currently trading at $0.00005708 as of the time of writing. The similarity of the monthly trend to the existing price predictions indicates that Terra Classic may already be priced for October 1st.
Although a hypothetical increase of over 10,000% compared to today’s price may appeal to Terra Classic owners considering the all-time high market value, this dream may still be too far away as LUNC has experienced a decline of over 12% in the past 30 days since the time of writing and has shown significant losses considering longer timeframes.
