Bitcoin Markets Blog

News:

It seems like the cryptospace generates copycats of the flavor-of-the-week at an alarming rate.  When anonymous coins started getting popular after Darkcoin's rise, every week saw a new coin based on a new anonymity mechanism, where each successive mechanism was more convoluted and more difficult to decipher than the last.  There was also the hype behind arbitrary asset issuance protocols like Nxt, Counterparty, followed by several others.  Now with Nubits, it seems pegged assets is the new hot thing.  I think, at this point, what's necessary are experts in mechanism design (http://bit.ly/1DTrfQK) and implementation theory (http://bit.ly/10gXV7m) to come into the cryptospace and separate the plausible from the impossible.  I also feel that many altcoin whitepapers are needlessly long and painful to read.  That being said, I think anyone who has the patience to thoroughly sift through the noise will find many opportunities in the altcoin markets.

In an unrelated note, I recently read a paper on the correlation between bitcoin market activity and Twitter sentiment: http://tinyurl.com/k5b27a3.

To summarize its findings:

• The paper looked at the number of times positive emotive words (e.g. happy, great, awesome, etc.), negative emotive words (e.g. sad, bad, unhappy, etc.), and uncertain emotive words (e.g. hope, worry, fear) were mentioned in tweets alongside the word "bitcoin".
• Correlation between negative tweets (but not positive tweets) and bitcoin price is found to be negative at the 1% statistically significant level.
• This means negative sentiment is reflected in the market much more than positive sentiment (i.e. it could be that the baseline for tweeting is that most people say positive things about bitcoin most of the time)
• Correlation between uncertain tweets and bitcoin price is found to be negative at the 1% statistically significant level.
• This means that price goes down as uncertainty goes up.
• Trading volume was correlated with positive tweets, negative tweets, the ratio of positive to negative tweets, uncertain tweets, and total emotive tweets at the 1% level.
• This means that more twitter action goes together with more trading volume.
• Wider spreads are correlated with negative sentiment.
• Narrower spreads are correlated with the ratio of positive to negative sentiment.
• It also found that sentiment (negative, uncertain, and positive to negative ratio) could lead price movements by up to 2 days but sentiment did not lead trading volume.
• This suggests that directional sentiment lingers for a few days but excitement does not linger.  In other words, people who are right in their sentiment but slow pay off people who are right in their sentiment but fast.
• Ultimately, the methodology could be better.  It would have been better to use price returns instead of price and de-trended tweet counts instead of tweet counts since the correlation of two non-stationary time series is less telling than the correlation between two stationary time series since correlation is a linear relationship.
• Also they could improve upon their word choices for what represents positive or negative emotions.  They could also look for not just the word "bitcoin" but its many variants like "BTC" or "XBT".
• If you want to get really fancy, you can build out a neural network of emotive words each with a weighting for "positiveness", "negativeness", and "uncertainness" off a list of seed words (e.g. happy = (1,0,0) (positive, negative, uncertain), sad = (0,1,0), unsure = (0,0,1)) and doing heavy machine learning as a first pass over Twitter.  Then in the second pass, apply the full neural network onto the word "bitcoin" plus variants.