Can you Predict the Rise and Fall of Bitcoin?

Bitcoin has been known to show irregularities in its value. This past Sunday, Bitcoin's price fell to a year-to-date low of $290.83. On December 4, 2013, the price had reached an all-time high of $1147. The prices exhibit a regular trend of fluctuations from time to time. Consider the past month of August, when prices varied in a range from $450 to $600 throughout the month. Even last month, there was consistent variation on a daily basis but in a lower range of $370 to $490 as compared to August.

The following illustrations show the Bitcoin price variations for August and September:

August 2014:

September 2014:

The stock market is a classic example of a speculative marketplace where people bet and make investments in companies which leads to changes in the stock prices of listed companies, notwithstanding of course the market forces that play a major role in prices too. Speaking of Bitcoin, it is important to determine whether we consider it an investment or a currency. If it’s an investment, some predictions can be made depending on the kind of money being flowing into the Bitcoin market. If it’s a currency, there are other methods which can be applied for predictions taking cues from the Forex market.

But how to conduct these predictions is a real dilemma. Consider the stock market as an example. Some models do exist which aim to predict prices. Some known models are:

Hidden Markov Model: the model analyses and predicts phenomena by relying on a time dependence or time series. The model is based on a set of unobserved underlying states which are used in transition with respect to an ordered dimension. Each state is associated with observations. The stock market can also be seen in a similar manner.

Residual Income Model: The RIM is a theoretical model which links a stock price to book value, earnings in excess of a normal capital charge (abnormal earnings), and other information. Other information can be interpreted as capturing value-relevant information about the firm’s intangibles, which are poorly measured by reported financials. This interpretation recognizes that a portion of valuation stems from factors not captured in the financial statements.

Artificial neural network (ANN): This model uses data mining techniques that are gaining increasing acceptance in the business field due to their ability to learn and detect relationships among nonlinear variables. Also, it allows for deeper analysis of large sets of data, especially those that have a tendency to fluctuate within a short of period of time. This makes ANN a good candidate for stock market prediction.

But can these models really be applied to the Bitcoin price index. One cannot just predict, control and create models for Bitcoins. There are various factors which come into play when it comes to designing a model. In the case of Bitcoin, the dominating factors themselves are out of control, which makes it difficult to predict with any accuracy. These out-of-control factors can be attributed to scams that had occurred around Bitcoin. Closures of companies operating in field of crypto-currencies can also be an attributable reason behind such out-of-control factors.

Various attempts have been made at creating models for Bitcoin price prediction. came up with a Bitcoin prediction tool based on Artificial neural networks. It predicts the price movements on Bitstamp every hour via a certain type of artificial intelligence.

But such models do have some form of error. The error may be minimal for some days but it isn’t always so. Such models simply cannot be established as standards just yet. The question still remains whether we can actually make Bitcoin price predictions and see them come true.