Will Machine Learning Improve Cryptocurrency Valuation Models?

Avatar
February 6, 2019
3,396 Views

 

Financial asset valuation has always been a complicated science. Andrew Tobias, the author of The Only Investment Guide that You Will Ever Need, stated that highly trained financial analysts with MBAs from Ivy League universities are about as reliable at predicting asset prices as monkeys throwing darts at a board. Big data has helped improve valuation models considerably over the last decade. This might be necessary for the cryptocurrency industry as well.

In fact, machine learning will be even more important for regulating cryptocurrency prices than it is with traditional investments. It will play a very important role in EOS trading in the future.

Why is machine learning necessary to set price in the cryptocurrency market?

Coin Telegraph published an article last year on the challenges of setting prices in the cryptocurrency market. Some of the issues that the article raised include:

  • Unlike traditional investments, cryptocurrencies don’t have any intrinsic value. When you are evaluating a stock, you can you use the dividend discount model or free cash flow model. These models make assumptions about the value based on the company’s cash flow or net profit.
  • There are insufficient records on cryptocurrency holdings. Although every bitcoin in existence is accounted for in the blockchain ledger, the accounts of various owners are not publicized. Someone with an unusually large crypto wallet could liquidate all of their virtual currencies and push the asset price down overnight.
  • The vast majority of cryptocurrencies are held by amateur investors. Unlike banking institutions and other institutional investors, they don’t follow rational, disciplined investing practices. It is difficult to gauge the overall demand for a cryptocurrency when it is being dictated by the whims of so many people in such a fragmented market.
  • Herd mentality is also a concern. The people that own cryptocurrencies are a very homogeneous group and buy and sell currencies in tandem.

The last factor is especially influential. Coin Telegraph points out that crypto investors are mostly millennials. This group tends to follow the herd mentality even more closely than other demographics. This is even more visible, since they tend to be less astute investors.

“Crypto is largely a phenomenon of millennials, who distrust government, are early adopters in tech, and have been mainly shunned out of investment wins earned in the last decade of rising real estate and stock market prices. But most millennials do not have the long-term investment experience of their more mature generational counterparts. They also tend to have less disposable income as a result of historically poor job economics, and less time in the workforce.”

Due to all of these factors, valuing cryptocurrencies is difficult enough even under ideal conditions. Establishing prices is considerably more challenging when a high level of turbulence is injected into the market. The insolvency of Mt. Gox and other unusual conditions made regulating market prices a nightmare.

Machine learning is going to be necessary to ensure market stability in the near future. Here are some factors that advances in machine learning should be able to address:

Machine learning algorithms are able to detect subtle patterns in human behavior. These patterns repeatedly surface in financial markets, which is the reason that technical analysis is a prominent theory among investors that have been jaded by traditional financial valuation methods. These algorithms can help them forecast price movements based on observable conditions in the cryptocurrency market.

Machine learning technology helps exchanges better account for sudden events. They will be able to factor for prices of things like the Mt. Gox collapse better in the future.

Machine learning algorithms can evaluate geographical crypto exchange activity. They recognize that people in different regions purchase and sell crypto in different quantities, based on different market conditions. These algorithms also account for the impact that price differences from activity in certain regions will have on behavior of investors in other parts of the world.

Machine learning is gradually leaving its footprint in all major financial markets. The cryptocurrency market is going to be affected significantly as well. What will the implications before the industry in the years to come? The future is still unwritten, but one thing is clear. Machine learning is going to play in increasingly important role.