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The answer to forecasting Bitcoin may lie in artificial intelligence

As Bitcoin tries to regain some of the lustre it held in late 2017 when it nearly reached US$20,000 in value, investors are still questioning how to predict such a volatile currency.

Consequently, many investors track the so-called technical trading indicators (geometric patterns constructed from historical prices and trading volumes) in order to understand and predict Bitcoin’s future movement.

The rationale for using moving averages is that if the price of Bitcoin today becomes greater or lower than the average price over the past 50 or 200 days, traders could expect the emergence of an upward or downward trend.

Using daily observations from 2011-2018, we created an ANN with three predictors: returns, 50-day buy-sell signal and 200-day buy-sell signal.

By combining Bitcoin technical analysis and neural networks, we hoped that the ANN would find a pattern among the data that allowed us to more accurately predict future returns.

Our ANN model did indeed succeed in reducing the prediction error of the random walk by about five to 10 per cent over the full observation period.

Put differently, Bitcoin trading with our model would be on average more profitable than placing random buy and sell orders that have a 50 per cent chance of making a profit.

We concluded that the historical evolution of daily Bitcoin prices followed predictive trends (or bubbles) that potentially arise from the speculative nature of cryptocurrency trading.

We believe that the future of forecasting Bitcoin — and perhaps investing in general — lies in the abilities of artificial intelligence and artificial neural networks.

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