Academic Research Supports Efficient Market Hypothesis for Bitcoin Trading
A recent paper from academic researchers at the International Hellenic University and Democritus University of Thrace in Greece has endorsed the “efficient market hypothesis” (EMH) for Bitcoin (BTC) trading. The researchers claim that EMH has led to the development of models capable of outperforming the hodl strategy by almost 300% in simulated crypto portfolios.
The EMH theory suggests that an asset’s share price reflects its fair market value and all applicable market information, making it impossible to outperform the market by trying to time it or predict winning stocks intuitively. Proponents recommend putting funds in low-cost passive portfolios rather than attempting to beat the market with well-timed undervalued stock picks.
Opponents argue that some investors, such as Warren Buffet, have succeeded in beating the market despite EMH. The research team in Greece, however, claims that EMH can be applied to cryptocurrency trading as a replacement for the standard “buy and hold” approach to avoiding market volatility.
AI Models and Testing
The researchers developed four distinct artificial intelligence models trained with multiple data sets. After training and testing, they selected models optimized against both “beat the market” and hodling strategies. According to the team, the optimal model beat baseline returns by as much as 297%, indicating that EMH can be a useful tool for Bitcoin and cryptocurrency traders.
However, it is important to note that the authors conducted their research using historical data and simulated portfolio management. Therefore, while empirical, these results may not change the minds of those strongly opposed to EMH efficacy.
Hot Take
In conclusion, the academic research from Greece provides compelling evidence supporting the use of efficient market hypothesis for Bitcoin trading. While some critics remain skeptical, the study’s findings suggest that EMH can be a valuable tool for cryptocurrency traders seeking to optimize their portfolio performance.
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