Cryptocurrency exchanges are marketplaces where users buy and sell cryptocurrencies using fiat currency such as USD or Euros. You can also buy and sell cryptocurrencies with bitprofit.software. These usually operate 24/7 offering various trading tools. However, when trading on margin, these fees can be substantially higher than required if the user doesn’t use leverage. In this sense, margin trading can either increase profits for traders if done correctly or result in a significant loss of money. If used incorrectly, leverage is an effective instrument for magnifying gains and losses during extreme volatility in prices across different asset classes, including stocks, bonds, and real estate markets.
This investigates the adaptive market hypothesis about high-frequency cryptocurrency trading. This is a new phenomenon, as algorithmic and automated trading strategies have only recently become available to retail investors through increasingly accessible platforms.
High-frequency trading (HFT) is an advanced form of algorithmic and automated trading that relies on complex algorithms for rapid microsecond decision-making and execution via computerized private networks. We look at historical data from various cryptocurrency exchanges. The weak form states that all information is reflected in prices, while the adaptive form argues that arbitrageurs will not let this happen. In other words, if there’s a discrepancy between two markets, someone will notice and use it to profit from their knowledge.
The AMH is a theory that markets are efficient in the long run but not in the short run. It suggests that arbitrageurs have enough time to correct mispricing before they become significant enough to be exploited by others who may wish to take advantage of these discrepancies. This means that these temporary imbalances tend to disappear over time as prices adjust accordingly; however, this does not mean all price movements will be explained by fundamentals, especially when financial innovations are involved!
Our work builds upon the related studies in this area. The most relevant are those who performed an empirical study on the cryptocurrency market and found evidence that high-frequency traders play a dominant role in price movements, with no signs of longer-term reversals or fundamental influences.
However, while both teams produced some exciting findings about HFTs’ impact on cryptocurrency markets, there are still several limitations that need to be addressed before one can draw strong conclusions about their implications for future prices:
Both teams’ work suffers from relatively small sample sizes, which makes it impossible for them to assess whether their results are statistically significant or not fully.
We begin by describing how we measured the efficiency and risk of each market we were interested in. We then discuss our procedures for conducting statistical tests and report on their results.
To measure market efficiency, we measured the cross-sectional variation in returns for each cryptocurrency concerning its average return over one year (Ri). If Ri is large, it implies a lot of variability in returns around an average return.
Therefore, it can be inferred that there is a high degree of suboptimality or inefficient behavior within this period. Conversely, if Ri measures low variation around an average return, there are fewer deviations from an optimal portfolio across crypto assets. Thus, they exhibit lower levels of suboptimality or inefficient behavior during this period. The higher R2 value would indicate greater cross-sectionally clustered volatility among cryptocurrencies based on their separate mean returns.
We found that high-frequency trading (HFT) is a positive force for market efficiency. The HFTs in our study reduced the spreads between quotes and filled prices by up to 50%. In addition, HFTs could reduce bid spreads by 25%. However, due to the lack of data from other exchanges, it is unclear whether these results are specific to crypto markets or apply more generally.
The cryptocurrency market has made substantial progress since its inception ten years ago. There is still much room for improvement in efficiency. HFT could play an essential role in improving efficiency within this market as well as other financial markets moving forward into the future
This is a follow-up to our previous work investigating the efficiency level of cryptocurrencies. We are now looking at another exciting finance topic: the Adaptive Market Hypothesis. However, there is still room for improvement over time due to the nature of cryptocurrency markets as an adaptive system.