For most purposes of technical analysis, valuation metrics and many other relevant financial methods, the price of the last transaction is considered representative of the market price. The straightforward argument is that at this price, supply and demand have last met. However, on closer examination, the question arises as to why a past event should be relevant to the future, and why other, potentially more recent information should not be used to discover a future price.
Building on this question, we apply a range of new price formation models to current data available on crypto currency exchanges that depict level II market data, and compare their short-term forecast accuracy against the common-used ticker price and mid-price.
Data on crypto currencies is used as the closest example to free markets, since crypto currency trading is continuous, markets never close, and interferences through oversight is extremely rare.
Various tests were conducted to determine whether any of these computational models could beat the ticker and can therefore be considered more representative of future trade. In a first step, different measures of forecast quality were created and discussed. Additionally, the price formation models were checked for systematic over- or underestimations using their mean errors.
It turned out that, depending on the currency pair and crypto market, the bias produced by a price formation model may shift. An analysis of the distribution of the forecast errors revealed a leptokurtosis for all price formation models. We find that two of the five price formation models investigated outperform the widely used ticker as a price indicator for the next trade. We conclude that the volume-limited clearing price (vlcp) best predicts the price of subsequent trades. It is superior to the mid-price and the ticker. this work has revealed a number of new findings that may prove very valuable in financial market practice and that furthermore provide a solid foundation for further research. The simple mid-price and vlcp models produced the most accurate forecast results and are therefore most representative of future transactions. Its usage can thus enhance the explanatory power of various financial analyses.