Forecasting Price Direction Using Different Sampling Methods

Abstract: To extract usable information from financial data the prices of financial instruments must be summarized in an efficient manner. Typically price quotes are sampled at discrete and equidistant points in time to create a time series of prices at fixed times. However, alternative methods that instead utilize certain changes in the price data, such as price changes or drawdowns, could potentially create time series with more relevant information. This thesis builds upon previous research on so called ”directional changes” to establish scaling laws using such alternative sampling methods. This has been studied extensively for foreign exchange rates, and some of those results are replicated in this thesis. But here we also extend the results to a new domain of instruments, namely futures. In addition, data sampled with different methods is investigated for predictability using a simple classifier for forecasting trend direction. The main findings are that the aforementioned scaling laws hold for the time period investigated (2016-2020), and that using other methods than the typical discrete time method yields a more predictable time series when it comes to price trend.