Indisputably, trend-following and mean-reversion are two key concepts in quantitative investing or technical analysis. What about the Bitcoin? Are there trend-following or mean-reversion patterns? Or are both effects present and co-exist? In this short research, we examine how Bitcoin’s price is affected by its maximal or minimal price over the previous 10 to 50 days. Our finding shows that when the BTC is at the local maxima, it tends to continue trending upwards. Furthermore, the local minima are also connected with abnormal price action.

The Russia-Ukraine war is a conflict that has not been in Europe since WW2. And it has great implications not only on human lives but also on security prices. It bears numerous characteristics of the cold war crises, where two nuclear powers (Soviet Union and USA/NATO) were often very close to hot war or were waging a proxy war in 3rd countries. We thought it might be wise to look at similar periods from the past to understand what happens in such situations. We selected five events and analyzed the performance of main equity factors (market, HML, SMB, momentum & 2x reversal) and energy and fixed income proxy portfolios.

Data are the key to systematic investing/trading strategies. The hypotheses testing, risk or return evaluations, correlations, and factor loadings rely on past data and backtests. With an increasing speed of publication in finance, critiques of quantitative strategies have emerged. Strategies seem to decay in alpha, post-publication returns tend to be lower, and many strategies become insignificant once rigorously tested (in or out-of-sample). Moreover, some might even appear profitable purely by chance and the repetitive examination of the same dataset, such as CRSP stocks after 1963. 

Is there any solution to overcome these limitations? Partially, the design of the novel machine learning strategies consisting of training, validation, and testing sets might help. Perhaps the most crucial part of such a scheme is the usage of the purely out-of-sample dataset. In this regard, the novel research by Baltussen et al. (2021) provides several valuable findings for the most recognized factors. The authors constructed a database of U.S. stocks, including dividends and market caps for 1488 major stocks from 1866 to 1926. The sample can be described as the pre-CRSP period, including independent, pre-publication, and “out-of-sample” data that can be a perfect test for the factors utilized today.