We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the...

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the...

Trader behavior in FX market -- The effects of information reliability on financial market -- Property price distributions of Taiwan and the UK -- Phase transition in estimation of parameters of low default portfolio -- A Cryptocurrency Fraud Spill in Japan -- Idiosyncratic volatility of the...

Research on loss absorption of financial group (bank network) -- The Mathematics of Human Contact -- Does it still matter in the new world where a refugee comes from? - Social network, Shocks, and Ethnicity - A multi-level analysis -- The Transferability of Human Capital, the Brain Drain, and...

Symbolic analysis of economic indicators and derived time series offers an advantage of transferring quantitative values into qualitative notions by indexing intervals of numerical data with symbols. While differences in the numerical indicators are routinely measured by subtraction, differences...

An analysis of minute-tick data from the Japanese stock index market is reported for a three-year period of 2000/7/4–2003/6/30. Correlation patterns and principal component distributions were determined for 180 constituents of the NIKKEI 225 index, excluding the effects of after-hours trading...