This systematic literature review analyses the recent advances of machine learning and
deep learning in finance. The study considers six financial domains: stock markets, portfolio …

Directional changes (DC) is a recent technique that summarises physical time data (eg daily
closing prices, hourly data) into events, offering traders a unique perspective of the market to …

Although the combination of halide perovskite photocatalysts and plasma ensures the
effective conversion of CO2, there is still much room to improve its conversion ratio and …

The importance of surrogate modeling techniques has been steadily growing over the recent
years in high-frequency electronics, including microwave engineering. Fast metamodels are …

With most technical fields, there exists a delay between fundamental academic research and
practical industrial uptake. Whilst some sciences have robust and well-established …

Machine learning (ML) techniques have shown to be useful in the field of financial
forecasting. In particular, genetic programming has been a popular ML algorithm with …

Directional changes (DC) have been shown to form an effective approach in algorithmic
trading by converting fixed time series into event-based series and focusing on key events …

Directional change (DC) is an alternative to time series in recording transactions: it only
records the transactions at which price changes to the opposite direction of the current trend …

The majority of algorithmic trading studies use data under fixed physical time intervals, such
as daily closing prices, which makes the flow of time discontinuous. An alternative approach …

In the Forex market, trend trading, where trend traders identify trends and attempt to capture
gains through the analysis of an asset's momentum in a particular direction, is a great way to …