Quantitative stock trading systems apply automated, data-driven models to invest in the
stock markets. In the last two decades, the machine learning community has deeply …

Advanced deep learning methods have been widely adopted in stock movement prediction
with technical analysis (TA), while researchers prefer technical indicators to technical charts …

This review paper thoroughly examines the role of technical analysis in energy and financial
markets with a primary focus on its application, effectiveness, and comparative analysis with …

This work proposes a supervised multi-channel time-series learning framework for financial
stock trading. Although many deep learning models have recently been proposed in this …

Forecasting financial data plays a crucial role in financial market. Relying solely on prices or
price trends as prediction targets often leads to a vast of invalid transactions. As a result …

This study proposes a customizable and committee data mining (DM) framework for stock
trading. The underlying conjecture is that different stocks may have different trading …

In a vacuum, the existence of a profitable wagering strategy would seem to point to an
inefficiency in that particular wagering market. Here, we examine 1,547 simple wagering …

The new concept of forecasting market movements is introduced to fill the gap in stock price
forecasting that uses a fixed time window. The candlestick data are grouped into waves …

We propose a new methodology for trading financial instruments based on deterministic
sign patterns. These patterns are obtained from the m-dimensional elementary sample …

[ES] La creación de estrategias de inversión siempre ha atraído la atención de los
académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha …