Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric,
complex and chaotic nature of the stock price time series. With a simple eight-trigram feature …

This paper develops an open-high-low-close (OHLC) data forecasting framework to forecast
EUA futures price based on EU ETS data and extended exogenous variables from 2013 to …

Candlestick charts display the high, low, opening, and closing prices in a specific period.
Candlestick patterns emerge because human actions and reactions are patterned and …

A large universe of technical trading rules applied to a set of technology industry and small
cap sector portfolios over the 1995–2010 period yields superior predictability after adjusting …

Epidemie chorób zakaźnych nękają społeczeństwa od zarania dziejów i to one należą do
głównych przyczyn śmierci ludzi na całym świecie. Ostra choroba układu oddechowego …

PRML, a novel candlestick pattern recognition model using machine learning methods, is
proposed to improve stock trading decisions. Four popular machine learning methods and …

While many technical trading rules are based upon patterns in asset prices, we lack
convincing explanations of how and why these patterns arise, and why trading rules based …

Over the past few years, cryptocurrencies have increasingly been discussed as alternatives
to traditional fiat currencies. These digital currencies have garnered significant interest from …

This paper aims to investigate the profitability of two-day candlestick patterns by buying on
bullish (bearish) patterns and holding until bearish (bullish) patterns occur. Our data set …

Billions of dollars are traded automatically in the stock market every day, including
algorithms that use neural networks, but there are still questions regarding how neural …