The decomposition-based vector autoregressive model (DVAR) provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relationships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and emp...

Mercurio is a project currently investigated at Politecnico di Milano whose aim is to support the decision-making process of financial investors. Mercurio identifies relevant events both from financial news articles and financial indexes and uses sequential pattern mining to predict exceptional events given their past occurrences and relationships with other events. The process of event recogni...

Indeks LQ45 terdiri dari 45 saham-saham aktif di BEI. Sektor saham konstruksi adalah salah satu sektor vital bagi negara. Analisis terbagi menjadi dua, yaitu analisis teknikal dan fundamental. sendiri yang mengasumsikan bahwa harga telah merefleksikan semua informasi tentang perusahaan tersebut. Indikator utama dalam analis grafik, satunya grafik candlestick. Agar akurat menentukan keputusan in...

The volatility features of financial data would considerably change in different periods, that is one the main factors affecting applications machine learning quantitative trading. Therefore, to effectively distinguish fluctuation patterns markets can provide meaningful information for trading decision. In this article, a novel intelligent system based on deep fuzzy self-organizing map (DFSOM) ...

The design of a novel-type of atomic beam source which provides for long term, stable operation at high emission rates is reported. The heart of the design is the “candlestick” where liquid source material is transported by capillary action to a localized hot emission region. A surrounding cavity kept at the melting point for the source material shields the vacuum chamber walls from this region...

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of stock price time series. With simple eight-trigram feature engineering scheme inter-day candlestick patterns, we construct novel ensemble machine learning framework for daily pattern prediction, combining traditional charting with latest artificial intelligence methods. Se...

Deep learning methods have achieved significant success in various applications, including trend signal prediction financial markets. However, most existing approaches only utilize price action data. In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the of Ethereum cryptocurrency. We conduct experiments investigate relationship b...