Innovative deep matching algorithm for stock portfolio selection using deep stock profiles.

Construction of a reliable stock portfolio remains an open issue in quantitative investment. Multiple machine learning models have been trained for stock portfolio selection, but their practical applicability remains limited due to the challenges posed by the characteristic of a low signal-to-noise ratio (SNR), the nature of time-series data, and non-independent identical distribution in financial data. Here, we transformed the stock selection task into a matching problem between a group of stocks and a stock selection target.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time.

An experimental analysis of acquired impulse control among adult humans intolerant to alcohol.

The ability to control tempting impulses impacts health, education, and general socioeconomic outcomes among people at all ages. Consequently, whether and how impulse control develops in adult populations is a topic of enduring interest. Although past research has shed important light on this question using controlled intervention studies, here we take advantage of a natural experiment in China, where males but not females encounter substantial social pressure to consume alcohol.