Integrating return prediction of traditional time series models in portfolio formation can
improve the performance of original portfolio optimization model. Since machine learning …

The back propagation neural network (BPNN) can easily fall into the local minimum point in
time series forecasting. A hybrid approach that combines the adaptive differential evolution …

Forecasting stock returns is an exacting prospect in the context of financial time series. This
study proposes a unique decision-making model for day trading investments on the stock …

This paper studies the impact of China's new energy policies on expected changes and
volatility in new energy stock prices. Considering different kinds of policies (energy …

The motivation for this paper is to introduce a hybrid neural network architecture of Particle
Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying …

We investigated the presence and changes in, long memory features in the returns and
volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently …

This research aims to compare the performance of ARIMA as a linear model with that of the
combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to …

Portfolio optimization is an important part of portfolio management. It realizes the trade-off
between maximizing expected return and minimizing risk. A better portfolio optimization …

The increasing richness in the volume and types of data in the financial domain provides
unprecedented opportunities for understanding the stock market more comprehensively and …

Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …