Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

The agricultural futures prices are generally considered difficult to forecast because the
causes of fluctuations are incredibly complicated. We propose a text-based forecasting …

The Shanghai crude oil futures market exudes distinct speculative attributes, underscoring
the pivotal significance of precise price forecasts. Accurate forecasting of Shanghai crude oil …

The paper presents a framework to forecast futures prices of stocks listed on the National
Stock Exchange (NSE) in India during normal (unaffected by the COVID-19 pandemic) and …

This paper offers an innovative approach to capture the trend of oil futures prices based on
the text‐based news. By adopting natural language processing techniques, the text features …

Using macroeconomic and financial conditions to forecast credit default swap (CDS)
spreads is a challenging task. In this paper, we propose the Merton-LSTM model, a modified …

Price forecasting is critical for business management decision making and planning.
However, accurate price predicting faces daunting challenges due to data drift and …

The crude oil market plays a vital role in the world economy. However, due to the noisy
characteristics of the market and the complex and non-stationary nature of the asset series …

This study explores the predictive effect of climate change attention on carbon futures
returns. Using climate‐related Google Trends and news, we construct five dimensions of the …

This study provides a holistic and quantitative overview of over 800 mathematical methods
(eg, financial and risk models, statistical tests, statistics and advanced algorithms) taken out …