Predicting stock index increments by neural networks: The role of trading volume under different horizons

By Xiaotian Zhu, Hong Wang, Li D. Xu, Huaizu Li. 2008

171 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, methodology): “Traditionally neural networks have been used to model stock prices as time series for the forecasting purpose, such as in (Kaastra and Boyd, 1991; Adya and Collopy, 1991; Chan et al., 2000; Skabar and Cloete, 2002; Zhu et al., 2008).”
A review of unsupervised feature learning and deep learning for time-series modeling (2014, background): “, 2011), trading volume (Zhu et al., 2008), market speci c domain knowledge, and political and economical factors, it can be combined together with the stock price data to achieve higher stock price predictions (Agrawal et al.”
Artificial neural networks in business: Two decades of research (2016, methodology): “Moreover, several more sophisticated methods for feedforward networks were found throughout the review such as Self-Scaling Parallel QuasiNewton algorithm [435], or Levenberg-Marquardt algorithm [28, 102, 293] which is a combination of Gauss-Newton optimization rule and gradient descent.”
Financial time series forecasting with machine learning techniques: a survey (2010, unknown): “Time-frame Number Publications Day 31 [1], [2], [3], [4], [6], [7], [8], [9], [10], [13], [14], [17], [19], [20], [21], [22], [24], [27], [28], [31], [32], [33], [34], [35], [36], [37], [40], [41], [42], [44], [45] Week 3 [18], [23], [43] Month 3 [26], [38], [39] Multiple / Other 9 [5], [11], [12], ”