Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles

By Qili Wang, Wei Xu, Han Zheng. 2018

80 citations recorded by the source corpus.

Abstract Many researchers and practitioners have attempted to predict financial market trends for excess returns using multiple information sources including social media. Recent studies have investigated the relation between public sentiment and stock price movements and demonstrated that investment decisions are affected by public opinion. In this paper, we design a novel framework that combines the wisdom of crowds and technical analysis for financial market prediction using a new fusion strategy. A machine learning technique called deep random subspace ensembles (DRSE), which integrates deep learning algorithms and ensemble learning methods, is proposed according to the characteristics of the prediction task. Based on collected real-world datasets, the experimental results show that our proposed method outperforms the baseline models in predicting stock market by at least 14.2% in terms of AUC value, indicating the efficacy of DRSE as a viable mechanism for financial market prediction.

Subfields: Artificial Intelligence, Finance, Management Science and Operations Research, Time-Series & Forecasting

How researchers see this paper

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

In Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. A. Ozbayoglu

In Finance

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. Omer Berat Sezer
4. Weiwei Jiang
5. Longbing Cao

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “[213] Sina Weibo, Stock market records 2012-2015 Technical indicators, sentences DRSE F1-score, precision, recall,”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[150] Sina Weibo, Stock market records 2012-2015 Technical indicators, sentences DRSE F1-score, precision, recall, accuracy, AU-”