An Integrated Early Warning System for Stock Market Turbulence

Wang, Peiwan; Zong, Lu; Ma, Ye

doi:10.48550/arxiv.1911.12596

Cited by 2 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of stock market prediction, stock trend prediction, return/volatility prediction and stock crisis warning are the three mainstream tasks that attract the most attention. Among the deep learning models that have been adopted in these tasks, classic neural networks, such as long short-term memory (LSTM) [35,14,44] and convolutional Neural Networks (CNN) [1,12] are proven to be the most reliable and widely-adopted techniques handling financial time series [11]. Due to recent advances in textual feature engineering which allows the extraction of public events and investor sentiments, integrated predictive methods, such as ensemble learning and attention-based neural networks, have also gained significant popularity in the field.…”

Section: Stock Market Prediction Based On Deep Learning Modelsmentioning

confidence: 99%

“…In this section, we use an attention-based LSTM network to construct an early warning model for stock market crises. To classify the crisis/non-crisis dates of the SSE index, the switching ARCH (SWARCH) model [18] is hired through the modeling of high/low volatility regimes that reflect respectively the tranquility and turmoil [44]. To determine whether a crisis occurs, a SWARCH(2,1) process is written as:…”

Section: Experimental Settingsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

Ma¹,

Zong²,

Wang³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and crossdocumental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market per se, whilst the latter draws more concerns on the banking sector and economic policies.

show abstract

Section: Stock Market Prediction Based On Deep Learning Modelsmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

Ma¹,

Zong²,

Wang³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Leading indicators of sovereign debt and currency crises: Comparative analysis of 2001 and 2018 shocks in Argentina

Gruszczyński

Majczak

2020

JEM

View full text Add to dashboard Cite

Aim/purpose – This paper investigates the accuracy of leading indicators in the case of the 2001 sovereign default crisis and the 2018 currency turmoil in Argentina.Design/methodology/approach – In this paper, we conducted early warning signals analysis based on a-priori selected variables. For each of the macroeconomic variables, we computed yearly changes and selected the threshold to minimise the noise-to-signal ratio, i.e. the ratio of percentage of false signals in ‘normal’ times to percentage of good signals in a two-year period preceding each of the crises.Findings – The predictive power of indicators differs significantly in various crisis epi-sodes. For the 2001 crisis, the decline in value of bank deposits was the best leading indicator based on the noise-to-signal ratio. For the 2018 currency crisis, the lowest noise-to-signal ratio was observed for the lending-deposit rate ratio.Research implications/limitations – The survey is limited mostly by the data availabil-ity and their quality.Originality/value/contribution – This paper gives a complex review of the major early warning indicators in the context of the most recent history of Argentina’s economy. It applies a set of classical leading indicators to two modern cases of financial crises. The paper proposes an original ‘knocking the window’ approach to the presentation of tradi-tional warning concepts in the context of current economic events.

show abstract

An Integrated Early Warning System for Stock Market Turbulence

Cited by 2 publications

References 51 publications

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

Leading indicators of sovereign debt and currency crises: Comparative analysis of 2001 and 2018 shocks in Argentina

Contact Info

Product

Resources

About