A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Deng, Shangkun; Wang, Chenguang; Wang, Mingyue; Sun, Zhe

doi:10.1016/j.asoc.2019.105652

Cited by 58 publications

(30 citation statements)

References 24 publications

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“…Among popular machine learning approaches, Gradient Boosting machines have been shown to be successful in various forecasting problems in Economics and Finance (see e.g. [5,6,16,28] among others).…”

Section: Related Workmentioning

confidence: 99%

“…A whole new set of big data analytics models and tools that are robust in high dimensions, like the ones from machine learning, are required [19,24]. In particular in our computational study we have chosen to rely on Gradient Boosting (GB) [14], a well-known machine-learning approach which has been shown to be successful in various modelling problems in Economics and Finance [5,6,16,28]. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.…”

Section: Big Data Analyticsmentioning

confidence: 99%

“…Harvard IV-4 Psychosocial Dictionary: http://www.wjh.harvard.edu/ ∼ inquirer/ homecat.htm 5. WordNet-Affect dictionary: http://wndomains.fbk.eu/wnaffect.html 6. Loughran and McDonald Sentiment Word Lists: https://sraf.nd.edu/textualanalysis/resources/.…”

mentioning

confidence: 99%

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Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Consoli

Pezzoli

Tosetti

2020

Machine Learning, Optimization, and Data Science

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The Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.

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Section: Related Workmentioning

confidence: 99%

Section: Big Data Analyticsmentioning

confidence: 99%

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Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Consoli

Pezzoli

Tosetti

2020

Machine Learning, Optimization, and Data Science

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“…In current research we are experimenting different models, ranging from traditional economic models to novel machine learning approaches, like Gradient Boosting Machines and Recurrent Neural Networks (RNNs), which have been shown to be successful in various forecasting problems in Economics and Finance (see e.g. [4,[6][7][8]16,18,29] among others).…”

Section: Introductionmentioning

confidence: 99%

Information Extraction From the GDELT Database to Analyse EU Sovereign Bond Markets

Consoli

Pezzoli

Tosetti

2021

Mining Data for Financial Applications

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In this contribution we provide an overview of a currently on-going project related to the development of a methodology for building economic and financial indicators capturing investor’s emotions and topics popularity which are useful to analyse the sovereign bond markets of countries in the EU.These alternative indicators are obtained from the Global Data on Events, Location, and Tone (GDELT) database, which is a real-time, open-source, large-scale repository of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. After providing an overview of the method under development, some preliminary findings related to the use case of Italy are also given. The use case reveals initial good performance of our methodology for the forecasting of the Italian sovereign bond market using the information extracted from GDELT and a deep Long Short-Term Memory Network opportunely trained and validated with a rolling window approach to best accounting for non-linearities in the data.

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“…Therefore, there is an urgent need to identify a modeling method that is suitable for the prediction of the water accumulation process in a longer prediction period. As a type of integrated learning algorithm, GBDT performs well in tasks such as classification and regression [19], [20] given its high efficiency, high precision and low deviation, which has attracted increasing attention [21], [22]. However, to the authors' best knowledge, the application of the GBDT algorithm to urban flood research is still rare, and no study has applied the GBDT algorithm to the prediction of water accumulation process.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Cited by 58 publications

References 24 publications

Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Information Extraction From the GDELT Database to Analyse EU Sovereign Bond Markets

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

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