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We propose a novel curvature-based indicator constructed on log-price time series that captures an interplay between trend, acceleration, and volatility found relevant to quantify risks and improve trading strategies. We apply it to diagnose ...

• A novel curvature-based indicator to capture geometric information in time series data is proposed.
• Analysis of both synthetic and real-world price time series data is conducted.
• The indicator demonstrates its effectiveness in ...

Using a previously validated agent-based model with fundamentalists and chartists, we investigate the usefulness and impact of direct market intervention. The policy maker diagnoses bubbles by forming an expectation of the future returns, then ...

This paper aims to investigate the influence of investors’ confidence in their portfolio holding relative to their social group and of various social network topologies on the dynamics of an artificial stock exchange. An investor’s confidence depends on ...

Forecasting the direction of the daily changes of stock indices is an important yet difficult task for market participants. Advances on data mining and machine learning make it possible to develop more accurate predictions to assist investment ...

• A cascaded learning architecture LR2GBDT is proposed to predict the direction of the daily changes of stock indices.
• Logistic regression and gradient boosted decision trees are combined in our approach.
• Technical indicators and the ...

In its original form, the self-excited Hawkes process is a cluster process where immigrants follow a Poisson process, and each immigrant may form a cluster of multi-generational offspring. The Hawkes process is generalized by replacing the Poisson ...

Using virtual stock markets with artificial interacting software investors, aka agent-based models, we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly ...

The accurate detection and classification of network anomalies based on traffic feature distributions is still a major challenge. Together with volume metrics, traffic feature distributions are the primary source of information of approaches scalable to ...

Tracking changes in feature distributions is very important in the domain of network anomaly detection. Unfortunately, these distributions consist of thousands or even millions of data points. This makes tracking, storing and visualizing changes over ...

Seeing the Earth crust as criss-crossed by faults filled with fluid at close to lithostatic pressures, we develop a model in which its elastic modulii are different in net tension versus compression. In constrast with standard nonlinear effects, this ''...