Change‐point analysis in financial networks

Banerjee, Sayantan; Guhathakurta, Kousik

doi:10.1002/sta4.269

Cited by 14 publications

(10 citation statements)

References 47 publications

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“…The role of informative flows on financial equilibria is well known, and as already shown in previous works [ 87 ], the excess of information and signals is one of the most relevant channel of transmission of volatility. This is easily confirmed by a vast literature on financial contagion [ 88 , 89 , 90 ], which can be jointly considered in order to analyze to what extent the individual ability of traders to resist and behave without sharp reactions to any possible small perceived disequilibria can influence the global volatility of markets. While this remains the aim of successive studies, the analogy that we are advancing here between psychomotor skills and psychological factors can represent a first step in the direction of recognizing—and then taming—ingredients of financial instability.…”

Section: Discussionmentioning

confidence: 90%

Noise and Financial Stylized Facts: A Stick Balancing Approach

Biondo

Mazzarino

Pluchino

2023

Entropy

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In this work, we address the beneficial role of noise in two different contexts, the human brain and financial markets. In particular, the similitude between the ability of financial markets to maintain in equilibrium asset prices is compared with the ability of the human nervous system to balance a stick on a fingertip. Numerical simulations of the human stick balancing phenomenon show that after the introduction of a small quantity of noise and a proper calibration of the main control parameters, intermittent changes in the angular velocity of the stick are able to reproduce the most basilar stylized facts involving price returns in financial markets. These results could also shed light on the relevance of the idea of the “planetary nervous system”, already introduced elsewhere, in the financial context.

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Section: Discussionmentioning

confidence: 90%

Noise and Financial Stylized Facts: A Stick Balancing Approach

Biondo

Mazzarino

Pluchino

2023

Entropy

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“…For example, models using autoregressive [37] and singular spectrum analysis [38,39] have been proposed. Several studies apply change point detection algorithms to financial time series analysis [40][41][42]. Wood et al [7] used a change point detection algorithm with a Gaussian process to construct an investment strategy.…”

Section: Time Series Change Point Detection and Its Applicationsmentioning

confidence: 99%

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

Taguchi,

Sakaji,

Izumi

et al. 2023

New Gener. Comput.

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This study demonstrates whether financial text is useful for the tactical asset allocation method using stocks. This can be achieved using natural language processing to create polarity indexes in financial news. We perform clustering of the created polarity indexes using the change point detection algorithm. In addition, we construct a stock portfolio and rebalanced it at each change point using an optimization algorithm. Consequently, the proposed asset allocation method outperforms the comparative approach. This result suggests that the polarity index is useful for constructing the equity asset allocation method.

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“…The CPD literature is vast and spans several decades [15,30,38,42,62,65,95] and has appeared across many disciplines and applications, including finance [14,86], genetic sequence analysis [90], anomaly and fault detection [94], and human activity recognition [8,68]. There are two broad classes of CPD algorithms: parametric and nonparametric.…”

Section: Introductionmentioning

confidence: 99%

Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

Chowdhury¹,

Aldeer²,

Shantanu³

et al. 2021

Preprint

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Timeseries partitioning is an essential step in most machine-learning driven, sensor-based IoT applications. This paper introduces a sample-efficient, robust, time-series segmentation model and algorithm. We show that by learning a representation specifically with the segmentation objective based on maximum mean discrepancy (MMD), our algorithm can robustly detect time-series events across different applications. Our loss function allows us to infer whether consecutive sequences of samples are drawn from the same distribution (null hypothesis) and determines the change-point between pairs that reject the null hypothesis (i.e., come from different distributions). We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition. Moreover, while many works on change-point detection exist in the literature, our model is significantly simpler and matches or outperforms state-of-the-art methods. We can fully train our model in 9-93 seconds on average with little variation in hyperparameters for data across different applications.CCS Concepts: • Computing methodologies → Learning latent representations; • Computer systems organization → Embedded and cyber-physical systems.

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Change‐point analysis in financial networks

Cited by 14 publications

References 47 publications

Noise and Financial Stylized Facts: A Stick Balancing Approach

Noise and Financial Stylized Facts: A Stick Balancing Approach

Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

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