Thermodynamic Entropy in Quantum Statistics for Stock Market Networks

Wang, Jianjia; Lin, Chenyue; Wang, Yilei

doi:10.1155/2019/1817248

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…To ensure continuous and stable tracking of the market structure, we select the constituents of the stock index whose historical data are available during the entire experimental period. In this paper, we select

stocks from the NYSE dataset [ 11 , 22 ], whose daily closing prices are available from 2 January 1986 to 7 May 2021.…”

Section: Methodsmentioning

confidence: 99%

Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

Xiu

Wang

Chan

2021

Entropy

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This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.

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stocks from the NYSE dataset [ 11 , 22 ], whose daily closing prices are available from 2 January 1986 to 7 May 2021.…”

Section: Methodsmentioning

confidence: 99%

Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

Xiu

Wang

Chan

2021

Entropy

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“…It takes into account the number of communities as well as the size of the communities [28] to determine the structural entropy, which is then used to continuously monitor the market. The thermodynamical entropy [29] can also be used to describe the dynamics of stock market networks as it acts like an indicator for the financial system. Very recently, based on the distribution properties of the eigenvector centralities of correlation matrices, Chakraborti & Pharasi [30] have proposed a computationally cheap yet uniquely defined and nonarbitrary eigen-entropy measure, to show that the financial market undergoes 'phase separation' and there exists a new type of scaling behaviour (data collapse) in financial markets.…”

Section: Introductionmentioning

confidence: 99%

Network geometry and market instability

et al. 2021

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The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier–Ricci curvature and Forman–Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or ‘business-as-usual’ periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.

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“…embryonic, larval(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), pupal(41-58), adult(59-66), which constitute a time-varying network.NYSE Financial Networks: New York Stock Exchange (NYSE) database[45] is composed of 347 stock and their associated daily closing prices over 6004 transaction days from January 1986 to February 2011. To extract market networks, we closely follow[46]. We use a sliding time window of 28 days to obtain a moving closing price sequence for each stock.…”

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confidence: 99%