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Stock and financial markets are examined from the perspective of communication-theoretical perspectives on the dynamics of information and meaning. The study focuses on the link between the dynamics of investors expectations and market price movement. This process is considered quantitatively in a model representation. On supposition that available information is differently processed by different groups of investors, market asset price evolution is described from the viewpoint of communicating the information and meaning generation within the market. A non-linear evolutionary equation linking investors expectations with market asset price movement is derived. Model predictions are compared with real market data.
Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of tradin
We introduce a framework to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode the causal structure of a system, infering lead-lag networks for many pairs of timescal
We investigated the network structures of the Japanese stock market through the minimum spanning tree. We defined grouping coefficient to test the validity of conventional grouping by industrial categories, and found a decreasing in trend for the coe
This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in a fixed t
Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market