Coupled Network Approach to Predictability of Financial Market Returns and News Sentiments

Abstract

We analyze the network structure of lagged correlations among daily financial news sentiments and returns of financial market indices of 40 countries from 2002 to 2012. Using a spectral method, we decompose the network into bipartite sub-structures, and show that these sub-structures are relevant to the performance of prediction models, bridging concepts from network theory and time series analysis. Our results suggest that, at the daily level, endogenous influences among financial markets overwhelm exogenous influences of news outlets, and that changes in financial news sentiments respond to market movements more substantially than they drive them.

Predicting future stock market structure by combining social and financial network information

“…Analyses on filtered correlation-based networks for information extraction [6,7,3] have widely been used to explain market interconnectedness from high-dimensional data. Applications include asset allocation [8], market stability assessments [9], hierarchical structure analyses [2,3,4,10,11] and the identification of lead-lag relationships [12].…”

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

“…Several works have showed the potential of online social media, in particular of the microblogging platform Twitter, for analyzing the public sentiment in general [1][2][3][4] or to predict stock markets movements or sales performance [5][6][7][8][9]. With the increasing importance of Twitter in political discussions, a considerable number of studies [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] also investigated the possibility to analyze political processes and predict political elections from data collected on Twitter.…”

Analysis of Cryptocurrency Interdependencies

“…Machine learning has been used to extract finance sentiments [18] to study stock price movements [6,24], the effect of news sentiments on corporate performance [19] and financial markets [4], and to forecast Bitcoin prices [5].…”