Quantifying the semantics of search behavior before stock market moves

Curme, Chester; Preis, Tobias; Stanley, H. Eugene; Moat, Helen Susannah

doi:10.1073/pnas.1324054111

Cited by 155 publications

(130 citation statements)

References 47 publications

(41 reference statements)

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“…The search behavior shows a clear pattern. Similar Google Trends patterns have proven to have significant correlations with a large variety of aspects in commerce, including stock market movements [67], trading behavior [68], automobile sales [69], company evaluations [70], and private consumption [71].…”

Section: Big Data: Informing Business Growth Strategiesmentioning

confidence: 88%

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Hilbert

2017

Entropy

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In our information age, information alone has become a driver of social growth. Information is the fuel of "big data" companies, and the decision-making compass of policy makers. Can we quantify how much information leads to how much social growth potential? Information theory is used to show that information (in bits) is effectively a quantifiable ingredient of growth. The article presents a single equation that allows both to describe hands-off natural selection of evolving populations and to optimize population fitness in uncertain environments through intervention. The setup analyzes the communication channel between the growing population and its uncertain environment. The role of information in population growth can be thought of as the optimization of information flow over this (more or less) noisy channel. Optimized growth implies that the population absorbs all communicated environmental structure during evolutionary updating (measured by their mutual information). This is achieved by endogenously adjusting the population structure to the exogenous environmental pattern (through bet-hedging/portfolio management). The setup can be applied to decompose the growth of any discrete population in stationary, stochastic environments (economic, cultural, or biological). Two empirical examples from the information economy reveal inherent trade-offs among the involved information quantities during growth optimization.

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Section: Big Data: Informing Business Growth Strategiesmentioning

confidence: 88%

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Hilbert

2017

Entropy

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“…In recent years, exploring the scientific impact of online big-data has attracted much attention of researchers from different fields. The massive new data sources resulting from human interactions with the internet offer a better understanding for the profound influence of external information on complex financial systems [31,32,33,34,35,36,37,38,39].…”

Section: Introductionmentioning

confidence: 99%

New approaches in agent-based modeling of complex financial systems

Chen

Zheng

et al. 2017

Front. Phys.

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Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogenous personal preferences and interactions, these models are successful to explain the microscopic origination of the temporal and spatial correlations of the financial markets. We then present a novel paradigm combining the big-data analysis with the agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces, and develop an agent-based model to simulate the dynamic behaviors of the complex financial systems.

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“…Nowadays, the Internet acts as a core information source for human worldwide, and many information gathering activities take place online [5,10]. Search activities widely exist in daily life, from which irrelevant information is filtered and useful one is extracted to support decision making.…”

Section: Introductionmentioning

confidence: 99%

“…On one hand, search becomes an essential part for consumers to find useful information in their decision making processes. On the other hand, by keeping track of the search patterns of the consumers, online merchants can have a better understanding of the consumers' behaviors and intentions [5].…”

Section: Introductionmentioning

confidence: 99%

Flexible Query Answering Systems 2015

Andreasen

Christiansen²,

Kacprzyk

et al. 2016

Advances in Intelligent Systems and Computing

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Search activity is an essential part for gathering useful information and supporting decision making. With the exponential growth of mobile e-commerce, consumers often search for products and services that are closely relevant to the current context such as location and time. This paper studies the search behaviors of mobile consumers, which reflect their customized purchase intentions. In light of machine learning, a probabilistic generative model is proposed to discover underlying search patterns, i.e., when to search,where to search and in what category. Furthermore, the predicting power of the proposed model is validated on the dataset released by Alibaba, the biggest e-commerce platform in the world. Experimental results show the advantages of the proposed model over the classical content-based methods, and also illustrate the effectiveness of integrating contextual factors into modeling consumers search patterns.

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Quantifying the semantics of search behavior before stock market moves

Cited by 155 publications

References 47 publications

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

New approaches in agent-based modeling of complex financial systems

Flexible Query Answering Systems 2015

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