Opinion dynamics in finance and business: a literature review and research opportunities

Zha, Quanbo; Kou, Gang; Zhang, Hengjie; Liang, Haiming; Xia, Chen; Li, Cong-Cong; Dong, Yucheng

doi:10.1186/s40854-020-00211-3

Cited by 127 publications

(40 citation statements)

References 133 publications

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“…Possible research directions may lie in a larger volume of media input (e.g., adding video sources) in sentiment analysis; updating baseline natural language processing model to perform more robust text preprocessing; applying neural networks in label training; extending samples in terms of holding period; transaction-fees; opinion dynamics (Zha et al 2020) and, user reputation research.…”

Section: Opportunities In Cryptocurrency Tradingmentioning

confidence: 99%

Cryptocurrency trading: a comprehensive survey

et al. 2022

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In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

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Section: Opportunities In Cryptocurrency Tradingmentioning

confidence: 99%

Cryptocurrency trading: a comprehensive survey

et al. 2022

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“…The popularity of the topic is also demonstrated by the number of recent review articles on opinion dynamics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Most opinion models can be classified into one of two main families: continuous or discrete opinion models [ 2 , 3 , 4 , 5 ]. The second family is dominated by models with binary opinions; however, some multi-state models have also been proposed [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, they show that this is an area that is attracting increasing attention from researchers in a variety of disciplines. The popularity of the topic is also demonstrated by the number of recent review articles on opinion dynamics [1][2][3][4][5][6][7]. Most opinion models can be classified into one of two main families: continuous or discrete opinion models [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

Doniec

Lipiecki

Sznajd-Weron

2022

Entropy

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In this work, we address the question of the role of the influence of group size on the emergence of various collective social phenomena, such as consensus, polarization and social hysteresis. To answer this question, we study the three-state noisy q-voter model with bounded confidence, in which agents can be in one of three states: two extremes (leftist and rightist) and centrist. We study the model on a complete graph within the mean-field approach and show that, depending on the size q of the influence group, saddle-node bifurcation cascades of different length appear and different collective phenomena are possible. In particular, for all values of q>1, social hysteresis is observed. Furthermore, for small values of q∈(1,4), disagreement, polarization and domination of centrists (a consensus understood as the general agreement, not unanimity) can be achieved but not the domination of extremists. The latter is possible only for larger groups of influence. Finally, by comparing our model to others, we discuss how a small change in the rules at the microscopic level can dramatically change the macroscopic behavior of the model.

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“…Opinion evolution is a process of fusion of agents’ opinions, in which agents interact with their friends and continuously update their opinions to reach a consensus, polarization (two conflicting or different opinions) or fragmentation (several clusters of different opinions) in the final stage [ 4 , 5 , 55 ]. Suppose that during the opinion evolution process, each agent is not only influenced by his/her friends in the network, but also maintains his/her own opinions to some extent.…”

Section: Social Network Degroot Model Based On Competition Gamementioning

confidence: 99%

“…The opinions can be interpreted as support in campaign [ 37 ], brand awareness in marketing [ 1 ] and market share in business [ 32 ]. In these cases, the benefits of both contestants are closely related to the collective opinion of all agents [ 5 ]. Suppose that each contestant is rational economic man, and the only thing he/she cares about is competing for the opinions of all agents to maximize the consensus and thus maximize his/her benefits.…”

Section: Social Network Degroot Model Based On Competition Gamementioning

confidence: 99%

Opinion dynamics in social networks under competition: the role of influencing factors in consensus-reaching

2022

Self Cite

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The rapid development of information technology and social media has provided easy access to the vast data on individual preferences and social interactions. Despite a series of problems, such as privacy disclosure and data sensitivity, it cannot be denied that this access also provides beneficial opportunities and convenience for campaigns involving opinion control (e.g. marketing campaigns and political election). The profitability of opinion and the finiteness of individual attention have already spawned extensive competition for individual preferences on social networks. It is necessary to investigate opinion dynamics over social networks in a competitive environment. To this end, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize opinion evolution in a competitive opinion dynamics. Social interactions based on trust relationships are captured in the DGCG model. From the model, we then obtain equilibrium results in a stable state of opinion evolution. We also analyse what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions, self-confidence and network structure. Theoretical analyses and numerical simulations show that these factors can significantly sway the consensus and even reverse competition outcomes.

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Opinion dynamics in finance and business: a literature review and research opportunities

Cited by 127 publications

References 133 publications

Cryptocurrency trading: a comprehensive survey

Cryptocurrency trading: a comprehensive survey

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

Opinion dynamics in social networks under competition: the role of influencing factors in consensus-reaching

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