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Information identification in different networks with heterogeneous information sources

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Abstract

Traditional cheap-talk game model with homogeneous information sources provided a conclusion that dishonest information sources will not be identified if he changes strategy stochastically. In this paper, the authors incorporate different information diffusion networks and heterogeneous information sources into an agent-based artificial stock market. The obtained results are different with traditional results that identification ability of uninformed agents has been highly improved with diffusion networks and heterogeneous information sources. Additionally, the authors find uninformed agents can improve identification ability only if there exists a sufficient number of heterogeneous information sources in stock market.

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References

  1. Kyle A S, Continuous auctions and insider trading, Econometrica: Journal of the Econometric Society, 1985, 53(6): 1315–1335.

    Article  MATH  Google Scholar 

  2. Easley D and O’hara M, Information and the cost of capital, Journal of Finance, 2004, 59(4): 1553–1583.

    Article  Google Scholar 

  3. Banerjee A V, A simple model of herd behavior, Quarterly Journal of Economics, 1992, 107(3): 797–817.

    Article  Google Scholar 

  4. Alevy J E, Haigh M S, and List J A, Information cascades: Evidence from a field experiment with financial market professionals, Journal of Finance, 2007, 62(1): 151–180.

    Article  Google Scholar 

  5. Benabou R and Laroque G, Using privileged information to manipulate markets: Insiders, gurus, and credibility, Quarterly Journal of Economics, 1992, 107(3): 921–958.

    Article  MATH  Google Scholar 

  6. Van Bommel J, Rumors, Journal of Finance, 2003, 58(4): 1499–1520.

    Article  Google Scholar 

  7. Chen Y, Perturbed communication games with honest senders and naive receivers, Journal of Economic Theory, 2011, 146(2): 401–424.

    Article  MATH  MathSciNet  Google Scholar 

  8. Crawford V P and Sobel J, Strategic information transmission, Econometrica, 1982, 50(6): 1431–1451.

    Article  MATH  MathSciNet  Google Scholar 

  9. Crawford V P, Lying for strategic advantage: Rational and boundedly rational misrepresentation of intentions, American Economic Review, 2003, 93(1): 133–149.

    Article  Google Scholar 

  10. Liu Q, Information acquisition and reputation dynamics, Review of Economic Studies, 2011, 78(4): 1400–1425.

    Article  MATH  MathSciNet  Google Scholar 

  11. Kartik N, Ottaviani M, and Squintani F, Credulity, lies, and costly talk, Journal of Economic Theory, 2007, 134(1): 93–116.

    Article  MATH  MathSciNet  Google Scholar 

  12. Kartik N, Strategic communication with lying costs, Review of Economic Studies, 2009, 76(4): 1359–1395.

    Article  MATH  MathSciNet  Google Scholar 

  13. Davies P L and Canes M, Stock prices and the publication of second-hand information, Journal of Business, 1978, 51(1): 43–56.

    Article  Google Scholar 

  14. Liu P, Smith S D, and Syed A A, Stock price reactions to the Wall Street Journal’s securities recommendations, Journal of Financial and Quantitative Analysis, 1990, 25(3): 399–410.

    Article  Google Scholar 

  15. Fang L and Peress J, Media coverage and the cross-section of stock returns, Journal of Finance, 2009, 64(5): 2023–2052.

    Article  Google Scholar 

  16. Klumpp T, Communication in financial markets with several informed traders, Economic Theory, 2007, 33(3): 437–456.

    Article  MATH  MathSciNet  Google Scholar 

  17. Becker B and Milbourn T, How did increased competition affect credit ratings?, Journal of Financial Economics, 2011, 101(3): 493–514.

    Article  Google Scholar 

  18. Markose S, Alentorn A, and Krause A, Dynamic learning, herding and guru effects in networks, Economics Department Working Paper, University of Essex, 2004, No. 582.

    Google Scholar 

  19. Tedeschi G, Iori G, and Gallegati M, Herding effects in order driven markets: The rise and fall of gurus, Journal of Economic Behavior and Organization, 2012, 81(1): 82–96.

    Article  Google Scholar 

  20. Hoffmann A O I, Jager W, and Eije J H V, Social simulation of stock markets: Taking it to the next level, Journal of Artificial Societies and Social Simulation, 2007, 10(2): 7.

    Google Scholar 

  21. Ponta L, Pastore S, and Cincotti S, Information-based multi-assets artificial stock market with heterogeneous agents, Nonlinear Analysis: Real World Applications, 2011, 12(2): 1235–1242.

    Article  MATH  MathSciNet  Google Scholar 

  22. Chen S H and Yeh C H, Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market, Journal of Economic Dynamics and Control, 2001, 25(3–4): 363–394.

    Article  MATH  Google Scholar 

  23. Arthur WB, Holland J H, LeBaron B, Palmer R, and Tayler P, MAsset pricing under endogenous expectations in an artificial stock market, The Economy as an Evolving Complex System II, Addison-Wesley, 1997, 15–44.

    Google Scholar 

  24. Watts D J and Strogatz S H, Collective dynamics of “small-world” networks, Nature, 1998, 393(6684): 440–442.

    Article  Google Scholar 

  25. Börgers T and Sarin R, Naive reinforcement learning with endogenous aspirations, International Economic Review, 2000, 41(4): 921–950.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. College of Management and Economics, Tianjin University, Tianjin, 300072, China

    Xu Feng, Wei Zhang, Yongjie Zhang & Xiong Xiong

  2. China Center for Social Computing, Tianjin University, Tianjin, 300072, China

    Xu Feng, Wei Zhang, Yongjie Zhang & Xiong Xiong

Authors
  1. Xu Feng
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  2. Wei Zhang
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  3. Yongjie Zhang
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  4. Xiong Xiong
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Corresponding author

Correspondence to Yongjie Zhang.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 71131007, 71271144, and 71271145, the New Century Excellent Talents Supporting Program by Ministry of Education under Grant No. NECT-10-0626, the Innovative Research Team in University Supporting Program by Ministry of Education under Grant No. IRT 1208.

This paper was recommended for publication by Editor WANG Shouyang.

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Feng, X., Zhang, W., Zhang, Y. et al. Information identification in different networks with heterogeneous information sources. J Syst Sci Complex 27, 92–116 (2014). https://doi.org/10.1007/s11424-014-3297-0

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  • DOI: https://doi.org/10.1007/s11424-014-3297-0

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