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Financial Analytics: Science and Experience
 

Simulation of emotional differences in the structured query language for databases of financial markets

Vol. 14, Iss. 2, JUNE 2021

Received: 11 February 2021

Received in revised form: 27 February 2021

Accepted: 10 March 2021

Available online: 28 May 2021

Subject Heading: ASSESSMENT AND APPRAISAL ACTIVITIES

JEL Classification: G32

Pages: 156–173

https://doi.org/10.24891/fa.14.2.156

Semen Yu. BOGATYREV Financial University under Government of Russian Federation, Moscow, Russian Federation
sbogatyrev@fa.ru

https://orcid.org/0000-0002-6080-5869

Subject. The article addresses a simulation in the structured query language (SQL) for the Bloomberg information base, the scientifically grounded tools for measuring emotions in markets in the face of financial and pandemic crisis and market imbalances, in addition to classic financial indicators, the creation of analytical tools based on state-of-the-art software tools that integrate the latest advances in behavioral finance and in financial and coefficient analysis, machine learning technologies, and open financial market data.
Objectives. The aim is to create a usable toolkit for balanced evaluation of financial and economic situation of companies, based on the analysis of the main Russian and foreign modern means to measure emotions.
Methods. The study employs methods of induction, deduction, and modeling. It demonstrates the link between methods and methodologies with new technical means of modern information systems.
Results. The paper studies the effect of the heuristics of insufficient reaction and heuristics of excessive self-confidence, using the examples of market drawdown. I developed and implemented a model, using the behavioral finance tools as a factor in stabilizing financial decisions.
Conclusions. New software and hardware tools enabled to identify and measure the actions of the heuristics of financial market participants. Financial analysts, when applying the new capabilities of modern information systems for programming and creating user models with required parameters and extensive database, will have new opportunities in the described models. The procedure for collecting and processing the original information required for valuation is simplified.

Keywords: Bloomberg query language, valuation, measuring emotions in markets, emotional finance, behavioral finance

References:

  1. Varian H.R. Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 2014, vol. 28, iss. 2, pp. 3–28. URL: Link
  2. Baker K.H., Nofsinger J.R. Behavioral Finance: Investors, Corporations, and Markets. Hoboken, New Jersey, John Wiley & Sons, Inc., 2010, 768 p.
  3. De Bondt W.F.M., Thaler R. Does the Stock Market Overreact? The Journal of Finance, 1985, vol. 40, iss. 3, pp. 793–805. URL: Link
  4. Hausman J. Contingent Valuation: From Dubious to Hopeless. Journal of Economic Perspectives, 2012, vol. 26, iss. 4, pp. 43–56. URL: Link
  5. Barberis N.C. Thirty Years of Prospect Theory in Economics: A Review and Assessment. Journal of Economic Perspectives, 2013, vol. 27, iss. 1, pp. 173–196. URL: Link
  6. Benartzi Sh., Thaler R.H. Myopic Loss Aversion and the Equity Premium Puzzle. The Quarterly Journal of Economics, 1995, vol. 110, iss. 1, pp. 73–92. URL: Link
  7. Lee C., Shleifer A., Thaler R. Investor Sentiment and the Closed-End Puzzle. The Journal of Finance, 1991, vol. 46, iss. 1, pp. 75–109. URL: Link
  8. Mehra R., Prescott E.C. The Equity Premium: A Puzzle. Journal of Monetary Economics, 1985, vol. 15, iss. 2, pp. 145–161. URL: Link90061-3
  9. Kadous K., Tayler W.B., Thayer J.M., Young D. Individual Characteristics and the Disposition Effect: The Opposing Effects of Confidence and Self-Regard. Journal of Behavioral Finance, 2014, vol. 15, iss. 3, pp. 235–250. URL: Link
  10. Jensen M.C. The Performance of Mutual Funds in the Period 1945–1964. The Journal of Finance, 1967, vol. 23, iss. 2, pp. 389–416. URL: Link
  11. Fama E.F., French K.R. The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 2004, vol. 18, iss. 3, pp. 25–46. URL: Link
  12. Fama E.F. Market Efficiency, Long Term Returns and Behavioral Finance. Journal of Financial Economics, 1998, vol. 49, iss. 3, pp. 283–306. URL: Link
  13. Fama E.F. Efficient Capital Markets: II. The Journal of Finance, 1991, vol. 46, iss. 5, pp. 1575–1617. URL: Link
  14. Banz R.W. The Relationship between Return and Market Value of Common Stocks. Journal of Financial Economics, 1981, vol. 9, pp. 3–18. URL: Link90018-0
  15. Basu S. The Relationship between Earnings' Yield, Market Value, and Return for NYSE Common Stocks: Further Evidence. Journal of Financial Economics, 1983, vol. 12, pp. 129–156. URL: Link90031-4
  16. Bates D. The Crash of ’87: Was it Expected? The Evidence from Options Markets. The Journal of Finance, 1991, vol. 46, iss. 3, pp. 1009–1044. URL: Link
  17. Bradford C., Damodaran A. Tesla: Anatomy of a Run-Up. The Journal of Portfolio Management Fall, 2014, vol. 41, iss. 1, pp. 139–151. URL: Link
  18. Blume L., Easley D. Evolution and Market Behavior. Journal of Economic Theory, 1992, vol. 58, iss. 1, pp. 9–40. URL: Link90099-4
  19. Brennan M.J., Chordia T., Subrahmanyam A. Alternative Factor Specifications, Security Characteristics, and the Cross-Section of Expected Stock Returns. Journal of Financial Economics, 1998, vol. 49, iss. 3, pp. 345–373. URL: Link00028-2

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