Looking into bubbles in financial markets and the emotional side of corporate forecast completion through modeling in the structured query language of financial databases
Subject. The study focuses on a set of financial and economic indicators of corporate performance and metrics of the financial market actors’ sentiment, financial indicators in markets and the way they change over time as markets face dramatic events. The article discusses techniques for applying the information of the financial and economic condition in modeling based on structured word language. Objectives. Analyzing key Russian and foreign sentiment measurement means, I try to create a toolkit, which would be applicable to the valuation and provides a balanced view of the financial and economic condition of companies with reference to the market sentiment. Methods. The article applies methods of induction and deduction, modeling. I demonstrate the relationship of methods and the methodology with new technological means of modern IT systems. Results. Modeling the completion of forecasts, plans and measuring the sentiment, I discovered that key statements and behavioral finance mechanisms manifested in the process. Modeling based on two scenarios and two types of models appeared to be the most illustrative. When forecasts, profit and revenue turn to be higher than expected and when they are lower. Studying bubbles in financial markets, I coined a respective model tested with cases of air lines. The applicable tests, such as the quality of revenue, percentage of deferred income in revenue, sentiment index of the companies show that revenue indicators and sentiment indices are on average poorer in companies with the worst indicators on the sample. Conclusions and Relevance. Given new capabilities of modern information systems, financial analysts get more opportunities for programming, creating user models with needed configurations and extensive database. There we have simplified primary data collection and processing when financial analysts use the data. The findings are applicable to practices of modern appraisers, cost and fundamental analysts. The use of models herein supplements and expands a conventional set of valuation tools and improves the quality of valuation.
Banz R.W. The Relation between Return and Market Value of Common Stocks. Journal of Financial Economics, 1981, vol. 9, iss. 1, pp. 3–18. URL: Link90018-0
Basu S. The Relationship between Earnings Yield, Market Value, and Return for NYSE Common Stocks: Further Evidence. Journal of Financial Economics, 1983, vol. 12, iss. 1, pp. 129–156. URL: Link90031-4
Benartzi Sh., Thaler R.H. Myopic Loss Aversion and the Equity Premium Puzzle. The Quarterly Journal of Economics, 1995, vol. 110, no. 1, pp. 73–92.
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
Hausman J. Contingent Valuation: From Dubious to Hopeless. Journal of Economic Perspectives, 2012, vol. 26, no. 4, pp. 43–56. URL: Link
Cornell B., Damodaran A. Tesla: Anatomy of a Run-Up Value Creation or Investor Sentiment? Behavioral & Experimental Finance eJournal, 2014. URL: Link
Varian H.R. Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 2014, vol. 28, no. 2, pp. 3–28. URL: Link
Baker K.H., Nofsinger J.R. Behavioral Finance: Investors, Corporations, and Markets. Hoboken, New Jersey, JohnWiley & Sons, Inc., 2010, 768 p.
Lee C.M.C., Shleifer A., Thaler R.H. Investor Sentiment and the Closed-End Puzzle. The Journal of Finance, 1991, vol. 46, iss. 1, pp. 75–109. URL: Link
Blume L., Easley D. Evolution and Market Behavior. Journal of Economic Theory, 1992, vol. 58, iss. 1, pp. 9–40. URL: Link90099-4
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
Barberis N.C. Thirty Years of Prospect Theory in Economics: A Review and Assessment. Journal of Economic Perspectives, 2013, vol. 27, no. 1, pp. 173–196. URL: Link
Jensen M.C. The Performance of Mutual Funds in the Period 1945–1964. The Journal of Finance, 1968, vol. 23, iss. 2, pp. 389–416. URL: Link
Fama E.F., French K.R. The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 2004, vol. 18, no. 3, pp. 25–46. URL: Link
Fama E. Market Efficiency, Long Term Returns and Behavioral Finance. Journal of Financial Economics, 1998, vol. 49, iss. 3, pp. 283-306. URL: Link00026-9
Fama E. Efficient Capital Markets: II. The Journal of Finance, 1991, vol. 46, iss. 5, pp. 1575–1617. URL: Link
Bates D. The Crash of 87: Was it Expected? The Evidence from Options Markets. Journal of Finance, 1991, vol. 46, iss. 3, pp. 1009–1044. URL: Link
Kumiega A., Van Vliet B.E. Automated Finance: The Assumptions and Behavioral Aspects of Algorithmic Trading. Journal of Behavioral Finance, 2012, vol. 13, iss. 1, pp. 51–55. URL: Link
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
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