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Finance and Credit
 

Looking into bubbles in financial markets and the emotional side of corporate forecast completion through modeling in the structured query language of financial databases

Vol. 27, Iss. 4, APRIL 2021

Received: 11 February 2021

Received in revised form: 25 February 2021

Accepted: 11 March 2021

Available online: 29 April 2021

Subject Heading: THEORY OF FINANCE

JEL Classification: G32

Pages: 833–850

https://doi.org/10.24891/fc.27.4.833

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

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

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.

Keywords: Structured Query Language, valuation, sentiment measurement, emotional finance, behavioral finance

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