Presenting a Meta Synthesis Model for Predicting Inflationary Crises in Iran's Capital Market in the Algorithm of Distributive Functions of Investors Monetary Illusion

Subject Areas : Computational economics

Homayoun Khosravi Golmetabadi ¹ , ali asghar taherabadi ² , Attaollah Mohamadi Melgharni ³ , Ahmad Ali Jadidiyan ⁴

1 - Ph.D. Candidate, Department of Accounting and Finance, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
2 - Assistant Prof., Department of Accounting, Kangavar Branch,Islamic Azad University, Kangavar, Iran.
3 - Assistant Prof, Department of Accounting and Finance, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
4 - Assistant Prof, Department of Psychology, Kermanshah(kangavar) Branch, Islamic, Azad University, Kermanshah, Iran

Received: 2024-08-27 Accepted : 2024-10-21 Published : 2025-01-27

Keywords: Dynamic psychological games, monetary illusion, meta synthesis model,

Abstract :

 

Extended Abstract

Purpose

The general object of presenting a comprehensive and meta synthesis model of the data collection model using the Delphi-fuzzy method, the structural equation model method, the analysis of hypotheses and the method of component factor analysis to identify and predict the signal atmospheres of the stock and currency markets as a phenomenon of cognitive biases affected by the monetary illusion Investors are related to two categories of expected and non-expected inflation and in the framework of the theory of dynamic psychological games. In this connection, due to the signal and mutual pressures of monetary inflation in the stock and currency markets of Iran in the period from the beginning of 2015 to the beginning of 2020, the next object was to identify the critical foci of one-way and two-way signal games of the followers of signal chains in the form of herding behaviors. To be able to provide more optimal forecasts related to the analysis of leaving or remaining in the capital market by primary people in the directions and manner of changes and dynamics of stock price impulses.

Methodology

The methodology of the research objects related to the meta synthesis model, which is fundamental-developmental in terms of implementation, causal-analytical in terms of action, and retrospective in terms of the time dimension of the data in three basic steps. In the first step, the process of data collection using Excel and SPSS software, the Delphi-fuzzy method to obtain cross-sectional data and coded selectively according to the theories of Strauss and Corbin, (1988) and also in connection with the "phenomenology" paradigm, which is simultaneously two categories of inflations. Expected and unexpected, the experts will include the main theories in the recursive algorithm of the discrete probability functions of the iDFT model according to the patterns of Thomas Cormen, (2001) in chain matrices and the expansion of zero and one binomials. In the second step, using the Smart-pls software, the main and sub-path hypothesis tests of the variables related to the pricing games will be analyzed following the inflation signals of the chains according to the issues raised in the research. In the third step, by using SPSS software and Varimax method in the rotation of component matrices, forecasting the inflationary signal pressures of two stock and currency markets, two-way and one-way tracking of chains related to leaving the stock market or holding shares by real people are analyzed. and will be analyzed and identified.

 Findings

The findings of the research show the significance of all signal chain paths in the structural equation model and related to the variable "inflationary impulses" in relation to the two categories of expected and unexpected monetary inflation according to the main hypotheses of the research. In the test of sub-hypotheses, the mediating variable of stock market inflation in two-way psychological games, pricing resulting from investors' monetary illusion, has an effective role, and in a meaningful difference, it has a low-effect role in connection with one-way signal games. The modulating variable of unexpected signal chains of the currency market, the findings indicate its significant effect on the dynamics and behavior of signal chains in relation to changes in inflationary impulses and can affect the two-way and one-way changes in the behavioral chains of investors.

Conclusion

The result of presenting a meta synthesis model to identify inflationary crises caused by monetary illusion is important from several aspects. First, in terms of the innovation aspect of the research in using the algorithm of discrete distribution functions in the direction of transformation of iDFT Fourier recurrence matrices and related to the Delphi-fuzzy method of data collection model. Second, the generalization of binomial distribution matrices with dual categories of experts of economic theories in a probability function and continuous variable "inflation impulses" to identify the critical points of the Iranian capital market in leaving one group of real investors in a five-year period from the beginning of 2015 to the beginning of 2020 and its third aspect is to separate the "expected" and "unexpected" groups of investors in the use of the distribution of dynamic psychological game models for the factor analysis of the components of the monetary illusion. The findings of the factor analysis of the components indicate that the capital market is left unilaterally in critical situations resulting from the presence of the stock market inflation signal, or in other words, the exit after the creation of stock price bubbles, while in the atmosphere of the inflation signal of the currency market, persistence or entry and exit The primary investors in the balanced points of the two-way dynamic situations of the signal games will be affected by the adjustment of the unexpected monetary inflation and the inflation signal of the stock market.

References:

- Azar, Adel (1994). Statistical Explanation of Assumptions in Behavioral-Management Research, Scientific, Research Quarterly Journal of Management Science. Tehran University of Administrative Sciences and Marketing Management, 26(2), 3-10. (inpersian)
- Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. J. Econ. Prospect,21 (2), 129–152. Doi.10.1111/j.1540-6261.2006.00885
- Baker, M., & Stein, J.C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, (15)7, 271-299 Doi:10.1257/jep.21.2.129.
- Banerjee, A.V. (1992). A Simple Model of Herd Behavior. the Quarterly Journal of Economics, 107(8), 797-817. Doi: jstor.org/stable/2118364
- Barberis, N. (2018). “Psychology-based models of asset prices and trading volume “in Handbook of Behavioral Economics—Foundations and Applications 1, B. D. Bern-heim, S. DellaVigna, Laibson, Eds, 25(3), 79−175. Doi: 10.1016/S0304-405X(03)00064-3
- Battigalli, P., & Dufwenberg, M. (2008). Dynamic psychological games. Journal of Economic Theory,144 (5), 1–35. Doi: /10.1006/jeth.1999.2555
- Bentia, B. S. & Haßb, D. & Stadtmann, Georg. (2021). Money illusion in free-to-play games. European University Viadrina Frankfurt. 422(1), 196-240
- Bikhchandani, Sushiland, & Sharma, Sunil. (2000). “Herd Behavior in. Financial Markets”. IMF Staff Papers, 47(3), 279-310. Doi: 10.1086/261849
- Bikhchandani, Sushil, & David Hirshleifer, & Welch, I. (1992). “A Theory of Fads, Fashion, Custom and Cultural Change as Informational Cascades.” Journal of Political Economy, 100(11),992–1027 Doi: /pii/S0304-3932(01)00091-5.
- Carhart, Mark. (1995). Survivor bias and mutual fund performance. Working paper, School of Business Administration, University of Southern California, Los Angeles, Cal,35(8),261-285. doi:10.1111/j.1540-6261. 1997.tb03808.x
- Christopher, Avery., & Zemsky, Peter. (1998). Multidimensional Uncertainty and Herd Behavior in Financial Markets. American Economic Review, 88(4), 724 – 748. Doi: 10.1257/000282805775014317
- Cohen, Randolph B & Polk, Christopher. Vuolteenaho, Tuomo. (2005). Money illusion in the stock market: the modigliani-cohn hypothesis. The Quarterly journal of economics. 120(2), 639-668. Doi: 10.1162/0033553053970133
- Cormen, Thomas, H. (2001). chapter 30: Polynomials and the FFT. MIT Press and Mc Graw_ Hill,56(14), 828-848.
- Danilo Leiva-Leon, Jaime Martínez-Martín, (2020). Exchange rate shocks and inflation comovement in the euro area. Working Paper Series, No 238. DOI:10.2139/ssrn.3470112
- Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47(2),427–465. Doi: 10.2307/2329112
- Fama, E.F., & French, K.R. (2012). Size, value, and momentum in international stock returns. Journal of Financial Economics ,105(16), 457-472. Doi:10.1016/0304-405X (93)90023-5
- Fehr, Ernst. & Tyran, Jean-Robert. (2007). Money illusion and coordination failure. Games and Economic Behavior. 58(12), 246-268. DOI:10.1016/j.geb.2006.04.005
- Fehr, E. & Tyran, J.R. (2005). Individual Irrationality and Aggregate Outcomes. Journal of Economic Perspectives. 19(4), 43–66 DOI: 10.1257/089533005775196651
- Fehr, E., & Tyran, J. R. (2001). Does money illusion matter. American Economic Review. 91(5), 1239-1262. DOI: 10.1257/aer.91.5.1239
- Hilde,B.C, Kai, L. (2005). Identifying the interdependence between US monetary policy and the stock market. Monetary Policy and Research Department, Bank of Finland Research. Discussion Papers, 17/2005. DOI: 10.1016/j.jmoneco.2008.12.001
- Hair Jr, J. F., & Hult, G. T. M., & Ringle, C., & Sartedt, M. (2013). A Primer on Partial least squares structural equation modeling (PLS-SEM). SAGE Publications, Incorporated,65(19),187-220. DOI: 10.1007/978-3-030-80519-7
- Hulland, J. (1999). Use a partial least squares (PlS) in strategic management research:
a review of four recent studies. Strategic management Journal, 20(2), 195-204. https://www.jstor.org/stable/3094025
- Kahneman, Daniel., & Amos, Tversky. (1979). Prospect theory: An analysis of decisionunder risk. Econometrica, 47(2), 263–291. Doi: RePEc:aea:aecrev:v:93:y:2003:i:5:p:1449-1475
- Modigliani, F., & R. Cohn. (1979). “Inflation, Rational Valuation and the Market.” Financial Analysts Journal, 35(2) ,24–44. doi.org/10.2469/faj.v35.n2.24
- Mumtaz, Haroon, Plassmann, Laura Sunder, (2010). Time-varying dynamics of the real, exchange rate, A structural VAR analysis. Working Paper. 382(15), 255-310. DOI:10.2139/ssrn.1568775
- Statman, M. (2011). Investor sentiment, stock characteristics, and returns. Journal of Portfolio Management, 37(3), 54-61. Doi: 10.1016/j.bir.2014.03.001
- Weber, B. & Rangel, A. & Wibral, M. & Falk, A. (2009). The medial prefrontal cortex exhibits money illusion. Proceedings of the National Academy of Sciences. 106(13), 5025-5078. DOI:10.1073/pnas.0901490106