A new method of measuring stock market manipulation through structural equation modeling (SEM)
-
DOIhttp://dx.doi.org/10.21511/imfi.14(3).2017.05
-
Article InfoVolume 14 2017, Issue #3, pp. 54-61
- Cited by
- 1248 Views
-
565 Downloads
This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License
This paper proposes a new model of measuring a latent variable, stock market manipulation. The model bears close resemblance with the literature on economic well-being. It interprets the manipulation of a stock as a latent variable, in the form of a multiple indicators and multiple causes (MIMIC) model. This approach exploits systematic relations between various indicators of manipulation and between manipulation and multiple causes, which allows it to identify the determinants of manipulation and an index of manipulation simultaneously. The main reason of stock market manipulation comes from the fact that information availability is not universally equal. The manipulation is thus critically linked to the creation, arrival and dissemination of information or rumors/mis-information. Thus, the immediate impact of manipulation is on the time profile of returns, or excess returns, from an asset and the excess volatility of returns in excess of the volatility explained by the fundamentals. In this basic setup, the model used these two variables as the indicators of stock market manipulation. The main intuition of the MIMIC approach is that some variables, or statistics, related to peace are indicators of manipulation, while others signify effects or outputs of causal factors, or inputs, of manipulation. In other words, distinction can be made between causes of manipulation and indicators of manipulation. The causal factors used in this model are classified into five different domains namely pure economic factors as determinants of manipulation, labor market conditions, international factors, quality of governance factors and systematic risk factors.
- Keywords
-
JEL Classification (Paper profile tab)G18, G28, C39
-
References26
-
Tables0
-
Figures1
-
- Figure 1. MIMIC 10-1-2
-
- Allen, F., & Gale, D. (1992). Stock-price manipulation. The Review of Financial Studies, 5(3), 503-529.
- Armson, E. (2009). False Trading and Market Rigging in Australia. Company and Securities Law Journal, 27(11), 411.
- Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
- Chatterjea, A., Cherian, J. A., & Jarrow, R. A. (1993). Market manipulation and corporate finance: A new perspective. Financial management, 200-209.
- Dell’Anno, R. (2003). Estimating the shadow economy in Italy: A structural equation approach.
- Dionigi, G., Charles, C., Christopher, W., & Paul, M. (2014). Stock Market Manipulation on the Hong Kong Stock Exchange. Australasian Accounting, 8(4), 105-140.
- Dixit, A. K. (2007) Lawlessness and economics: alternative modes of governance. Princeton University Press.
- Ervik, A. O. (2003). IQ and the Wealth of Nations. The Economic Journal, 113(488).
- Gerace, D., Chew, C., Whittaker, C., & Mazzola, P. (2014). Stock Market Manipulation on the Hong Kong Stock Exchange. Australasian Accounting Business & Finance Journal, 8(4), 105.
- Goldwasser, V. (1999). Regulating Manipulation in Securities Markets: Historical Perspectives and Policy Rationales. Australian Journal of Legal History, 5, 149.
- Jarrow, R. A. (1992). Market manipulation, bubbles, corners, and short squeezes. Journal of financial and Quantitative Analysis, 27(3), 311-336.
- John, K., & Narayanan, R. (1997). Market manipulation and the role of insider trading regulations. The Journal of Business, 70(2), 217-247.
- Jöreskog, K. G. (1970). A general method for estimating a linear structural equation system. ETS Research Report Series, 1970(2).
- Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70(351a), 631-639.
- Knott, M., & Bartholomew, D. J. (1999). Latent variable models and factor analysis. Edward Arnold.
- Lynn, R., & Vanhanen, T. (2006). IQ and global inequality. Washington Summit Publishers.
- Mia, M. A. H. (2012). Origin of & Solution to Global Financial Meltdown: An Islamic View. International Journal of Business and Management, 7(12), 114.
- Morris, M. D. (1979). Measuring the condition of the worlds poor: the physical quality of life index.
- Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29(1), 81-117.
- Pickholz, M. G., & Pickholz, J. (2001). Manipulation. Journal of Financial Crime, 9(2), 117-133.
- Richardson, K. (2004). IQ and the Wealth of Nations. Nature Publishing Group.
- Sen, G. (1997). Empowerment as an approach to poverty. Background paper to the Human Development Report, 1997. Bangalore, India: Unpublished paper.
- Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Crc Press.
- UNDP (1990). Human Development Report 1990: Concept and Measurement of human development. New York: Oxford University Press.
- Uppal, J. Y., & Mangla, I. U. (2006). Market volatility, manipulation, and regulatory response: a comparative study of Bombay and Karachi stock markets. The Pakistan Development Review, 1071-1083.
- Vila, J.-L. (1989). Simple games of market manipulation. Economics Letters, 29(1), 21-26.