Investment consulting plays a major role in any individual or corporate financial planning. Competent consultants research on technical analysis of different companies and stocks. This study uses the Bayesian statistical principle to apply the random effect model to binary outcomes. Furthermore, there seems to be little or no approach to develop quality metrics for financial advisers’ historic data and their plans offer. The relevance of monitoring stock broker recommendations and finding the predictive factors connected with it is addressed in this study. The main goal is to investigate the impact of variation in profit and loss call types within each cluster for the 2020 dataset acquired from an investment advisory. This study incorporates the essential module of Bayesian approach, construction of priors, suitable inferences using posterior distribution of the parameters. It is observed that the recommendation made for Sell call has higher odds for profit in the outcomes and a source of heterogeneity has been observed across the stratification variables.

Bayesian, heterogeneity, investment consulting, stratification variables, trading.

Received: February 17, 2022; Revised: March 13, 2022; Accepted: April 1, 2022; Published: April, 23, 2022

How to cite this article: S. Mythreyi Koppur and Dr. B. Senthilkumar, Random effect model for the causes of recommendation - a Bayesian approach, Advances and Applications in Statistics 76 (2022), 53-74. http://dx.doi.org/10.17654/0972361722036

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:

[1] F. S. Al-Duais, Bayesian reliability analysis based on the Weibull model under weighted general entropy loss function, Alexandria Engineering Journal 61 (2022), 247-255.
[2] F. S. Al-Duais, Approximate Bayes estimation of the Weibull distribution under weighted linex loss function, Advances and Applications in Statistics 72 (2022), 25-39.
[3] B. Baujat, C. Mahé, J. P. Pignon and C. Hill, A graphical method for exploring heterogeneity in meta‐analyses: application to a meta-analysis of 65 trials, Stat. Med. 21(18) (2002), 2641-2652.
[4] B. Carpenter, A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li and A. Riddell, Stan: a probabilistic programming language, Journal of Statistical Software 76(1) (2017), 1-32.
[5] D. Costantini and A. P. Møller, A meta-analysis of the effects of geolocator application on birds, Current Zoology 59(6) (2013), 697-706.
[6] I. Domowitz and H. Yegerman, Measuring and Interpreting the Performance of Broker Algorithms, Algorithmic Trading: A Buyside Handbook, 2005.
[7] M. Firth, An analysis of the stock market performance of new issues in New Zealand, Pacific-Basin Finance Journal 5(1) (1997), 63-85.
[8] V. F. Froelicher, S. Perdue, W. Pewen and M. Risch, Application of meta-analysis using an electronic spread sheet to exercise testing in patients after myocardial infarction, The American Journal of Medicine 83(6) (1987), 1045-1054.
[9] R. J. Hardy and S. G. Thompson, Detecting and describing heterogeneity in meta‐analysis, Stat. Med. 17(8) (1998), 841-856.
[10] Larry V. Hedges and Ingram Olkin, Statistical Methods for Meta-analysis, Academic Press, Orlando, 1985.
[11] K. A. L’abbé, A. S. Detsky and K. E. I. T. H. O’Rourke, Meta-analysis in clinical research, Ann. Intern. Med. 107(2) (1987), 224-233.
[12] M. Lavielle and E. Lebarbier, An application of MCMC methods for the multiple change-points problem, Signal Processing 81(1) (2001), 39-53.
[13] H. C. Lucas, Jr. and V. K. Spitler, Technology use and performance: a field study of broker workstations, Decision Sciences 30(2) (1999), 291-311.
[14] D. Lunn, J. Barrett, M. Sweeting and S. Thompson, Fully Bayesian hierarchical modelling in two stages, with application to meta-analysis, Journal of the Royal Statistical Society, Series C, Applied Statistics 62(4) (2013), 551-572.
[15] J. F. Madden, Performance-support bias and the gender pay gap among stockbrokers, Gender and Society 26(3) (2012), 488-518.
[16] M. L. McHugh, The odds ratio: calculation, usage, and interpretation, Biochemia Medica 19(2) (2009), 120-126.
[17] M. Pathak, S. N. Dwivedi, S. V. S. Deo, V. Sreenivas and B. Thakur, Which is the preferred measure of heterogeneity in meta-analysis and why? A revisit, Biostatistics and Biometrics Open Access Journal 1 (2017), 14-20.
[18] S. R. Patil, R. Morales, S. Cates, D. Anderson and D. Kendall, An application of meta-analysis in food safety consumer research to evaluate consumer behaviors and practices, Journal of Food Protection 67(11) (2004), 2587-2595.
[19] H. S. Sacks, D. Reitman, D. Pagano and B. Kupelnick, Meta-analysis: an update, The Mount Sinai Journal of Medicine 63(3-4) (1996), 216-224.
[20] S. Shim, B. H. Yoon, I. S. Shin and J. M. Bae, Network meta-analysis: application and practice using Stata, Epidemiol. Health 39 (2017), e2017047.
doi:10.4178/epih.e2017047.
[21] F. Song, T. A. Sheldon, A. J. Sutton, K. R. Abrams and D. R. Jones, Methods for exploring heterogeneity in meta-analysis, Evaluation and the Health Professions 24(2) (2001), 126-151.
[22] A. J. Sutton and J. P. Higgins, Recent developments in meta-analysis, Stat. Med. 27(5) (2008), 625-650.
[23] G. R. Warnes, B. Bolker, G. Gorjanc, G. Grothendieck, A. Korosec, T. Lumley and J. Rogers, gdata: various R programming tools for data manipulation, R Package Version 2(3) (2014), 35.