This study, a part of a larger work on decomposing rating migration matrices from market prices, investigates the matter of the associated optimization problem, in plain form, yielding multiple possible local solutions. The sources of nonlinearity and complexity of the optimization problem are outlined. This includes the rating category migration variance results of solutions, in terms of values and spacing, and within matrix rating category structures. Plain optimization problem decompositions struggle to surface, and correct both rating category migration variance values and spacing, and rating category matrix structures. Generally, full matrix decompositions require good initial solutions to yield good results. Matrix averaging and matrix sampling are considered. Matrix averaging is based on limited coefficient counts or sets-not using the full coefficient count, but rather grouping coefficients. Matrix sampling forms an approximation of the matrix, in a sense through parsimony. Matrix averaging represents simple(r) optimization problems, and offers easy solutions, with good results and information, but offers poor initial solutions for full matrix decomposition. Matrix sampling can offer good indications, and good initial solutions for full matrix decomposition. Full matrix decomposition from initial solutions sourced through matrix sampling offers good results. Overall, revisiting and re-examining the way that the optimization algorithm searches optimal solutions based on the initial solution provided, can further improve decomposition results.

The paper evaluates the impact of rainfall forecast announcement on six selected commodity spot price fluctuation with reference to Indian commodity market. Using daily spot price data from January 2, 2017 to December 13, 2017, four research questions have been addressed by considering two rainfall forecasting announcement dates, April 18, 2017 and June 6, 2017, by the Indian Meteorological Department, Government of India. By adopting dummy regression, Bai-Perron (2003) multiple structural break point tests and lagged regression, the empirical results suggest that rainfall forecasting announcement is a very temporary phenomenon and there is no strong evidence of fluctuations in return on commodity spot prices. This observation is also supported by ARIMA modeling, where it has been found that the return on spot price fluctuation behavior can be explained better with the help of lower order moving average models like MA1, MA2, etc. The results are statistically parsimonious because they are consistent with the real-life fact that agriculture product pricing is subject to the matter of seasonality which can persist for a short span of time, and this observation has been supported by the finding from lagged regression that lag 1 is significant at 1% level in maximum cases. The policy suggestions are that there is a need to introduce weather derivatives in Indian market to hedge risk due to weather variability and the meteorological department should improve its penetration of information among traders and farmers to achieve price efficiency.

This paper examines how the herd mentality and regret bias of investors impact their investment decisions. Primary data was collected from employees, stock brokers, students and others who are either active investors in the stock market or following it regularly. The questionnaire is divided into two parts, consisting of demographical questions and technical questions on herd mentality and regret bias. Regression analysis and descriptive analysis were done on the data. The approach of this study is different as it uses situation-based questions and demographics to find out how their affect the investor's biases.