A study on behavioral biases and it's impact on financial risk tolerance among investors of Uttar Pradesh.

Investment decision these days play a crucial role in planning different life long events 
as well as financial contingencies. In Indian Investors, where an individual needs to 
allocate funds on yearly basis in accordance with different governmental policies, this 
task becomes much complex and difficult. In order to reduce this mental burden, every 
individual takes help from different consultancies, people, and other organizations. As 
seen in various financial decisions individual investors generally try to find his/her 
assets and liabilities and then do financial planning in accordance; but the most 
important criteria they generally omit is they are risk tolerance level. 
The study where has been conducted with a primary objective to develop the financial 
model to profile and investors (belonging to different tolerance levels) according to 
different behavioural biases. However, the study has been conducted among investor of 
Uttar Pradesh Region only, the replication of results on a larger population needs to be 
analysed. The data has been collected from 500 individual investors (the absolute figure 
is 487 after data editing) in the form of a structured Questionnaire having closed ended 
questions. The questions related to behavioural biases have been developed on a 5 point 
Likert scale. 
The results show that the investors of Uttar Pradesh are found to be affected by different 
behavioural biases which is prominently reflected in their irrational behaviour towards 
financial decision making. The study found 8 behavioural biases (as propounded by NM 
Waweru in the year 2008) namely representativeness, overconfidence, gamblers fallacy, 
availability bias, loss aversion, regret aversion, mental accounting, and herd behaviour. 
However, one biases named as anchoring bias does not seem to influence the investors 
of Uttar Pradesh. The results also categories the investors into five different categories 
of risk tolerance as conservative, above moderate, moderate, below moderate and 
aggressive one (as defined in GL-RTS model in the year 2015). 
The study here attempts to generate an association between these biases and different 
risk tolerance levels. It is found that four biases (representativeness, over confidence, 
gamblers fallacy, availability bias) are strongly, positively associated with risk 
tolerance. However other 4 biases (loss aversion, regret aversion, mantel accounting, 
and herd behaviour) are strongly, negatively associated with risk tolerance level. 
The study also claims that various demographical factors (gender, age, marital status, 
education, employment income, investment preference) do have an impact on the 
financial risk tolerance level of an individual and hence this must be taken into account 
while considering any financial alternative for an investment decision. 
This study will help an investor to construct his/her own behaviourally modified 
portfolio or to suggest it to others. However, future research with more behaviour biases 
and on a larger population will contribute much to the present study.