  With the advent of Web 2.0, various types of data are being produced every
day. This has led to the revolution of big data. Huge amount of structured and
unstructured data are produced in financial markets. Processing these data
could help an investor to make an informed investment decision. In this paper,
a framework has been developed to incorporate both structured and unstructured
data for portfolio optimization. Portfolio optimization consists of three
processes: Asset selection, Asset weighting and Asset management. This
framework proposes to achieve the first two processes using a 5-stage
methodology. The stages include shortlisting stocks using Data Envelopment
Analysis (DEA), incorporation of the qualitative factors using text mining,
stock clustering, stock ranking and optimizing the portfolio using heuristics.
This framework would help the investors to select appropriate assets to make
portfolio, invest in them to minimize the risk and maximize the return and
monitor their performance.
