Subject: CPS615 - final project proposal

From: Hyun Kim <[email-masked]>
Date: Mon, 10 Apr 2000 14:04:04 -0400

By: Hyun Kim
Title: Portfolio Optimization using Parallel Genetic Algorithms

We consider finding the optimal portfolio among all possible set of
portfolios under risk. This is the problem of maximizing the expected
profit on a portfolio and minimizing its corresponding risk by computing
the tradeoff between profit and risk. We introduce the concept of the
Sharpe Ratio and an approach which generates a solution of investing the
optimum amount on each security using parallel genetic algorithms and
report some computational results. In our problem, a candidate solution
is a linear combination of investment amounts on each security and
genetic algorithms apply operations of crossover, mutation and
reproduction to candidate solutions according to relative fitness values
in successive populations of candidates.
With the term of parallelization, we provide the concept of
parallelization when the genetic algorithms are applied on our portfolio
problems.  The most time-consuming part of genetic algorithms is to
compute the fitness of each candidate. So we divide the population
matrix to each processor, which computes the portion of candidates'
fitness and communicates with other processors.
The resulting optimal portfolio is evaluated by explicitly maximizing
the Sharpe Ratio and analyzed on data that are publicly available. The
results based on the Sharpe Ratio maximization using parallel genetic
algorithms show that the goal of the portfolio optimization can be
achieved with this approach.

---

From: Saleh Elmohamed <[email-masked]>
Date: Mon, 10 Apr 2000 14:45:11 -0400 (EDT)

Hi Hyun,

It sounds an interesting project. Make sure to clearly define those
concepts you mentioned such as 'sharpe ratio', 'optimal portfolio', and
other financially related terms. Without a clear cut definitions of these
ideas it would be hard to judge the 'goodness' of the performance of the
GAs on this problem... 

Also as I pointed out to two other students who would be using GAs for
their project, you'd need to define the 'fitness' as well as the
'selection, and 'crossover'... Also, watch out how you represent your
'solution'... Typically, a solution is represented as a collection of
genes with particular values, and there are so many ways you could
construct this 'representation'. In addition, the ability of GA to form
good solution would depend ALMOST completely upon your choice of this 
representation. Poor representation will result in the search being
effectively random or may be even worse.

---

From: Hyun Kim <[email-masked]>
Date: Mon, 10 Apr 2000 19:57:39 -0400

I added more explanations to the context I emailed you today. It will be
available on my web page.
http://carver.npac.syr.edu:3768/cps615spring00-docs/cc00kha/
If there is some problem, let me know it again. Thank you.