Portfolio optimization: A multi-period model with dynamic risk preference and minimum lots of transaction

Sufficient description of stock returns is essential to generate an efficient model of portfolio optimization. Security returns are considered to be random variables where there exist sufficient data of historical returns. Nonetheless, uncertain variables may be applied to increase the effectiveness of security returns. The following research entails an optimization objective problem focusing on minimum lots of transaction in uncertain environments of dynamic trading. Also, the changing risk preference of the investor over the horizon of investment has been factored in the model. An average- Value at Risk (VaR) framework has been used to maximize wealth creation using genetic algorithms.