Optimizing an investment portfolio to maximize returns while minimizing risk is the ultimate goal for investors and their advisers. However, there is no set path and challenges always arise. One such limitation is the high-dimensional, small-sample problem (HDSS). HDSS refers to a portfolio with a large number of assets but little historical data, leading to unreliable portfolio optimization and resulting in weak investment performance.
Researcher overcomes portfolio optimization limitations with new approach
