2019/11/15 by Dmytro Ivasiuk, Ivasiuk, Dmytro
Economics, Econometrics and Finance · Engineering · #Electric Power System Optimization #Energy Load and Power Forecasting #FOS: Economics and business #Financial Markets and Investment Strategies #Financial Risk and Volatility Modeling #Portfolio Management (q-fin.PM) #Smart Grid Energy Management #Stochastic processes and financial applications #q-fin.PM
paper · pdf · doi:10.48550/arxiv.1911.06552
openalex publication_date 2019/11/15 · openalex created_date 2021/02/15 · arxiv created 2021/10/12 · arxiv updated 2021/10/13 · openalex updated_date 2026/07/28
This work derives an approximate analytical single period solution of the portfolio choice problem for the power utility function. It is possible to do so if we consider that the asset returns follow a multivariate normal distribution. It is shown in the literature that the log-normal distribution seems to be a good proxy of the normal distribution in case if the standard deviation of the last one is way smaller than its mean. So we can use this property because this happens to be true for gross portfolio returns. In addition, we present a different solution method that relies on the machine learning algorithm called Gradient Descent. It is a powerful tool to solve a wide range of problems, and it was possible to implement this approach to portfolio selection. Besides, the paper provides a simulation study, where we compare the derived results with the well-known solution, which uses a Taylor series expansion of the utility function.