Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach

Jiayang Yu,Kuo-Chu Chang

doi:10.3390/jrfm13110285

Abstract

Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR.

Highlights

Markowitz 1952 pioneered the construction of an optimal portfolio by proposing a Mean-Variance model, which created an efficient frontier to model a portfolio’s risk and return trade-off
With the proposed Economic Factor-Based Predictive Model (EFPM) model and three benchmarks above, there are a total of 154 months of out-of-sample returns
After Jondeau et al proposed to estimate joint distribution by combining conditional dependency from copulas in the generalized autoregressive conditional heteroskedasticity (GARCH) context in (Jondeau and Rockinger 2006), many researchers have conducted studies utilizing this framework in the field of conditional asset allocation and risk assessment (Wang et al 2010) in non-normal settings

Summary

Introduction

Markowitz 1952 pioneered the construction of an optimal portfolio by proposing a Mean-Variance model, which created an efficient frontier to model a portfolio’s risk and return trade-off. This laid the foundation for a continuous development of Modern Portfolio Theory (MPT)—a mathematical framework for assembling and allocating a portfolio of assets (equities and bonds are the most common asset classes) with the goal of either maximizing its expected return for a given risk constraint or minimizing its risk for a given expected return constraint. Due to the fact that VaR is neither subadditive nor convex, and that the distribution of real-world financial asset returns data is found to exhibit substantial heavy tails and asymmetry around the mean (see (Shaik and Maheswaran 2019)), (Artzner et al 1999) proved that VaR is not a coherent measure of risk for asymmetric distribution

Objectives

Results

Conclusion