A Dynamic Target Volatility Strategy for Asset Allocation using Artificial Neural Networks

Abstract

A challenge to developing data-driven approaches in finance and trading is the limited availability of data because periods of instability, such as during financial market crises, are relatively rare. This study applies a stability-oriented approach (SOA) based on statistical tests to compare data for the current period to a past set of data for a stable period, providing higher reliability due to a more abundant source of data. Based on an SOA, this study uses an artificial neural network (ANN), which is one of the commonly applied machine learning algorithms, for simultaneously forecasting the volatility and classifying the level of market stability. In addition, this study develops a dynamic target volatility strategy for asset allocation using an ANN to enhance the ability of a target volatility strategy that is established for automatically allocating capital between a risky asset and a risk-free cash position. In order to examine the impact of the proposed strategy, the results are compared to the buy-and-hold strategy, the static asset allocation strategy, and the conventional target volatility strategy using different volatility forecasting methodologies. An empirical case study of the proposed strategy is simulated in both the Korean and U.S. stock markets.

Department(s)

Engineering Management and Systems Engineering

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Commerce; Financial markets; Investments; Learning algorithms; Learning systems; Neural networks; Applied machine learning; Asset allocation; Buy-and-hold strategy; Data-driven approach; Dynamic target; Empirical case studies; Market stability; Volatility forecasting; Electronic trading

International Standard Serial Number (ISSN)

0013-791X; 1547-2701

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Taylor & Francis, All rights reserved.

Publication Date

01 Oct 2018

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