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.

中文翻译：

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基于人工神经网络的资产配置动态目标波动策略

摘要 在金融和交易中开发数据驱动方法的一个挑战是数据的可用性有限，因为不稳定时期，例如在金融市场危机期间，相对罕见。本研究采用基于统计检验的面向稳定性的方法 (SOA)，将当前时期的数据与稳定时期的过去一组数据进行比较，由于数据来源更丰富，因此提供了更高的可靠性。本研究基于 SOA，使用人工神经网络 (ANN)，这是一种常用的机器学习算法，用于同时预测波动性和对市场稳定性水平进行分类。此外，本研究使用 ANN 开发了一种用于资产配置的动态目标波动率策略，以增强目标波动率策略的能力，该策略是为在风险资产和无风险现金头寸之间自动分配资本而建立的。为了检查所提出策略的影响，使用不同的波动率预测方法将结果与买入并持有策略、静态资产配置策略和传统目标波动率策略进行比较。拟议策略的实证案例研究在韩国和美国股票市场进行了模拟。以及使用不同波动率预测方法的传统目标波动率策略。拟议策略的实证案例研究在韩国和美国股票市场进行了模拟。以及使用不同波动率预测方法的传统目标波动率策略。拟议策略的实证案例研究在韩国和美国股票市场进行了模拟。