ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)
Tzu-Pu Chang, Yu-Cheng Chang and Po-Ching Chou
Correspondence: Tzu-Pu Chang, changtp@yuntech.edu.tw
Department of Finance, National Yunlin University of Science and Technology, Taiwan
pdf (553.15 Kb) | doi: https://doi.org/10.47260/bae/912
The essential goal of trend-following investing is to precisely identify where the uptrend and downtrend are located. This paper thus provides a two-layer stacking technique, which is a novel ensemble learning approach, to predict such trends for the Taiwan Top 50 ETF. The proposed stacking technique stacks the predictors of support vector machine (SVM), multi-layer perception (MLP), adaptive boosting (Adaboost), and extreme gradient boosting (Xgboost), presenting empirical results whereby following the trends obtained from the stacking technique can generate positive returns and beat both conventional moving-average crossover and buy-and-hold strategies.
Trend-following investing; Stacking technique; Ensemble learning; Machine learning; Taiwan Top 50 ETF
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Zura Kakushadze and Willie Yu
Correspondence: Zura Kakushadze
Quantigic® Solutions LLC, USA
pdf (553.15 Kb) | doi: https://doi.org/10.47260/bae/911
We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-) binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of [Kakushadze, 2015b] (for binary classifications) or general risk model construction of [Kakushadze and Yu, 2016a] (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings.
ETF, risk model, covariance, correlation, risk factor, optimization, growth, value, industry classification, quant, trading, stock, bond, equity, commodity, currency, volatility, real estate, alternatives, multi-asset, diversification, portfolio, credit rating, duration, maturity, market cap.
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