Abstract
Recently, various machine learning techniques have been applied to solve online portfolio optimization (OLPO) problems. These approaches typically explore aggressive strategies to gain excess returns due to the existence of irrational phenomena in financial markets. However, existing aggressive OLPO strategies rarely consider the downside risk and lack effective trend representation, which leads to poor prediction performance and large investment losses in certain market environments. Besides, prediction with a single model is often unstable and sensitive to the noises and outliers, and the subsequent selection of optimal parameters also become obstacles to accurate estimation. To overcome these drawbacks, this paper proposes a novel kernel-based aggregating learning (KAL) system for OLPO. It includes a two-step price prediction scheme to improve the accuracy and robustness of the estimation. Specifically, a component price estimator is built by exploiting additional indicator information and the nonstationary nature of financial time series, and then an aggregating learning method is presented to combine multiple component estimators following different principles. Next, this paper conducts an enhanced tracking system by introducing a kernel-based increasing factor to maximize the future wealth of next period. At last, an online learning algorithm is designed to solve the system objective, which is suitable for large-scale and time-limited situations. Experimental results on several benchmark datasets from diverse real markets show that KAL outperforms other state-of-the-art systems in cumulative wealth and some risk-adjusted metrics. Meanwhile, it can withstand certain transaction costs.
Highlights
Portfolio optimization is a fundamental issue of computational finance which aims to invest wealth in a set of assets to meet some financial demands in the long run. ere are two major schools of principles and theories for this problem: (i) Markowitz [1] introduces the mean-variance theory that illustrates the relationship between portfolio expected return and risk; (ii) Kelly [2] presents the Kelly investment criterion, which focuses on multiperiod portfolio selection and tends to maximize the expected log return
We focus on six benchmark datasets: (1) New York Stock Exchange (NYSE)(O), (2) NYSE(N), (3) DJIA, (4) SP500, (5) TSE, and (6) HS300
The datasets mentioned above cover much long trading periods from 1962 to 2017 and diversified markets, which enables us to examine how the proposed kernel-based aggregating learning (KAL) system performs under different events and crises such as the dot-com bubble from 1995 to 2001 and the subprime mortgage from 2007 to 2009
Summary
Portfolio optimization is a fundamental issue of computational finance which aims to invest wealth in a set of assets to meet some financial demands in the long run. ere are two major schools of principles and theories for this problem: (i) Markowitz [1] introduces the mean-variance theory that illustrates the relationship between portfolio expected return and risk; (ii) Kelly [2] presents the Kelly investment criterion, which focuses on multiperiod portfolio selection and tends to maximize the expected log return. Portfolio optimization is a fundamental issue of computational finance which aims to invest wealth in a set of assets to meet some financial demands in the long run. Due to the sequential nature of financial market data, it is suitable to solve online portfolio optimization (OLPO) problems following the last framework. Trend representation is one of the main methods to make future price predictions following this principle. In the survey by Li and Hoi [14], there are three categories for trend representation: patternmatching, trend-reversing, and trend-following. Patternmatching tries to find historical patterns that are similar to the current pattern and uses the historical results to predict the asset price. Gyorfi et al [15] identify the similarity set by comparing two market windows via Euclidean distance and conduct nonparametric kernel-based sequential investment strategies. Gyorfi et al [16] further discuss the nonparametric nearest neighbor system to search for historical patterns which are located in the l nearest
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