Moving average reversion strategy for on-line portfolio selection

Abstract

Please cite this article in press as: B. Li et al., Moving Average Reversion Strategy for On-Line Portfolio Selection, Artificial Intelligence (2015), http://dx. AbstractOn-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interests from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving AverageReversion" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the data sets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.

Kernel-Based Aggregating Learning System for Online Portfolio Optimization

“…e problem setting in this paper is consistent with the standard and common one that has been used by many previous research studies [8][9][10][11][12][13][14][15][16]. Consider an investment task over a financial market with d assets.…”

“…Online moving average reversion (OLMAR) and robust median reversion (RMR) are two state-of-the-art defensive strategies based on the mean reversion principle. Li et al [8] assume that the asset price in the next period will reverse to its moving average (MA) and takes MA as a reference of the asset price trend. ere are two types of MA.…”

“…Due to the sequential nature of financial market data, it is suitable to solve online portfolio optimization (OLPO) problems following the last framework. In recent years, we have witnessed much research effort from machine-learning and artificial intelligence communities to design OLPO strategies through diverse prediction models and online learning algorithms (see [3][4][5][6][7][8][9][10][11][12][13] and references therein for more details).…”

“…Trend-reversing assumes that poor performing assets will perform well in the subsequent periods and vice versa. For example, Li et al [8] present the online moving average reversion (OLMAR) strategy that takes the moving average in a recent time window as a prediction of the future asset price. Huang et al [9] propose the robust median reversion (RMR) strategy which exploits the L 1 -median of recent asset prices as a robust statistic against noise and outliers.…”

“…Few aggressive systems in OLPO that could catch up with those of the state-of-the-art defensive ones are investigated. As an aggressive system, Lai et al [11] have achieved better investing performance than OLMAR and RMR [8,9], but it lacks effective trend representation and does not take into account the downside risk which may lead to poor prediction performance and large investment losses in some market environments. Besides, in financial practice, technical analysis is one common method to analyze the asset price, which can identify price patterns from historical data by exploiting technical indicators and then suggest future activities.…”