On-Line Portfolio Selection with Moving Average Reversion

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the 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 in some real datasets, leading to poor performance when the assumption does not hold. To overcome the 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 Average Reversion" (OLMAR), which exploits MAR by applying powerful online learning techniques. From our empirical results, we found that OLMAR can overcome the drawback of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where the existing mean reversion algorithms failed. In addition to superior trading performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications.Comment: ICML201

Sources of Over-performance in Equity Markets: Mean Reversion, Common Trends and Herding

In the field of optimisation models for passive investments, we propose a general portfolio construction model based on principal component analysis. The portfolio is designed to replicate the first principal component of a group of stocks, instead of a traditional benchmark, thus capturing only the common trend in the stock returns. The main advantage of this approach is that the reduction of the noise present in stock returns facilitates the replication task considerably and the optimal portfolio structure is very stable. We analyse the portfolio performance over different time horizons and in different international equity markets. The strategy over-performs both equally weighted and price weighted benchmarks, even after transaction costs. A market premium, a value premium associated with mean reversion in stock returns, and a volatility premium which give the strategy characteristics of a benchmark enhancer, all explain the over-performance, but have time-varying contributions to it. A behavioural explanation for the mean reversion mechanism leads to the conclusion that the portfolio performance is influenced by the extent of investors herding towards the common trend in stock returns.common trends, mean revrsion, herding, principal component analysis, abnormal returns, value strategies, behavioural finance

On-Line Portfolio Selection Strategy Based on Weighted Moving Average Asymmetric Mean Reversion

Mean reversion is an important property for constructing efficient on-line portfolio selection strategy. The existing strategies mostly suppose that the mean reversion is multi-period symmetric or single-period asymmetric. However, the mean reversion is multi-period and asymmetric in the real market. Taking this into account, on-line strategies based on multi-period asymmetric mean reversion is proposed. With designing multi-piecewise loss function and imitating passive aggressive algorithm, we propose a new on-line strategy WMAAMR. This strategy runs in linear time, and thus is suitable for large-scale trading applications. Empirical results on four real markets show that WMAAMR can achieve better results and bear higher transaction cost rate

Testing the predictability and efficiency of securitized real estate markets

This paper conducts tests of the random walk hypothesis and market efficiency for 14 national public real estate markets. Random walk properties of equity prices influence the return dynamics and determine the trading strategies of investors. To examine the stochastic properties of local real estate index returns and to test the hypothesis that public real estate stock prices follow a random walk, the single variance ratio tests of Lo and MacKinlay (1988) as well as the multiple variance ratio test of Chow and Denning (1993) are employed. Weak-form market efficiency is tested directly using non-parametric runs tests. Empirical evidence shows that weekly stock prices in major securitized real estate markets do not follow a random walk. The empirical findings of return predictability suggest that investors might be able to develop trading strategies allowing them to earn excess returns compared to a buy-and-hold strategy. --Securitized real estate,weak-form market efficiency,random walk hypothesis,variance ratio tests,runs test,trading strategies

Optimal life cycle portfolio choice with housing market cycles

During the last decades households in the U.S. have experienced that residential house prices move in a persistent manner, i.e. that returns are positively serially correlated. Since an owner-occupied home is usually the largest investment of a household it is important to understand how households act when they base their consumption and investment decisions on this experience. We show in a setting with housing market cycles and households who can decide whether they rent or own the home, that - besides the consumption and the precautionary savings motive - serial correlation in house prices generates a new speculative motive for homeownership. In particular, we show how good and bad housing market cycles affect homeownership rates, leverage, stock investments and consumption and can explain empirically observed household behavior during housing market boom and bust periods. Keywords: Asset Allocation , Portfolio Choice , Housing Market Cycles , Real Estate JEL Classification: G11, D9