Trend following strategies are well-known to exhibit excellent excess return performance across a wide range of asset classes in various global markets. For the equity asset class in particular, while the securities selection part is relatively a straightforward procedure, the weight allocation part is more debatable and it has traditionally been identified with the equal-weighted allocation strategy. In this paper, we examine security’s own return-based weight allocation strategy for trend following investing and find that this strategy generates superior returns to several well-established weight allocation schemes. In particular, if the true return of the holding period is used ex ante for weight allocation, it is found that this strategy can generate absolutely huge excess returns. Motivated by this finding, we investigate the efficacy of machine learning techniques for regression of securities’ returns to improve the weight calculation in this framework. Empirical results indicate that deep learning provides the means of regression with which largest excess return gains are possible. In particular, it is demonstrated that the return-based weight allocation strategy defined by our proposed deep learning architecture produces substantial abnormal returns outperforming all other broadly recognised weight allocation schemes compared in this paper.

We consider the problem of low-volatility portfolio selection which has been the subject of extensive research in the field of portfolio selection. To improve the currently existing techniques that rely purely on past information to select low-volatility portfolios, this paper investigates the use of time series regression techniques that make forecasts of future volatility to select the portfolios. In particular, for the first time, the utility of support vector regression and its enhancements as portfolio selection techniques is provided. It is shown that our regression-based portfolio selection provides attractive outperformances compared to the benchmark index and the portfolio defined by a well-known strategy on the data-sets of the S&P 500 and the KOSPI 200.

Pairs trading is an instance of statistical arbitrage that relies on heavy quantitative data analysis to profit by capitalising low-risk trading opportunities provided by anomalies of related assets. A key element in pairs trading is the rule by which open and close trading triggers are defined. This paper investigates the use of time series regression to define the rule which has previously been identified with fixed threshold-based approaches. Empirical results indicate that our approach may yield significantly increased excess returns compared to ones obtained by previous approaches on large capitalisation stocks in the Korean equities market.

In this paper, we study a two-user interference channel with a causal relay, where the relay's transmit symbol depends not only on its past received symbols, but also on its present received symbol. This is an appropriate model for studying amplify-and-forward type relaying when the bandwidth delay-spread product is much smaller than one. For the discrete memoryless interference channel with a causal relay, we derive a genie-aided outer bound. For the Gaussian interference channel with a causal relay, we define strong and very strong interference conditions and propose an outer bound for each case. We also propose an achievable scheme based on instantaneous amplify-and-forward (AF) relaying for the Gaussian interference channel with a causal relay and so it achieves capacity under some conditions. Our result extends the previous result by El Gamal, Hassanpour, and Mammen on the optimality of instantaneous AF relaying for the Gaussian relay channel with a causal relay to that of the Gaussian interference channel with a causal relay under strong and very strong interference.

In this letter, we implement a simplified version of the Cover van der Meulen Hajek Pursley (CMHP) coding originally characterized by Wajcer, Wiesel, and Shamai. The vector Gaussian broadcast channel with imperfect channel state information at the transmitter (CSIT) is considered where the transmitter only knows the channel mean and variance. Our focus is on the implementation and performance analysis of CMHP under the imperfect CSIT model using practical codes. Turbo codes described in IEEE 802.20 draft specification and quadrature amplitude modulation are used to implement CMHP. In order to find the optimal power allocation and beamforming vectors which maximize the sum rate with practical codes, we introduce the SINR penalty factor. The SNRs that achieve various target spectral efficiency are presented and analyzed.

MOTIVATION: With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein beta-turn prediction. In this study, we perform protein beta-turn prediction using a k-nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure information. Traditional beta-turn prediction from k-nearest neighbor method is modified to account for the unbalanced ratio of the natural occurrence of beta-turns and non-beta-turns. RESULTS: Our prediction scheme is tested on a set of 426 non-homologous protein sequences. The prediction scheme consists of two stages: k-nearest neighbor method stage and filtering stage. Variations of the k-nearest neighbor method were used to take property of beta-turns into consideration. Our filtering method uses beta-turn/non-beta-turn estimates from the k-nearest neighbor method stage and predicted protein secondary structure information from PSI-PRED in order to get new beta-turn/non-beta-turn estimate. Our result is compared with the previously best known beta-turn prediction method on the dataset of 426 non-homologous protein sequences and is shown to give slightly superior performance at significantly lower computational complexity. AVAILABILITY: Contact the author for information on the source code of the programs used.