Performance Of Winners Research Articles

Do smart beta ETFs deliver persistent performance?

This paper analyses smart beta ETF performance and provides the first evidence on the funds’ performance persistence. Our sample is comprised of 152 US equity smart beta ETFs over the period June 2000 to May 2017. We found that as per the risk-adjusted performance, about 40% of smart beta ETFs outperformed their related traditional ETFs after expenses. The analysis of performance persistence conducted based on the relative performance of smart beta ETFs showed that the performance of winners and losers does persist in the year ahead. The persistence in performance was documented in seven out of nine peer categories.

Do Smart Beta ETFs Deliver Persistent Performance?

This paper analyses Smart Beta ETF performance and provides the first evidence on the funds’ performance persistence. Our sample is comprised of 152 US equity smart beta ETFs over the period June 2000 to May 2017. We found that as per the risk-adjusted performance about 40% of Smart Beta ETFs outperformed their related traditional ETFs after expenses. The analysis of performance persistence conducted based on the relative performance of Smart Beta ETFs showed that the performance of winners and losers does persists in the year ahead. The persistence in performance was documented in 7 out of 9 peer categories.

Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization

Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβSMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β-hill climbing, AβHC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβSMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβSMA-1 and BTβSMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβSMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβSMA algorithm in terms of solution precision.

Industry Herding and the Profitability of Momentum Strategies During Market Crises

abstractThe degree of industry herding is significantly related to the subsequent performance of winner and loser industries. While the herding effect on losers is not inconsistent with investors’ tendency to herd on negative information, the herding effect on winners reflects institutional demand for overpriced securities. An alternative momentum strategy based on the degree of herding within an industry significantly outperforms the conventional industry momentum strategy over the subsequent 1, 3, 6, and 12 months. The findings suggest that behavioral patterns could be utilized to generate enhanced momentum profits, even during market stress periods when the conventional momentum strategy performs poorly.

Optimizing feature vectors and removal unnecessary channels in BCI speller application

In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) system in speller application based on single-trial classification of electroencephalogram (EEG) signal. The idea is to employ proper methods for reducing the number of channels and optimizing feature vectors. Removal unnecessary channels and reducing feature dimension result in cost decrement, time saving and improve the BCI implementation eventually. Optimal channels will be gotten after two stages sifting. In the first stage, the channels reduced up to 30% based on channels of the important event related potential (ERP) components and in the next stage, optimal channels were extracted by backward forward selection (BFS) algorithm. Also we will show that suitable single-trial analysis requires applying proper feature vector that was constructed by recognizing important ERP components, so as to propose an algorithm to distinguish less important features in feature vectors. F-Score criteria used to recognize effective features which created more discrimination between different classes and feature vectors were reconstructed based on effective features. Our algorithm has tested on dataset II of BCI competition III. The results show that we achieve accuracy up to 31% in single-trial, which is better than the performance of winner who is in this competition (about 25.5%). Also we use simple classifier and few channels to compute output performances while more complicated classifier and all channels are used by them.

Do mutual funds with few holdings outperform the market?

This paper investigates the performance of mutual funds that hold a small number of stocks in their portfolio. Similar to results reported in the literature for the average diversified mutual fund, our results indicate that the average small holding fund does not outperform the S&P 500 index. On average, small holding funds under-perform the market on a risk and investment style adjusted basis by about −20 basis points per month, or by −2.40 per cent per year. We also find that there is a sharp contrast between the performance of Winner and Loser portfolios. On average, Winner portfolios outperform the S&P composite index by 410 basis points per month, or an astounding 49.2 per cent per annum, whereas Losers under-perform by 320, or −38.4 per cent per annum, over the same period. Cross sectional regressions indicate that Winner portfolio abnormal performance is positively and significantly related to fund turnover and the per cent of the fund's assets invested in their top 10 most heavily weighted holdings. Results for Loser portfolios show that abnormal performance deteriorates significantly with turnover, concentration and expenses, but rises with Load and Size.