Profitability of Index-based Size and Style Rotation Strategies in the UK Equity Markets

The objective of this paper is to examine whether short-term directional variation in the size and style spreads of indices in the UK equity market is predictable and exploitable by means of active style rotation strategies. Using a set of market related, macroeconomic and fundamental variables chosen by the Principal Component Analysis (PCA) method, we employ a recursive dynamic modelling approach (logit model) to predict the direction of the style index return spreads. Our style rotation strategies are based on small-capitalisation, large-capitalisation, value and growth segments of the market, using the appropriate style benchmark indices as proxies for styles, namely: FTSE 350 Value, FTSE 350 Growth, FTSE Small Cap and FTSE 100. The period analysed is January 1987 to May 2005. The results indicate that the optimal long only and long/short style rotation strategies are profitable for UK investors and that both the size of transaction costs and the strength of the forecasting signal play an important role in determining the profitability of the rotation strategy. Finally, we believe that there are two comparatively simple and cheap ways in which the suggested rotation strategies can be applied by a real-world investor: through ETFs and stock index futures.PCA, Logit model, value/growth and small/large style rotation

A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options

Under the assumption of no-arbitrage, the pricing of American and Bermudan options can be casted into optimal stopping problems. We propose a new adaptive simulation based algorithm for the numerical solution of optimal stopping problems in discrete time. Our approach is to recursively compute the so-called continuation values. They are defined as regression functions of the cash flow, which would occur over a series of subsequent time periods, if the approximated optimal exercise strategy is applied. We use nonparametric least squares regression estimates to approximate the continuation values from a set of sample paths which we simulate from the underlying stochastic process. The parameters of the regression estimates and the regression problems are chosen in a data-dependent manner. We present results concerning the consistency and rate of convergence of the new algorithm. Finally, we illustrate its performance by pricing high-dimensional Bermudan basket options with strangle-spread payoff based on the average of the underlying assets.Comment: Published in at http://dx.doi.org/10.1214/105051607000000249 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Benchmark-adjusted performance of US equity mutual funds and the issue of prospectus benchmarks

This study examines the impact of mismatch between prospectus benchmark and fund objectives on benchmark-adjusted fund performance and ranking in a sample of 1281 US equity mutual funds. All funds in our sample report S&P500 index as a prospectus benchmark, yet 2/3 of those are placed in the Morningstar category with risk and objectives different to those of the S&P500 index. We identify more appropriate ‘category benchmarks’ for those mismatched funds and obtain their benchmark-adjusted alphas using recent Angelidis et al. (J Bank Finance 37(5):1759–1776, 2013) methodology. We find that S&P500-adjusted alphas are higher than ‘category benchmark’-adjusted alphas in 61.2% of the cases. In terms of fund quartile rankings, 30% of winner funds lose that status when the prospectus benchmark is substituted with the one better matching their objectives. In the remaining performance quartiles, there is no clear advantage of using S&P 500 as a benchmark. Hence, the prospectus benchmark can mislead investors about fund’s relative performance and ranking, so any reference to performance in a fund’s prospectus should be treated with caution

CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network -- CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. The evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches.Comment: Accepted to IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Writing and reading of single magnetic domain per bit perpendicular patterned media

By fabricating patterned media with a large number of nanoscale single domain magnetic particles embedded in a nonmagnetic substrate, and by writing the magnetization for each of these particles in a desired direction, nonvolatile magnetic storage of information could reach densities much higher than what is currently thought possible for longitudinal continuous media. We have fabricated high aspect ratio perpendicular nickel columnar nanoparticles embedded in a hard Al2O3/GaAs substrate. We show that the magnetization states of the individual magnets can be controlled by demonstrating that prototype patterned "single magnetic domain per bit" data tracks can be written and read back using current magnetic information storage technology