Growth Optimal Investment Strategy Efficacy: An Application on Long Run Australian Equity Data

A number of investment strategies designed to maximise portfolio growth are tested on a long run Australian equity data set. The application of these growth optimal portfolio techniques produces impressive rates of growth, despite the fact that the assumptions of normality and stability that underlie the growth optimal model are shown to be inconsistent with the data. Growth optimal portfolios are constructed by rebalancing the portfolio weights of 25 Australian listed companies each month with the aim of maximising portfolio growth. These portfolios are shown to produce growth rates that are up to twice those of the benchmark, equally weighted, minimum variance and 15% drift portfolios. The key to the success of the classic, no short-sales, growth optimal portfolio strategy lies in its ability to select for portfolio inclusion a small number of Australian stocks during their high growth periods. The study introduces a variant of ridge regression to form the basis of one of the growth focussed investment strategies. The ridge growth optimal technique overcomes the problem of numerically unstable portfolio weights that dogs the formation of short-sales allowed growth portfolios. For the short sales not allowed growth portfolio, the use of the ridge estimator produces increased asset diversification in the growth portfolio, while at the same time reducing the amount of portfolio adjustment required in rebalancing the growth portfolio from period to period.growth optimal portfolios; australian equity returns; feasible investment strategy; ridge regression

A discrete/rhythmic pattern generating RNN

Biological research supports the concept that advanced motion emerges from modular building blocks, which generate both rhythmical and discrete patterns. Inspired by these ideas, roboticists try to implement such building blocks using different techniques. In this paper, we show how to build such module by using a recurrent neural network (RNN) to encapsulate both discrete and rhythmical motion patterns into a single network. We evaluate the proposed system on a planar robotic manipulator. For training, we record several handwriting motions by back driving the robot manipulator. Finally, we demonstrate the ability to learn multiple motions (even discrete and rhythmic) and evaluate the pattern generation robustness in the presence of perturbations

Feedback control by online learning an inverse model

A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made

The spectral radius remains a valid indicator of the echo state property for large reservoirs

In the field of Reservoir Computing, scaling the spectral radius of the weight matrix of a random recurrent neural network to below unity is a commonly used method to ensure the Echo State Property. Recently it has been shown that this condition is too weak. To overcome this problem, other more involved - sufficient conditions for the Echo State Property have been proposed. In this paper we provide a large-scale experimental verification of the Echo State Property for large recurrent neural networks with zero input and zero bias. Our main conclusion is that the spectral radius method remains a valid indicator of the Echo State Property; the probability that the Echo State Property does not hold, drops for larger networks with spectral radius below unity, which are the ones of practical interest

A new representation of the Adler function for lattice QCD

We address several aspects of lattice QCD calculations of the hadronic vacuum polarization and the associated Adler function. We implement a representation derived previously which allows one to access these phenomenologically important functions for a continuous set of virtualities, irrespective of the flavor structure of the current. Secondly we present a theoretical analysis of the finite-size effects on our particular representation of the Adler function, based on the operator product expansion at large momenta and on the spectral representation of the Euclidean correlator at small momenta. Finally, an analysis of the flavor structure of the electromagnetic current correlator is performed, where a recent theoretical estimate of the Wick-disconnected diagram contributions is rederived independently and confirmed.Comment: 9 pages, 5 figure

Comparing trotting and turning strategies on the quadrupedal Oncilla Robot

In this paper, we compare three different trotting techniques and five different turning strategies on a small, compliant, biologically inspired quadrupedal robot, the Oncilla. The locomotion techniques were optimized on the actual hardware using a treadmill setup, without relying on models. We found that using half ellipses as foot trajectories resulted in the fastest gaits, as well as the highest robustness against parameter changes. Furthermore, we analyzed the importance of using the scapulae for turning, from which we observed that although not necessary, they are needed for turning with a higher speed

Charge transport and vector meson dissociation across the thermal phase transition in lattice QCD with two light quark flavors

We compute and analyze correlation functions in the isovector vector channel at vanishing spatial momentum across the deconfinement phase transition in lattice QCD. The simulations are carried out at temperatures $T/T_c=0.156, 0.8, 1.0, 1.25$ and $1.67$ with $T_c\simeq203$MeV for two flavors of Wilson-Clover fermions with a zero-temperature pion mass of $\simeq270$MeV. Exploiting exact sum rules and applying a phenomenologically motivated ansatz allows us to determine the spectral function $\rho(\omega,T)$ via a fit to the lattice correlation function data. From these results we estimate the electrical conductivity across the deconfinement phase transition via a Kubo formula and find evidence for the dissociation of the $\rho$ meson by resolving its spectral weight at the available temperatures. We also apply the Backus-Gilbert method as a model-independent approach to this problem. At any given frequency, it yields a local weighted average of the true spectral function. We use this method to compare kinetic theory predictions and previously published phenomenological spectral functions to our lattice study.Comment: 28 pages, 6 figure