The delay of stock price adjustment to information: A country-level analysis

This study measures the speed with which the aggregate stock market in 49 countries responds to global market-wide public information. Our empirical results show that there are wide variations in the aggregate price delay values over time and across countries. Subsequent panel analysis confirms previous firm-level evidence that market size, trading volume, short sales restrictions and the degree of investability are significant determinants of price delay even at the country level.Informational efficiency, speed of adjustment, price delay, aggregate stock market

Corporate Shareholdings and the Liquidity of Malaysian Stocks: Investor Heterogeneity, Trading Account Types and the Underlying Channels

This paper examines the relationship between shareholdings of various investor groups and stock liquidity for Malaysian public listed firms over the 2002-2009 sample period. Using the Amihud illiquidity ratio, we extend the literature by addressing the issues of investor heterogeneity, trading account types and the interactions of competing liquidity channels. The analysis reveals that only local institutions and local individual investors who trade through the direct accounts are significantly associated with the liquidity of domestic firms. In contrast, the significant liquidity effect for foreign investors operates through the nominee accounts. While institutional ownership exhibits a linear negative relationship, our findings on local individuals and foreign nominees differ greatly from previous studies in that their relationship with stock liquidity is non-monotonic. Apart from the widely researched information asymmetry and trading effects, we find that liquidity is also driven by the largely ignored information competition channel. An important insight from our findings is that the large shareholdings by any particular investor group is detrimental to stock liquidity as they exacerbate information asymmetry, reduce the degree of competition and lower the level of trading activity

Model predictive control of a two-motor drive with five-leg inverter supply

Model predictive control (MPC) for a two-motor drive, supplied from a five-leg inverter, is presented in this paper. As an alternative to existing methods, use of MPC in multimachine drives has the advantages of independent fast current control of the machines, elimination of the closed-loop system's cascaded structure, and a reduced number of microcontrollers. A vector control algorithm is required, necessitating state-space modeling, with each machine's direct- and quadrature-axis currents chosen as state variables. Prediction of future states is via a discrete-time model of the five-leg inverter and a piecewise-affine model of two permanent-magnet synchronous motors (PMSMs). A method which eliminates unfeasible switching states inherent in reduced-switch-count inverters while reducing computation and sampling times is proposed. The algorithm is implemented in a TMS320F28335 DSP microcontroller, which controls the five-leg inverter and the two PMSMs. Simulation and experimental results validate the presented control concept

Adaptive rough radial basis function neural network with prototype outlier removal

A new rough neural network (RNN)-based model is proposed in this paper. The radial basis function network with dynamic decay adjustment (RBFNDDA) is applied to learn information directly from a data set and group it in terms of prototypes. Then, a neighborhood rough set-based procedure is applied to detect prototype outliers. This hybrid model is named rough RBFNDDA1. However, the removal of all outliers may cause information loss because some outliers may represent rare yet useful information in a classification task. As such, the parameters of a prototype outlier, i.e., its radius and weight, are exploited to gauge whether the information encoded by the prototype is meaningful. This hybrid model is named rough RBFNDDA2. The results from a benchmark experimental study show that rough RBFNDDA2 can retain meaningful prototype outliers and, at the same time, significantly reduce the number of prototypes from the original RBFNDDA model while maintaining classification accuracy. A real-world application in a power generation plant is used to evaluate and demonstrate the effectiveness of the proposed model

Reducing the complexity of an adaptive radial basis function network with a histogram algorithm

In this paper, a constructive training technique known as the dynamic decay adjustment (DDA) algorithm is combined with an information density estimation method to develop a new variant of the radial basis function (RBF) network. The RBF network trained with the DDA algorithm (i.e. RBFNDDA) is able to learn information incrementally by creating new hidden units whenever it is necessary. However, RBFNDDA exhibits a greedy insertion behaviour that absorbs both useful and non-useful information during its learning process, therefore increasing its network complexity unnecessarily. As such, we propose to integrate RBFNDDA with a histogram (HIST) algorithm to reduce the network complexity. The HIST algorithm is used to compute distribution of information in the trained RBFNDDA network. Then, hidden nodes with non-useful information are identified and pruned. The effectiveness of the proposed model, namely RBFNDDA-HIST, is evaluated using a number of benchmark data sets. A performance comparison study between RBFNDDA-HIST and other classification methods is conducted. The proposed RBFNDDA-HIST model is also applied to a real-world condition monitoring problem in a power generation plant. The results are analysed and discussed. The outcome indicates that RBFNDDA-HIST not only can reduce the number of hidden nodes significantly without requiring a long training time but also can produce promising accuracy rates

FCS-MPC-based control of a five-phase induction motor and its comparison with PI-PWM control

This paper presents an investigation of the finite-control-set model predictive control (FCS-MPC) of a five-phase induction motor drive. Specifically, performance with regard to different selections of inverter switching states is investigated. The motor is operated under rotor flux orientation, and both flux/torque producing (d-q) and nonflux/torque producing (x-y) currents are included into the quadratic cost function. The performance is evaluated on the basis of the primary plane, secondary plane, and phase (average) current ripples, across the full inverter's linear operating region under constant flux-torque operation. A secondary plane current ripple weighting factor is added in the cost function, and its impact on all the studied schemes is evaluated. Guidelines for the best switching state set and weighting factor selections are thus established. All the considerations are accompanied with both simulation and experimental results, which are further compared with the steady-state and transient performance of a proportional-integral pulsewidth modulation (PI-PWM)-based current control scheme. While a better transient performance is obtained with FCS-MPC, steady-state performance is always superior with PI-PWM control. It is argued that this is inevitable in multiphase drives in general, due to the existence of nonflux/torque producing current component

A comparative study of synchronous current control schemes based on FCS-MPC and PI-PWM for a two-motor three-phase drive

A two-motor drive, supplied by a five-leg inverter, is considered in this paper. The independent control of machines with full dc-bus voltage utilization is typically achieved using an existing pulsewidth modulation (PWM) technique in conjunction with field-oriented control, based on PI current control. However, model predictive control (MPC), based on a finite number of control inputs [finite-control-set MPC (FCS-MPC)], does not utilize a pulsewidth modulator. This paper introduces three FCS-MPC schemes for synchronous current control in this drive system. The first scheme uses all of the available switching states. The second and third schemes are aimed at reducing the computational burden and utilize a reduced set of voltage vectors and a duty ratio partitioning principle, respectively. Steady-state and transient performances are analyzed and compared both against each other and with respect to the field-oriented control based on PI controllers and PWM. All analyses are experimental and use the same experimental rig and test conditions. Comparison of the predictive schemes leads to the conclusion that the first two schemes have the fastest transient response. The third scheme has a much smaller current ripple while achieving perfect control decoupling between the machines and is of low computational complexity. Nevertheless, at approximately the same switching loss, the PI-PWM control yields the lowest current ripple but with slower electrical transient response