An analysis of the nature and effectiveness of corporate governance in smaller listed Australian companies

The objective of this thesis is to investigate the corporate governance attributes of smaller listed Australian firms. This study is motivated by evidence that these firms are associated with more regulatory concerns, the introduction of ASX Corporate Governance Recommendations in 2004, and a paucity of research to guide regulators and stakeholders of smaller firms. While there is an extensive body of literature examining the effectiveness of corporate governance, the literature principally focuses on larger companies, resulting in a deficiency in the understanding of the nature and effectiveness of corporate governance in smaller firms. Based on a review of agency theory literature, a theoretical model is developed that posits that agency costs are mitigated by internal governance mechanisms and transparency. The model includes external governance factors but in many smaller firms these factors are potentially absent, increasing the reliance on the internal governance mechanisms of the firm. Based on the model, the observed greater regulatory intervention in smaller companies may be due to sub-optimal internal governance practices. Accordingly, this study addresses four broad research questions (RQs). First, what is the extent and nature of the ASX Recommendations that have been adopted by smaller firms (RQ1)? Second, what firm characteristics explain differences in the recommendations adopted by smaller listed firms (RQ2), and third, what firm characteristics explain changes in the governance of smaller firms over time (RQ3)? Fourth, how effective are the corporate governance attributes of smaller firms (RQ4)? Six hypotheses are developed to address the RQs. The first two hypotheses explore the extent and nature of corporate governance, while the remaining hypotheses evaluate its effectiveness. A time-series, cross-sectional approach is used to evaluate the effectiveness of governance. Three models, based on individual governance attributes, an index of six items derived from the literature, and an index based on the full list of ASX Recommendations, are developed and tested using a sample of 298 smaller firms with annual observations over a five-year period (2002-2006) before and after the introduction of the ASX Recommendations in 2004. With respect to (RQ1) the results reveal that the overall adoption of the recommendations increased from 66 per cent in 2004 to 74 per cent in 2006. Interestingly, the adoption rate for recommendations regarding the structure of the board and formation of committees is significantly lower than the rates for other categories of recommendations. With respect to (RQ2) the results reveal that variations in rates of adoption are explained by key firm differences including, firm size, profitability, board size, audit quality, and ownership dispersion, while the results for (RQ3) were inconclusive. With respect to (RQ4), the results provide support for the association between better governance and superior accounting-based performance. In particular, the results highlight the importance of the independence of both the board and audit committee chairs, and of greater accounting-based expertise on the audit committee. In contrast, while there is little evidence that a majority independent board is associated with superior outcomes, there is evidence linking board independence with adverse audit opinion outcomes. These results suggest that board and chair independence are substitutes; in the presence of an independent chair a majority independent board may be an unnecessary and costly investment for smaller firms. The findings make several important contributions. First, the findings contribute to the literature by providing evidence on the extent, nature and effectiveness of governance in smaller firms. The findings also contribute to the policy debate regarding future development of Australia’s corporate governance code. The findings regarding board and chair independence, and audit committee characteristics, suggest that policy-makers could consider providing additional guidance for smaller companies. In general, the findings offer support for the “if not, why not?” approach of the ASX, rather than a prescriptive rules-based approach.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Inexact uniformization method for computing transient distributions of Markov chains

The uniformization method (also known as randomization) is a numerically stable algorithm for computing transient distributions of a continuous time Markov chain. When the solution is needed after a long run or when the convergence is slow, the uniformization method involves a large number of matrix-vector products. Despite this, the method remains very popular due to its ease of implementation and its reliability in many practical circumstances. Because calculating the matrix-vector product is the most time-consuming part of the method, overall efficiency in solving large-scale problems can be significantly enhanced if the matrix-vector product is made more economical. In this paper, we incorporate a new relaxation strategy into the uniformization method to compute the matrix-vector products only approximately. We analyze the error introduced by these inexact matrix-vector products and discuss strategies for refining the accuracy of the relaxation while reducing the execution cost. Numerical experiments drawn from computer systems and biological systems are given to show that significant computational savings are achieved in practical applications.

Generalized binomial Tau-leap method for biochemical kinetics incorporating both delay and intrinsic noise

The delay stochastic simulation algorithm (DSSA) by Barrio et al. [Plos Comput. Biol.2, 117–E (2006)] was developed to simulate delayed processes in cell biology in the presence of intrinsic noise, that is, when there are small-to-moderate numbers of certain key molecules present in a chemical reaction system. These delayed processes can faithfully represent complex interactions and mechanisms that imply a number of spatiotemporal processes often not explicitly modeled such as transcription and translation, basic in the modeling of cell signaling pathways. However, for systems with widely varying reaction rate constants or large numbers of molecules, the simulation time steps of both the stochastic simulation algorithm (SSA) and the DSSA can become very small causing considerable computational overheads. In order to overcome the limit of small step sizes, various τ-leap strategies have been suggested for improving computational performance of the SSA. In this paper, we present a binomial τ- DSSA method that extends the τ-leap idea to the delay setting and avoids drawing insufficient numbers of reactions, a common shortcoming of existing binomial τ-leap methods that becomes evident when dealing with complex chemical interactions. The resulting inaccuracies are most evident in the delayed case, even when considering reaction products as potential reactants within the same time step in which they are produced. Moreover, we extend the framework to account for multicellular systems with different degrees of intercellular communication. We apply these ideas to two important genetic regulatory models, namely, the hes1 gene, implicated as a molecular clock, and a Her1/Her 7 model for coupled oscillating cells.

Numerical methods for second-order stochastic differential equations

We seek numerical methods for second‐order stochastic differential equations that reproduce the stationary density accurately for all values of damping. A complete analysis is possible for scalar linear second‐order equations (damped harmonic oscillators with additive noise), where the statistics are Gaussian and can be calculated exactly in the continuous‐time and discrete‐time cases. A matrix equation is given for the stationary variances and correlation for methods using one Gaussian random variable per timestep. The only Runge–Kutta method with a nonsingular tableau matrix that gives the exact steady state density for all values of damping is the implicit midpoint rule. Numerical experiments, comparing the implicit midpoint rule with Heun and leapfrog methods on nonlinear equations with additive or multiplicative noise, produce behavior similar to the linear case.

Stochastic simulation of chemical reactions in spatially complex media

Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.