Accounting and Finance Journal Articles

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    Interpolation of financial time series data in a three-dimensional spatialisation
    (2022-08-28) Borna, Kambiz.; Moore, A.B.; Unitec Institute of Technology; University of Otago
    This paper introduces a new approach to interpolating and visualising financial time series data, e.g., Bitcoin prices, in a spatial domain using the notion of spatialisation: forming a spatial representation of non-spatial phenomena. The proposed algorithm first utilises the temporal components of the Bitcoin prices, i.e., date and time of day, to build a 2D vector map based on four observations per day, i.e., opening, high, low and closing prices. It then uses the coordinates assigned to the prices and their values to convert the 2D vector map into a 3D raster map. This transformation is performed using the Natural Neighbour Interpolation (NNI) algorithm. We then apply the 3D map to interpolate time series data with a 30-minute frequency and compare it with the actual observed data to assess the quality of the map. The RMSE results show an improvement of 12% using the proposed method compared with conventional interpolation methods
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    Improving the quality of information: Does Integrated Reporting matter? Evidence from Sri Lankan listed companies
    (STIE Malang Kucecwara, 2023-11-04) Bandara, Saman; Wijesinghe, N.; Unitec, Te Pūkenga; Te Pūkenga; Swinburne University
    This study examines the information quality of integrated reporting (IR) adopted companies in comparison to non-adopted companies. Information quality was measured in terms of the decision usefulness approach based on fundamental qualitative characteristics (QCs) of financial information. Data were collected through annual reports of listed companies of 26 IR-adopters and 27 non-adopters for 2010 (pre adoption year) and 2019 (post-adoption year). The results revealed IR-adopters have significantly improved information quality from 2010 to 2019 compared to non-adopters. Also, there is a significant positive relationship between the information quality of IR adopters with the number of years of experience in IR. Our novel QCs-based quality measurement index provides numerical measures for evaluating information quality. The study shows that IR has achieved its overall objective of improving information quality in the Sri Lankan context. Thus, it provides confidence for the firms expecting to adopt IR to improve their information quality in the future.
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    Does the threat of takeover affect default risk?
    (Wiley, 2022-10-23) Balachandran, B.; Duong, H. N.; van Zijl, T.; Zudana, Arfian; Unitec,Te Pūkenga; Te Pūkenga; La Trobe University; Monash University; Victoria University of Wellington
    We examine the impact of the threat of takeovers on default risk. Using a sample of 50,189 firm-year observations for US firms over the period 1990–2015, we find that the threat of takeovers has a negative relation with default risk. We use difference-in-difference analysis to address potential endogeneity concerns and propensity score matching to control for self-selection bias. The results are robust to alternative measures of default risk and exclusion of the dot com and financial crisis periods. Our results also hold after controlling for Governance Index and Entrenchment Index. We identify improvement in performance and earnings quality in response to the threat of takeovers as channels underlying our main result. The effect of the threat of takeovers on default risk is more pronounced for firms with opaque information environment and low institutional ownership. Our findings provide important insights for the market for corporate control as a disciplining mechanism in reducing default risk.
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    Forecasting accuracy of Holt-Winters Exponential Smoothing : evidence from New Zealand.
    (New Zealand Journal of Applied Business Research, 2020) Dassanayake, Wajira; Ardekani, Iman; Jayawardena, C.; Sharifzadeh, Hamid; Gamage, N.; Unitec Institute of Technology; Sri Lanka Institute of Information Technology (SLIIT)
    Financial time series is volatile, dynamic, nonlinear, nonparametric, and chaotic. Accurate forecasting of stock market prices and indices is always challenging and complex endeavour in time series analysis. Accurate predictions of stock market price movements could bring benefits to different types of investors and other stakeholders to make the right trading strategies. Adopting a technical analysis perspective, this study examines the predictive power of Holt-Winters Exponential Smoothing (HWES) methodology by testing the models on the New Zealand stock market (S&P/NZX50) Index. Daily time-series data ranging from January 2009 to December 2017 are used in this study. The forecasting performance of the investigated models is evaluated using the root mean square error (RMSE], mean absolute error (MAE) and mean absolute percentage error (MAPE). Employing HWES on the undifferenced S&P/NZX50 Index (model 1) and HWES on the differenced S&P/NZX50 Index (model 2) we find that model 1 is the superior predictive algorithm for the experimental dataset. When the tested models are evaluated overtime of the sample period we find the supportive evidence to our original findings. The evaluated HWES models could be employed effectively to predict the time series of other stock markets or the same index for diverse periods (windows) if substantiate algorithm training is carried out.
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    Institutional determinants of carbon financial accounting practices
    (2020-06-08) Kashyap, Varsha; Hooks, J.; Rahman, Md.; Bhuiyan, Md. B.U.; Unitec Institute of Technology
    This paper investigates how and why firms affected by Emissions Trading Schemes (ETSs) are financially accounting for carbon in a voluntary setting. Using institutional theory, the authors seek to identify the determinants of a firm’s decision to adopt a particular carbon financial accounting practice. We identify the recognition and measurement practices for carbon-emission allowances using data gathered from the annual reports of ETS-affected firms in Australia. These practices are identified in the five stages of carbonemission allowance transactions, namely, when these are: (1) received for free, (2) purchased, (3) used, (4) sold and (5) surrendered. Inconsistencies in carbon financial accounting practices are observed. The findings reveal that carbon-emission allowances are recorded as intangible assets, but most firms provide incomplete information on their carbon financial accounting practices. Firms also exhibit inconsistencies in specifying how they are ‘recognising’ and ‘measuring’ carbon-emission allowances. The results provide evidence of coercive (regulation) and mimetic (size, leverage and listing status) pressures being the main determinants of carbon financial accounting practice. The findings will help accounting policy-makers in understanding how and why ETS-affected firms financially account for their carbon allowances. This can assist the development of a uniform carbon financial accounting guidance. Given the few studies in the field of financial accounting of carbon emissions under ETSs, this research will also give meaningful insights to academics and researchers.