Deep and surface approaches to learning within introductory accounting: a phenomenographic study

This paper reports the findings of a phenomenographic research study which sought to identify students' approaches to learning introductory accounting and their conceptions of accounting. The findings reveal that, in common with other disciplines, deep and surface approaches to learning can be identified. However, the main contribution of this study lies in two areas. First, it distinguishes those features that are characteristic of the deep and surface approaches within the discipline of accounting. Secondly, it identifies contextual features surrounding these approaches to learning and which are central to an understanding of them. The paper suggests how these findings can be used immediately to make changes within teaching and assessment practice through a phenomenographic pedagogy which: seeks to make students' conceptions of the subject matter explicit; provides diagnostic tools for the identification of distinctively different conceptions of the subject and approaches to learning; and addresses issues of preconceptions and relevance within teaching and assessment. The findings can also be used as the basis for further research into the identification of statistical variation in approaches to learning between students.     

Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms

The behaviourally based portfolio selection problem with investor’s loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are: developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large-scale computational results for the prospect theory model have been obtained for real financial market data with up to 225 assets. Previously, as far as we are aware, only small laboratory tests (2–3 artificial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a differential evolution algorithm and a genetic algorithm which are capable of dealing with a large universe of assets have been developed. Specific breeding and mutation, as well as normalization, have been implemented in the algorithms. A tabulated comparative analysis of the algorithms’ parameter choice is presented. The prospect theory model with the reference point being the index is compared to the index tracking model. A cardinality constraint has been implemented to the basic index tracking and the prospect theory models. The portfolio diversification benefit has been found. The aggressive behaviour in terms of returns of the prospect theory model with the reference point being the index leads to better performance of this model in a bullish market. However, it performed worse in a bearish market than the index tracking model. A tabulated comparative analysis of the performance of the two studied models is provided in this paper for in-sample and out-of-sample tests. The performance of the studied models has been tested out-of-sample in different conditions using simulation of the distribution of a growing market and simulation of the t-distribution with fat tails which characterises the dynamics of a decreasing or crisis market.