ORiON

Editorial 39 2

Jaco Visagie — Fri, 22 Dec 2023 23:23:37 +0000

A portfolio selection problem arising from real-estate investments

Shaun Butler, Kit Searle, Philip Christian Malan — Fri, 22 Dec 2023 22:58:18 +0000

Investments have been used as a medium to mitigate the effects of inflation for many years
and it is expected that they will be used for many years to come. Not only do investments
allow for an opportunity to increase the net worth of a sum of money, they also allow for
a source of passive income. With every investment, however, there is an associated risk
but, higher risks are typically associated with a more enticing reward. From the broad
scope of potential investment opportunities, the primary focus of this paper is to consider
investments within the realm of real estate.
Although property investments have the potential of generating a satisfactory return and
may generally be considered a safe investment, as with any investment, poor decision
making may still results in the loss of capital. A multi-period portfolio selection model may
assist a potential investor in determining an optimal investment plan. The multi-period
portfolio selection model considers the future value and rental incomes of a set of potential
properties, in determining the optimal investment plan over a given time horizon, aimed at
maximising the expected net present value of the portfolio. The quality of the investment
plan, however, is dependent on the accuracy of the predicted future value and rental income
of the properties under consideration. Therefore, in order to determine these future values
accurately, a time series forecasting model is proposed. The forecasting model predicts the
values for property values and rental income over a selected time horizon, which serves as
input to the multi-period portfolio selection model.
In short, the goal of this paper is to apply suitable time series forecasting methods in order
to generate predicted values with an acceptable accuracy, to serve as input for a multi-period
portfolio selection model to determine an optimal investment plan as output.

A Hybrid Predictive Prototype for Portfolio Selection using Probability-based Quadratic Programming and Ensemble Artificial Neural Networks

Brian Muganda, Bernard Kasamani — Fri, 22 Dec 2023 22:59:42 +0000

Investors are usually limited by cognitive and emotional biases in their investment decision making and thereby end up making poor portfolio investment decisions. Robo-advisors can assist in overcoming these biases. This paper sought to develop a financial robo-advisor prototype based on hybrid programming by use of ensemble artificial neural networks to predict portfolio returns and variances with input nodes of Ornstein–Uhlenbeck process (OU) and geometric brownian motion (GBM) processes’ estimates. The results were subsequently channeled into a probability quadratic optimization algorithm which considers target return probability and value-at-risk constraints as proxies for investor’s risk tolerance so as to provide the optimal portfolio allocation strategy that minimizes portfolio risk given a prescribed investment horizon and target return. The results showed that the ensemble artificial neural network method implementation predicted correctly the level of 2 of 5 assets and also predicted correctly the trends of the remaining 3 assets. It however yielded low standard deviations and low returns compared to the OU and GBM estimates for short horizons. The quadratic optimization algorithm supported investment in shorter time horizons since portfolio risk was lowest. Diversified allocation was achieved in the shorter time horizons. Longer horizons allocations were biased towards assets with lower standard deviations. Lowest risk portfolio were the ones with a lower certainty probability of target return and vice versa. This paper is a clear demonstration that ensemble methods are accurate in prediction and that a hybrid programming paradigm is an effective approach to leverage on strengths, speed and functionality of different programming languages; an elixir for multifaceted dissociable programming problems.

A framework for a structured problem-solving approach with experimental design as the focus for industrial processing environment

William Henry van Blerk, Charles van der Vyver — Fri, 22 Dec 2023 00:00:00 +0000

This article focuses on developing a framework for industrial environments to provide a structured problem-solving approach based on experimentation as a basis to assist analysts and management for strategic decision making for process improvement. The process for developing this generic framework was through an analytical process improvement process case study for a company in an industrial environment. The research methodology includes an interpretivist approach followed by a positivist approach and ends with a constructivism worldview due to the Experimental design approach design depicted by this framework. The summarized goals were, expanding Design of Experiments (DOE) as a statistical approach to complement existing methods and methodologies used for Data Mining, validate the integrity of data through a refining process and applying DOE in combination with traditional Data Mining techniques. The importance for developing this framework was to experiment with historical data, based on real process data then to predict future process behaviour, using full and fractional DOE design scenarios which allows the analyst not to have a one-dimensional analytical approach but to evaluate which design fits the data best. No risk of costly process failures due to experimenting into the unknown by utilizing historic data by applying experimentation when evaluating different processing scenarios for possible product improvement and to provide an alternative statistical approach for Data Mining in an industrial environment for screening independent and dependent variables for a DOE model.

Honours course timetabling and classroom assignment

Fri, 22 Dec 2023 22:57:35 +0000

This work addresses the timetabling problem faced by the Department of Statistical Sciences at the University of Cape Town each year for the honours course or fourth-year class. The approach taken by the department to design the timetable has been a manual one, which is time consuming. An automated approach is proposed in this work, based on mathematical programming models, with the aim to alleviate the burden caused by the manual approach to create such timetable. The proposed mixed integer programming model consists of three phases: The first phase involves the allocation of lecturers to modules based on preferences and expertise, the second phase consists of assigning modules to time slots, and the third phase involves the allocation of available venues for classes on the timetable. The model was applied to real data, collected from the department. The resulting timetabling solutions were compared to the 2022 timetable and validated by the course convenors.