The design of dynamic and nonlinear models in cash flow prediction

This thesis is concerned with designing a novel model for cash flow prediction. Cash flow and earnings are both important measures of a firm’s profit. The extant literature has discussed different models that have been applied to cash flow prediction. However, previous studies have not made attempts to address the dynamics in the cash flow model parameters, which are potentially nonlinear processes. This thesis proposes a grey-box model to capture the nonlinearity and dynamics of the cash flow model parameters. The parameters are modelled as a black box, which adopts a Padé approximant as the functional form and two exogenous variables as input variables that are considered to have explanatory power for the parameter process. Besides, this thesis also employs a Bayesian forecasting model in an attempt to capture the parameter dynamics of the cash flow modelling process. The Bayesian model has the advantage of applicability in the case of a limited number of observations. Compared with the grey-box model, the Bayesian model places linear restriction on the parameter dynamics. The prior is required for the implementation of the Bayesian model and this thesis uses the results of a random parameter model as the prior. In addition, panel data estimation methods are also applied to see whether they could outperform the pooled regression that is widely applied in the extant literature. There are four datasets employed in this thesis for the examination of various models’ performance in predicting cash flow. All datasets are in panel form. This work studies the pattern of net operating cash flow (or cash flow to asset ratio) along with time for different datasets. Out-of-sample comparison is conducted among the applied models and two measures of performance are selected to compare the practical predictive power of the models. The designed grey-box model has promising and encouraging performance in all the datasets, especially for U.S. listed firms. However, the Bayesian model does not appear to be superior compared to the simple benchmark models in making practical prediction. Similarly, the panel data models also cannot beat pooled regression. In this thesis, the traditional discounted cash flow model for equity valuation is employed to take account of the cash flow prediction models that have been developed to obtain the theoretical value of equities based on the cash flows predicted by the various models developed in this thesis. The reported results show that simpler models such as the random walk model is closer to market expectation of future cash flows because it leads to a better fitness for the market share prices using the new discounting model. The results reported in this thesis show that the new valuation models developed in this thesis could have investment value. This thesis has made contributions in both theoretical and practical aspects. Through the derivation of various models, it is found that there exists potential nonlinearity and dynamic feature in cash flow prediction models. Therefore, it is crucial to capture the nonlinearity using particular tools. In addition, this thesis builds up a framework, which can be used to analyse problems of similar kinds, such as panel data prediction. The models are derived from theoretical level and then applied to analyse empirical data. The promising results suggest that in practice, the models developed in this work could provide useful guidance for people who make decisions

Predicting airline corporate bankruptcies using a modified Altman Z -score model

Since 1979, 150 airlines have filed for bankruptcy. The airline industry was officially deregulated in October 1978, which brought about many changes including the strengthening of hub and spoke operations, fare-cutting, and the entry of new competitors into the industry. However, following deregulation, the airline industry has suffered financially from various problems: the economic recession of the early 1980s; rising jet fuel costs; rising labor costs; maintenance and interest costs; rising insurance costs; and intensified competition. The transition, from a regulated to a deregulated environment, increased the instability of the carriers\u27 operating profits. In 1998, airlines earned record profits, but by 2002, only two of the major carriers turned a profit. Since 1998, six major or national North American airlines filed for bankruptcy; The objective of this study was to analyze bankrupt and non-bankrupt airlines using a traditional bankruptcy prediction model, the Altman Z-score model, in order to evaluate its ability to predict financial distress in the airline industry. The four financial ratios used in the model represented liquidity, cumulative profitability, productivity, and solvency. A second objective of this study was to develop and test a new statistical model that would better differentiate between bankrupt and non-bankrupt airlines; The new model used only three variables, predicted membership to only one of two groups, and used a simple zero as a cut-off to distinguish whether a firm belonged to the bankrupt group or the non-bankrupt group. Furthermore, the new model\u27s predictions were accurate up to four years in advance of a bankruptcy filing. The Z model, on the other hand, used four variables, did not always give a classification to one of two groups, and used two cut-offs. Furthermore, it performed no better than a naive prediction in determining whether an airline firm should be classified as bankrupt or non-bankrupt

EverFarm® - Climate adapted perennial-based farming systems for dryland agriculture in southern Australia

AbstractAustralian dryland agriculture will be affected by climate change in a number of ways. First, higher temperatures and changes to rainfall are likely to create greater variability of crop yields and livestock productivity. Second, government policies introduced to mitigate greenhouse gas emissions are likely to influence production costs and commodity prices. Third, global trade patterns are likely to alter as populations increase, and as climate change continues to affect producers and consumers worldwide. This will create both challenges and opportunities for Australian agriculture.Farmers will have to respond to the additional challenge of climate change even when it is compounded by existing long term stresses associated with declining terms of trade, climate variability and existing environmental issues. Investing in new land-use options to combat climate change, with their associated risks, is made more difficult by being set against a backdrop of declining profitability. The opportunity to create transformational change in farming enterprises was tested by combining the multiple components of the potential future perennial‐based dryland farming systems and assessing their expected contribution to climate change adaptation. This project has found that adopting perennial pastures for livestock grazing and tree crops for biomass production, when planted on appropriate soils, can improve profitability when compared to the existing land uses facing a changing climate.  In some farming systems increased cropping is likely to result in improved future farm profits.This work demonstrated that Mallees as a biomass tree crop can be cohesively integrated into existing farming systems with minimal interruption to normal operations of livestock and cropping enterprises. A woody biomass crop can be profitable and diversify revenue risk by enabling farmers to supply biomass and sequester carbon to relevant markets. This work demonstrates suitable designs of a mallee belt planting layout that minimizes costs and maximizes benefits when planted in appropriate agro‐climatic zones and where there are adequate soil conditions. Knowledge developed from this work will help build farmers capacity about climate change adaptation and assist in achieving positive social, environmental and economic outcomes.Please cite this report as:Farquharson, R, Abadi, A, Finlayson, J, Ramilan, T,  Liu, DL, Muhaddin, A, Clark, S, Robertson, S, Mendham, D, Thomas, Q,  McGrath, J 2013 EverFarm® – Climate adapted perennial-based farming systems for dryland agriculture in southern Australia, National Climate Change Adaptation Research Facility, Gold Coast, pp. 159.AbstractAustralian dryland agriculture will be affected by climate change in a number of ways. First, higher temperatures and changes to rainfall are likely to create greater variability of crop yields and livestock productivity. Second, government policies introduced to mitigate greenhouse gas emissions are likely to influence production costs and commodity prices. Third, global trade patterns are likely to alter as populations increase, and as climate change continues to affect producers and consumers worldwide. This will create both challenges and opportunities for Australian agriculture.Farmers will have to respond to the additional challenge of climate change even when it is compounded by existing long term stresses associated with declining terms of trade, climate variability and existing environmental issues. Investing in new land-use options to combat climate change, with their associated risks, is made more difficult by being set against a backdrop of declining profitability. The opportunity to create transformational change in farming enterprises was tested by combining the multiple components of the potential future perennial‐based dryland farming systems and assessing their expected contribution to climate change adaptation. This project has found that adopting perennial pastures for livestock grazing and tree crops for biomass production, when planted on appropriate soils, can improve profitability when compared to the existing land uses facing a changing climate.  In some farming systems increased cropping is likely to result in improved future farm profits.This work demonstrated that Mallees as a biomass tree crop can be cohesively integrated into existing farming systems with minimal interruption to normal operations of livestock and cropping enterprises. A woody biomass crop can be profitable and diversify revenue risk by enabling farmers to supply biomass and sequester carbon to relevant markets. This work demonstrates suitable designs of a mallee belt planting layout that minimizes costs and maximizes benefits when planted in appropriate agro‐climatic zones and where there are adequate soil conditions. Knowledge developed from this work will help build farmers capacity about climate change adaptation and assist in achieving positive social, environmental and economic outcomes

Improved water and land management in the Ethiopian highlands: its impact on downstream stakeholders dependent on the Blue Nile; Intermediate Results Dissemination Workshop February 5-6, 2009, Addis Ababa, Ethiopia

River basin management, Watershed management, Farming systems, Water balance, Reservoirs, Water supply, Irrigation requirements, Irrigation programs, Simulation models, Sedimentation, Rainfall-Runoff relationships, Erosion, Soil water, Water balance, Soil conservation, Institutions, Organizations, Policy, Water governance, International waters, Institutional and Behavioral Economics, Land Economics/Use, Resource /Energy Economics and Policy,

A nonlinear dynamic approach to cash flow forecasting

We propose a novel grey-box model to capture the nonlinearity and the dynamics of cash flow model parameters. The grey-box model retains a simple white-box model structure, while their parameters are modelled as a black-box with a Padé approximant as a functional form. The growth rate of sales and firm age are used as exogenous variables because they are considered to have explanatory power for the parameter process. Panel data estimation methods are applied to investigate whether they outperform the pooled regression, which is widely used in the extant literature. We use the U.S. dataset to evaluate the performance of various models in predicting cash flow. Two performance measures are selected to compare the out-of-sample predictive power of the models. The results suggest that the proposed grey-box model can offer superior performance, especially in multi-period-ahead predictions

Predictive AI for SME and Large Enterprise Financial Performance Management

Financial performance management is at the core of business management and has historically relied on financial ratio analysis using Balance Sheet and Income Statement data to assess company performance as compared with competitors. Little progress has been made in predicting how a company will perform or in assessing the risks (probabilities) of financial underperformance. In this study I introduce a new set of financial and macroeconomic ratios that supplement standard ratios of Balance Sheet and Income Statement. I also provide a set of supervised learning models (ML Regressors and Neural Networks) and Bayesian models to predict company performance. I conclude that the new proposed variables improve model accuracy when used in tandem with standard industry ratios. I also conclude that Feedforward Neural Networks (FNN) are simpler to implement and perform best across 6 predictive tasks (ROA, ROE, Net Margin, Op Margin, Cash Ratio and Op Cash Generation); although Bayesian Networks (BN) can outperform FNN under very specific conditions. BNs have the additional benefit of providing a probability density function in addition to the predicted (expected) value. The study findings have significant potential helping CFOs and CEOs assess risks of financial underperformance to steer companies in more profitable directions; supporting lenders in better assessing the condition of a company and providing investors with tools to dissect financial statements of public companies more accurately.Comment: 8 pages plus appendix. Thesis for MSc in AI at QMU

An Empirical Analysis of the Liquidity, Solvency and Financial Health of Small and Medium Sized Enterprises in Kisii Municipality, Kenya

Research findings have shown that the liquidity, profitability and solvency position of most Small and Medium Eenterprises (SMEs) are in average position with the causal factors behind this position being unsound financial management, inadequate working capital, slow conversion of receivables and inventory into cash, lower position of sales and higher amount of debt. Therefore, the purpose of this paper was to carry out a Financial diagnosis of the SMEs financial performance by focusing on their  liquidity, solvency and profitability positions using ratio analysis. Data for the study covered the period 2009-2011 and was obtained from the financial statements of three SMEs which were purposively sampled from the SMEs operating in Kisii Municipality. The sampled SMEs were those which had financial statements for the years under consideration. Data collected through the analysis of key ratios were analyzed using the mean, standard deviation, coeffifient of variation, Student-t test and through the use of the Altman’s Z-score model. The findings of the study showed that the liquidity position of the SMEs was on average low; their solvency was low and their financial Health was on average not good.  Further,the results show that there is a significant impact of current ratio, quick ratio and Debt to Total Assets ratio on Return on Assets (ROA). The results of the study demonstrate that the liquidity position of the SMEs was  well below the acceptable global norm of 2 for current ratio and 1 for quick ratio. Further, the results indicated that the financial health of the SMEs needed to be improved hence the recommendation that SMEs make liquidity, solvency management and financial stability an integral driver of their policy frameworks. Key words: Liquidity, Solvency and Financial Healt