Institutional investment horizon and investment–cash flow sensitivity

This paper examines the relevance of institutional investors’ investment horizon, as reflected in the response of firm investment to internal cash flows. We argue that institutional investors with longer investment horizons have greater incentives and efficiencies to engage in effective monitoring. This improved monitoring mitigates asymmetric information and agency problems, and in turn reduces the wedge between the costs of internal and external funds. As a result, the sensitivity of firms’ investment outlays to internal cash flows decreases in the presence of institutional investors with long-term investment horizons. Using a sample of 8402 US firms over the period 1981–2008, we provide empirical evidence consistent with these arguments.     

Economic burden,work, and school productivity in individuals with tuberous sclerosis and their families

Aims: Tuberous sclerosis complex (TSC) is a multi-organ autosomal-dominant, genetic disorder with incomplete penetrance. The multiple manifestations of TSC and impacts to numerous organ systems represent significant disease, healthcare, and treatment burden. The economic and employment burden of the disease on individuals and their families is poorly understood. This study assessed the cost of illness and work and school productivity burden associated with TSC in a cross-sectional web-survey sample.

Materials and methods: Eligible TSC individuals and caregivers were invited through the Tuberous Sclerosis Alliance advocacy group to complete a web-based survey about illness characteristics, treatment, disease burden, direct and indirect healthcare costs, work and school impairment.

Results: Data from 609 TSC adults or caregiver respondents with no cognitive impairments were analyzed. TSC adults (>18 years of age) had significantly higher direct out-of-pocket costs for ER visits, expenses for medical tests and procedures, alternative treatments, medications and lifetime cost of surgeries compared to TSC pediatric individuals. Both TSC adults and TSC caregivers reported work and school absenteeism and presenteeism; however, adults reported significantly higher absenteeism and presenteeism and overall activity impairment due to TSC, as might be expected, compared to TSC caregivers. TSC adults had significantly higher absenteeism and presenteeism rates compared to adults with moderate-to-severe plaque psoriasis and muscular sclerosis.

Conclusions: TSC results in considerable direct out-of-pocket medical costs and impairment to work productivity, especially for adults. Future studies should include the comparator group and examine direct cost burden in the US using electronic medical records and insurance databases.     

The mediating role of alliances in the international market orientation-performance relationship of smes

Enhancing international performance is a critical issue for internationalizing SMEs. Researchers have suggested that firms need to enhance their international market orientation (IMO) in order to increase performance; but the way this association works is rather unexplored. This is important for both theory and practice since its exploration would lead to a better understanding on how SMEs can effectively enhance their international performance. In this paper, we examine whether alliances through which firms access knowledge and resources play a mediating role in the IMO and performance relationship. We investigate our hypothesis on a sample of 94 companies based in the United Arab Emirates. We discovered that alliances mediate the IMO-performance relationship and we extend the knowledge- and resource-based views of the firm.     

Effects of Mandatory Audit Firm Rotation Upon Quality of Audit： The Perception of Audit Firms Evidence From Bahrain

The objectives of this study are： （1） to explore current audit appointment practices by audit firms in Bahrain; （2） to look into the opinions of audit firms in Bahrain on potential effects provided by implementing mandatory audit firm rotation （audit quality）; and （3） to investigate audit firms＇ views in implementing mandatory audit firm rotation in Bahrain. To achieve these objectives, a questionnaire was developed and distributed to respondents that consist of all auditors working in audit firms in Bahrain. The findings indicated that there is a significant relationship between mandatory audit firm rotation and quality of audit. The study also indicated that longer partner tenure makes the auditor＇s performance lack the quality in the auditing process. The average mean for all questions of the hypothesis together is 2.73 with average standard deviation of 0.94 which is less than half of the mean. This means that there is no dispersion among respondents about the questions of the hypothesis. Also, the analysis shows that the t-value is 29.922, which is greater than the table critical value of t （1.66）, and the p-value obtained is 0.000 which is less than the value of significance at p 〈 0.05. These results confirm statistically that there is a significant relationship, so the null hypothesis is rejected and the alternative hypothesis is accepted.     

Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks

In this paper, a hybrid system combining neural networks and genetic training is designed to forecast future oil prices. The architectural design is that of the multilayer back propagation network that is fed monthly prices for West Texas Intermediate covering the period 1986–2014. The model’s predictions are compared to those of the one, two, three, and four-month futures prices and are evaluated both on their level of accuracy as well as correctness. While accuracy measures the degree of error, correctness tests the model’s ability to predict the direction of the movement. By processing information more efficiently, and identifying patterns that may be ill-defined as a result of pronounced price volatility, this paper aims to improve the accuracy of oil price forecasts.     

TYPES OF FARMING IN NORTH WALES*

This paper presents the results of a classification of farming undertaken for the four counties of North Wales.? Essentially a type-of-farming classification results in the subdivision of a large area into regions within which a relatively homogeneous association of factors influence the prevailing types of agriculture. Ideally, a type-of-farming area would consist of all that territory within which the choice of products and the possible combinations of factors tend to be similar. The nature of any classification will depend, however, on the main purpose for which it is made. In the present study this was to group parishes according to the type of agricultural activity occurring within them. A further objective was to map the location of the different farming types and thereby to define the predominant type-of-farming areas in North Wales. Among other uses of this classification is an attempt to calculate the contribution to production of each type of farming in the area.     

Roads out of poverty? Assessing the links between aid,public investment,growth, and poverty reduction

This paper presents a dynamic macroeconomic model that captures key linkages between foreign aid, public investment, growth, and poverty. Public capital is disaggregated into education, core infrastructure, and health. Dutch disease effects associated with aid are accounted for by endogenizing changes in the relative price of domestic goods. The impact of shocks on poverty is assessed through partial elasticities and household survey data. The model is calibrated for Ethiopia and changes in the level of nonfood aid are simulated. The amount by which (nonfood) aid should increase to reach the poverty targets of the Millennium Development Goals is also calculated, under alternative assumptions about the degree of efficiency of public investment.     

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.