The economic value of information provided by milk biomarkers under different scenarios : case-study of an ex-ante analysis of fat-to-protein ratio and fatty acid profile to detect subacute ruminal acidosis in dairy cows

Monitoring systems (MS) provide additional information that many developers and researchers expect will reduce the uncertainty surrounding decision-making in livestock production and therefore enhance management decisions. However, the actual economic value of the information (VoI) yielded by MS has hardly been investigated. The aim of this study was to fill that void based on two objectives. The first is to estimate the VoI of MS prior to implementation using decision analysis based on scarce data from different sources. The second objective is to identify which factors most influence the VoI of MS and to develop recommendations about the focus of future MS development. To illustrate our objectives, we used a case study of two milk biomarkers used to monitor subacute ruminal acidosis (SARA) in dairy cows: fat-to-protein ratio (FPR) and the fatty acid profile (FAP). FPR is presently used to monitor SARA, while FAP is a newly developed test, currently in the pre-commercial phase, with reports of better accuracy than FPR. A stochastic decision tree model was used to estimate the expected monetary value of three levels of information with regards to SARA: (i) no monitoring, monitoring (ii) with FPR or (iii) with FAP. The VoI of FPR and FAP were calculated as the difference in expected monetary value of monitoring with FPR and FAP as compared with no monitoring, respectively. Several scenarios were modeled using sensitivity and elasticity analyses. The aim was not only to compensate for the scarcity of data for some variables, but also to identify under which conditions decisions based on FAP monitoring were indeed the best. In all the scenarios, monitoring SARA with FPR had the lowest expected monetary value. No monitoring was a better decision in 70% of the iterations in the scenario that described the most probable situation. The VoI of FAP was positive when SARA prevalence was between 0.21 and 0.79 with its maximum value at 0.61, when the treatment costs were lower than (sic)116/case/year and when the disease costs were higher than (sic)260/case/year. Moreover, an increase of specificity of the FAP to 0.95 yielded a positive VoI, whereas an increase of its sensitivity to 1.0 still yielded a negative VoI, suggesting that developers of the FAP should focus on improving its specificity rather than its sensitivity. To avoid suboptimal use of finite resources while developing MS, we recommend ex-ante investigation of the VoI of the MS under development

A systemic integrative framework to describe comprehensively a swine health system, Flanders as an example

A well-functioning swine health system is crucial to ensure a sustainable pig production. Yet, little attention has been paid to understand it. The objective of this study was to unravel the complexity of a swine health system by using a systems-thinking approach for the case of Flanders (Northern part of Belgium). To that end, qualitative interviews were held with 33 relevant stakeholders. A hybrid thematic analysis was conducted which consisted of two phases. First, an inductive thematic analysis was conducted and second, the resulting themes were classified into the building blocks of a systemic framework. This framework combined a structural and a functional analysis that allowed to identify the key actors and their functions. Additionally, a transformational analysis was performed to evaluate how structures and the entire swine health system enable or disable functions. Findings revealed that the Flemish swine health system presents several merits such as the synchronization of policies and sector's agreements to reduce the antimicrobial use in the pig sector and the presence of a rich network of universities and research institutes that contribute to the education of health professionals. Nevertheless, several systemic failures were observed at different levels such as the lack of a good professional body representing the swine veterinarians, the tradition that veterinary advice is provided for 'free' by feed mill companies, and the shortage of reliable farm productivity data. Both latter failures may hinder swine practitioners to provide integrative advice. While few veterinarians are remunerated per hour or per visit by farmers, the most common business model used by veterinarians is largely based on the sale of medicines. Thus, veterinarians encounter often a conflict of interest when advising on preventive vaccinations and, in turn, farmers distrust their advice. On a positive note, alternatives to the traditional business model were suggested by both veterinarians and farmers which may indicate that there is intention to change; however, the broader institutional and socio-cultural environment does not enable this evolution. The results of this study can aid policy makers to anticipate the effects of proposed interventions and regulations so that they can be fine-tuned before they are enforced

On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (I): Empowering Discounted Cash Flow Valuation

The Discounted Cash Flow (DCF) method is probably the most extended approach used in company valuation, its main drawbacks being probably the known extreme sensitivity to key variables such asWeighted Average Cost of Capital (WACC) and Free Cash Flow (FCF) estimations not unquestionably obtained. In this paper we propose an unbiased and systematic DCF method which allows us to value private equity by leveraging on stock markets evidences, based on a twofold approach: First, the use of the inverse method assesses the existence of a coherentWACC that positively compares with market observations; second, different FCF forecasting methods are benchmarked and shown to correspond with actual valuations. We use financial historical data including 42 companies in five sectors, extracted from Eikon-Reuters. Our results show that WACC and FCF forecasting are not coherent with market expectations along time, with sectors, or with market regions, when only historical and endogenous variables are taken into account. The best estimates are found when exogenous variables, operational normalization of input space, and data-driven linear techniques are considered (Root Mean Square Error of 6.51). Our method suggests that FCFs and their positive alignment with Market Capitalization and the subordinate enterprise value are the most influencing variables. The fine-tuning of the methods presented here, along with an exhaustive analysis using nonlinear machine-learning techniques, are developed and discussed in the companion paper

Opening the 21st Century Technologies to Industries: On the Special Issue Machine Learning for Society

Machine learning techniques, more commonly known today as artificial intelligence, are playing an increasingly important role in all aspects of our lives. Their applications extend to all areas of society where similar techniques can be accommodated to provide efficient and interesting solutions to a wide range of problems. In this Special Issue entitled Machine Learning for Society [1], we present some examples of the applications of this type of technique. From the valuation of unlisted companies to the characterization of clients, through the detection of financial crises or the prediction of the behavior of the exchange rate, this group of works presented here has in common the search for efficient solutions based on a set of historical data, and the application of artificial intelligence techniques. The techniques and datasets used, as well as the relevant findings developed in the different articles of this Special Issue, are summarized below

On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (II): Applying Machine-Learning Techniques for Unbiased Enterprise Value Assessment

The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company’s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods –including entropy, mutual information, and the Compounded Aggregated Positivity Index– allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties’ positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations

On the Statistical and Temporal Dynamics of Sentiment Analysis

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at ..