Showing papers by "Claudio Conversano published in 2020"
TL;DR: An integrated approach is proposed, which identifies a long list of key quality indicators (KQI), defines their properties, involves experts to elicit judgments for each KQI, evaluates the long list, and points out the most promising set.
Abstract: Recent interests in transit services have captured attention of experts on the monitoring of public transport quality. Previous research focused on relevant models and methods to monitor the quality of transit services and showed where and when different service quality levels occur. However, there was little attention to detect objectively a pool of key quality indicators (KQI) to be monitored, from a large set. This paper covers this gap by the proposal of an integrated approach, which identifies a long list of KQI, defines their properties, involves experts to elicit judgments for each KQI, evaluates the long list, and points out the most promising set. This integrated approach is demonstrated with an application based on an international survey and a Monte Carlo simulation method. Moreover, a restricted and relevant set of 9 overlapping KQI is derived by linking these results with those obtained from two different approaches.
28 citations
TL;DR: The main methodological features and the goals of pharmacoeconomic models that are classified in three major categories: regression models, decision trees, and Markov models are presented and decision makers are advised to interpret the results with extreme caution.
Abstract: We present an overview of the main methodological features and the goals of pharmacoeconomic models that are classified in three major categories: regression models, decision trees, and Markov models. In particular, we focus on Markov models and define a semi-Markov model on the cost utility of a vaccine for Dengue fever discussing the key components of the model and the interpretation of its results. Next, we identify some criticalities of the decision rule arising from a possible incorrect interpretation of the model outcomes. Specifically, we focus on the difference between median and mean ICER and on handling the willingness-to-pay thresholds. We also show that the life span of the model and an incorrect hypothesis specification can lead to very different outcomes. Finally, we analyse the limit of Markov model when a large number of states is considered and focus on the implementation of tools that can bypass the lack of memory condition of Markov models. We conclude that decision makers should interpret the results of these models with extreme caution before deciding to fund any health care policy and give some recommendations about the appropriate use of these models.
20 citations
TL;DR: Under few conditions, using Monte Carlo simulations with different scenarios, it is proved that the Kelly criterion beats any other approach in many aspects and has the best performance in the long run.
Abstract: We develop a general framework to apply the Kelly criterion to the stock market data, and consequently, to portfolio optimization. Under few conditions, using Monte Carlo simulations with different scenarios we prove that the Kelly criterion beats any other approach in many aspects. In particular, it maximizes the expected growth rate and the median of the terminal wealth. We also show that, under a normal distribution of returns, the Kelly criterion has the best performance in the long run. Next, we optimize a portfolio with the Kelly criterion with no leverage and no short selling conditions and show that this portfolio lays in the mean-variance efficient frontier and has higher expected return and higher variance, although it is less diversified, respect to the tangent portfolio optimized under the Markowitz approach. Finally, we implement a dynamic strategy applied on the European stock market data and compare the results between the tangent and the optimal Kelly portfolios. In a dynamic setting, the rolling Kelly portfolio outperforms competitors particularly in the case of rebalanced portfolios optimized with a 2-years window width.
12 citations
TL;DR: An index of social media popularity for both presidential candidates is derived and results show that Trump wisely exploited Twitter to attract more people by tweeting in a well-organized and desirable manner and that his tweeting style has increased his popularity in social media.
Abstract: Popularity in social media is mostly interpreted by drawing a relationship between a social media account and its followers. Although understanding popularity from social media has been explored for about a decade, to our knowledge, the extent to which the account owners put efforts to enhance their popularity has not been evaluated in detail. In this paper, we focus on Twitter, a popular social media, and consider the case study of the 2016 US elections. More specifically, we aim to assess whether candidates endeavor to improve their style of tweeting over time to be more attractive to their followers. An ad hoc-defined predictive model based on a recurrent random forest is used for this purpose. To this end, we build a classification model whose features are obtained from the characteristics of a set of content/sentiment information extracted from the tweets. Next, we derive an index of social media popularity for both candidates. Results show that Trump wisely exploited Twitter to attract more people by tweeting in a well-organized and desirable manner and that his tweeting style has increased his popularity in social media. The differences in the tweeting styles of the two presidential candidates and the links between the sentiments arising from candidates’ tweets and their popularity index are also investigated.
8 citations
TL;DR: In this article, the authors analyzed the relationship between the four basic elements and other managerial factors, such as extra fees for cleaning or similar services, on the probability of being a superhost.
Abstract: Airbnb is currently one of the most developed new forms of hospitality. It is essentially an online platform that connects the owners of apartments or rooms with potential guests. The core of the Airbnb model is the host, who offers a support to guests and favor a link between the guests and the tourism destination. Airbnb awards the best hosts with the badge of “Superhost”, which is attributed according to four elements: the occupancy rate, the number of reservations, the response rate and the cancellation policy. This paper focuses on hosts and their activities. Specifically, the main goal is understanding if the four aforementioned elements actually influence the attribution of the “Superhost” badge to hosts operating in two of the main Italian touristic destinations: Sardinia and Sicily. Furthermore, the link between the four basic elements and other “managerial factors” is analyzed. Logistic and Probit models are used for these purposes and the main findings are derived from the computation of marginal effects. The results show a direct impact of the four Airbnb variables and of other “managerial” variables, as for instance the presence of extra fees for cleaning or similar services, on the probability to be a superhost.
3 citations
TL;DR: An analysis of comments from open-source repositories of software systems shows that when in presence of impolite or negative comments, the probability of the next comment being impolites or negative is 14% and 25%, respectively; anger however, has a probability of 40% of being followed by a further anger comment.
Abstract: In this paper, we present an analysis of more than 500K comments from open-sourcerepositories of software systems.Our aim is to empirically determine how developers interact with each otherunder certain psychological conditions generated by politeness, sentiment andemotion expressed within developers' comments.Developers involved in an open-source projects do not usually know each other; they mainly communicate through mailing lists, chat rooms, and tools such as issue tracking systems.The way in which they communicate affects the development process and the productivity of the people involved in the project.We evaluated politeness, sentiment and emotions of comments posted by developers and studied the communication flow to understand how they interacted in the presence of impolite and negative comments (and vice versa).Our analysis shows that when in presence of impolite or negative comments, the probability of the next comment being impolite or negative is 14% and 25%, respectively; anger however,has a probability of 40% of being followed by a further anger comment.The result could help managers take control the development phases of a system, since social aspects can seriously affect a developer's productivity. In a distributed environment this may have a particular resonance.
1 citations
TL;DR: A supervised classification model is used to distinguish bots from legitimate users on Twitter and shows the importance of sentiment features in bot-human account detection.
Abstract: The role of Twitter as a platform to share opinions has been growing in the recent years especially since it has been widely used by public personae such as politicians, personalities of the show business, and other influencers to communicate with the public. For these reasons, the use of social bots to manipulate information and influence people's opinions is also growing. In this paper, we use a supervised classification model to distinguish bots from legitimate users on Twitter. More specifically, we show the importance of sentiment features in bot-human account detection. Moreover, we evaluate our detection model by testing on Russian bot accounts who are the most recent set of social bots that appeared on Twitter to show that these techniques may be easily adapted to work on new, unseen types of social bots.
TL;DR: Protected Adaptive Asset Allocation (PAAA) as discussed by the authors is a tactical asset allocation model that targets an optimal risk/returns ratio using both a momentum index to capture the short-run dynamics and cash protection in negative market periods to reduce drawdowns.