A study of efficiency in New Zealand election prediction markers

<p>The development of prediction markets has naturally given rise to studies of their efficiency. Most studies of efficiency in prediction markets have focused on the speed with which they incorporate information. A necessary (but not sufficient) condition of efficiency is that arbitrage opportunities must non-existent or transitory in nature so that the systematic generation of abnormal profits is not possible. Using data from New Zealand’s first prediction market, iPredict, I examine the potential for arbitrage in the contracts for the party vote for the 2011 General Election. Relative to the risk-free interest rate, the returns from arbitrage are generally low, consistent with an efficient market. Regression analysis requires that the data not be subject to the possibility of spurious regressions - something that is not addressed in the literature. After confirming the non-stationarity of the price level and the stationarity of the price changes by the unit root test, I use the iPredict data in conjunction with opinion poll data to test whether the polls impact on market pricing behaviour. Using a number of different model types, I find that the opinion poll data has a very limited impact on market prices, suggesting that the information contained in the poll is largely already incorporated into market prices.</p>

A study of efficiency in New Zealand election prediction markers

<p>The development of prediction markets has naturally given rise to studies of their efficiency. Most studies of efficiency in prediction markets have focused on the speed with which they incorporate information. A necessary (but not sufficient) condition of efficiency is that arbitrage opportunities must non-existent or transitory in nature so that the systematic generation of abnormal profits is not possible. Using data from New Zealand’s first prediction market, iPredict, I examine the potential for arbitrage in the contracts for the party vote for the 2011 General Election. Relative to the risk-free interest rate, the returns from arbitrage are generally low, consistent with an efficient market. Regression analysis requires that the data not be subject to the possibility of spurious regressions - something that is not addressed in the literature. After confirming the non-stationarity of the price level and the stationarity of the price changes by the unit root test, I use the iPredict data in conjunction with opinion poll data to test whether the polls impact on market pricing behaviour. Using a number of different model types, I find that the opinion poll data has a very limited impact on market prices, suggesting that the information contained in the poll is largely already incorporated into market prices.</p>

Study and analysis of various sentiment classification strategies: A challenging overview

In large-scale social media, sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions, including public emotional status monitoring, political election prediction, and so on. On the other hand, textual sentiment classification is well studied by various platforms, like Instagram, Twitter, etc. Sentiment classification has many advantages in various fields, like opinion polls, education, and e-commerce. Sentiment classification is an interesting and progressing research area due to its applications in several areas. The information is collected from various people about social, products, and social events by web in sentiment analysis. This review provides a detailed survey of 50 research papers presenting sentiment classification schemes such as active learning-based approach, aspect learning-based method, and machine learning-based approach. The analysis is presented based on the categorization of sentiment classification schemes, the dataset used, software tools utilized, published year, and the performance metrics. Finally, the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contribution in devising significant sentiment classification strategies. Moreover, the probable future research directions in attaining efficient sentiment classification are provided.

Spanish electoral archive. SEA database

This paper introduces the SEA database (acronym for Spanish Electoral Archive). SEA brings together the most complete public repository available to date on Spanish election outcomes. SEA holds all the results recorded from the electoral processes of General (1979–2019), Regional (1989–2021), Local (1979–2019) and European Parliamentary (1987–2019) elections held in Spain since the restoration of democracy in the late 70 s, in addition to other data sets with electoral content. The data are offered for free and is presented in a homogeneous and friendly format. Most of the databases are available for download with data from various electoral levels, including from the ballot box level. This paper details how the information is organized, what the main variables are on offer for each election, which processes were applied to the data for their homogenization, and discusses future areas of work. This data has many applications, for example, as inputs in election prediction models and in ecological inference algorithms, to study determinants of turnout or voting, or for defining marketing strategies.

Election Prediction on Twitter: A Systematic Mapping Study

Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.

Devising an Algorithm for Election Prediction Using Survey of Voters Opinions

The purpose of this research study is to analyze how we use voter polls to predict elections and to design an algorithm to predict elections. We propose a method of prediction based on learning algorithm to determine the political profile of a voter group by obtaining a linear hierarchy on the attributes that weights the number of instances that are more relevant. Our process starts with opinion survey collected directly from the target group of voters. Having a linear attribute hierarchy that expresses the political preferences of voters allows the application of a holistic approach to distribute the potential number of votes among the parties involved. We applied our electoral outlook model in the Kosovo election case study in from February 2021. The devised algorithmic model may also be applied to other situations. Data analysis not only provides new analysis opportunities, but also faces many challenges. In our case, we listed the limitations of the research. The research attempts to promote the implementation of the algorithm by extending the processing of the information generated by the learning algorithm to improve the prediction of elections and winning parties. Discussed of all data analysis challenges, and present, discuss, and argue insights.

Implicit Factors and Voting Behavior Inconsistency: From an Attitude to Behavior

Several recent elections and referendums were marked by a dramatic failure in pre-election prediction based on large-scale surveys among voters. The focus of the present study is poorly studied limitations on the accuracy of forecasts which are based on the explicit intentions of voters and do not take into consideration implicit (unconscious, latent) factors influencing voting behavior. To identify those factors the author introduces Graphic Association Test of Attitude (GATA) - a simple but powerful tool which enables measurement of implicit factors/intentions and helps to “enrich” traditional forecasting models dealing with explicit factors with a set of implicit effects. How these “upgraded” models work can be illustrated by the inconsistency phenomenon showcasing functionality of the general concept. The findings of the study proves an assumption stating that implicit factors affecting attitudes and intentions are real phenomena, and inconsistencies in explicit and implicit elements of the voter’s attitudes and intentions are typical of many voters. These issues were examined in detail in the previous article (Implicit Factors and Voting Behavior Inconsistency: from Theoretical Concept to Empirical Phenomenon// Monitoring of Public Opinion: Economic and Social Changes, 2020, No. 4). The present article argues that implicit factors, particularly due to the inconsistencies in the voting behavior components, can have a real impact on the voters’ behavior. Taking this fact into account can considerably and sustainably improve the forecast accuracy.

Implicit Factors and Voting Behaviour Inconsistency: From Theoretical Concept to Empirical Phenomenon

Several recent elections and referendums were marked by a dramatic failure in pre-election prediction based on large-scale surveys among voters. As a reaction to public anger and discontent among politicians alternative strategies (prediction markets, Implicit Association Test (IAT), expectation-based forecast, etc.) are being developed. The industry of election polling has also made progress: a number of studies have shown that a relatively low accuracy of forecasts was caused by inconsistencies in sample design and implementation. The present article considers another factor behind election forecast errors: insufficiency of data about the declared intentions needed to make an accurate prediction. For this purpose, the author introduces a tool called GATA (Graphic Association Test of Attitude) measuring implicit attitudes/intentions and proposes to add a “stream” of implicit effects to the usual Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB). According to the findings, implicit factors are an actual and clearly detected phenomenon; inconsistency in explicit and implicit attitudes/intentions is typical of many voters. The present article aims to present this phenomenon. This will be followed by another article (Implicit Factors and Voting Behavior Inconsistency: From an Attitude to Behavior) in the next issue of the Monitoring of Public Opinion: Economic and Social Changes (2020, no.5) which will highlight behavioral effects of inconsistencies and the results of a combined use of implicit and explicit factors in the election forecast model.