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1. Public opinion without polls : investigating the feasibility of Twitter-based election forecasts

Author

Loynes, Niklas, Elliot, Mark, Gibson, Rachel, and Cantijoch Cunill, Marta

Subjects
computational social science, forecasting, American politics, elections, Twitter, public opinion
Abstract

This thesis investigates whether, and if so, how digital trace data taken from the social media platform Twitter can be used as a valid and reliable basis for measuring public opinion. While Twitter data has been used in a range of spheres to measure public opinion, in the political research sphere work has typically centred on forecasting the outcome of elections. This functions as a predictive exercise in its own right, but can also be framed as providing a test for the robustness of measures derived from Twitter data as a new proxy for voter opinion: election outcomes offer a 'ground truth' against which bias in these data can be assessed and the extent of error measured. Results to date have been mixed, with studies showing varying levels of accuracy in matching their estimates of vote intention against polls and/or electoral outcomes. So far, however, none have consistently achieved the standards of accuracy, reproducibility and reliability that would make them an unquestioned alternative to surveys, thereby offering ample scope for new research, which this thesis contributes to. The research documented in this thesis is guided by the assumption that the sheer amount of political content on Twitter provides enough pertinent and varied information to measure public opinion, but the tools necessary for achieving this reliably and at scale do not yet exist. The core goals of this thesis are exploring which methods are required, and contributing to their development. This is approached in three papers. First, 'Finding Friends' demonstrates a new approach to estimating any given Twitter user's home location. Second, 'Understanding Political Sentiment' benchmarks existing approaches to measuring public opinion by extracting sentiment from tweets, as well as a new approach to estimating public opinion metrics derived from hand-labelled and machine-propagated sentiment scores, while applying twelve aggregate vote share prediction models in a comparative framework in three US 2016 presidential primaries. This paper, as well as third paper employ samples of Twitter users generated using the geo-locating algorithm outlined in paper 1. Paper 3, 'Listening in on the noise' introduces a novel approach to estimating individual-level political preferences using distant supervision and Machine Learning. This is applied on two distinct, geo-located samples of users, one selected based on indication of previous voting, one selected randomly, in order to trace public opinion in the 2018 US midterm elections, both nationally and in the four most populous states. Each of my papers marks a significant contribution to its particular issue-area in the field of Twitter-based public opinion research. Paper 1 adds a reproducible geo-locating pipeline with an open source software package. Paper 2 adds evidence to the evaluation of the usefulness of sentiment analysis on tweets for public opinion measurement. Paper 3 introduces an approach to estimating individual-level political preferences, as well as highlighting the impact sampling decisions have on research outcomes. The best models for predicting vote shares in papers 2 and 3 achieve mean errors on par with opinion polling, and offer, through their application in diverse election scenarios and by following a theory- grounded and reproducible framework, a strong contribution to existing practice in the field. Furthermore, the ability to estimate individual users' home locations significantly improves sampling capabilities for researchers employing Twitter data across fields.

Published
2021

2. Sustainable Directions in Tourism.

Author

Espino-Rodríguez, Tomás

Subjects
A'WOT, Apuseni mountains, China, ICT, LSTM model, PM10, TOWS matrix, agritourism, air pollution, all-for-one tourism, behavioral intentions, campsites, certification, choice experiment, climate change, cluster analysis, community-based tourism, compartmentalisation, constraint, deep learning framework, destination development, discrete choice experiments, ecolabel, ecolabel adoption, electricity, essential marketing, event quality, forecast through a logistic model, forecasting, forecasting performance, hotel accommodation demands, hotel industry, hotel management, inbound tourism, internet search index, invasive species control, islands, learning-based tourism, local community, motivation, mountain areas, museum planning, negative externalities, physical environment (PhE), place attachment, pro-social/pro-environmental behavior, purchasing, qualitative methodology, regional disparity, regression discontinuity design, science museum, semantic analysis, sense of belonging, spatial analysis, sport tourism, sporting event, strategic planning, supply chain, sustainability, sustainability services marketing matrix, sustainable daily practices, sustainable development, sustainable tourism, the "health" of rural settlements, time series, tourism, tourism and sustainability, tourism evaluation, tourism indicators, tourist satisfaction, tourists' preferences, two stage on-site sampling, value and tourism, visitor behavior, visitor satisfaction, young adults
Abstract

Summary: Within the framework of tourism companies and tourist destinations, the question of sustainability is gaining importance. Tourists are increasingly aware of the importance of sustainability criteria, awarding greater value to sustainable destinations. Sustainability refers to a wide range of aspects related to climate change, the economic organization of tourism, social values or questions, job creation, and the necessary protection of the culture of destinations and the environment. Therefore, there is a need for studies that consider these aspects in order to achieve the sustainable development of tourist destinations. Fundamental to this is discovering to what degree tourism companies and destinations approach these questions in the strategies they use to deal with problems stemming from their attempts to be more sustainable. Conceptual papers and empirical research on the economic, social, cultural, and environmental aspects related to tourism companies and destinations are welcome. Studies that analyze how these questions and the concept of sustainability are included in tourism companies and destinations are necessary in these modern times. This book was established for these reasons, dedicated to examining sustainability in tourism. The papers included in this Special Issue can help us to determine the new directions being addressed in the research on sustainability tourism.

3. Quantitative Methods for Economics and Finance.

Author

Trinidad-Segovia, J.E., Sánchez-Granero, Miguel Ángel, and Trinidad-Segovia, J.E.

Subjects
Coins, banknotes, medals, seals (numismatics), Bitcoin, Chinese listed companies, DCC, DEA, EGARCH, EVT, Ethereum, FD4 approach, GARCH, Hurst exponent, Markov Chain Monte Carlo simulation, P 500, P500, Ripple, S&amp, SRA approach, Tobin's q, United States, VaR, academic cheating, asset pricing, autoregressive integrated moving average (ARIMA), bilateral investment treaties, biotechnological firms, bitcoin, cash flow management, centered model, co-movement, cointegration, commodity prices, computational finance, copula, copulas, corporate prudential risk, correlation risk premium, cryptocurrency, decision-making process, decreasing impatience, deep learning, deep recurrent convolutional neural networks, delay, derivation, detection, discount, dispersion trading, dynamically simulated autoregressive distributed lag (DYS-ARDL), econometrics, eigenvalues, elasticity, energy consumption, ensemble empirical mode decomposition (EEMD), essential multicollinearity, financial distress, financial distress prediction, financial markets, forecasting, foreign direct investment, futures prices, generalized Pareto distribution, genetic algorithm (GA), gold, historical simulation approach, hurst exponent, induced risk aversion, informality, intercept, intertemporal choice, liquidity constraints, liquidity risk, local optima vs. local minima, long memory, macroeconomic propagation, mean square error, multicollinearity, multiperiod financial management, multiple periods, non-linear macroeconomic modelling, non-parametric efficiency, noncentered model, nonessential multicollinearity, number of factors, option arbitrage, pairs trading, peaks-over-threshold, pharmaceutical industry, policy uncertainty, precautionary savings, probability, probability of volatility cluster, productivity, profitability, raise regression, regional trade agreements, risk, scale economies, stock prices, structural gravity model, student t-copula, support vector regression (SVR), tax evasion, the financial accelerator, threshold regression, unconstrained distributed lag model, variance inflation factor, volatility cluster, volatility series, volatility trading
Abstract

Summary: This book is a collection of papers for the Special Issue "Quantitative Methods for Economics and Finance" of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice.

4. Centralised demand information sharing in supply chains

Author

Ali, Mohammad Mojiballah

Subjects
338.0068, Supply chains, Information sharing, Business and management, Centralised demand information sharing, CDIS, Bullwhip Effect, Inventory management, Downstream Demand Inference, Forecasting
Abstract

This thesis explores Centralised Demand Information Sharing (CDIS) in supply chains. CDIS is an information sharing approach where supply chain members forecast based on the downstream member’s demand. The Bullwhip Effect is a demand variance amplification phenomenon: as the demand moves upstream in supply chains, its variability increases. Many papers in the literature show that, if supply chain members forecast using the less variable downstream member’s demand, this amplification can be reduced leading to a reduction in inventory cost. These papers, using strict model assumptions, discuss three demand information sharing approaches: No Information Sharing (NIS), Downstream Demand Inference (DDI) and Demand Information Sharing (DIS). The mathematical analysis in this stream of research is restricted to the Minimum Mean Squared Error (MMSE) forecasting method. A major motivation for this PhD research is to improve the above approaches, and assess those using less restrictive supply chain assumptions. In this research, apart from using the MMSE forecasting method, we also utilise two non-optimal forecasting methods, Simple Moving Averages (SMA) and Single Exponential Smoothing (SES). The reason for their inclusion is the empirical evidence of their high usage, familiarity and satisfaction in practice. We first fill some gaps in the literature by extending results on upstream demand translation for ARMA (p, q) processes to SMA and SES. Then, by using less restrictive assumptions, we show that the DDI approach is not feasible, while the NIS and DIS approaches can be improved. The two new improved approaches are No Information Sharing – Estimation (NIS-Est) and Centralised Demand Information Sharing (CDIS). It is argued in this thesis that if the supply chain strategy is not to share demand information, NIS-Est results in less inventory cost than NIS for an Order Up To policy. On the other hand, if the strategy is to share demand information, the CDIS approach may be used, resulting in lower inventory cost than DIS. These new approaches are then compared to the traditional approaches on theoretically generated data. NIS-Est improves on NIS, while CDIS improves on the DIS approach in terms of the bullwhip ratio, forecast error (as measured by Mean Squared Error), inventory holding and inventory cost. The results of simulation show that the performance of CDIS is the best among all four approaches in terms of these performance metrics. Finally, the empirical validity of the new approaches is assessed on weekly sales data of a European superstore. Empirical findings and theoretical results are consistent regarding the performance of CDIS. Thus, this research concludes that the inventory cost of an upstream member is reduced when their forecasts are based on a Centralised Demand Information Sharing (CDIS) approach.

Published
2008

5. Assessment of Renewable Energy Resources with Remote Sensing.

Author

Martins, Fernando Ramos and Martins, Fernando Ramos

Subjects
Research & information: general, Baltic area, CSP plants, GES-CAL software, Hazaki Oceanographical Research Station, artificial neural networks, climate, cloud, cloud coverage, cloud detection, coastal wind measurements, coastline, computational design method, convection, data processing, digitized image processing, electrical resistivity tomography, extreme value analysis, feature engineering, feature importance, forecasting, geophysical prospecting, geothermal energy, global radiation, graphical user interface software, hydropower reservoir, image processing, lake breeze influence, light gradient boosting machine, machine learning, machine learning techniques, metaheuristic, multistep-ahead prediction, parameter extraction, passive design strategy, photovoltaic power plant, plan position indicator, point cloud data, potential well field location, remote sensing, remote sensing data acquisition, renewable energy resource assessment and forecasting, satellite, scanning LiDAR, scatterometer, shading envelopes, sky camera, smart island, solar energy, solar energy resource, solar irradiance enhancement, solar irradiance estimation, solar irradiance forecasting, solar photovoltaic, solar radiation forecasting, statistical analysis, surface solar radiation, time domain electromagnetic method, total sky imagery, velocity volume processing, voxel-design approach, whale optimization algorithm, wind speed
Abstract

Summary: The book "Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security.

6. Bayesian Econometrics.

Author

Bernardi, Mauro, Bernardi, Mauro, Grassi, Stefano, and Ravazzolo, Francesco

Subjects
Technology: general issues, Bayesian TVP-SV-VAR, Bayesian VAR, Bayesian econometrics, Bayesian estimation, Bayesian nonlinear mixed-effects regression, Bitcoin, CES function, DSGE model, ES, MCMC methods, cryptocurrency, density forecast, density forecasting, dynamic model averaging, dynamic model selection, fiscal policy, forecasting, forgetting factors, macroeconomic and financial applications, military and civilian spending, monetary policy, point forecast, portfolio choice, sentiments, stock market predictability, time-varying volatility, transmission channel, unconventional monetary policy
Abstract

Summary: Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb-Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.

7. The 8th International Conference on Time Series and Forecasting.

Author

Rojas, Ignacio, Herrera, Luis, Kaufman, Peter, Pomares, Hector, Rojas, Fernando, Rojas, Ignacio, and Valenzuela, Olga

Subjects
Computer science, Information technology industries, : financial market volatility, ARIMA, CNN, COVID-19, DCCA method, K-Means, K-mean clustering, LSTM, Markov chain, NARNN, VAR-DCC-GARCH, accessibility, all sky images, anomaly detection, cloud-base height, clustering, convolutional neural network, cross-correlation, diagnosis, dynamic convergence, dynamic mode decomposition with control, ecosystem respiration, energy, error diagnosis, faults, forecasting, health forecasting, hydrological data, intensive care unit (ICU), longshort-term memory (LSTM), machine learning, machine learning (ML), machinelearning, mobile data traffic, model evaluation, multivariate prediction, non-stationary time series, oil derivatives, oil production, ordinal patterns, outlier detection, outlier detection in time series, prediction, prediction intervals, predictive maintenance, privacy, readmission prediction, recurrent neural network (RNN), retainability, seq2seq, shareable data, signal processing, spatial, spectrum, stationarity, structural breaks, synthetic data, temporal, time delay embedding, time series, time series analysis, time series cluster evaluation, time series clustering, time series data, time series forecasting, time-series, time-series forecasting, unit root, utilization, wavelet-based random forest, PV systems, automated machine learning
Abstract

Summary: The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields.

8. Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics.

Author

Yeomans, Julian Scott, Kozlova, Mariia, and Yeomans, Julian Scott

Subjects
Mathematics & science, Research & information: general, C-vine copula, CO2 emissions, DEA, Germany, Iowa food-energy-water nexus, LASSO, Monte Carlo simulation, South Texas, biofuel policy, biomass gasification, boron, business aviation, classification, computer modeling, computer simulation, desalination, dynamic programming, eco-efficiency, ecological indicators, ecological relationship, electric motor, electricity production, energy modeling, energy system design, environmental footprint, factorial analysis, feature selection, feed-in tariff, financial market, forecasting, fuzzy, generation profile, input-output analysis, interval, investing, investment profitability analysis, joint dependencies, machine learning, model reduction, modelling, n/a, nitrogen export, nonpoint source pollution, operational flexibility, optimal allocation, optimal path, parameter estimation, point source pollution, pollutant loadings, quantile regression, reduction, regression, renewable energy, renewable energy support, reverse osmosis, seawater, simulation, simulation decomposition, sourcing, specific power, streamflow forecasting, sustainability, system modeling, the pay-off method, turboprop, unlisted companies, urban solid waste system, water quality, water resource management, water resources, watershed management, weather modeling
Abstract

Summary: Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the "real world" frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis - be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies.

9. Renewable Energy Resource Assessment and Forecasting.

Author

Galanis, George and Galanis, George

Subjects
Research & information: general, ANFIS, Granger causality, Kalman filtering, Kalman-Bayesian filter, Markov chains, WRF, anthropogenic waste processing, biofuel, biomass, biotechnology, central solar receiver, clearness coefficient, climate policy, complex terrain, concentrated solar, concentrating solar power, cross border trading, deep learning, deformable models, developing countries, different horizontal resolution, direct normal irradiance, economic efficiency, electric energy demand, electricity trading, energy, energy resource assessment, forecast, forecast errors, forecasting, functional statistics, global horizontal irradiance (GHI), heat supply of industrial processes, high-resolution, momentum sink, nowcasting, numerical weather prediction model, open channel flows, operational strategies, particle swarm optimization, photovoltaic solar energy, prediction, ramp rates, renewable energy, renewable energy forecasting, renewable energy sources, risk analysis, shape-invariant model, shark algorithm, shock-capturing capability, short-term forecasts, solar collectors, solar irradiance forecasts, solar irradiation, solar radiation, solar resource, spot prices, sustainable development, system advisor model, the European Green Deal, thermochemical, thrust force coefficient, tidal-stream energy, unbounded flow, validation, weather research and forecasting model, wind
Abstract

Summary: In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on 'Renewable Energy Resource Assessment and Forecasting' aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today's technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources.

10. Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development.

Author

Hashemi, Shervin and Hashemi, Shervin

Subjects
Environmental science, engineering & technology, History of engineering & technology, Technology: general issues, Bangladesh, CO2, COVID-19, Clean Development Mechanism, Internet of Things (IoT), MENA Islamic cities, Mexico, PLS-SEM, SDGs, VECM, aquaculture, arsenic, benchmarking, biodiesel, blockchain, brand loyalty, circular economy, construction sector, construction waste reduction, consumer brand relationships, corrugated cardboard, cost-benefit analysis, crumb rubber, developing countries, disposal, dose-response, eco-friendly sound-absorbing material, economic reactivation, economy, emission reductions, emissions, engine performance, environmental regulations, ethical marketing, ethnobotany, extended marketing mix, fisheries, food security, forecasting, geometric mean weighting, green recovery, groundwater level, groundwater management models, groundwater monitoring system, groundwater resource, human health, humanitarian logistics, industry 4.0, innovation, intersectoral linkages, landfill, manufacturing supply chains, metals, modelling of waste (reduce, reuse and recycle), multi-frequency resonator, multiobjective linear programming, n/a, natural feedstocks, pandemic, penalty weighting, perforated corrugated cardboard, pollution, potential solutions, poverty, power plants, product life cycle cost, production method, response surface methodology, revised multichoice goal programming, rice husk, rural Gansu, rural clean heating project, scorecard, seed rate, seed recycling, self-compacting concrete, silica fume, sound transmission loss, sound-absorption coefficient, spatial modelling, strength, supplier selection, sustainability, sustainable agriculture, sustainable built environment, sustainable construction, sustainable development, sustainable development goals, traditional knowledge, transfer function method, transfer matrix method, urban management, urban solid waste, waste management, wheat, wireless sensor network, yield effect
Abstract

Summary: The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, "We do not inherit the Earth from our ancestors; we borrow it from our children." This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled "Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development" during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability.

11. Frontiers of Asset Pricing.

Author

Kolari, James W., Kolari, James W., and Pynnonen, Seppo

Subjects
Philosophy, Bitcoin, GARCH-jump, Poisson model, S&P 500 index, abnormal returns, announcements, asset pricing, at-the-money, bias adjustments, carry cost rate, clustered event days, commodity market, conditional hedge ratio, cross-sectional correlation, cryptocurrencies, cumulated ranks, deep-out-of-the-money, direction, earnings, economics, efficient market hypothesis, efficient portfolios, event study, expectation-maximization (EM) regression, finance, forecasting, free-boundary problem, hedge ratio, informed trading, latent variable, market factor, market index, market volume, metals, momentum, multifactors, net buying pressure, options, out-of-the-money, outliers, pairs trading, portfolio profitability, pricing, rank test, return dispersion, risk factors, spectral analysis, standardized abnormal returns, stochastic control, survivor stocks, term structure, time-varying jumps, trading strategies, transaction costs, transaction regions, unit root, volatility, yield spread, zero-beta CAPM
Abstract

Summary: This book is comprised of articles published in a Special Issue of the Journal of Risk and Financial Management entitled "Frontiers in Asset Pricing" with Guest Editors Professor James W. Kolari and Professor Seppo Pynnonen. The book contains papers in various areas related to asset pricing: (1) models; (2) multifactors; (3) theory; (4) empirical tests; (5) applications; (6) other asset classes; and (7) international tests.