Search Results (15)

This study applies Gradient Boosting Machines (GBMs) and principal component regression (PCR) to forecast the closing price of the Johannesburg Stock Exchange (JSE) All-Share Index (ALSI), using daily data from 2009 to 2024, sourced from the Wall Street Journal. The models are evaluated under three training–testing split ratios to assess short-term forecasting performance. Forecast accuracy is assessed using standard error metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE). Across all test splits, the GBM consistently achieves lower forecast errors than PCR, demonstrating superior predictive accuracy. To validate the significance of this performance difference, the Diebold–Mariano (DM) test is applied, confirming that the forecast errors from the GBM are statistically significantly lower than those of PCR at conventional significance levels. These findings highlight the GBM’s strength in capturing nonlinear relationships and complex interactions in financial time series, particularly when using features such as the USD/ZAR exchange rate, oil, platinum, and gold prices, the S&P 500 index, and calendar-based variables like month and day. Future research should consider integrating additional macroeconomic indicators and exploring alternative or hybrid forecasting models to improve robustness and generalisability across different market conditions. Full article

Push notifications are a digital strategy for outlets to provide news and a convenient way for audiences to absorb information. Past research shows the effectiveness of push notifications and how they are framed, but few studies have explored their relationship with clickbait. However, clickbait often has a negative connotation. Through an exploratory mixed methods study involving textual analysis of push notifications (n = 639) sent by three credible mainstream media outlets, namely The Associated Press, The New York Times, and The Wall Street Journal, and a survey of readers’ (n = 368) perception of push notifications and clickbait, this research explores how credible news outlets directly engage with their respective audiences by framing push notifications in the form of clickbait. This study builds on framing theory by proposing the concept of credible clickbait and illustrating how push notifications shape readers’ immediate perceptions of content being shared with them by news outlets they subscribe to. This research also aims to be a resource for journalists to increase audience interaction and foster sustained attention with stories. Full article

Forward-looking metrics of uncertainty based on options-implied information should be highly predictive of equity market returns in accordance with asset pricing theory. Empirically, however, the ability of the VIX, for example, to predict returns is statistically weak. In contrast to other studies that typically analyze a short time-series of option prices, I make use of a ‘VIX-type’ but a text-based measure of uncertainty starting in 1890, which is constructed using the titles and abstracts of front-page articles of the Wall Street Journal. I hypothesize that uncertainty timing might increase Sharpe ratios because changes in uncertainty are not necessarily correlated with changes in equity risk and, therefore, not offset by proportional changes in expected returns. Using a major US equity portfolio, I propose a dynamic trading strategy and show that lagged news-based uncertainty explains future excess returns on the market portfolio at the short horizon. While policy- and war-related concerns mainly drive these predictability results, stock market-related news has no predictive power. A managed equity portfolio that takes more risk when news-based uncertainty is high generates an annualized equity risk-adjusted alpha of 5.33% with an appraisal ratio of 0.46. Managing news-based uncertainty contrasts with conventional investment knowledge because the strategy takes relatively less risks in recessions, which rules out typical risk-based explanations. Interestingly, I find that the uncertainty around governmental policy is lower and, by taking less risk, it performs better during periods when the Republicans control the senate. I conclude that my text-based measure is a plausible proxy for investor policy uncertainty and performs better in terms of predictability compared to other options-based measures. Full article

Self-supervised learning (SSL) is an effective way of learning rich and transferable speech representations from unlabeled data to benefit downstream tasks. However, effectively incorporating a pre-trained SSL model into an automatic speech recognition (ASR) system remains challenging. In this paper, we propose a network architecture with light-weight adapters to adapt a pre-trained SSL model for an end-to-end (E2E) ASR. An adapter is introduced in each SSL network layer and trained on the downstream ASR task, while the parameters of the pre-trained SSL network layers remain unchanged. By carrying over all pre-trained parameters, we avoid the catastrophic forgetting problem. At the same time, we allow the network to quickly adapt to ASR task with light-weight adapters. The experiments using LibriSpeech and Wall Street Journal (WSJ) datasets show that (1) the proposed adapter-based fine-tuning consistently outperforms full-fledged training in low-resource scenarios, with up to 17.5%/12.2% relative word error rate (WER) reduction on the 10 min LibriSpeech split; (2) the adapter-based adaptation also shows competitive performance in high-resource scenarios, which further validates the effectiveness of the adapters. Full article

During 2018, three academics employed what they referred to as “reflective ethnography” to examine the hypothesis that many disciplines (e.g., sociology, educational philosophy, and critical race theory) are motivated by extreme ideologies, as opposed to generating knowledge. The authors published, or had accepted, seven “hoax” articles in a number of peer-reviewed journals. When the story broke in the Wall Street Journal, the authors stated that the articles advocated a number of ludicrous, inhumane, and appalling ideas. For example, one argued that men should be trained like dogs with shock collars. Their acceptance for publication was therefore taken as evidence for the kind of ideas that many academic disciplines will advocate. In the present article, I will show that the central aspects of the hoax articles do not match with how they were later described by the hoax authors and many other commentators (e.g., journalists). Despite the vast amount of media coverage, this has (virtually) gone unnoticed. I will suggest that the widely accepted narrative of the so-called Grievance Studies affair is incorrect. Full article

Climate change and rapid urbanization increase/amplify urban heat islands (UHIs). Green infrastructure (GI) is an effective and popularly strategy used to moderate UHIs. This paper aims to better understand the progress of different GI types (urban parks, urban forests, street trees, green roofs, green walls) in mitigating UHIs, and what benefits they provide. Firstly, this paper used CiteSpace to analyze 1243 publications on the Web of Science from 1990 to 2021, then analyzed the function/regulation of ecosystem services/benefits and values of GI types in reducing UHIs. The historical review results show that research on all GI types showed rapid growth since 2013, and their GR increased rapidly. The highest-ranking keywords were urban heat island/heat island, climate/climate change/microclimate, and temperature/land surface temperature/air temperature. “Design,” “vegetation,” “quality,” and “reduction” are the top four strongest keyword bursts. The most published countries are the People’s Republic of China, USA, Australia, Germany, and Italy, and the top three institutions are the Chinese Academy of Sciences, Arizona State University, and the National University of Singapore. Landscape and Urban Planning, Building and Environment, Energy and Building, and Urban Forestry and Urban Greening are the most published journals. In urban areas, different GI types as a form of ecosystem hardware provide multiple functions (reduced land surface temperatures, lower building energy usage, improved thermal comfort and enhanced human health, reduced morbidity and mortality, etc.). GI thus provides a regulated ecosystem service to ameliorate UHIs primarily through temperature regulation and shade. At the same time, GI provides benefits and values (ecological, economic, social, and cultural) to humans and urban sustainable development. GI types determine the functions they provide, afford corresponding regulated ecosystem services, and provide benefits and values in a logical/recycle system. Overall, this review highlights the development and importance of GI, as well as the relationship of GI types and functions of regulating the ecosystem service benefits and values to mitigate UHI, and advances the study of climate change adaptation in cities. Full article

Sentiment analysis of news headlines is an important factor that investors consider when making investing decisions. We claim that the sentiment analysis of financial news headlines impacts stock market values. Hence financial news headline data are collected along with the stock market investment data for a period of time. Using Valence Aware Dictionary and Sentiment Reasoning (VADER) for sentiment analysis, the correlation between the stock market values and sentiments in news headlines is established. In our experiments, the data on stock market prices are collected from Yahoo Finance and Kaggle. Financial news headlines are collected from the Wall Street Journal, Washington Post, and Business-Standard website. To cope with such a massive volume of data and extract useful information, various embedding methods, such as Bag-of-words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), are employed. These are then fed into machine learning models such as Naive Bayes and XGBoost as well as deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Various natural language processing, andmachine and deep learning algorithms are considered in our study to achieve the desired outcomes and to attain superior accuracy than the current state-of-the-art. Our experimental study has shown that CNN (80.86%) and LSTM (84%) are the best performing models in relation to machine learning models, such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%). Moreover, two novel methods, BERT and RoBERTa, were applied with the expectation of better performance than all the other models, and they did exceptionally well by achieving an accuracy of 90% and 88%, respectively. Full article

Food and gastronomy are significant ingredients of everyday leisure and lifestyle practices. Food is part of culture and culture is part of the media. The current research analyzes the mediatization of food in legacy media. Drawing from a quantitative approach, the paper reviews food-based contents in New York City’s newspapers. In particular, AM New York, El Diario, Metro, The New York Times and The Wall Street Journal are studied over a period of 50 days. As a result, a total of 287 articles are analyzed. This research highlights the features of food and gastronomy contents and describes the differences and similarities between traditional newspapers and free dailies. Furthermore, the referent role of The New York Times in communicating food is confirmed. Full article

A recent study by the Wall Street Journal reveals that the hydrofractured horizontal wells in shales have been producing less than the industrial forecasts with the empirical hyperbolic decline curve analysis (DCA). As an alternative to DCA, we introduce a simple, fast and accurate method of estimating ultimate recovery in oil shales. We adopt a physics-based scaling approach to analyze oil rates and ultimate recovery from 14,888 active horizontal oil wells in the Bakken shale. To predict the Estimated Ultimate Recovery (EUR), we collapse production records from individual horizontal shale oil wells onto two segments of a master curve: (1) We find that cumulative oil production from 4845 wells is still growing linearly with the square root of time; and (2) 6401 wells are already in exponential decline after approximately seven years on production. In addition, 2363 wells have discontinuous production records, because of refracturing or changes in downhole flowing pressure, and are matched with a linear combination of scaling curves superposed in time. The remaining 1279 new wells with less than 12 months on production have too few production records to allow for robust matches. These wells are scaled with the slopes of other comparable wells in the square-root-of-time flow regime. In the end, we predict that total ultimate recovery from all existing horizontal wells in Bakken will be some 4.5 billion barrels of oil. We also find that wells completed in the Middle Bakken formation, in general, produce more oil than those completed in the Upper Three Forks formation. The newly completed longer wells with larger hydrofractures have higher initial production rates, but they decline faster and have EURs similar to the cheaper old wells. There is little correlation among EUR, lateral length, and the number and size of hydrofractures. Therefore, technology may not help much in boosting production of new wells completed in the poor immature areas along the edges of the Williston Basin. Operators and policymakers may use our findings to optimize the possible futures of the Bakken shale and other plays. More importantly, the petroleum industry may adopt our physics-based method as an alternative to the overly optimistic hyperbolic DCA that yields an ‘illusory picture’ of shale oil resources. Full article

In this work, we aim to re-rank the n-best hypotheses of an automatic speech recognition system by punishing the sentences which have words that are semantically different from the context and rewarding the sentences where all words are in semantical harmony. To achieve this, we proposed a topic similarity score that measures the difference between topic distribution of words and the corresponding sentence. We also proposed another word-discourse score that quantifies the likeliness for a word to appear in the sentence by the inner production of word vector and discourse vector. Besides, we used the latent semantic marginal and a variation of log bi-linear model to get the sentence coordination score. In addition we introduce a fallibility weight, which assists the computation of the sentence semantically coordination score by instructing the model to pay more attention to the words that appear less in the hypotheses list and we show how to use the scores and the fallibility weight in hypotheses rescoring. None of the rescoring methods need extra parameters other than the semantic models. Experiments conducted on the Wall Street Journal corpus show that, by using the proposed word-discourse score on 50-dimension word embedding, we can achieve 0.29% and 0.51% absolute word error rate (WER) reductions on the two testsets. Full article

As demonstrated in hybrid connectionist temporal classification (CTC)/Attention architecture, joint training with a CTC objective is very effective to solve the misalignment problem existing in the attention-based end-to-end automatic speech recognition (ASR) framework. However, the CTC output relies only on the current input, which leads to the hard alignment issue. To address this problem, this paper proposes the time-restricted attention CTC/Attention architecture, which integrates an attention mechanism with the CTC branch. “Time-restricted” means that the attention mechanism is conducted on a limited window of frames to the left and right. In this study, we first explore time-restricted location-aware attention CTC/Attention, establishing the proper time-restricted attention window size. Inspired by the success of self-attention in machine translation, we further introduce the time-restricted self-attention CTC/Attention that can better model the long-range dependencies among the frames. Experiments with wall street journal (WSJ), augmented multiparty interaction (AMI), and switchboard (SWBD) tasks demonstrate the effectiveness of the proposed time-restricted self-attention CTC/Attention. Finally, to explore the robustness of this method to noise and reverberation, we join a train neural beamformer frontend with the time-restricted attention CTC/Attention ASR backend in the CHIME-4 dataset. The reduction of word error rate (WER) and the increase of perceptual evaluation of speech quality (PESQ) approve the effectiveness of this framework. Full article

According to the latest report by the World Health Organization, air pollution, one of the planet’s most dangerous environmental carcinogens, has become one of the leading causes of cancer-related deaths. In China this is a particularly crucial issue, with more than 100 cities and close to one billion individuals threatened by haze due to heavy air pollution in recent years. Beyond traditional channels, the rise of social media has led to greater online haze-related information sharing. Formative research suggests that Weibo is playing a larger role in the process of information seeking than traditional media. Given the severity of haze and the influential role of Weibo, a textual analysis was conducted based on Sina Weibo (Chinese Twitter) to provide health decision-makers and media consumers knowledge on how environmental health issues such as haze are framed in Chinese social media. Framing theory served to explain the differences across various outlets: People’s Daily, China Daily, and the Chinese version of the Wall Street Journal. By analyzing 407 Weibo posts, five major frames emerged: (1) governmental concern, (2) public opinion and issue management, (3) contributing factors and effects, (4) socializing haze-related news, and (5) external haze-related news. Full article

This study looks at some non-conventional determinants of economic growth, with the help of the newly developed economic freedom index datasets of the Heritage Foundation/Wall Street Journal(HF/WSJ), which is a cumulative index derived from several sub-indices (trade freedom index, financial freedom, labor freedom, business and fiscal freedom index). The cumulative economic freedom index show us how open and business friendly a country is. The sub-indices show us openness across different sector of the economy, for example, the financial sector or the trade sector etc. Traditional neo-classical economic theories have explained economic growth looking at the supply of labor, capital and state of technology, with little attention being paid to institutional factors. The study presents evidence based on two panel data-sets. The first set consists of 186 countries over the period 2013, 2014 and 2015 that show institutional factors play a crucial role in economic growth. A second data-set with data for 57 countries for the period 2004–2014 also show a positive impact on the index on the growth rate of per capita GDP. Full article

We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time. Full article

Given the volatile nature of animal rights issues and the extensive array of writings on the topic, one might expect several introductory anthologies to be available. The only anthologies in print, however, are scholarly tomes (listed below) geared towards more advanced readers. Fortunately, Lauri S. Friedman, author of dozens of anthologies on controversial topics such as gun control, national security, terrorism, fast food, sexually transmitted diseases, and many other topics, fills this void well with her volume titled Introducing Issues with Opposing Viewpoints: Animal Rights. She has included articles by influential authors in a diverse range of lay outlets such as The Wall Street Journal, Slate, Guardian, Christianity Today, Food & Wine, among others. Below, I describe the contents of the book, its strengths and weaknesses, and how educators might use the book in classroom settings. Full article