Analyst Underreaction to Past Information About Earnings: reporting, processing or plain old misspecification bias?

We revisit the debate concerning the interpretation given to prior year’s earnings changes in predicting future earnings as discussed by Abarbanell & Bernard (1992), Francis & Philbrick (1993) and Easterwood and Nutt (1999). We advance a new specification of this relationship which distinguishes between earnings reversion and momentum. On a large UK dataset, we find there is substantial underreaction, particularly in situations of earnings momentum, approximately six times as large as that identified by Abarbanell & Bernard. This suggests that analysts behaviour is still a candidate to explain post earnings announcement drift. We also show that our model performs well relative to a specification recently proposed by Easterwood and Nutt (1999)

Sustainable typography

We need to radically re-think typography for text-rich business documents & publications (not referring to books). Most designers assume people have time to read. In reality the following occurs: Observations: 1) We browse/forage (71%) then read (11%) 2) People have different time tolerances and requirements for detail i.e. the same information is required to different levels of detailing dependent on the time the reader can allocate to it (Senior directors will have less time than juniors). 3) People want choice as to whether they wish to view information on paper, i-phone, PowerPoint or via web/screen. 4) Most publications do not follow the cognitive principles of how we are Œwired‚ to interpret visual signals. Message-based Design & Message-based Writing (MBD/MBW) is a system that addresses these 4 points and allows key messages to be understood prior to reading simply by scanning the page with its embedded Œvisual hooks‚ to draw the reader in. Thus it overcomes Œfilter failure‚ a phrase coined and first used by Clay Shirky at the Web 2.0 Expo. It collapses to a summary and exploits the way we are wired. Additionally it caters for up to 4 time tolerances of readers and morphs‚ from paper to screen effortlessly

Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context

Validating the predictions of case-based decision theory

Real-life decision-makers typically do not know all possible outcomes arising from alternative courses of action. Instead, when people face a problem, they may rely on the recollection of their past personal experience: the situation, the action taken, and the accompanying consequence. In addition, the applicability of a past experience in decision-making may depend on how similar the current problem is to situations encountered previously. Case-based decision theory (CBDT), proposed by Itzhak Gilboa and David Schmeidler (1995), formalises this type of analogical reasoning. While CBDT is intuitively appealing, only a few experimental and empirical studies have attempted to validate its predictions. This thesis reports two laboratory experiments and an empirical study that attempt to confirm the predictive power of CBDT vis-à-vis Bayesian reasoning

Stock price change prediction using news text mining

Along with the advent of the Internet as a new way of propagating news in a digital format, came the need to understand and transform this data into information. This work presents a computational framework that aims to predict the changes of stock prices along the day, given the occurrence of news articles related to the companies listed in the Down Jones Index. For this task, an automated process that gathers, cleans, labels, classifies, and simulates investments was developed. This process integrates the existing data mining and text algorithms, with the proposal of new techniques of alignment between news articles and stock prices, pre-processing, and classifier ensemble. The result of experiments in terms of classification measures and the Cumulative Return obtained through investment simulation outperformed the other results found after an extensive review in the related literature. This work also argues that the classification measure of Accuracy and incorrect use of cross validation technique have too few to contribute in terms of investment recommendation for financial market. Altogether, the developed methodology and results contribute with the state of art in this emerging research field, demonstrating that the correct use of text mining techniques is an applicable alternative to predict stock price movements in the financial market.Com o advento da Internet como um meio de propagação de notícias em formato digital, veio a necessidade de entender e transformar esses dados em informação. Este trabalho tem como objetivo apresentar um processo computacional para predição de preços de ações ao longo do dia, dada a ocorrência de notícias relacionadas às companhias listadas no índice Down Jones. Para esta tarefa, um processo automatizado que coleta, limpa, rotula, classifica e simula investimentos foi desenvolvido. Este processo integra algoritmos de mineração de dados e textos já existentes, com novas técnicas de alinhamento entre notícias e preços de ações, pré-processamento, e assembleia de classificadores. Os resultados dos experimentos em termos de medidas de classificação e o retorno acumulado obtido através de simulação de investimentos foram maiores do que outros resultados encontrados após uma extensa revisão da literatura. Este trabalho também discute que a acurácia como medida de classificação, e a incorreta utilização da técnica de validação cruzada, têm muito pouco a contribuir em termos de recomendação de investimentos no mercado financeiro. Ao todo, a metodologia desenvolvida e resultados contribuem com o estado da arte nesta área de pesquisa emergente, demonstrando que o uso correto de técnicas de mineração de dados e texto é uma alternativa aplicável para a predição de movimentos no mercado financeiro

Complacency and Intentionality in IT Use and Continuance

Decision makers’ initial and continued use of information technology has traditionally been viewed as a mindful and intentional behavior. However, when a decision aid makes mostly correct recommendations, its users may become complacent. That is, users may accept recommendations without mindfully considering the recommendations or involvement with the aid. As such, they may be more likely to accept inaccurate recommendations. We draw on dual-processing theory to describe why users might behave in a mindless and complacent rather than mindful manner when using a decision aid. In our experimental investigation, we manipulated the accuracy of the recommendations provided by a decision aid and observe users’ performance on a complex decision task. Using the decision aid, participants completed five task trials. To assess complacency and intentionality, we compared subjective (i.e., self-report) and objective (i.e., gaze data via an eye tracker) use measures. Our analysis and comparison of the subjective and objective responses indicate that, contrary to widespread theorizing, decision aid usage and continuance appear to be less intentional than commonly believed. Further, we found that a decision aid’s users can be vulnerable to complacency even when recommendations are known to be inaccurate. Based on the findings of our study, we offer theoretical and practical implications regarding complacency and intentionality in technology use

Agricultural Crop Recommendation, Crop Disease Detection and Price Prediction Using Machine Learning

India's foundation is its agriculture. With over 60% of the workforce employed and producing over 18% of the nation's GDP, it is a vital sector of the Indian economy. Although there are many ways in which we can use technology to increase product production, a farmer can only profit if he is able to sell his crops. Three laws have been passed by the Indian government to encourage the export of agricultural products across the nation. But today, we witness farmers all over the nation fighting against these regulations to protect their rights. Farmers worry that big merchants will exploit them as puppets and undercut the price at which they sell their goods. After doing a thorough analysis of the situation, we developed the concept of creating an agricultural produce application that facilitates direct communication between farmers and retailers, allows for product reviews and crop yielding rate prediction, and predicts the price of agricultural produce based on quantity produced and previous years' sales rates. Unpredictable rains, unexpected temperature decreases, and heat waves have all been brought on by the shifting climate, and the ecosystem has suffered significant harm. Thankfully, machine learning has produced useful methods for tackling international problems, such as agriculture. These climate change-related agricultural issues can be resolved by using various machine learning methods. The purpose of this piece is to Create a method to identify crop diseases and suggest crops. For both objectives, publicly accessible datasets were utilized. Regarding the crop recommendation system, feature extraction was done, and a variety of machine learning methods were used to train the dataset, including Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron. 99.30% accuracy was attained via the random forest algorithm.CNN architectures such as ResNet50, and EfficientNetV2 were trained and compared for the plant disease identification system. EfficientNetV2 outperformed the rest, with a high accuracy of 96.08%