Valuation of Apple Inc.

JEL Classification: G30 (Corporate Finance); O22 (Projects Analysis)Generally valuation models can be divided into two main categories: Absolute Valuation and Relative Valuation. Under each criterion there is a wide range of models. In order to define the fair value of Apple’s stocks, several models are applied, including FCFF model, FCFE model, DDM, Residual Income Model and Multiples Valuation Model. Key financial indicators show that Apple still maintains a high profit margin in the recent years, while competition in electronics industry is expected to become fiercer in the coming future. In terms of the capital structure, Apple has increased its debts continuously in the recent years, and the growing ROE actually results from the combined effect of high profitability and leverage effect. The majority of valuation models suggest that the shares of Apple are undervalued as the estimated fair price is higher than the current market price. Despite the suspicious comments on the company after the loss of Steve Jobs, the share price of Apple is expected to appreciate and reach the implied fair value in the long run. As different models provide various results, they have different hypothesis and limitations. Though Apple is assumed as a mature company, the payout ratio is relatively low and there is not enough historical data for the prediction of future dividends, which make the result from DDM unreliable. Based on the results generated by valuation models and financial indicators, the recommendation for the customers is to buy or hold shares of Apple. In addition, the author also suggests that the company should consider the increase of dividends when there is a shortage of good investment opportunities.De um modo geral, os modelos de avaliação inserem-se em duas categorias: avaliação absoluta e avaliação relativa. Sob cada critério, existe uma ampla gama de modelos. Para se poder definir o valor justo das ações da Apple, vários modelos são aplicados, como Fluxos de Caixa Descontados e Múltiplos. Os indicadores-chave financeiros demostram que a Apple mantém uma margem de lucro elevada mesmo recentemente, enquanto que se prevê que a concorrência na indústria eletrónica irá ficar mais renhida no futuro próximo. Quanto à estrutura do capital, a Apple tem, nos últimos anos, aumentado as suas dívidas continuamente, e o crescente ROE resulta da combinação do efeito da alta rentabilidade com o efeito de alavanca. Grande parte dos modelos de avaliação demonstram que as ações da Apple continuam subvalorizadas, com o preço justo estimado mais alto que o preço de mercado. Apesar da empresa ter sido alvo de comentários de suspeição após o falecimento de Steve Jobs, prevê-se que o preço das ações da Apple valorizem, e, a longo prazo, atinjam o seu valor justo implícito. Dado o facto que modelos diferentes têm resultados também diferentes, as suas hipóteses e limitações também diferem entre si. Apesar da Apple ser considerada uma empresa madura, a sua proporção de pagamentos de dividendos é baixa, e não existem dados históricos suficientes para prever dividendos futuros, tornando assim o resultado proveniente do DDM pouco fiável. Com base nos resultados criados a partir dos modelos de avaliação e indicadores financeiros, recomenda-se aos clientes que comprem ou detenham ações da Apple. Também se sugere que a empresa considere aumentar os dividendos quando se verificar uma falta de boas oportunidades de investimento

The current opportunities and challenges of Web 3.0

With recent advancements in AI and 5G technologies,as well as the nascent concepts of blockchain and metaverse,a new revolution of the Internet,known as Web 3.0,is emerging. Given its significant potential impact on the internet landscape and various professional sectors,Web 3.0 has captured considerable attention from both academic and industry circles. This article presents an exploratory analysis of the opportunities and challenges associated with Web 3.0. Firstly, the study evaluates the technical differences between Web 1.0, Web 2.0, and Web 3.0, while also delving into the unique technical architecture of Web 3.0. Secondly, by reviewing current literature, the article highlights the current state of development surrounding Web 3.0 from both economic and technological perspective. Thirdly, the study identifies numerous research and regulatory obstacles that presently confront Web 3.0 initiatives. Finally, the article concludes by providing a forward-looking perspective on the potential future growth and progress of Web 3.0 technology

TF-IDF Based Contextual Post-Filtering Recommendation Algorithm in Complex Interactive Situations of Online to Offline: An Empirical Study

O2O accelerates the integration of online and offline, promotes the upgrading of industrial structure and consumption pattern, meanwhile brings the information overload problem. This paper develops a post-context filtering recommendation algorithm based on TF-IDF, which improves the existing algorithms. Combined with contextual association probability and contextual universal importance, a contextual preference prediction model was constructed to adjust the initial score of the traditional recommendation combined with item category preference to generate the final result. The example of the catering industry shows that the proposed algorithm is more effective than the improved algorithm

Information Entropy Theory Based Recognition of the Validity of Contextual Information of Restaurants: An Empirical Study

Contextual information plays a key role in personalized recommendations. However, not all contextual information plays a positive role in personalized recommendations. Therefore, it is critical to identify the effective contextual information to realize personalized recommendations. This study aims to develop a set of feasible context importance calculation methods that can identify effective contextual information in different application scenarios. The information entropy of each contextual dimension is calculated, and the validity of the context compared according to the magnitude of its entropy is determined based on the informational entropy theory. Subsequently, this approach is applied to hotel and catering service data to determine the valid context in the dining domain. The experimental results indicate that location, work-rest condition, weather, mood and companionship considerably influence consumers’ behaviour and decisions in a catering environment, and the user preference in such contexts should be carefully considered

Effects of green credit policy on the risk of stock price crash

Green credit policy (GCP) is a specific instrument for the credit resource allocation dimension in the financial sector, and stock price crashes are an important manifestation of financial market risks that cannot be ignored. However, there are gaps in existing research on how green credit policies affect the stock price crash risk (SPCR). Using the Green Credit Guidelines as a quasi-natural experiment, this paper examines the impact of green credit policies on SPCR of heavily polluting firms. It confirms the crash risk is significantly increased for heavily polluting enterprises, mainly due to facing greater financing pressure; and that corporate governance mechanisms reduce its impact, finding that firms with higher analyst attention, higher levels of independent directors, and higher shares held by institutional investors. The effect between GCP and SPCR is not significant for companies with higher analyst attention, higher levels of independent directors, and higher shareholdings of institutional investors. At the same time, it is less significant in regions with high level of financial development. These results of this paper not only enrich the literature in green credit-related fields, but also provide a reference value for understanding the implementation effect of GCP in China to the stock price crash in the capital market

KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture to a multimodal model with visual and textual inputs. We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Generation (KCG) boosts model performance on the VCG task by leveraging commonsense knowledge from a large language model pretrained on external commonsense knowledge graphs. To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on the VCG task. Experimental results show that our model reaches state-of-the-art performance on the VCG task by applying these novel pretraining tasks.Comment: ACL-IJCNLP 2021 main conference. The first three authors contribute equally to this wor

Efficacy and safety of Argatroban in patients with acute ischemic stroke: a systematic review and meta-analysis

ObjectiveArgatroban is a highly promising drug for the treatment of acute ischemic stroke (AIS), but there is currently insufficient strong evidence regarding the efficacy and safety of using Argatroban in the treatment of AIS. Therefore, we conducted a systematic review and meta-analysis to evaluate the effectiveness and safety of Argatroban in the treatment of AIS.MethodsArticles on PubMed, Embase and the Cochrane Library databases were searched from these websites’ inceptions to 2th February 2023. Randomized controlled trials and observational studies on Argatroban therapy for acute ischemic stroke were included. Meta-analyses were conducted using a random-effects model.ResultsFourteen studies involving 10,315 patients were included in the meta-analysis. The results showed a significant reduction in the rate of early neurological deterioration (END) in the Argatroban group compared with the control group (OR = 0.47, 95% CI: 0.31–0.73, I2 = 15.17%). The rates of adverse events were no significant difference between the two groups (ICH: OR = 1.02, 95% CI: 0.68–1.51, I2 = 0.00%; major extracranial bleeding: OR = 1.22, 95% CI: 1.01–1.48, I2 = 0.00%; mortality: OR = 1.16, 95% CI: 0.84–1.59, I2 = 0.00%). However, the rates of mRS score of 0–1 (OR = 1.38, 95% CI: 0.71–2.67, I2 = 77.56%) and mRS score of 0–2 (OR = 1.18, 95% CI: 0.98–1.42, I2 = 0.00%) during the 90 days did not significantly improved in the Argatroban group. Subgroup analyses showed that the rate of END (OR = 0.41, 95% CI: 0.26–0.65, I2 = 2.77%) and mRS score of 0–2 (OR = 1.38, 95% CI: 1.06–1.81, I2 = 0.00%) had significantly improved when the intervention group adopted Argatroban plus Antiplatelet.ConclusionArgatroban can improve neurological deterioration, with a low incidence of adverse events such as bleeding and death, and general analysis showed no improvement in mRS. However, subgroup analysis suggests that compared to mono-antiplatelet therapy, combination therapy of Argatroban combined with antiplatelet therapy significantly reduced the incidence of END and improved mRS scores. After using Argatroban, there was no increase in the risk and mortality of intracranial hemorrhage and other bleeding sites

Audio-Visual Segmentation

We propose to explore a new problem called audio-visual segmentation (AVS), in which the goal is to output a pixel-level map of the object(s) that produce sound at the time of the image frame. To facilitate this research, we construct the first audio-visual segmentation benchmark (AVSBench), providing pixel-wise annotations for the sounding objects in audible videos. Two settings are studied with this benchmark: 1) semi-supervised audio-visual segmentation with a single sound source and 2) fully-supervised audio-visual segmentation with multiple sound sources. To deal with the AVS problem, we propose a novel method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process. We also design a regularization loss to encourage the audio-visual mapping during training. Quantitative and qualitative experiments on the AVSBench compare our approach to several existing methods from related tasks, demonstrating that the proposed method is promising for building a bridge between the audio and pixel-wise visual semantics. Code is available at https://github.com/OpenNLPLab/AVSBench.Comment: ECCV 2022; Correct the equation (3) and update the notation of the evaluation metrics in the last arxiv version; Code is available at https://github.com/OpenNLPLab/AVSBenc