Eat the Rich : The Aftermath of the Meme Stock Craze: An Empirical Analysis

Recently, the r/wallstreetbets subreddit has sent shock waves through the financial industry. The subreddit has been the subject of widespread discussion regarding the unintuitive price actions caused by the platform activity. This thesis examines recent changes in the WallStreetBets (WSB) phenomenon, particularly after the meme stock craze in 2021. In addition, it examines relationships between WSB activity and stock data and whether it is possible to utilize findings in a trading algorithm producing abnormal returns. The results identify the meme stock craze as an outlier period, where in the following period the impact of WSB activity returns to normal levels corresponding to the pre-2021 period. In addition, the results demonstrate a significant relationship between WSB activity and stock data, particularly for the mentions activity on the subreddit. Moreover, this thesis finds a profitable strategy adjusted for biases by maximizing the Sharpe ratio, which also provides a strong abnormal return. In a broader sense, its results are promising in relation to the utilization of big data and quantitative methods in analyzing social media and applying the findings to a financial strategy.nhhma

Stock Exchange Frequent Topics @NYSE

The utilization of Twitter content to predict the market is necessary since the fundamental perception related to the activities of the market influenced by the opinion in the market space. Therefore this research is utilizing the tweet of @NYSE and compare the research finding @IDX_BEI the stock exchange office in Indonesia. There is 2069 tweet extracted from the period January until June 2018. Further analysis using the Exploratory Factor Analysis conducted, and show that @NYSE delivering more varieties of information related to the stock market. It is not only information related to the IPO, but also an opinion from the recognized economist and analyst. The findings show that Twitter could be improved for further utilization and reduce asymmetric information related to the market Keywords: @NYSE, Frequent Topics, Unstructured Dataset DOI: 10.7176/RJFA/10-16-10 Publication date: August 31st 201

Notes on Cloud computing principles

This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that Cloud Computing first and foremost is a new business model, a new model to sell computational resources, the understanding of these concepts is facilitated by treating them in unison. Here, we review some of the most important concepts and how they relate to each other

The Power of the Stakeholders' Voice: The Effects of Social Media Activism on Stock Markets

This is the author accepted manuscript. The final version is available fromWiley via the DOI in this record.Building on social movement theory, this study assesses the influence of social media activism on the stock market performance of targeted firms. We focus on information published on Twitter by two critical stakeholders: consumer associations and trade unions. To the extent that social media represent a valid medium to mobilize stakeholders' activism, protests on Twitter may damage firm reputation, leading to capital market reactions. Using a corpus of over 1.5 million tweets referring to Spanish listed banks, we study the impact of activism by looking at targeted firms' abnormal variations in price and trading volume. Our findings suggest that the Twitter activism of key stakeholders has a significant impact on investors' decisions. Further, our empirical analyses indicate that the mechanisms affecting investors' behavior differ depending on the characteristics of the stakeholder group. Hence, this study contributes to understanding how social movements influence corporate behavior via social media

Economic Policy Uncertainty: A Review on Applications and Measurement Methods with Focus on Text Mining Methods

Economic Policy Uncertainty (EPU) represents the uncertainty realized by the investors during economic policy alterations. EPU is a critical indicator in economic studies to predict future investments, the unemployment rate, and recessions. EPU values can be estimated based on financial parameters directly or implied uncertainty indirectly using the text mining methods. Although EPU is a well-studied topic within the economy, the methods utilized to measure it are understudied. In this article, we define the EPU briefly and review the methods used to measure the EPU, and survey the areas influenced by the changes in EPU level. We divide the EPU measurement methods into three major groups with respect to their input data. Examples of each group of methods are enlisted, and the pros and cons of the groups are discussed. Among the EPU measures, text mining-based ones are dominantly studied. These methods measure the realized uncertainty by taking into account the uncertainty represented in the news and publicly available sources of financial information. Finally, we survey the research areas that rely on measuring the EPU index with the hope that studying the impacts of uncertainty would attract further attention of researchers from various research fields. In addition, we propose a list of future research approaches focusing on measuring EPU using textual material.Comment: JEL Classification: C53, C38, A13, O38, H5

Trading on mood? Analysing the efficacy of sentiment and behavioural biases in predicting stock market movements and investor behaviour

Traditional finance theory rests on the assumption that investors are rational in aggregation. However, a wealth of behavioural finance research has shown this not to be the case. This paper examines whether biases that have been evidenced to impact individual’s behaviour can be witnessed in the stock market as a whole, and whether these can be utilised as a bellwether to future price changes. The results mirror the findings of the behaviourists, evidencing a susceptibility to biases among investors, and a promising forecasting ability when incorporated into a systematic volatility trading model

Sensitivity to sentiment: News vs social media

© 2019 We explore the rapidly changing social and news media landscape that is responsible for the dissemination of information vital to the efficient functioning of the financial markets. Using the sheer volume of social and news media activity, commonly known as buzz, we document three distinct regimes. We find that between 2011 and 2013 the news media coverage stimulates activity in social media. This is followed by a transition period of two-way causality. From 2016, however, changes in levels of social media activity seem to lead and generate news coverage volumes. We uncover similar evolution of lead-lag pattern between sentiment measures constructed from the tonality contained in textual data from social and news media posts. We discover that market variables exert stronger impact on investor sentiment than the other way around. We also find that return responses to social media sentiment almost doubled after the transition period, while return responses to news-based sentiment almost halved to its pre-transition level. The linkage between volatility and sentiment is much more persistent than that between returns and sentiment. Overall, our results suggest that social media is becoming the dominant media source

Fed+: A Unified Approach to Robust Personalized Federated Learning

We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-specific datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local computation intact. Without making any statistical assumption on the degree of heterogeneity of local data across parties, we provide convergence guarantees for Fed+ for convex and non-convex loss functions and robust aggregation. The Fed+ theory is also equipped to handle heterogeneous computing environments including stragglers without additional assumptions; specifically, the convergence results cover the general setting where the number of local update steps across parties can vary. We demonstrate the benefits of Fed+ through extensive experiments across standard benchmark datasets as well as on a challenging real-world problem in financial portfolio management where the heterogeneity of party-level data can lead to training failure in standard federated learning approaches

Social Media for Exploring Adverse Drug Events Associated with Multiple Sclerosis

Multiple Sclerosis (MS) affects 400,000 people in the USA and almost 2.5 million people worldwide. There is no cure for MS. A variety of disease-modifying therapies are currently available. They aim to reduce disease activity that ultimately leads to disability. However, such drugs have adverse effects that vary widely among patients making the choice of a suitable drug particularly challenging. With the proliferation of social media, this research aims to understand the perspective of people with MS on social media (Twitter) in regard to Adverse Drug Events (ADEs) and to analyze ADEs as perceived by MS patients. This study helps in understanding ADEs associated with MS drugs and can further inform future medical research by highlighting and prioritizing additional clinical trials needed to better assess such adverse drug effects