WallStreetBets: An Analysis of Investment Advice Democratization

Reddit's WallStreetBets (WSB) community has come to prominence due to its role in the hype around GameStop and other meme stocks. Yet very little is known about the reliability of the investment advice disseminated on WSB. We investigate whether an anonymous, investment-focused community such as WSB can be a valuable source for investment advice and thus may constitute a way of democratizing access to financial knowledge. Our analysis reviews data spanning 28 months to assess how successful an investor relying on WSB recommendations could have been. We detect buy and sell signals and define a WSB portfolio based on the community's most popular stocks. Our evaluation shows that this portfolio has grown significantly, outperforming the S&P 500 over the reviewed time frame. We find that filtering for proactive posts yields higher returns and our review of the period before 2021 shows that the GameStop hype merely amplified previously existing characteristics

WallStreetBets Beyond GameStop, YOLOs, and the Moon: The Unique Traits of Reddit’s Finance Communities

While the effect of established social media on stock markets has been thoroughly investigated, the recent surge in retail investing and the emergence of different finance-related Reddit communities with unique new traits have led to new research questions. In this work, we aim to understand the linguistic and thematic characteristics and differences of the largest financial Reddit communities, r/WallStreetBets, r/stocks, and r/investing. Using different techniques for the analysis of linguistic features and topic modeling, we identify keywords and phrases that are most prominent in each community and determine each community’s thematic focus and risk affinity. An analysis of users that post on all of these communities confirm these findings, as they appear to adapt to the respective target audience when posting. The stock returns for each community prove consistent with their respective risk profile. Overall, we conclude that understanding these communities can help investors in making more informed investment decisions

FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras

Estimating the speed of vehicles using traffic cameras is a crucial task for traffic surveillance and management, enabling more optimal traffic flow, improved road safety, and lower environmental impact. Transportation-dependent systems, such as for navigation and logistics, have great potential to benefit from reliable speed estimation. While there is prior research in this area reporting competitive accuracy levels, their solutions lack reproducibility and robustness across different datasets. To address this, we provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras to achieve greater robustness. Our model employs novel techniques to estimate the length of road segments via depth map prediction. Additionally, our framework is capable of handling realistic conditions such as camera movements and different video stream inputs automatically. We compare our model to three well-known models in the field using their benchmark datasets. While our model does not set a new state of the art regarding prediction performance, the results are competitive on realistic CCTV videos. At the same time, our end-to-end pipeline offers more consistent results, an easier implementation, and better compatibility. Its modular structure facilitates reproducibility and future improvements

Comparative analysis of neural NLP models for information extraction from accounting documents

Natural Language Processing has reached a high importance in research and business applications. The state-of-the-art techniques are being used to automate tasks like extracting relevant entities from documents or translating texts from one language to another. This thesis focuses on the task of selecting models that have performed well on standard benchmarks for those tasks, and adapting them to a new and specialised problem: the labelling of entities in invoice documents. For this purpose, five state-of-the-art Neural Network models are presented, applied and evaluated. The results show that four out of the five selected models, based on recurrent and convolutional architectures, can be implemented successfully and perform similarly well on test documents with average F1 performance scores of 68-71% on word level and 67-69% on entity level. A detailed error analysis reveals that low data quality and suboptimal choice of labels due to the dataset’s origins are the main factors that influence the models’ performances. The thesis proposes a ranking of the five models with regards to their prediction performance as well as their cost and difficulty of implementation in order to answer the main research question. Possible improvements are proposed for future work, while the limitations of the project’s setting are explored and discussed. This project aims to contribute a different perspective to NER research by analysing and discussing errors and poor design choices in order to propose future improvements

Comparative analysis of neural NLP models for information extraction from accounting documents

Natural Language Processing has reached a high importance in research and business applications. The state-of-the-art techniques are being used to automate tasks like extracting relevant entities from documents or translating texts from one language to another. This thesis focuses on the task of selecting models that have performed well on standard benchmarks for those tasks, and adapting them to a new and specialised problem: the labelling of entities in invoice documents. For this purpose, five state-of-the-art Neural Network models are presented, applied and evaluated. The results show that four out of the five selected models, based on recurrent and convolutional architectures, can be implemented successfully and perform similarly well on test documents with average F1 performance scores of 68-71% on word level and 67-69% on entity level. A detailed error analysis reveals that low data quality and suboptimal choice of labels due to the dataset’s origins are the main factors that influence the models’ performances. The thesis proposes a ranking of the five models with regards to their prediction performance as well as their cost and difficulty of implementation in order to answer the main research question. Possible improvements are proposed for future work, while the limitations of the project’s setting are explored and discussed. This project aims to contribute a different perspective to NER research by analysing and discussing errors and poor design choices in order to propose future improvements

NICER detection of two X-ray bursts in the follow-up observations of Terzan 5 X-3

MAXI/GSC reported a new outburst from a source in Terzan 5 on 2023 February 27 at 17:26 UT (ATel #15917). NICER conducted observations scanning a grid of pointings centered on the MAXI coordinates, finding a source position R.A. = 267.0137 deg. and Dec. = -24.7658 deg. (J2000) consistent with being just 1 arcmin from the nominal center of the cluster. The position was later refined with Swift-XRT, suggesting the association with Terzan 5 X-3 (also known as Swift J174805.3-244637), a neutron star low-mass X-ray binary located in the globular cluster Terzan 5 (ATel #15919). Given the large number of X-ray sources in the field, Chandra observations could confirm the association of the current burster with Terzan 5 X-3.NICER collected ~7.3 ks of pointed observations starting on 2023 February 28 18:00:40 UT. The source lightcurve shows a gradually increasing count-rate, rising from ~160 cts/s up to ~230 cts/s in the 0.5 - 10 keV energy range; we note that the low starting count rate may partially result from the initially offset NICER pointing before the source coordinates were firmly established. The power spectrum in this band does not present significant periodic signals in the frequency range 0.005-2500 Hz. However, we detected a broad-band (0.1-20 Hz) noise component with fractional rms of ~20%.Two Type-I X-ray bursts from this source were detected on 2023 February 28 18:04:04 and 2023 March 1 06:46:26 UT. The tail of a likely third Type-I X-ray burst is partially detected starting from 2023 March 1 08:07:56 UT, suggesting a burst rate of one every ~40 minutes. Both Type-I bursts displayed a fast rise over a few seconds and exponential decay over ~100 seconds. The burst decay is significantly longer than the only previously observed burst from this source (Bahramian et al. 2014), likely implying different accretion rates or different elemental abundances in the accreted fuel. We searched for burst oscillations using a moving window of duration 2, 4, and 8 seconds and steps of 0.5 seconds in the frequency range 10-1000 Hz. We found no significant signals.We performed spectral analysis of the persistent emission with ~2.4 ks exposure (corresponding to the last four orbits of the dataset where no Type-I bursts are present) using an absorbed disk blackbody plus blackbody model in the 1-10 keV range. The inferred hydrogen column density is (2.2\pm0.1) \times 10^{22} cm^{-2} using the tbabs model and assuming ISM abundances (Wilms et al. 2001), a value similar to that reported by Bahramian et al. (2014). The best-fit temperatures of the disk blackbody and blackbody components are 0.95\pm0.05 and 1.94\pm0.04 keV, respectively. The derived unabsorbed flux in the 0.5-10 keV range is 2.61\times10^{-9} erg/s/cm^{-2}.Time-resolved spectroscopy of two Type-I bursts did not show any evidence of photospheric radius expansion. The bolometric peak fluxes of these bursts are estimated to be 1.27\pm0.09 and 2.00\pm0.37 \times 10^{-8} erg/s/cm^{-2}, corresponding to ~30% and ~50% Eddington luminosity (for a NS mass of 1.4 Msun and assuming a distance of 5.9 kpc for Terzan 5), respectively. The maximum blackbody temperatures are 2.05\pm0.08 and 2.17\pm0.22 keV, respectively, while the apparent emitting radius of the blackbody settles around 6 and 7 km for about 30 seconds in the cooling tail of both bursts.Further NICER observations are planned, and we encourage additional observations of this source with other facilities. NICER is a 0.2–12 keV X-ray telescope operating on the International Space Station. The NICER mission and portions of the NICER science team activities are funded by NASA