Evaluation of sentiment analysis in finance: from lexicons to transformers
Financial and economic news is continuously monitored by financial market participants.
According to the efficient market hypothesis, all past information is reflected in stock prices …
According to the efficient market hypothesis, all past information is reflected in stock prices …
Using ML and Explainable AI to understand the interdependency networks between classical economic indicators and crypto-markets
In a global world, no country, market, or economy is isolated. Interconnectivity is becoming a
fundamental feature of economic systems, including macroeconomic trends, traditional …
fundamental feature of economic systems, including macroeconomic trends, traditional …
Simulation-informed revenue extrapolation with confidence estimate for scaleup companies using scarce time-series data
Investment professionals rely on extrapolating company revenue into the future (ie revenue
forecast) to approximate the valuation of scaleups (private companies in a high-growth …
forecast) to approximate the valuation of scaleups (private companies in a high-growth …
Bitcoin price prediction using transfer learning on financial micro-blogs
We present a methodology for predicting the price of Bitcoin using Twitter data and historical
Bitcoin prices. Bitcoin is the largest cryptocurrency that, in terms of market capitalization …
Bitcoin prices. Bitcoin is the largest cryptocurrency that, in terms of market capitalization …
Analysis of cryptocurrency interdependencies
In a world where no country, market, or economy is an island, interconnectivity is becoming
a fundamental feature of almost all social and economic systems. In the case of digital …
a fundamental feature of almost all social and economic systems. In the case of digital …
Multivariate dynamic modeling for Bayesian forecasting of business revenue
Forecasting enterprise‐wide revenue is critical to many companies and presents several
challenges and opportunities for significant business impact. This case study is based on …
challenges and opportunities for significant business impact. This case study is based on …
A computational analysis of financial and environmental narratives within financial reports and its value for investors
F Armbrust, H Schäfer, R Klinger - … of the 1st Joint Workshop on …, 2020 - aclanthology.org
Public companies are obliged to include financial and non-financial information within their
cor-porate filings under Regulation SK, in the United States (SEC, 2010). However, the …
cor-porate filings under Regulation SK, in the United States (SEC, 2010). However, the …
[HTML][HTML] OptionNet: A multiscale residual deep learning model with confidence interval to predict option price
L Lin, M Wang, H Cheng, R Liu, F Chen - The Journal of Finance and Data …, 2023 - Elsevier
Option is an important financial derivative. Accurate option pricing is essential to the
development of financial markets. For option pricing, existing time series models and neural …
development of financial markets. For option pricing, existing time series models and neural …
Applications of Deep Learning Models in Financial Forecasting
A Rostamian - 2024 - repository.essex.ac.uk
In financial markets, deep learning techniques sparked a revolution, resha** conventional
approaches and amplifying predictive capabilities. This thesis explored the applications of …
approaches and amplifying predictive capabilities. This thesis explored the applications of …
Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue
AK Yanchenko, G Tierney, J Lawson… - arxiv preprint arxiv …, 2021 - arxiv.org
Forecasting enterprise-wide revenue is critical to many companies and presents several
challenges and opportunities for significant business impact. This case study is based on …
challenges and opportunities for significant business impact. This case study is based on …