We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public . • NLP data from social media improve the accuracy of cryptocurrency forecasting models. • As a target variable, local extrema are a valid alternative to daily price changes. • Deep learning language models substantially outperform dictionary-based methodologies. • Both pre-trained and fine-tuned language models effectively quantify market sentiment.

The M6 competition evaluated investment performance over a period of one year, contributing to the efficient market hypothesis debate. This paper provides further insights into the outcomes of the competition by unraveling the effect that team engagement and performance consistency had on the final results. First, we identify three different types of engagement and investigate their relationship with portfolio efficiency, also making useful observations about the learning effect implied by a re-submission process. Then, we analyze the monthly performance of the teams and determine whether it aligned with their global performance or was affected significantly by extreme instances. Our results suggest that consistency is more important than engagement for making profitable investments. Nevertheless, we identify many cases where both regular portfolio updates and luck provided an advantage.

Forecast value added (FVA) analysis is commonly used to measure the improved accuracy and bias achieved by judgmentally modifying system forecasts. Assessing the factors that prompt such adjustments, and their effect on forecast performance, is important in demand forecasting and planning. To address these issues, we collected the publicly available data on around 147,000 forecasts from six studies and analysed them using a common framework. Adjustments typically led to improvements in bias and accuracy for only just over half of stock keeping units (SKUs), though there was variation across datasets. Positive adjustments were confirmed as more likely to worsen performance. Negative adjustments typically led to improvements, particularly when they were large. The evidence that forecasters made effective use of relevant information not available to the algorithm was weak. Instead, they appeared to respond to irrelevant cues, or those of less diagnostic value. The key question is how organizations can improve on their current forecasting processes to achieve greater forecast value added. For example, a debiasing procedure applied to adjusted forecasts proved effective at improving forecast performance.