Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
4,510 documents
This study applies natural language processing to Chinese analyst reports, using a customized BERT model to classify their tone as positive, neutral, or negative. It examines whether report sentiment helps explain subsequent stock outcomes, including excess…
This study examines how different investor groups traded Nokia shares from 2003 to 2008 and compares the roles of market conditions and news. It treats returns and volatility as endogenous factors, while daily news volume and a sentiment measure derived from…
This paper examines whether changes in the investor base of cryptocurrencies are associated with changes in their idiosyncratic volatility. It uses changes in subreddit follower counts as a proxy for shifts in investor base, then relates that measure to…
This paper studies information gaps in the Japanese stock market and asks whether investors outside conventional statistical arbitrage research can find useful signals in public online material. Its approach analyzes a large volume of blog data, then builds…
This document reports two studies of crypto rewards offered as conditional gifts to charitable donors. The first examines the Ukrainian government’s fundraising appeal accepting Bitcoin and Ethereum. When crypto rewards were present, the hourly donation…
This study compares weekly stock momentum strategies in China using raw returns, idiosyncratic returns, and different measures of idiosyncratic risk. Its sample covers individual A-share stocks from January 1997 through December 2017. The authors first…
The document describes CryptoTrade, a cryptocurrency trading agent built around a large language model. Its approach combines on-chain information, which the authors characterize as transparent and immutable, with off-chain signals such as news, which can…
The study examines whether news and social media text can improve Bitcoin and Ethereum price forecasts when combined with financial and blockchain data. It compares pre-trained and fine-tuned language models with dictionary-based sentiment methods, and uses…
The document examines why more capable large language models may sometimes trade cryptocurrency less effectively than weaker models. It attributes part of this pattern to stronger models favoring factual information over subjective interpretation. The…
PulseReddit aligns large-scale Reddit discussions with high-frequency cryptocurrency market statistics to study whether social information can support short-term trading. The paper evaluates LLM-based multi-agent systems that incorporate this data and…
This study combines language-model sentiment from financial news with macroeconomic indicators and technical signals to construct portfolios of Russell 2000 stocks. Its uncertainty-aware approach separates predicted risk into aleatoric and epistemic…
The paper proposes a model for predicting Bitcoin’s short-horizon price direction by combining hourly price and volume features with sentiment from Reddit. A rolling volatility measure labels each observation as stable or volatile, and a learned gate changes…
This paper extends a threshold model of collective behavior to include global nodes, or trend followers. In a standard local cascade, a node activates in response to its neighbors; in this extension, some nodes also respond to the overall fraction of…
The paper proposes a dynamic cryptocurrency portfolio method that combines technical signals with news sentiment. It uses the 14-day Relative Strength Index and Simple Moving Average to represent momentum, while VADER analyzes news sentiment and Google…
The study describes a trading approach that combines sentiment extracted from financial news with technical indicators and time-series forecasting. It uses GPT-2 and FinBERT for sentiment analysis, pairs the resulting signals with momentum and trend…
This paper outlines an experimental framework for testing whether large language models can extract useful sentiment signals from Chinese financial news. It applies three different models, selected to represent distinct approaches to improving model…
The document examines how Twitter promotion relates to cryptocurrency pump-and-dump events. It analyzes abnormal returns, trading volume, and tweet activity to assess how promotion draws attention to these schemes and how market behavior changes around the…
DeepTrust is a framework for finding and assessing information that may explain extreme equity price changes. Its workflow has three parts: machine-learning detection of anomalous moves from historical prices, retrieval of related unstructured information…
This study examines whether information beyond price history can help forecast Bitcoin–dollar volatility. Its proposed CoMForE system combines historical trading data with sentiment from related tweets, search-volume measures of public interest, and…
This study asks when market text should be followed and when it should be faded. It separates institutional statements (Say), media repetition (Echo), and revealed institutional positioning (Do). A linear-quadratic model describes an informed institution…
This study models minute-level Bitcoin return volatility with generalized autoregressive conditional heteroskedasticity methods. It combines historical returns and volatility, as represented by variants of the GARCH family, with the Mixture of Distribution…
This study uses a lab-in-the-field experiment to examine how people guess whether financial markets will rise or fall. Volunteers receive controlled financial information based on real data from global indices and make repeated direction predictions. The…
This study investigates whether the timing and subject matter of cryptocurrency social media discussions are related to later price behavior. It applies dynamic topic modelling to communication in social media communities, then uses a Hawkes model to examine…
The document presents a general workflow for designing, implementing, and assessing algorithmic trading strategies across stock and crypto markets. It uses four examples: a moving average crossover, a volume-weighted execution approach, sentiment-based…