Weekly Research Recap
Latest research on investing and trading
It’s time for another roundup of the latest investing research. Below is a carefully curated selection of last week’s highlights, with each title linking directly to its source for further reading.
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Behavioral Finance
FoMO in Investment: A Critical Literature Review of Fear of Missing Out in Investment (Nirun and Asgarli)
Many investors are influenced by the fear of being left out when others profit, especially in fast-moving or hype-driven markets. This literature review shows how that fear can lead to both helpful behaviors like diversification and harmful ones like impulsive trading. It suggests that investors can protect themselves by improving financial literacy, following structured strategies, avoiding emotionally charged content on social media, and focusing on informed, independent decision-making.
Commodities
Do Sectors Matter for Commodity Pricing? (Bianchi and Jung)
Commodity investors often focus on commodity-specific traits like momentum or carry, but many of these signals reflect broader sector trends. By breaking each signal into sector-level and commodity-specific parts, the authors show that some strategies, like carry and inflation sensitivity, earn most of their returns from sector effects. In contrast, others like momentum still work well beyond those commonalities. The findings suggest a more nuanced approach to signal construction, where sector-aware versions may lead to more targeted and potentially more robust strategies.
Crypto
The Dangers of Excessive Crypto Exposure During a Bear Market (Krause)
Cryptocurrencies are sometimes described as digital gold and a store of value. However, this paper shows that during five recent periods of steep equity drawdowns between 2018 and 2025, most digital assets, including Bitcoin, fell sharply and declined alongside stocks. Gold, in contrast, often held up better. So far, crypto has not proven to be a reliable hedge during turbulent times.
Currencies
Covered Interest Parity in Emerging Markets (Dao and Gourinchas)
Deviations from covered interest rate parity (CIP) in emerging markets are often large and volatile, typically reflecting hidden credit risks and market frictions. Unlike developed markets, where such deviations became widespread after 2008, they have long persisted in emerging markets. Even when supranational bonds are used to strip out credit risk, significant deviations remain, driven by global funding constraints. Rather than signaling arbitrage opportunities, CIP deviations in these markets reveal structural frictions and limits to arbitrage.
Equities
Range of Financial Analyst Opinions (Miwa)
Analyst opinions often vary widely, and prior research has found limited predictive power in changes to consensus price targets. However, this paper shows that stock prices underreact when the lowest target price is revised upward, signaling that even the most bearish analysts are becoming less negative. Investors may benefit from tracking these shifts in the bottom range of price targets, as they often lead to delayed price reactions.
A Tale of Two Anomalies: The Implications of Investor Attention for Price and Earnings Momentum (Hou, Loh, Peng, and Xiong)
Investors often struggle to fully absorb all financial information, especially when attention is limited. This paper finds that price trends are more profitable in stocks that attract lots of attention, while earnings surprises and earnings momentum tend to work better when few are watching. The takeaway: Investors should consider buying earnings winners in overlooked stocks and riding price trends in stocks that are already in the spotlight.
ESG
ESG Return and Portfolio Attribution via Shapley Values (Ang, Basu, and Corsi)
A key challenge in ESG investing is understanding which factors, environmental, social, or governance, actually drive performance. This paper introduces a method to break down portfolio returns, risk, and other metrics into contributions from each ESG component. In addition to applying it to a real portfolio, the authors find that imposing a 50% cut in carbon emissions relative to the benchmark has virtually no cost to returns. The latter suggests investors can align with sustainability goals without sacrificing performance.
A Systematic Approach to Sustainable and ESG Investing (Ang, Garvey, and Schwaiger)
Many ESG investors avoid companies with poor sustainability records, but excluding entire industries can hurt portfolio performance and reduce diversification. This paper suggests a better approach: Selecting stocks with ESG traits that either predict stronger future profits or align with proven investment factors. By focusing on ESG signals tied to cashflows or historically rewarded characteristics, investors can enhance returns without narrowing their investment universe.
Fixed Income
Harvesting the Term Premium: International Out-of-Sample Evidence (Vincenz)
A rich academic literature has explored international bond risk premia, often finding that global factors outperform local ones in explaining returns. This paper evaluates several forecasting models across six developed countries and shows that combining international data, rather than relying solely on local signals, improves out-of-sample forecasts. While most models struggle to consistently beat a historical average, a few, particularly those using global panel data and interest rate cycles, deliver modest improvements.
U.S. Treasury Market Functioning from the GFC to the Pandemic (Adrian, Fleming, and Nikolaou)
Even US Treasuries can suffer from worsening trading conditions when markets panic. The paper points out that since 2008, tighter regulations and rising public debt have limited dealers’ ability to smooth out disruptions in the Treasury market. While technology and new participants support liquidity in calm times, they often pull back during periods of stress. The authors highlight several policy initiatives aimed at strengthening liquidity backstops and improving market transparency.
Machine Learning and Large Language Models
Enhancing Trend-Following Strategies Using Machine Learning and Time Series Models (Chandrinos and Lagaros)
Many trend-following strategies rely on moving averages or variations of past price trends. This paper focuses on the Ichimoku Cloud and enhances it using a range of machine learning and time series models. Applied to major currency pairs, models like XGBoost among the machine learning approaches, and Kalman filters among the time series models, significantly improve returns and reduce risk, resulting in much higher Sharpe ratios.
The News in Earnings Announcement Disclosures: Capturing Word Context Using LLM Methods (Siano)
A large body of research has found that large language models are highly effective at classifying news sentiment to predict returns. This study shows that a BERT-based language model (RoBERTa), fine-tuned to predict short-term stock returns, captures three times more information than traditional methods.
Automate Strategy Finding with LLM in Quant Investment (Kou, Yu, Luo, Peng, and Chen)
This paper uses a large language model to generate potential trading signals from diverse financial data, such as charts, news, and earnings reports and combines it with a team of AI agents that evaluate and adapt these signals based on current market conditions. The resulting trading strategy is found to significantly outperform the market.
Leveraging LLMS for Top-Down Sector Allocation In Automated Trading (Heng, Vittori, Ong, Mao, Cambria, and Mengaldo)
Most research on large language models and news sentiment focuses on stock picking and firm-specific headlines. This paper takes a different route, using a top-down approach where multiple large language models process economic reports, Fed minutes, and news articles to decide which sectors to favor and which stocks to include. By adjusting sector allocations based on real-time macro conditions and sentiment, the strategy outperforms traditional momentum-based methods.
Large language models in finance : what is financial sentiment? (Kirtac and Germano)
Traditional methods of gauging investor sentiment often struggle with subtle language and context in financial news. This paper reviews the progress in sentiment classification, from traditional dictionary-based approaches to the latest large language models. Moreover, the authors explain when to use different types of models for classification versus interpretation.
Macro
How Markets Process Macro News: The Importance of Investor Attention (Kroner)
The market reaction to economic news varies greatly across types of news and time periods. For example, during the 2021–2023 inflation spike, investor attention to CPI reports was high, leading to large price moves around announcements. Using measures like Bloomberg headline counts before each release, the paper shows that higher attention around macro news leads to much stronger, and often excessive, price reactions. The key takeaway for investors is that high attention is more likely to trigger short-lived overreactions, creating opportunities for short-term reversal trades.
Blogs
Walking Forward Optimal Strategy Combinations (Allocate Smartly)
Front Running in Country ETFs, or How to Spot and Leverage Seasonality (Quantpedia)
Weekly Research Insights (QuantSeeker)
GitHub
Streaming Indicators - A Python library for computing technical analysis indicators on streaming data.
Investing Algorithm Framework - Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.
Pyfolio - Portfolio and risk analytics in Python.
Medium
Increasing equity trading profits with macroeconomic information (Macrosynergy)
Backtesting News Sentiment with Asset Prices (With Full Code) (LZP Data Science)
Podcasts
Thoughts on Bill Dunn with Michael Covel on Trend Following Radio (Michael Covel)
Trends, Tall Heads, and Transformations with Transtrend’s Harold de Boer (The Derivative)
Carry Analysis - Small versus Large Universe (ReSolve Asset Management)
Last Week’s Most Popular Links
Momentum at Long Holding Periods (Calluzo, Moneta, and Topaloglu)
Direction is More Important than Speed: A Comparison of Direction and Value Prediction of Stock Excess Returns (Cheng, Shang, and Zhao)
Investment Base Pairs (Goulding and Harvey)
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