Weekly Recap
Latest research on quant investing and trading
Welcome to this week’s collection of links featuring the latest research on quant investing and useful resources. Below, you'll find a curated list, with each title linking to the source for more information. Thank you for reading!
Bonds
Fear in the "Fearless" Treasury Market (Wang et al.)
A fear index based on the Thomson Reuters (now LSEG) MarketPsych dataset strongly predicts Treasury bond returns, controlling for other factors.
Crypto
This paper sheds light on the factors influencing the staking of Ethereum, finding distinct patterns among different participant groups and market conditions.
Currencies
Political Risk and Commodity Currencies (Dodd et al.)
The established positive relation between commodity and currency returns for resource-exporting countries is shown to break down in periods of high political risk.
Equity
Stock Splits are Not Dead: Implications of Reappearing Stock Splits (Li et al.)
Stock splits have evolved beyond their traditional role of improving liquidity and are now also used to garner interest from customers and investors.
Insights from the Geopolitical Sentiment Index made with Google Trends (Desai et al.)
Changes in geopolitical sentiment are shown to predict the spread between small and large-cap stocks.
Dissecting Flow-induced Trading (Qin)
The author studies the price impact of flow-related trading by mutual funds, finding that investors could possibly exploit it.
Threshold Overnight Comovement Analysis of Intraday and Overnight Returns (Jung et al.)
This paper studies the lead-lag effect between stock markets in different time zones, focusing specifically on the ETFs FXI and SPY.
Shades of Momentum: Alternative Momentum Metrics and their Dissipation in Indian Equities (Raju)
The author explores momentum strategies in Indian equities, finding that some alternative momentum signals outperform traditional momentum.
Valuation Fundamentals (Décaire and Graham)
This paper examines how financial analysts value companies, specifically focusing on how they form cash flow expectations and discount rates.
Machine Learning and Large Language Models
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (Mirzadeh et al.)
This study finds significant limitations in large language models' mathematical reasoning abilities, relying more on pattern recognition than logical reasoning.
Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets (Ramdas and Wells)
The authors train a convolutional neural network to identify characteristics and patterns of stock trades that predict the signs of future price movements.
The author explores various statistical and machine learning techniques to predict stock market returns, finding mixed results but where tree-based models show the most promise.
Statistical Arbitrage in Rank Space (Li and Papanicolaou)
A statistical arbitrage strategy based on stock rankings and neural networks generates a meaningful Sharpe ratio, although being sensitive to transaction costs.
Unlocking the Power of AI: Deep Learning of Conditional Volatility is Indispensable (Ma and Yan)
This paper trains a neural network to jointly predict returns and volatilities, finding substantial performance gains compared to solely predicting returns.
Can GANs Learn the Stylized Facts of Financial Time Series? (Kwon and Lee)
The authors explore the abilities of Generative Adversarial Networks to generate synthetic time series data, finding somewhat mixed results.
Regime Models
Dynamic Factor Allocation Leveraging Regime-Switching Signals (Shu and Mulvey)
This paper considers a regime-switching approach to equity-factor allocation, outperforming benchmarks.
Volatility
The Risk of Finance Words (Chen et al.)
The authors construct a volatility dictionary that differs from existing return-predicting dictionaries and predicts future levels of implied and realized volatility.
Leverage Volatility Risk (Shentu et al.)
Sorting stocks on their exposure to aggregate financial leverage volatility is shown to generate a statistically significant return spread.
Blogs
How macro-quantamental trading signals will transform asset management (Macrosynergy)
GitHub
“Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.”
“…flexible backtesting for Python”
Technical analysis
Medium
FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable Models (Kennedy)
Beyond Value at Risk: Understanding Conditional VaR and Entropic VaR (Lewinson)
33 Mindblowing Python Code Snippets for Everyday Problems (Uddin)
Advanced Python Techniques for Efficient Data Analysis (CyCoderX)
Podcasts
Eric Balchunas on The Hidden Gems of the ETF World You Need to Know! (Resolve Asset Management)
Eric Crittenden - A Portfolio for All Seasons (The Algorithmic Advantage)
Owen Lamont on Bubble Fever, Index Gripers & Inefficient Markets (Meb Faber Show)
Practical Lessons from Jerry Parker (Excess Returns)
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