Weekly Recap
Recent research on quant investing and trading
Welcome to this week’s collection of links to the latest research and insights on quant investing. Below, you’ll find a curated list where each title links to the source for more information. Thank you for reading!
Crypto
Crypto exchange tokens (Garratt and van Oordt)
This paper develops a pricing model for tokens and finds that token issuance with a buyback pledge is a costly way of financing and opens up for manipulation of token supply by large investors.
Lottery-Like Effect and Cryptocurrency (Wu et al.)
Cryptos with lottery-like characteristics underperform other cryptos, consistent with earlier research on lottery stocks.
Does Monetary Liquidity Affect Bitcoin Price? (Zhao and Miao)
This paper constructs a measure of monetary liquidity based on the Fed balance sheet and compares its influence on Bitcoin prices.
Currencies
The Informational Role of Forex Option Volume (Bao et al.)
This paper explores predictability of EUR/USD returns and finds that the put-call volume ratio of EUR/USD options is a statistically and economically significant predictor of returns.
Derivatives
Joint calibration to SPX and VIX options with signature-based models (Cuchiero et al.)
The authors consider a stochastic volatility and signature-based model that jointly prices and calibrates SPX and VIX options without resorting to jumps and that jointly calibrates short and longer maturities.
Asymmetry and the Cross-section of Option Returns (Wang et al.)
The authors propose a broader measure of return asymmetries in delta-hedged option returns that is found to predict option returns in the cross-section.
Equities
Improved Tracking-Error Management for Active and Passive Investing (De Nard et al.)
This paper explores various shrinkage estimators for managing the tracking error of passive and active portfolios.
High-beta stock valuation around macroeconomic announcements (Chen and Jiang)
The authors study stock returns around macro announcements and find large swings for high-beta stocks and consequently for betting-against-beta strategies.
Local-Thinking Bias (deHaan et al.)
The study reveals that sell-side analysts overemphasize news from their own coverage portfolios while underreacting to economically linked firms, affecting market prices and enabling profitable trading strategies.
The Effect of New Information Technologies on Asset Pricing Anomalies (Hirshleifer and Ma)
The introduction of EDGAR and XBRL technologies has reduced mispricing in accounting-based asset pricing anomalies, enhancing market efficiency.
So Many Jumps, So Few News (Ait-Sahalia et al.)
The paper investigates the relationship between stock price jumps and news, finding most jumps lack identifiable news explanations.
Fixed Income
Estimating Mean Reversions in Interest Rate Models (Duchitskii and Piterbarg)
The paper presents a framework for estimating mean reversion speeds in interest rate models, handling noisy market data and are demonstrated through simulations and real-world applications on zero-coupon rates across currencies and tenors.
The author describes the evolution of the US government bond market starting in the 18th century and constructs a new monthly series of government bond returns, spanning 231 years.
Lecture Notes
Linear Algebra for Data Science (Kang and Cho)
Lecture notes prepared by Professors Wanmo Kang and Kyunghyun Cho.
Machine Learning and Large Language Models
A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin (Jabbar and Jalil)
The authors explore the ability of a range of machine learning models for predicting Bitcoin returns, using a mix of price and volume-based features.
Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow (Guo and Hauptmann)
This paper finetunes three different large language models (LLMs) using firm-specific news items for predicting stock returns, and form long-only and long-short portfolios.
This paper considers the problem of predicting changes in credit ratings using SEC filings, accounting data, and macro data.
The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models (Roszyk and Ślepaczuk)
The paper demonstrates that combining machine learning with traditional models enhances the accuracy of predicting S&P 500 volatility.
The authors find promising results when estimating EUR/USD tail risk using various machine learning models.
Macro
Inflation Preferences (Afrouzi et al.)
Consumers are found to prefer, on average, a 0.2% annual inflation, as opposed to the Fed inflation target of 2%.
Monetary Policy Without Moving Interest Rates: The Fed Non-Yield Shock (Boehm and Kroner)
The paper explores a monetary policy mechanism by the Federal Reserve that impacts global stock prices and exchange rates without altering interest rates, highlighting a significant risk premium channel.
The author proposes a new recession indicator based on the unemployment rate and the yield curve slope and which is shown to improve on earlier indicators.
Market Microstructure
The authors propose new estimators for bid-ask spreads that account for serial dependence in returns and trades.
Market Making with Exogenous Competition (Boyce et al.)
The paper explores how a market maker optimizes trading strategies amidst competition by balancing inventory risk and unfilled orders.
Understanding the worst-kept secret of high-frequency trading (Pulido et al.)
This paper explores how volume imbalance in a limit order book is used by market makers to predict future price movements and optimize trading strategies.
Pairs Trading
This recent book chapter discusses Kalman filtering and pairs trading, including extensions such as partial co-integration and the potential use of reinforcement learning.
Blogs
Trading the mean reversion curve (Quantitativo)
Pricing a Sports Bet like an Option (Robot Wealth)
Podcasts
Kris Abdelmessih - Life Through a Volatility Lens (Flirting with Models)
How the Hottest Hedge Funds on Wall Street Really Manage Risk (Odd Lots)
Dave Aspell of Mt Lucas - An Institutional Perspective on the Simplicity of Trend Following (The Algorithmic Advantage)
If Trend Following was an Olympic Sport… ft. Mark Rzepczynski (Top Traders Unplugged)
Value Investing, the Mag 7 and Making Sense of a Changing Market with Tobias Carlisle (Excess Returns)
How to Thrive in Your (Data Science) Career, with Daliana Liu (Super Data Science)
Scott Patterson's Chaos Kings, Part 2 (Nassim Taleb)
The Edge in Trading IPOs Strategy (Quantitativo/Quantopian)
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