Financial Network Learning for Momentum Strategies
The L2GMOM machine learning framework enhances portfolio profitability and risk management by learning financial networks and optimizing trading signals.
7 shares4 citations todaySource ↗
Quant LetterNo. 13
August 2023, Week 4
81 items across 8 sections, as sent to readers on 24 August 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
- Items
- 81
- Papers
- 53
- Sections
- 8
- Issue
- No. 13
arXiv
Quantitative-finance and ML-for-finance preprints from arXiv.
17 items
Finance8
Time-Inconsistent Portfolio Optimization with Stocks
A proposed machine learning algorithm for portfolio optimization includes options and a risk-free bond, resulting in a more stable stock allocation and less need for drastic re-allocations.
6 shares4 citations todaySource ↗
Valuing Vulnerable Derivative Contracts
A model for valuing a vulnerable derivative considers bilateral cash flows, funding, credit, and wrong-way risks, with findings indicating more sensitivity to funding factors than credit ones.
5 sharesSource ↗
Analyzing Collective Trading Events on Social Media
A study finds a strong correlation between Twitter activity and stock volatility, but a weak connection between tweet sentiment and stock performance, suggesting Reddit has a more significant impact on these events.
5 shares2 citations todaySource ↗
Asset Class Network Momentum
The article discusses network momentum, a trading signal from asset momentum spillover, and its use in a multi-asset investment strategy that yielded a 22% annual return from 2000 to 2022.
4 shares6 citations todaySource ↗
Delayed Hedging
The research focuses on the problem of maximizing exponential utility within the context of semistatic hedging.
3 shares1 citation todaySource ↗
Black-Litterman, Bayesian Shrinkage, Factor Models
The paper introduces a Bayesian model that combines shrinkage estimation with view inclusion, applied to Fama-French approach factor models, outperforming simple and optimal portfolios based on sample estimators.
3 sharesSource ↗
Hierarchical Clustering for Portfolio Management
The study improves the Markowitz Model by integrating machine learning through a hierarchical clustering approach, enhancing portfolio performance on a risk-adjusted basis.
3 shares2 citations todaySource ↗
Economics4
Hidden Dissents in FOMC Meetings
A deep learning model study reveals that disagreement among FOMC members is primarily driven by current or forecasted macroeconomic data, and intensifies with more aggressive monetary policy action.
5 shares1 citation todaySource ↗
Student's Mixture Models for Stock Indices
A study comparing equity indices finds that a combination of three Student's t distributions best describes the log-returns of the indices.
10 shares10 citations todaySource ↗
Data-Driven Guide to WSB
Research shows that activity on the WallStreetBets forum directly impacts the returns of several assets, including 'meme stocks'.
5 sharesSource ↗
Happiness Search & Stock Returns
Google Trends' search volume for 'happiness' can predict future stock returns, particularly for large and value firms, indicating it mirrors a company's societal impact.
5 shares1 citation todaySource ↗
Miscellaneous3
Retail Demand Forecasting with Macroeconomic Variables
The study creates improved retail demand prediction models using macroeconomic factors and past sales data.
4 shares28 citations todaySource ↗
Enhancing Robustness of Neural Network Models in Finance: An Attack-Defense Game
An Attack-Defense Game: The research explores the strengths and weaknesses of neural network models in finance by conducting a competition, offering insights on model security and suggesting new attack or defense strategies.
3 sharesSource ↗
NLP-based Anomaly Detection in Consumer Complaints
The paper introduces a method using NLP to identify patterns in consumer complaints by turning stories into measurable data for algorithm analysis.
4 shares8 citations todaySource ↗
Historical Trending2
Volatility Trading System for Stock Market Forecasting
The article introduces a new trading strategy based on volatility, statistical analysis, and machine learning, which effectively identifies profitable stock market trends.
14 shares8 citations todaySource ↗
Deep RL for High Frequency Trading
A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
194 shares51 citations todaySource ↗
SSRN
Working papers in finance and economics from SSRN.
20 items
Quantitative11
Cost-sensitive ML for Stock Return Prediction
The research investigates the use of cost-sensitive loss functions in machine learning models to predict equity market index movement, using option prices as a measure of error costs.
2 sharesSource ↗
Price Limit Expansion's Impact on Stock Volatility
The research suggests that expanding price limits can either increase or decrease stock volatility, primarily driven by the magnet effect and inherent volatility levels, with the correction effect having a lesser impact.
2 sharesSource ↗
Limited Practicality of Sentiment Analysis in Financial Forecasting
The paper criticizes the overemphasis on sentiment analysis in financial forecasting, arguing that its practical use is often overstated and misleading, and calls for more methodological rigor and transparency in its application.
2 sharesSource ↗
Self-Exciting Jump Structure in Cryptocurrencies vs. S&P 500
The research introduces a model that captures the dynamics of daily cryptocurrency returns, showing evidence of self-triggered clustering and more identified jumps than previous models.
2 sharesSource ↗
ML & Deep Learning for Electricity Price Forecasting
The paper introduces a seasonal attention mechanism through the BiLSTM model, improving forecasts of extreme prices in the British electricity market.
2 sharesSource ↗
Exploring the Double Bottom Trading Strategy
The Quantpedia article discusses the use of technical analysis in trading, specifically double bottom and double top strategies, asserting its continued relevance despite skepticism.
2 sharesSource ↗
High-Dimensional Datasets: Tensor PCA
Tensor PCA: New techniques for analyzing high-dimensional tensor datasets, including a tensor principal component analysis (TPCA) estimation algorithm and a unique test for the number of factors in a tensor factor model, have been developed.
2 sharesSource ↗
Bridging the Gap in Legal Document Analysis
The article promotes the application of legal theory in machine learning to extract information from legal texts, with a focus on the interest theory of rights and the Hohfeldian taxonomy of legal relations.
2 sharesSource ↗
Bayesian Approach for Credit Risk Parameters
The article introduces a Bayesian model to estimate default probabilities in low-default portfolios, using credit derivatives market data and observed default data for better risk differentiation.
98 sharesSource ↗
Stock Returns and Factor Models in Big Data
A model with a high-dimensional state space and multiple assets can solve several asset pricing puzzles, predicting many high Sharpe ratio strategies that do not overlap.
2 sharesSource ↗
GARCH Model Selection Bias
Information criteria can impact the robustness of the News Impact Curve in financial time series due to their restrictive or slack nature when dealing with asymmetric volatility.
2 sharesSource ↗
Financial9
News Media Sentiment & Market Mispricing
The research suggests that news media sentiment can predict returns during periods of high volatility, low returns, high economic policy uncertainty, and heavily skewed returns.
27 sharesSource ↗
Nonlinear Forecasting of Volatility
A new framework for forecasting implied volatility in European put and call options is introduced, using the functional Neural Tangent Kernel estimator to handle the nonlinear and asymmetric dependencies inherent to implied volatility.
2 sharesSource ↗
Credit Ratings & Bond Volatility: Early Evidence
Early Evidence: The study reveals that the introduction of credit ratings in the early 20th century reduced bond volatility.
2 sharesSource ↗
Managing Stock Portfolios with Default Events
The paper discusses managing equity risk in stock portfolios with defaults, deriving formulas for loss distributions and applying them to Value-at-Risk calculations.
7 sharesSource ↗
Dollar Shorting and Stock Market Surge
The research shows that global market shocks significantly affect the hedging behavior of institutional investors, leading to the sale of US dollar forwards and exchange rate appreciation.
125 sharesSource ↗
Panel Data Nowcasting: P/E Ratios
P/E Ratios: The paper highlights the superior performance of structured machine learning regressions for nowcasting with panel data of different frequencies, especially in predicting corporate earnings.
2 sharesSource ↗
Price Discovery in Derivatives
The study proposes a theory of price discovery across derivative markets, detailing informed demand, price impact, and information efficiency of prices, and suggesting strategies for trading at any given time.
2 sharesSource ↗
Mixed-Asset Portfolio Choice via Third-Degree Stochastic Dominance
A study finds that adding direct real estate investments and bonds to a mixed-asset portfolio of stocks significantly enhances the portfolio's efficient frontiers.
4 shares1 citation todaySource ↗
Volatility Control Strategies for Crypto or Digital Assets in Portfolios: Effectiveness
Effectiveness: Volatility control strategies are effective in managing risk in high-volatility assets like crypto, but their risk-adjusted performance varies, as per a study on portfolio construction techniques.
2 sharesSource ↗
RePEc
Economics working papers from RePEc's NEP field reports.
16 items
Finance6
Tail Factor Modeling for Financial Data
The Factor-HGH model is proposed for the joint distribution of financial factors and asset returns, offering advantages in data interpretation and applicability in large dimensions due to a fast estimation algorithm.
14 sharesSource ↗
Forecasting GCC Financial Stress on Oil & Markets
The research uses a 1D-CNN to predict financial stress in the GCC region's markets, finding that financial stress indices and oil significantly improve forecasting and risk hedging.
17 sharesSource ↗
Sectoral & Regional Volatility: CDS Spreads & Equities
CDS Spreads & Equities: The research finds that volatility connectedness between the CDS and equity markets in the US, UK, EU, and Japan is higher during crises, with equity being the main transmitter of volatility.
17 sharesSource ↗
Trends in Volatility
A replication of a 2001 study found that idiosyncratic volatility increased from 1962 to 1997, but decreased in other periods, suggesting the original finding was specific to its sample.
16 sharesSource ↗
Volatility & Expected Returns: Past & Present
Past & Present: The research confirms previous findings that stock returns from 1963 to 2000 are influenced by aggregate-volatility risk and idiosyncratic volatility, and recent asset-pricing models do not consistently account for this.
22 sharesSource ↗
Volatility and Foreign Trade in ECOWAS Economies
A study found that exchange rate volatility negatively affects foreign trade in the short-term but has a positive impact in the long-term, supporting the J curve effect.
14 sharesSource ↗
Historical Trending10
Econometrics and Analytics for Movie Success Forecasts
Machine learning and social media data enhance forecast accuracy in commercial applications, with combined econometrics and machine learning strategies being the most precise.
22 sharesSource ↗
Machine Learning for Housing Prices
Housing price trends can be accurately modeled using machine learning algorithms, considering time lag effects, physical conditions, and socio-economic factors.
21 sharesSource ↗
COVID-19's Impact on Sustainable Stocks Volatility
COVID-19 significantly affected the volatility of sustainable and market-capitalisation-based stocks, with the largest impact on Large-Cap and Mid-Cap indices.
19 sharesSource ↗
Enhancing Return Predictability with ML
A new machine learning model that reclassifies stocks based on forecasted financial performance enhances return predictability and lowers momentum stocks' risk.
19 sharesSource ↗
ML Approach for Predicting Pig Iron Production
Machine learning models can accurately predict production levels in pig iron plants, offering valuable insights to improve production efficiency.
19 sharesSource ↗
Credit Cycles & Returns
Research indicates that high leverage credit booms often lead to lower returns on risky equities, while fixed income provides slightly higher returns as a safer option.
17 sharesSource ↗
Uncertainty Indices & Macroeconomics
The study explores the relationship between major uncertainty indices and macroeconomic variables in the U.S. and Japan, showing varied responses to different events and their effects on business cycles.
17 sharesSource ↗
Scaling SMEs Credit Scoring
A new method using Gradient Boosting Decision Trees and SHapley Additive exPlanation values aims to enhance credit scoring for Small and Medium Size Enterprises, providing high predictability and explainability.
15 sharesSource ↗
Machine Learning vs. Dictionary for Sentiment
Machine-learning methods, particularly the random-forest-regression-tree method, significantly improve the capture of disclosure sentiment at 10-K filing and conference-call dates compared to dictionary-based measures.
13 sharesSource ↗
Cluster Analysis for Missing Values
The study shows that clustering analysis effectively imputes more representative values in missing data cases, ensuring the data structure remains intact.
12 sharesSource ↗
Papers with code
Papers that shipped their code, from the Papers with Code feed (2023-25).
2 items
Trending2
SpecInfer: Accelerating Language Model Serving
Accelerating Language Model Serving: Specinfer uses several small language models to predict the outputs of Large Language Models, arranging predictions in a token tree format.
930 shares
EasyEdit: Easy-to-use Knowledge Editing Framework
Easy-to-use Knowledge Editing Framework: Large Language Models often face issues with knowledge cutoff or fallacies, resulting in ignorance of unseen events or production of text with incorrect information due to outdated or noisy data.
387 shares
GitHub
Repositories the letter featured.
10 items
Finance5
AutoGluon: AutoML for Data Types
AutoML for Data Types: AutoML for processing various types of data including image, text, time series, and tabular data.
6,120 shares
Enhanced Event-Driven Backtester: Python Implementation
Python Implementation: Python-based, event-driven backtester with improved coding structure, data handling, and trading strategies, based on QuantStart articles.
30 shares
Structural Time Series in JAX: stsjax
Stsjax: The implementation of Structural Time Series in JAX, a high-performance machine learning library.
158 shares
Offline ChatGPT: Llama-Powered Chatbot
Llama-Powered Chatbot: Private chatbot that works offline, powered by Llama 2, ensuring data privacy as no information leaves the device.
3,959 shares
IncognitoPilot: AI Interpreter for Sensitive Data
AI Interpreter for Sensitive Data: AI code interpreter, powered by GPT4 or Llama 2, specifically designed to handle sensitive data.
108 shares
Trending5
Financial Info Exchange C Library
A C library specifically designed for exchanging financial information.
239 shares
Podcasts
Episodes on markets, quant methods and economics.
4 items
Quantitative4
ML Training: Cuttlefish Model Tuning
Cuttlefish Model Tuning: Hongyi Wang, a Senior Researcher at Carnegie Mellon University, shares his research on improving the training of machine learning models, introducing the Cuttlefish model.
13 shares
Alternative Data with Jason DeRise
Jason DeRise, a pioneer employee of UBS's Evidence Lab, shares his insights on the platform's growth and the future of alternative data.
10 shares
Market Predictions with Michael Howell: Liquidity Dynamics
Liquidity Dynamics: Michael Howell, a market researcher, discusses global liquidity, economic indicators, and future market trends, emphasizing the roles of the Federal Reserve and the People's Bank of China.
10 shares
WorldQuant CEO emphasizes AI-human trader collaboration
Exgame developer turned hedge fund manager promotes a cooperative relationship between human investors and AI.
1 shares
X / Twitter
Posts from quant researchers on X.
5 items
Quantitative5
Machine Learning Survey
The survey paper explores different aspects of machine learning and forecasting such as nowcasting, textual data panel, tensor data, high-dimensional Granger causality tests, and time series cross-validation.
7 shares
Treasury Market Sentiment Index as Predictor
A study shows that the Sentix Survey's Treasury market investor sentiment index can predict US bond returns, likely because it can forecast near-term macro variables.
2 shares
Investor Overconfidence Determinants
The article analyzes how overconfidence can negatively affect investment outcomes, based on the UBS-Gallup Investor Optimism Survey.
2 shares
Market Microstructure and Feature Extraction Link
The article's content is unclear due to lack of sufficient information.
0 shares
Microrostructure Feature Extraction in Market Paper
The article discusses the process of identifying key characteristics from the detailed structure of a market.
0 shares
Threads from r/quant, r/algotrading and friends.
7 items