July 2023, Week 2

Dynamic Model Selection for Portfolio Optimization:

An algorithm using reinforcement learning is developed to select the best model from many, improving portfolio performance using macroeconomic and price data.

7 Jul 20233 sharesSource ↗

Efficient Scenario Extraction from Panel Data:

Two new algorithms are introduced to extract key scenarios from large, complex data, showing potential in portfolio optimization.

8 Jul 20233 shares1 citation todaySource ↗

Spatial-Temporal-Aware Graph Transformer for Fraud Detection:

A new graph neural network, STA-GT, is proposed for transaction fraud detection, effectively learning and incorporating spatial-temporal and global information.

11 Jul 20232 shares10 citations todaySource ↗

Comparative Portfolio Optimization Study

A study comparing three portfolio design methods found that the mean-variance portfolio is best for risk-adjusted returns, while autoencoder portfolios have the highest annual returns.

11 Jul 20234 shares4 citations todaySource ↗

Structural Time Relationships Testing

The article discusses a method to examine the relationship between time variables in daily monetary flows in retail brokerage using the first eigenvalue distribution of lagged correlation matrices.

11 Jul 20232 sharesSource ↗

Adaptive Portfolio Management using Dynamic Black-Litterman Approach

The paper presents a new adaptive portfolio management framework that merges dynamic Black-Litterman optimization with the general factor model and Elastic Net regression, showing computational benefits and promising trading results.

7 Jul 20232 shares1 citation todaySource ↗

Identifying Dragon Kings in Stock Volatility

A study of market volatility over 50 years shows that the highest volatility aligns with major economic crises, and these instances are classified as Black Swans, Dragon Kings, or Negative Dragon Kings based on their statistical significance.

7 Jul 20233 shares5 citations todaySource ↗

Demographic Risk Modeling

The research provides formulas for measuring demographic risk in insurance portfolios and evaluating the Solvency Capital Requirement of unique and systematic risks.

6 Jul 20235 sharesSource ↗

Optimizing Trading Strategies for Market Makers

The study investigates automated market makers, particularly constant product market makers, and develops two optimal trading strategies using stochastic optimal control tools, as demonstrated with Uniswap v3 data.

7 Jul 202310 shares49 citations todaySource ↗

Understanding Fill Probability in HFT Algorithms

The research employs high-frequency data and survival analysis to study the order book dynamics in high-frequency trading algorithms, using a multi-layer perceptron to determine the fill probability function, and applies this model to a fixed time horizon execution issue.

10 Jul 20233 sharesSource ↗

Derivatives Price Discovery

A new theory has been developed for price discovery across derivative markets, defining informed demand, price impact, and price information efficiency, and proposing a theory of insider trading on higher moments of the underlying payoff.

26 Feb 202336 shares1 citation todaySource ↗

Market Microstructure and Asset Pricing

The article presents a new model for valuing contingent claims, considering market microstructure effects to prevent arbitrage.

5 Apr 202352 sharesSource ↗

Rough Volatility: Fact or Artifact

Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.

24 Mar 202247 shares52 citations todaySource ↗

Financial Market Forecasting with Semantic Network Analysis

A novel textual data index has been utilized to forecast Italian stock and bond market returns and volatilities, showing significant predictability, especially for bond market data during the COVID-19 crisis.

9 Sep 202046 shares20 citations todaySource ↗

Expert Aggregation for Forecasting

The Bernstein Online Aggregation procedure merges predictions from various machine learning models to enhance portfolio performance, surpassing individual algorithms and providing a superior portfolio Sharpe Ratio.

25 Nov 202140 shares12 citations todaySource ↗

Measuring Informed Trading Intensity with ML

A machine learning technique is used to create a new measure of informed trading intensity, which rises before significant announcements and affects return reversal and asset pricing.

15 Jun 20212 sharesSource ↗

Self-Supervised Learning for Diversifying Portfolios

The article discusses the use of self-supervised learning to analyze financial time series data for portfolio diversification, specifically for index tracking and minimum variance portfolio optimization.

15 Aug 20222 sharesSource ↗

SMARTboost: Efficient Tabular Learning

Efficient Tabular Learning: SMARTboost, a new machine learning model, is designed to fit complex functions in large dimensions, adjust model complexity, manage various features, and cater to specific financial needs.

6 Dec 2021398 sharesSource ↗

ML Predicts Fund Performance

Machine learning can forecast top-performing mutual funds using fund characteristics, particularly fund momentum and fund flow.

7 Dec 20215,001 sharesSource ↗

Unsupervised ML in Financial Time-Series Analysis

The study merges ontological methodology and temporal clustering to detect structural changes and crucial periods in financial time series, building on prior research in commodity markets.

14 May 20232 sharesSource ↗

Forecasting Financial Risk with Quantile RF

The study introduces a financial risk forecasting model using Generalized Quantile Random Forests, which offers competitive risk and shortfall forecasts and generates appealing Sharpe, Sortino, and Omega ratios.

17 Jan 20232 sharesSource ↗

Estimating Panel Data Models with Heteroskedasticity

The research provides a condition for accurately estimating structural parameters in panel data models with cross-sectionally heteroskedastic data.

15 Jan 20233 sharesSource ↗

Markowitz's Asset Risk Measurement Challenges

The article challenges the traditional concept of asset risk, arguing that it's the asset risk that causes volatility, not vice versa, and volatility doesn't necessarily decrease asset value.

11 Jul 202314 sharesSource ↗

Machine Learning for Merger Arbitrage Takeover Failure Prediction

The study explores the use of feed forward neural networks (FFNNs) in making merger arbitrage investment decisions, highlighting the effectiveness of machine learning in predicting takeover failures and improving risk-standardized deal returns.

8 Jul 20232 sharesSource ↗

ML, AI, and PCA Insights for Profitability Prediction

AI and machine learning models have proven effective in predicting the profitability of companies listed on the China Ashare market.

6 Jul 20233 sharesSource ↗

Machine Learning for Credit Risk Modeling Variable Selection

The piece proposes a machine learning-based method for selecting variables in clustered credit risk modeling, using the most influential risk drivers as clustering variables.

11 Jul 20233 shares5 citations todaySource ↗

A Supply-Chain-Centric View of Redefined Supply Chain Finance

The chapter provides a detailed overview of Integrated Supply Chain Finance (iSCF), discussing key aspects such as working capital financing, financial hedging, integrated risk management, and supply chain contracts and risk management.

6 Jul 20232 sharesSource ↗

Side-by-Side Management and Bond Fund Performance

The research reveals that bond mutual funds managed by managers with performance-based fees receive fewer fund flows and inflate their asset values, indicating potential conflicts of interest in side-by-side management.

3 Jul 20235 sharesSource ↗

Time-Varying Equity Premia & Sentiment

From 1990 to 2022, equity market returns can be predicted using a simple model, with higher returns following high implied volatility and lower returns after high market sentiment.

19 Jun 2023108 sharesSource ↗

Thematic Investing: Fund Performance

Fund Performance: Mutual fund managers can outperform by using thematic investment strategies, with a higher thematic concentration index leading to significant superior performance.

5 Aug 2022427 sharesSource ↗

Market Concentration & Wealth Dynamics

A new theory suggests that financial market concentration is dynamic, with risk and wealth distribution determining market power, and wealth changing over time due to strategic portfolio decisions.

28 Nov 2021153 sharesSource ↗

International Corporate Bond Returns Prediction with ML

Machine learning is used to forecast global corporate bond returns, showing varying influential factors in U.S. and non-U.S. markets and different levels of bond integration among countries.

27 Jun 2022239 sharesSource ↗

Credit Market Fragility: Evidence from Asset Demand System

Evidence from Asset Demand System: A two-layer asset demand framework is created to study the fragility of the corporate bond market, using microdata to assess the impact of unconventional monetary and liquidity policies on asset prices and institutions.

12 Dec 2022372 sharesSource ↗

ML in Financial Markets: A Survey

A Survey: A review of the emerging literature on machine learning in financial markets identifies promising research areas and provides insights for financial economists and machine learners.

1 Jul 202314 shares2 citations todaySource ↗

Anomaly Detection in High-Frequency Markets with Deep Learning

A new anomaly detection framework for stock trading data uses a modified Transformer autoencoder to spot fraudulent time series.

6 Jul 20233 sharesSource ↗

VIX1D: New Index for Volatility Forecasting

New Index for Volatility Forecasting: The Cboe's 1-Day Volatility Index overestimates S&P 500 volatility, but a simple proxy can correct this for more accurate forecasts with less data.

10 Jul 20233 shares3 citations todaySource ↗

Retail Investors' Behavior: The Impact of Digitalization

The Impact of Digitalization: Technological innovations like mobile apps and roboadvisors have transformed retail investing, making it more accessible but also increasing investment biases, though roboadvisors can help minimize errors.

10 Jul 20232 shares6 citations todaySource ↗

Green vs. Conventional Bonds' Liquidity Patterns

Green bonds from governments and energy companies are more liquid than traditional bonds, particularly if they are externally verified or meet international standards.

7 Jul 20232 shares2 citations todaySource ↗

Pricing 0DTE Options: Capturing Volatility Dynamics

Capturing Volatility Dynamics: The market for ultra short-term zero days-to-expiry options has expanded, with a new pricing formula developed to account for factors like leverage and volatility-of-volatility.

7 Jul 20232 shares8 citations todaySource ↗

SFDR Article 9: Impact vs. ESG Investments

Impact vs. ESG Investments: Under the EU Sustainable Finance Disclosure Regulation, 60% of Article 9 funds aim for impact-oriented investment, while 40% use an ESG strategy, with lower returns from downgraded funds.

10 Jul 20234 shares3 citations todaySource ↗

Alpha-factor Risk Parity for Global Equity FoFs

The study introduces a risk parity strategy for Fund-of-Funds portfolios, using a two-phase optimization technique, which provides a more stable risk-return profile, particularly in volatile and down-market periods.

12 Jul 202326 sharesSource ↗

Factor-based Portfolio Optimization with Forward Returns

The research applies a factor model and machine learning to include forward-looking information in portfolio optimization, which reduces idiosyncratic noise and enhances out-of-sample performance.

12 Jul 202323 sharesSource ↗

Fama-French Five-Factor Model vs. Machine Learning

The paper develops a seven-factor model for the A-share market, compares five machine learning algorithms, and discovers that SVM and random forests enhance fitting power, while the performance of lasso, ridge, and neural networks varies.

12 Jul 202320 sharesSource ↗

VaR and ES Forecasting in Large Portfolios: A Dynamic Factor Model Approach

A Dynamic Factor Model Approach: The article introduces two superior methods for predicting and estimating Value-at-Risk (VaR) and Expected Shortfall (ES) in large portfolios.

12 Jul 202319 sharesSource ↗

Algorithmic Trading and Block Ownership Initiation: An Information Perspective

An Information Perspective: The paper reveals that algorithmic trading in U.S. public companies discourages informed investors and decreases the chances of initiating block ownership.

12 Jul 202317 sharesSource ↗

Factor Models for Large and Incomplete Data Sets with Unknown Group Structure

The article proposes a new technique for managing large economic time series databases, which performs better than the standard expectation-maximization algorithm, especially with grouped factor structure data.

12 Jul 202314 sharesSource ↗

Crude Oil Volatility Prediction with Structural Regime Switching

The article introduces a new model using Markov regime switching for better prediction of volatility in the crude oil market, outperforming other high-frequency prediction models.

12 Jul 202315 sharesSource ↗

Machine Learning for Inflation Forecasting in Brazil

The paper investigates the use of machine learning for improved inflation forecasting in Brazil, outdoing traditional econometric models and aiding in identifying crucial inflation predictors.

12 Jul 202313 sharesSource ↗

Artificial Neural Networks Enhance Credit Risk Prediction

The study reveals that machine learning is superior to logistic regression in predicting company bankruptcy, and its predictive accuracy increases when factors like changes in operating expenditure are included in the model.

12 Jul 202311 sharesSource ↗

Mixed-frequency ML for weekly claims

A new method combining mixed-data sampling and machine learning, using Google Trends data, enhances the accuracy of predicting weekly unemployment insurance claims, especially during the COVID-19 crisis.

12 Jul 202320 sharesSource ↗

Bagging vs combination for oil futures volatility

The bagging method in machine learning is more effective than traditional models in predicting oil futures volatility, especially during the COVID-19 pandemic, with economic policy uncertainty indices being more useful than macroeconomic variables.

12 Jul 202316 sharesSource ↗

Herding effect and volatility forecast in Chinese stock market

The market herding effect significantly enhances the prediction of market volatility in the Chinese stock market, particularly in long-term predictions, with machine learning algorithms performing better than linear models.

12 Jul 202315 sharesSource ↗

Enhancing Stock Market Volatility Prediction with New Bagging Model

A new model combining an autoregressive model and bagging method is more effective in predicting U.S. stock market volatility than traditional models, a study found.

12 Jul 202314 sharesSource ↗

Text-Based Managerial Climate Attention Index for Chinese Listed Firms

A measure of managerial climate attention for Chinese listed companies, created through textual mining and machine learning, can reveal the subjective drivers of firms' climate actions.

12 Jul 202311 sharesSource ↗

High-Frequency Trading Volume Prediction with Neural Networks

A study successfully used a neural network to predict trading volumes of the CSI300 futures index using short-term data, finding that adding additional data did not improve predictions.

12 Jul 202323 sharesSource ↗

Hawkes Model Parameter Estimation with Recurrent Neural Networks

A recurrent neural network was used to estimate parameters of a Hawkes model using high-frequency financial data, showing faster performance and similar accuracy to traditional methods, allowing for real-time volatility measurement.

12 Jul 202314 sharesSource ↗

Convolutional Neural Network for Enterprise Default Risk Prediction

The study proposes a comprehensive metric model to address imbalanced datasets and redundant features in machine learning models for default risk prediction.

18 Nov 202217 sharesSource ↗

Loss-Cutting and Gain-Riding Strategies

A new trading strategy is proposed that targets left tail risk and generates an annualized alpha of 180 bps over 5 years, outperforming the contrarian mean-variance optimal strategy.

20 Jan 202215 sharesSource ↗

Adaptive Data Clustering Method with Density Peaks

The proposed AMDPC method improves data clustering accuracy by over 22.58% compared to traditional methods and can automatically determine the correct number of clusters.

26 Feb 202214 sharesSource ↗

Long Memory and Fractality in Volatility Indices

A study of nine volatility indices reveals evidence of long memory and fractality, providing new insights for investment decisions and trading strategies.

26 Sep 202214 sharesSource ↗

GLM0B: Bilingual Pretrained Model

Bilingual Pretrained Model: GLM130B is a new bilingual English and Chinese language model with 130 billion parameters.

10 Jul 20238,358 shares

hoGPT: Democratizing Language Models

Democratizing Language Models: Large Language Models like GPT4 are transforming AI with their human-level language processing capabilities.

10 Jul 20234,121 shares

CodeGen2: Training LLMs on Programming and Natural Languages

Training LLMs on Programming and Natural Languages: The research aims to improve the efficiency of Large Language Models training for program synthesis.

11 Jul 20234,021 shares

WizardCoder: Empowering Large Language Models

Empowering Large Language Models: The model outperforms other large language models like Anthropics Claude and Google's Bard on HumanEval.

10 Jul 20233,891 shares

ChatLaw: OpenSource Legal LLM

OpenSource Legal LLM: A self-attention method is suggested to improve large models' error correction and problem-solving abilities.

10 Jul 20233,703 shares

Bridging Data and Humans

Different industries such as finance, weather forecasting, and energy generate a vast amount of varied data every day.

10 Jul 2023415 shares

Large Language Models Survey

The research community has coined the term large language models (LLM) to differentiate larger parameter scale predictive language models (PLMs).

10 Jul 20233,527 shares

LengthExtrapolatable Transformer

The article discusses the importance of position modeling in the functioning of Transformer models in machine learning.

10 Jul 20231,855 shares

Efficient Web-QA System

WebGLM is a web-based question-answering system that employs the General Language Model (GLM) to function effectively.

10 Jul 20231,080 shares

Efficient Library for Optimization

TorchOpt provides a superior distributed execution runtime.

10 Jul 2023390 shares

Evaluation of Language Models

The use of large language models (LLMs) is growing due to their outstanding performance in numerous applications.

10 Jul 2023306 shares

DataCopilot: Data and Humans

Data and Humans: DataCopilot has launched an autonomous workflow to streamline the interaction between massive amounts of data and human users.

9 Jun 2023417 shares

barterdatars: Market Data Streaming

Market Data Streaming: A high-performance WebSocket integration library is being utilized to stream public market data in the barterrs project.

9 Oct 202254 shares

EasyTemporalPointProcess: Temporal Point Processes

Temporal Point Processes: EasyTPP introduces a novel method for open benchmarking of Temporal Point Processes.

29 May 202346 shares

MOGymnasium: Multiobjective RL Environments

Multiobjective RL Environments: Development is underway for Multiobjective Gymnasium environments aimed at enhancing reinforcement learning.

11 Apr 2022149 shares

Polars Cookbook: Python Recipes

Python Recipes: The article gives a guide on using Python's polars library.

27 May 2023132 shares

InternLM: Chat Model

Chat Model: InternLM has released an open-source chat model and training system with 7 billion parameters for practical use.

6 Jul 20231,434 shares

MetaGPT: MultiAgent Framework

MultiAgent Framework: The MultiAgent Meta Programming Framework can create PRD design tasks and repositories from a single line requirement.

30 Jun 20232,316 shares

Tinygrad: PyTorch Alternative

PyTorch Alternative: Tinygrad is a must-try for enthusiasts of Pytorch and micrograd.

18 Oct 202017,598 shares

FinetuneChatGLM26B: Efficient Fine-tuning

Efficient Fine-tuning: ChatGLM26B provides comprehensive fine-tuning and efficient adjustments for multi-round conversations.

4 Jul 2023122 shares

ToolQA: Evaluating LLMs in Answering Questions

Evaluating LLMs in Answering Questions: ToolQA is a newly created dataset aimed at testing the ability of LLMs to answer complex questions using external tools, featuring two difficulty levels across eight real-world scenarios.

6 Jun 2023105 shares

Unshakeable Portfolio Strategies

Jason Buck explores defensive investment strategies and the influence of market shifts on volatility cycles.

8 Jul 202321 shares

Predicting Recessions and Asset Allocation

Aaron Soderstrom provides insights on predicting market trends, understanding business cycles, and the Federal Reserve's role in managing wage inflation and unemployment.

6 Jul 202310 shares

Thematic Investing Strategies

Chris Versace talks about thematic investing, AI's disruptive potential, and the significance of understanding holdings and position sizing in investing.

9 Jul 20238 shares

AI Opportunities in Healthcare

Terence Flynn discusses the potential of AI and machine learning to transform the healthcare sector, especially in biopharmaceutical firms, by cutting costs and increasing the success rate of new drugs.

6 Jul 20238 shares

Removing Biases in Multi-Asset Investing

Trevor Greetham discusses the structure for multi-asset investing, decreasing inflation, and increasing growth.

7 Jul 20237 shares

Wall St. Anniversary

Kim Sokoloff, a Wall Street expert, talks about her career and opportunities for listeners to become prop traders funded by APEX Trader or Topstep Funding.

7 Jul 20237 shares

Brain AI

Lin Zhao and Lu Zhang explore the links between the brain and neural networks, and how this understanding can enhance artificial intelligence systems.

11 Jul 20236 shares

Profits in Metal and Mining

Gwen Preston delves into mining investing, the performance of gold and base metal miners, and the influence of China's demand on the metal market.

7 Jul 20236 shares

AI Technologies' Duality

Shawn Kim, Head of Morgan Stanley's Asia Technology Research Team, discusses the potential $275 billion artificial intelligence market by 2027 and key considerations for investors.

11 Jul 20235 shares

Earnings in Focus

Mike Wilson, Chief Investment Officer for Morgan Stanley, talks about high market valuations as earnings season begins and the significance of liquidity for investors.

10 Jul 20235 shares

Seasonal Equity Returns

The Equities Entity Store has determined that July yields the highest average return of 1.67 for the S&P500 index.

7 Jul 20236 shares

Harry Markowitz: Finance Legend

Finance Legend: Nobel Laureate Harry Markowitz, known for creating modern portfolio theory, died on June 22, 2023, leaving a substantial influence on quantitative finance.

11 Jul 20235 shares

Simulation of Multivariate Normal Distribution

Robert Wedderburn's study presents a new algorithm for simulating samples from a multivariate normal Gaussian distribution with a known mean vector and covariance matrix.

6 Jul 20235 shares

Harry Markowitz and Modern Portfolio Theory

Harry Markowitz, the founder of modern portfolio theory and a Nobel Laureate, died on June 22, 2023.

11 Jul 20232 shares

Cryptonite: Crypto and AI Intersection

Crypto and AI Intersection: The article explores the convergence of cryptocurrency and artificial intelligence.

9 Jul 20230 shares

Reinforcement Learning for Portfolios

The Hudson and Thames Reading Group explored the potential of Deep Reinforcement Learning (DRL) to transform financial decision-making by viewing portfolio allocation as a continuous control optimization issue.

6 Jul 202311 shares

Time Series Learning Books

The author suggests a sequence of books for beginners studying time series, highlighting the need to begin with basic principles.

9 Jul 202324 shares

Train LLMs in 50 Lines

The author provides a guide on how to train or fine-tune a Language Model (LLM) with minimal or no code, using various libraries and autotrainadvanced.

12 Jul 202393 shares

Paper Financial Machine Learning: A Resource

A Resource: Article 1: AQR researcher compiles a comprehensive paper on financial machine learning, serving as a guide for new quants and data scientists.

11 Jul 20238 shares

Rough Path Theory for Deep Learning Stock Predictions

The paper investigates the application of rough path theory in extracting signature path features from LOB data for accurate stock predictions.

11 Jul 20232 shares

Comparing NLP Approaches for Earnings Surprises Prediction

Chapados and team find that only finance-objective trained LLMs can accurately predict both positive and negative earnings surprises and future firm returns, outperforming classical NLP approaches.

10 Jul 20230 shares

Restructuring Regression

The article presents a new regression model that focuses on structural analysis.

9 Jul 20230 shares

Market Predicts Stock Return Anomalies

The paper reveals that the state of the market, whether positive or negative, can predict the returns of known stock return anomalies.

7 Jul 20230 shares

Online Order Flow Learning

The article investigates the use of Bayesian ChangePoint Detection Methods in understanding online order flow and market impact.

9 Jul 20230 shares

Open-Source Coding Model Status

The current status and future potential of open-source coding models are discussed in the article.

7 Jul 20230 shares

Strategy Parameter Optimization

8 Jul 202316 shares

DTale Integration for Large Datasets

6 Jul 202333 shares

Measure Theory in Quantitative Finance

9 Jul 202316 shares

Project Selection for Quantitative Trading/Research Interest

9 Jul 202336 shares