---
title: Quant Letter No. 7: July 2023, Week 2
url: https://www.ml-quant.com/issues/2023-07-12/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
issue_date: 2023-07-12
---


# Quant Letter No. 7: July 2023, Week 2

Sent 2023-07-12. 110 items.

## arXiv

### Quantitative

- __[Dynamic Model Selection for Portfolio Optimization:](https://arxiv.org/abs/2307.04754)__: An algorithm using reinforcement learning is developed to select the best model from many, improving portfolio performance using macroeconomic and price data. (2023-07-07, shares: 3) · https://www.ml-quant.com/papers/arxiv/2307.04754/
- __[Efficient Scenario Extraction from Panel Data:](https://arxiv.org/abs/2307.03927)__: Two new algorithms are introduced to extract key scenarios from large, complex data, showing potential in portfolio optimization. (2023-07-08, shares: 3) · https://www.ml-quant.com/papers/arxiv/2307.03927/
- __[Spatial-Temporal-Aware Graph Transformer for Fraud Detection:](https://arxiv.org/abs/2307.05121)__: A new graph neural network, STA-GT, is proposed for transaction fraud detection, effectively learning and incorporating spatial-temporal and global information. (2023-07-11, shares: 2) · https://www.ml-quant.com/papers/arxiv/2307.05121/

### Finance

- __[Comparative Portfolio Optimization Study](https://arxiv.org/abs/2307.05048)__: 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. (2023-07-11, shares: 4) · https://www.ml-quant.com/papers/arxiv/2307.05048/
- __[Structural Time Relationships Testing](https://arxiv.org/abs/2307.04953)__: 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. (2023-07-11, shares: 2) · https://www.ml-quant.com/papers/arxiv/2307.04953/
- __[Adaptive Portfolio Management using Dynamic Black-Litterman Approach](https://arxiv.org/abs/2307.03391)__: 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. (2023-07-07, shares: 2) · https://www.ml-quant.com/papers/arxiv/2307.03391/
- __[Identifying Dragon Kings in Stock Volatility](https://arxiv.org/abs/2307.03693)__: 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. (2023-07-07, shares: 3) · https://www.ml-quant.com/papers/arxiv/2307.03693/
- __[Demographic Risk Modeling](https://arxiv.org/abs/2307.03090)__: The research provides formulas for measuring demographic risk in insurance portfolios and evaluating the Solvency Capital Requirement of unique and systematic risks. (2023-07-06, shares: 5) · https://www.ml-quant.com/papers/arxiv/2307.03090/

### Crypto & Blockchain

- __[Optimizing Trading Strategies for Market Makers](http://dx.doi.org/10.2139/ssrn.4144743)__: 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. (2023-07-07, shares: 10) · https://www.ml-quant.com/papers/ssrn/4144743/
- __[Understanding Fill Probability in HFT Algorithms](https://arxiv.org/abs/2307.04863)__: 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. (2023-07-10, shares: 3) · https://www.ml-quant.com/papers/arxiv/2307.04863/

### Historical Trending

- __[Derivatives Price Discovery](https://arxiv.org/abs/2302.13426)__: 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. (2023-02-26, shares: 36) · https://www.ml-quant.com/papers/arxiv/2302.13426/
- __[Market Microstructure and Asset Pricing](https://arxiv.org/abs/2304.02356)__: The article presents a new model for valuing contingent claims, considering market microstructure effects to prevent arbitrage. (2023-04-05, shares: 52) · https://www.ml-quant.com/papers/arxiv/2304.02356/
- __[Rough Volatility: Fact or Artifact](https://arxiv.org/abs/2203.13820)__: Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise. (2022-03-24, shares: 47) · https://www.ml-quant.com/papers/arxiv/2203.13820/
- __[Financial Market Forecasting with Semantic Network Analysis](http://dx.doi.org/10.1002/for.2936)__: 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. (2020-09-09, shares: 46) · https://www.ml-quant.com/papers/doi/10-1002-for-2936/
- __[Expert Aggregation for Forecasting](https://arxiv.org/abs/2111.15365)__: 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. (2021-11-25, shares: 40) · https://www.ml-quant.com/papers/arxiv/2111.15365/

## SSRN

### Quantitative

- __[Measuring Informed Trading Intensity with ML](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4505962)__: 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. (2021-06-15, shares: 2) · https://www.ml-quant.com/papers/ssrn/4505962/
- __[Self-Supervised Learning for Diversifying Portfolios](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4504962)__: 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. (2022-08-15, shares: 2) · https://www.ml-quant.com/papers/ssrn/4504962/
- __[SMARTboost: Efficient Tabular Learning](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4501547)__: 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. (2021-12-06, shares: 398) · https://www.ml-quant.com/papers/ssrn/4501547/
- __[ML Predicts Fund Performance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4505281)__: Machine learning can forecast top-performing mutual funds using fund characteristics, particularly fund momentum and fund flow. (2021-12-07, shares: 5001) · https://www.ml-quant.com/papers/ssrn/4505281/
- __[Unsupervised ML in Financial Time-Series Analysis](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4503933)__: 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. (2023-05-14, shares: 2) · https://www.ml-quant.com/papers/ssrn/4503933/
- __[Forecasting Financial Risk with Quantile RF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4504950)__: 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. (2023-01-17, shares: 2) · https://www.ml-quant.com/papers/ssrn/4504950/
- __[Estimating Panel Data Models with Heteroskedasticity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4503961)__: The research provides a condition for accurately estimating structural parameters in panel data models with cross-sectionally heteroskedastic data. (2023-01-15, shares: 3) · https://www.ml-quant.com/papers/ssrn/4503961/
- __[Markowitz's Asset Risk Measurement Challenges](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4506410)__: 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. (2023-07-11, shares: 14) · https://www.ml-quant.com/papers/ssrn/4506410/
- __[Machine Learning for Merger Arbitrage Takeover Failure Prediction](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4504043)__: 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. (2023-07-08, shares: 2) · https://www.ml-quant.com/papers/ssrn/4504043/
- __[ML, AI, and PCA Insights for Profitability Prediction](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502775)__: AI and machine learning models have proven effective in predicting the profitability of companies listed on the China Ashare market. (2023-07-06, shares: 3) · https://www.ml-quant.com/papers/ssrn/4502775/
- __[Machine Learning for Credit Risk Modeling Variable Selection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4506537)__: 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. (2023-07-11, shares: 3) · https://www.ml-quant.com/papers/ssrn/4506537/
- __[A Supply-Chain-Centric View of Redefined Supply Chain Finance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502534)__: 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. (2023-07-06, shares: 2) · https://www.ml-quant.com/papers/ssrn/4502534/

### Financial

- __[Side-by-Side Management and Bond Fund Performance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4506409)__: 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. (2023-07-03, shares: 5) · https://www.ml-quant.com/papers/ssrn/4506409/
- __[Time-Varying Equity Premia & Sentiment](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4505699)__: 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. (2023-06-19, shares: 108) · https://www.ml-quant.com/papers/ssrn/4505699/
- __[Thematic Investing: Fund Performance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4501859)__: Fund Performance: Mutual fund managers can outperform by using thematic investment strategies, with a higher thematic concentration index leading to significant superior performance. (2022-08-05, shares: 427) · https://www.ml-quant.com/papers/ssrn/4501859/
- __[Market Concentration & Wealth Dynamics](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502970)__: 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. (2021-11-28, shares: 153) · https://www.ml-quant.com/papers/ssrn/4502970/
- __[International Corporate Bond Returns Prediction with ML](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4506223)__: 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. (2022-06-27, shares: 239) · https://www.ml-quant.com/papers/ssrn/4506223/
- __[Credit Market Fragility: Evidence from Asset Demand System](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4501772)__: 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. (2022-12-12, shares: 372) · https://www.ml-quant.com/papers/ssrn/4501772/
- __[ML in Financial Markets: A Survey](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4501707)__: 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. (2023-07-01, shares: 14) · https://www.ml-quant.com/papers/ssrn/4501707/
- __[Anomaly Detection in High-Frequency Markets with Deep Learning](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4502662)__: A new anomaly detection framework for stock trading data uses a modified Transformer autoencoder to spot fraudulent time series. (2023-07-06, shares: 3) · https://www.ml-quant.com/papers/ssrn/4502662/
- __[VIX1D: New Index for Volatility Forecasting](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4505785)__: 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. (2023-07-10, shares: 3) · https://www.ml-quant.com/papers/ssrn/4505785/
- __[Retail Investors' Behavior: The Impact of Digitalization](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4506007)__: 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. (2023-07-10, shares: 2) · https://www.ml-quant.com/papers/ssrn/4506007/
- __[Green vs. Conventional Bonds' Liquidity Patterns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4503175)__: Green bonds from governments and energy companies are more liquid than traditional bonds, particularly if they are externally verified or meet international standards. (2023-07-07, shares: 2) · https://www.ml-quant.com/papers/ssrn/4503175/
- __[Pricing 0DTE Options: Capturing Volatility Dynamics](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4503344)__: 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. (2023-07-07, shares: 2) · https://www.ml-quant.com/papers/ssrn/4503344/
- __[SFDR Article 9: Impact vs. ESG Investments](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4505637)__: 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. (2023-07-10, shares: 4) · https://www.ml-quant.com/papers/ssrn/4505637/

## RePEc

### Finance

- __[Alpha-factor Risk Parity for Global Equity FoFs](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1057521923001709%3Bh%3Drepec%3Aeee%3Afinana%3Av%3A88%3Ay%3A2023%3Ai%3Ac%3As1057521923001709)__: 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. (2023-07-12, shares: 26) · https://www.ml-quant.com/papers/repec/eee-finana-v-88-y-2023-i-c-s1057521923001709/
- __[Factor-based Portfolio Optimization with Forward Returns](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176523001623%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A228%3Ay%3A2023%3Ai%3Ac%3As0165176523001623)__: 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. (2023-07-12, shares: 23) · https://www.ml-quant.com/papers/repec/eee-ecolet-v-228-y-2023-i-c-s0165176523001623/
- __[Fama-French Five-Factor Model vs. Machine Learning](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F13%2F2988%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A13%3Ap%3A2988-%3Ad%3A1186815)__: 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. (2023-07-12, shares: 20) · https://www.ml-quant.com/papers/repec/gam-jmathe-v-11-y-2023-i-13-p-2988-d-1186815/
- __[VaR and ES Forecasting in Large Portfolios: A Dynamic Factor Model Approach](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS2452306221000563%3Bh%3Drepec%3Aeee%3Aecosta%3Av%3A27%3Ay%3A2023%3Ai%3Ac%3Ap%3A1-15)__: 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. (2023-07-12, shares: 19) · https://www.ml-quant.com/papers/repec/eee-ecosta-v-27-y-2023-i-c-p-1-15/
- __[Algorithmic Trading and Block Ownership Initiation: An Information Perspective](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0890838922000828%3Bh%3Drepec%3Aeee%3Abracre%3Av%3A55%3Ay%3A2023%3Ai%3A4%3As0890838922000828)__: 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. (2023-07-12, shares: 17) · https://www.ml-quant.com/papers/repec/eee-bracre-v-55-y-2023-i-4-s0890838922000828/
- __[Factor Models for Large and Incomplete Data Sets with Unknown Group Structure](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169207022000723%3Bh%3Drepec%3Aeee%3Aintfor%3Av%3A39%3Ay%3A2023%3Ai%3A3%3Ap%3A1205-1220)__: 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. (2023-07-12, shares: 14) · https://www.ml-quant.com/papers/repec/eee-intfor-v-39-y-2023-i-3-p-1205-1220/

### Statistical

- __[Crude Oil Volatility Prediction with Structural Regime Switching](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0301420723003239%3Bh%3Drepec%3Aeee%3Ajrpoli%3Av%3A83%3Ay%3A2023%3Ai%3Ac%3As0301420723003239)__: 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. (2023-07-12, shares: 15) · https://www.ml-quant.com/papers/repec/eee-jrpoli-v-83-y-2023-i-c-s0301420723003239/
- __[Machine Learning for Inflation Forecasting in Brazil](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS2666143823000042%3Bh%3Drepec%3Aeee%3Alajcba%3Av%3A4%3Ay%3A2023%3Ai%3A2%3As2666143823000042)__: 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. (2023-07-12, shares: 13) · https://www.ml-quant.com/papers/repec/eee-lajcba-v-4-y-2023-i-2-s2666143823000042/
- __[Artificial Neural Networks Enhance Credit Risk Prediction](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F23322039.2023.2210916%3Bh%3Drepec%3Ataf%3Aoaefxx%3Av%3A11%3Ay%3A2023%3Ai%3A1%3Ap%3A2210916)__: 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. (2023-07-12, shares: 11) · https://www.ml-quant.com/papers/repec/taf-oaefxx-v-11-y-2023-i-1-p-2210916/

### Machine Learning

- __[Mixed-frequency ML for weekly claims](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169207022000656%3Bh%3Drepec%3Aeee%3Aintfor%3Av%3A39%3Ay%3A2023%3Ai%3A3%3Ap%3A1122-1144)__: 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. (2023-07-12, shares: 20) · https://www.ml-quant.com/papers/repec/eee-intfor-v-39-y-2023-i-3-p-1122-1144/
- __[Bagging vs combination for oil futures volatility](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1059056023001594%3Bh%3Drepec%3Aeee%3Areveco%3Av%3A87%3Ay%3A2023%3Ai%3Ac%3Ap%3A457-467)__: 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. (2023-07-12, shares: 16) · https://www.ml-quant.com/papers/repec/eee-reveco-v-87-y-2023-i-c-p-457-467/
- __[Herding effect and volatility forecast in Chinese stock market](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffor.2968%3Bh%3Drepec%3Awly%3Ajforec%3Av%3A42%3Ay%3A2023%3Ai%3A5%3Ap%3A1275-1290)__: 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. (2023-07-12, shares: 15) · https://www.ml-quant.com/papers/repec/wly-jforec-v-42-y-2023-i-5-p-1275-1290/
- __[Enhancing Stock Market Volatility Prediction with New Bagging Model](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1059056023001600%3Bh%3Drepec%3Aeee%3Areveco%3Av%3A87%3Ay%3A2023%3Ai%3Ac%3Ap%3A445-456)__: 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. (2023-07-12, shares: 14) · https://www.ml-quant.com/papers/repec/eee-reveco-v-87-y-2023-i-c-p-445-456/
- __[Text-Based Managerial Climate Attention Index for Chinese Listed Firms](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1544612323002830%3Bh%3Drepec%3Aeee%3Afinlet%3Av%3A55%3Ay%3A2023%3Ai%3Apa%3As1544612323002830)__: 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. (2023-07-12, shares: 11) · https://www.ml-quant.com/papers/repec/eee-finlet-v-55-y-2023-i-pa-s1544612323002830/

### Deep Learning

- __[High-Frequency Trading Volume Prediction with Neural Networks](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11408-022-00421-y%3Bh%3Drepec%3Akap%3Afmktpm%3Av%3A37%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1007_s11408-022-00421-y)__: 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. (2023-07-12, shares: 23) · https://www.ml-quant.com/papers/repec/kap-fmktpm-v-37-y-2023-i-2-d-10-1007-s11408-022-00421-y/
- __[Hawkes Model Parameter Estimation with Recurrent Neural Networks](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1544612323002945%3Bh%3Drepec%3Aeee%3Afinlet%3Av%3A55%3Ay%3A2023%3Ai%3Apa%3As1544612323002945)__: 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. (2023-07-12, shares: 14) · https://www.ml-quant.com/papers/repec/eee-finlet-v-55-y-2023-i-pa-s1544612323002945/

### Historical Trending

- __[Convolutional Neural Network for Enterprise Default Risk Prediction](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdownloads.hindawi.com%2Fjournals%2Fcomplexity%2F2022%2F5139562.pdf%3Bh%3Drepec%3Ahin%3Acomplx%3A5139562)__: The study proposes a comprehensive metric model to address imbalanced datasets and redundant features in machine learning models for default risk prediction. (2022-11-18, shares: 17) · https://www.ml-quant.com/papers/repec/hin-complx-5139562/
- __[Loss-Cutting and Gain-Riding Strategies](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1350486X.2023.2224354%3Bh%3Drepec%3Ataf%3Aapmtfi%3Av%3A29%3Ay%3A2022%3Ai%3A5%3Ap%3A402-438)__: 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. (2022-01-20, shares: 15) · https://www.ml-quant.com/papers/repec/taf-apmtfi-v-29-y-2022-i-5-p-402-438/
- __[Adaptive Data Clustering Method with Density Peaks](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdownloads.hindawi.com%2Fjournals%2Fcomplexity%2F2022%2F6742120.pdf%3Bh%3Drepec%3Ahin%3Acomplx%3A6742120)__: 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. (2022-02-26, shares: 14) · https://www.ml-quant.com/papers/repec/hin-complx-6742120/
- __[Long Memory and Fractality in Volatility Indices](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdownloads.hindawi.com%2Fjournals%2Fcomplexity%2F2022%2F6728432.pdf%3Bh%3Drepec%3Ahin%3Acomplx%3A6728432)__: A study of nine volatility indices reveals evidence of long memory and fractality, providing new insights for investment decisions and trading strategies. (2022-09-26, shares: 14) · https://www.ml-quant.com/papers/repec/hin-complx-6728432/

## Papers with code

### Trending

- __[GLM0B: Bilingual Pretrained Model](https://github.com/thudm/chatglm2-6b)__: Bilingual Pretrained Model: GLM130B is a new bilingual English and Chinese language model with 130 billion parameters. (2023-07-10, shares: 8358)
- __[hoGPT: Democratizing Language Models](https://github.com/h2oai/h2ogpt)__: Democratizing Language Models: Large Language Models like GPT4 are transforming AI with their human-level language processing capabilities. (2023-07-10, shares: 4121)
- __[CodeGen2: Training LLMs on Programming and Natural Languages](https://github.com/salesforce/CodeGen)__: Training LLMs on Programming and Natural Languages: The research aims to improve the efficiency of Large Language Models training for program synthesis. (2023-07-11, shares: 4021)
- __[WizardCoder: Empowering Large Language Models](https://github.com/nlpxucan/wizardlm)__: Empowering Large Language Models: The model outperforms other large language models like Anthropics Claude and Google's Bard on HumanEval. (2023-07-10, shares: 3891)
- __[ChatLaw: OpenSource Legal LLM](https://github.com/pku-yuangroup/chatlaw)__: OpenSource Legal LLM: A self-attention method is suggested to improve large models' error correction and problem-solving abilities. (2023-07-10, shares: 3703)

### Rising

- __[Bridging Data and Humans](https://github.com/zwq2018/data-copilot)__: Different industries such as finance, weather forecasting, and energy generate a vast amount of varied data every day. (2023-07-10, shares: 415)
- __[Large Language Models Survey](https://github.com/rucaibox/llmsurvey)__: The research community has coined the term large language models (LLM) to differentiate larger parameter scale predictive language models (PLMs). (2023-07-10, shares: 3527)
- __[LengthExtrapolatable Transformer](https://github.com/microsoft/torchscale)__: The article discusses the importance of position modeling in the functioning of Transformer models in machine learning. (2023-07-10, shares: 1855)
- __[Efficient Web-QA System](https://github.com/thudm/webglm)__: WebGLM is a web-based question-answering system that employs the General Language Model (GLM) to function effectively. (2023-07-10, shares: 1080)
- __[Efficient Library for Optimization](https://github.com/metaopt/torchopt)__: TorchOpt provides a superior distributed execution runtime. (2023-07-10, shares: 390)
- __[Evaluation of Language Models](https://github.com/mlgroupjlu/llm-eval-survey)__: The use of large language models (LLMs) is growing due to their outstanding performance in numerous applications. (2023-07-10, shares: 306)

## GitHub

### Finance Applicable

- __[DataCopilot: Data and Humans](https://github.com/zwq2018/Data-Copilot)__: Data and Humans: DataCopilot has launched an autonomous workflow to streamline the interaction between massive amounts of data and human users. (2023-06-09, shares: 417)
- __[barterdatars: Market Data Streaming](https://github.com/barter-rs/barter-data-rs)__: Market Data Streaming: A high-performance WebSocket integration library is being utilized to stream public market data in the barterrs project. (2022-10-09, shares: 54)
- __[EasyTemporalPointProcess: Temporal Point Processes](https://github.com/ant-research/EasyTemporalPointProcess)__: Temporal Point Processes: EasyTPP introduces a novel method for open benchmarking of Temporal Point Processes. (2023-05-29, shares: 46)
- __[MOGymnasium: Multiobjective RL Environments](https://github.com/Farama-Foundation/MO-Gymnasium)__: Multiobjective RL Environments: Development is underway for Multiobjective Gymnasium environments aimed at enhancing reinforcement learning. (2022-04-11, shares: 149)
- __[Polars Cookbook: Python Recipes](https://github.com/escobar-west/polars-cookbook)__: Python Recipes: The article gives a guide on using Python's polars library. (2023-05-27, shares: 132)

### Trending

- __[InternLM: Chat Model](https://github.com/InternLM/InternLM)__: Chat Model: InternLM has released an open-source chat model and training system with 7 billion parameters for practical use. (2023-07-06, shares: 1434)
- __[MetaGPT: MultiAgent Framework](https://github.com/geekan/MetaGPT)__: MultiAgent Framework: The MultiAgent Meta Programming Framework can create PRD design tasks and repositories from a single line requirement. (2023-06-30, shares: 2316)
- __[Tinygrad: PyTorch Alternative](https://github.com/tinygrad/tinygrad)__: PyTorch Alternative: Tinygrad is a must-try for enthusiasts of Pytorch and micrograd. (2020-10-18, shares: 17598)
- __[FinetuneChatGLM26B: Efficient Fine-tuning](https://github.com/SpongebBob/Finetune-ChatGLM2-6B)__: Efficient Fine-tuning: ChatGLM26B provides comprehensive fine-tuning and efficient adjustments for multi-round conversations. (2023-07-04, shares: 122)
- __[ToolQA: Evaluating LLMs in Answering Questions](https://github.com/night-chen/ToolQA)__: 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. (2023-06-06, shares: 105)

## Podcasts

### Quantitative

- __[Unshakeable Portfolio Strategies](https://www.buzzsprout.com/2034153/13183829-unveiling-defensive-investment-strategies-creating-an-unshakeable-portfolio-with-jason-buck.mp3)__: Jason Buck explores defensive investment strategies and the influence of market shifts on volatility cycles. (2023-07-08, shares: 21)
- __[Predicting Recessions and Asset Allocation](https://www.buzzsprout.com/2034153/13171875-decoding-market-dynamics-predicting-recessions-and-mastering-asset-allocation-with-aaron-soderstrom.mp3)__: Aaron Soderstrom provides insights on predicting market trends, understanding business cycles, and the Federal Reserve's role in managing wage inflation and unemployment. (2023-07-06, shares: 10)
- __[Thematic Investing Strategies](https://www.buzzsprout.com/2034153/13188006-exploring-thematic-investing-strategies-with-chris-versace-ai-commodity-futures-and-emerging-themes.mp3)__: Chris Versace talks about thematic investing, AI's disruptive potential, and the significance of understanding holdings and position sizing in investing. (2023-07-09, shares: 8)
- __[AI Opportunities in Healthcare](https://chrt.fm/track/E5A66E/pdst.fm/e/rss.art19.com/episodes/4c2945d1-48b4-4fa2-8e12-6c388b35ab13.mp3?rss_browser=BAhJIgtTYWZhcmkGOgZFVA%3D%3D--e8daa48e4e049c2293a0ad1663b4a762c475e386)__: 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. (2023-07-06, shares: 8)
- __[Removing Biases in Multi-Asset Investing](https://macrohive.libsyn.com/trevor-greetham-on-spike-flation-equities-and-removing-biases)__: Trevor Greetham discusses the structure for multi-asset investing, decreasing inflation, and increasing growth. (2023-07-07, shares: 7)

### Related

- __[Wall St. Anniversary](https://rss.com/podcasts/confessionsmm/1028104)__: 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. (2023-07-07, shares: 7)
- __[Brain AI](https://dataskeptic.com/blog/episodes/2023/brain-inspired-ai)__: Lin Zhao and Lu Zhang explore the links between the brain and neural networks, and how this understanding can enhance artificial intelligence systems. (2023-07-11, shares: 6)
- __[Profits in Metal and Mining](https://www.buzzsprout.com/2034153/13179409-unearthing-profits-a-deep-dive-into-metal-and-mining-investments-with-gwen-preston.mp3)__: 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. (2023-07-07, shares: 6)
- __[AI Technologies' Duality](https://chrt.fm/track/E5A66E/pdst.fm/e/rss.art19.com/episodes/c2844953-ebfa-458b-8e47-d4cb2c5c917d.mp3?rss_browser=BAhJIgtDaHJvbWUGOgZFVA%3D%3D--d05363d83ce333c74f32188013892b2863ad051c)__: 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. (2023-07-11, shares: 5)
- __[Earnings in Focus](https://chrt.fm/track/E5A66E/pdst.fm/e/rss.art19.com/episodes/727578cb-a67c-4cdd-8df5-0a4665869d7a.mp3?rss_browser=BAhJIglFZGdlBjoGRVQ%3D--4bda49189709ff5bf5f2839774d878c8bff25936)__: Mike Wilson, Chief Investment Officer for Morgan Stanley, talks about high market valuations as earnings season begins and the significance of liquidity for investors. (2023-07-10, shares: 5)

## Blogs

### Quantitative

- __[Seasonal Equity Returns](https://jonathankinlay.com/2023/07/seasonality-in-equity-returns/)__: The Equities Entity Store has determined that July yields the highest average return of 1.67 for the S&P500 index. (2023-07-07, shares: 6)
- __[Harry Markowitz: Finance Legend](https://wilmott.com/harry-markowitz-an-appreciation-part-ii/)__: Finance Legend: Nobel Laureate Harry Markowitz, known for creating modern portfolio theory, died on June 22, 2023, leaving a substantial influence on quantitative finance. (2023-07-11, shares: 5)

### Related

- __[Simulation of Multivariate Normal Distribution](https://portfoliooptimizer.io/blog/simulation-from-a-multivariate-normal-distribution-with-exact-sample-mean-vector-and-sample-covariance-matrix/)__: 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. (2023-07-06, shares: 5)
- __[Harry Markowitz and Modern Portfolio Theory](https://wilmott.com/harry-markowitz-an-appreciation-part-i/)__: Harry Markowitz, the founder of modern portfolio theory and a Nobel Laureate, died on June 22, 2023. (2023-07-11, shares: 2)
- __[Cryptonite: Crypto and AI Intersection](https://stockviz.substack.com/p/cryptonite-june-2023)__: Crypto and AI Intersection: The article explores the convergence of cryptocurrency and artificial intelligence. (2023-07-09, shares: 0)

## Videos

### Quantitative

- __[Reinforcement Learning for Portfolios](https://www.youtube.com/watch?v=xPUDyaPggNQ)__: 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. (2023-07-06, shares: 11)
- __[Time Series Learning Books](https://www.youtube.com/watch?v=vksFF3QrrSc)__: The author suggests a sequence of books for beginners studying time series, highlighting the need to begin with basic principles. (2023-07-09, shares: 24)
- __[Train LLMs in 50 Lines](https://www.youtube.com/watch?v=JNMVulH7fCo)__: 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. (2023-07-12, shares: 93)

## X / Twitter

### Quantitative

- __[Paper Financial Machine Learning: A Resource](https://twitter.com/carlcarrie/status/1678717192433545216)__: A Resource: Article 1: AQR researcher compiles a comprehensive paper on financial machine learning, serving as a guide for new quants and data scientists. (2023-07-11, shares: 8)
- __[Rough Path Theory for Deep Learning Stock Predictions](https://twitter.com/carlcarrie/status/1678578109539287041)__: The paper investigates the application of rough path theory in extracting signature path features from LOB data for accurate stock predictions. (2023-07-11, shares: 2)
- __[Comparing NLP Approaches for Earnings Surprises Prediction](https://twitter.com/quantseeker/status/1678334051327832065)__: 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. (2023-07-10, shares: 0)

### Miscellaneous

- __[Restructuring Regression](https://twitter.com/carlcarrie/status/1678171397661573120)__: The article presents a new regression model that focuses on structural analysis. (2023-07-09, shares: 0)
- __[Market Predicts Stock Return Anomalies](https://twitter.com/quantseeker/status/1677244332993044487)__: The paper reveals that the state of the market, whether positive or negative, can predict the returns of known stock return anomalies. (2023-07-07, shares: 0)
- __[Online Order Flow Learning](https://twitter.com/carlcarrie/status/1677877456575901696)__: The article investigates the use of Bayesian ChangePoint Detection Methods in understanding online order flow and market impact. (2023-07-09, shares: 0)
- __[Open-Source Coding Model Status](https://twitter.com/Yampeleg/status/1677378719478890496)__: The current status and future potential of open-source coding models are discussed in the article. (2023-07-07, shares: 0)

## Reddit

### Rising

- __[Strategy Parameter Optimization](https://www.reddit.com/r/algotrading/comments/14tu8qa/how_can_i_take_a_potentially_good_strategy_and/)__:  (2023-07-08, shares: 16)
- __[DTale Integration for Large Datasets](https://www.reddit.com/r/algotrading/comments/14sjpyu/the_free_pandas_visualizer_dtale_has_now_been/)__:  (2023-07-06, shares: 33)
- __[Measure Theory in Quantitative Finance](https://www.reddit.com/r/quant/comments/14vbcu4/measure_theory/)__:  (2023-07-09, shares: 16)
- __[Project Selection for Quantitative Trading/Research Interest](https://www.reddit.com/r/quant/comments/14v5cpr/projects_to_improve_portfolio/)__:  (2023-07-09, shares: 36)