---
title: Quant Letter No. 12: August 2023, Week 3
url: https://www.ml-quant.com/issues/2023-08-17/
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: 2023-08-17
---


# Quant Letter No. 12: August 2023, Week 3

Sent 2023-08-17. 95 items.

## arXiv

### Finance

- __[Essential Factors from Order Flow Data](https://arxiv.org/abs/2308.08135)__: A new model has been suggested to better predict stock trends and improve order execution by analyzing high-frequency order flow data in stock investment. (2023-08-16, shares: 7) · https://www.ml-quant.com/papers/arxiv/2308.08135/
- __[Portfolio Selection with Topological Analysis](https://arxiv.org/abs/2308.07944)__: A two-stage method using Topological Data Analysis for building a stock investment portfolio consistently outperforms traditional methods across various time periods. (2023-08-15, shares: 6) · https://www.ml-quant.com/papers/arxiv/2308.07944/
- __[Company Similarity using Language Models](https://arxiv.org/abs/2308.08031)__: Large language models can learn company profiles from SEC filings, accurately replicating GICS classifications and reflecting financial performance metrics. (2023-08-15, shares: 5) · https://www.ml-quant.com/papers/arxiv/2308.08031/
- __[AI Stock Selection for Portfolios](https://arxiv.org/abs/2308.06260)__: The research shows that AI model ChatGPT is useful in selecting stocks from the S&P500 index, but may not be as efficient in determining the best stock weights in a portfolio. (2023-08-11, shares: 5) · https://www.ml-quant.com/papers/arxiv/2308.06260/
- __[Quantifying Fund Miscategorization with ML](https://arxiv.org/abs/2308.06882)__: The study uses machine learning to measure the impact of mutual fund miscategorization, revealing a significant link between outlier measures of funds and their future returns. (2023-08-14, shares: 4) · https://www.ml-quant.com/papers/arxiv/2308.06882/
- __[Managing Liquidity Risk in Loan Portfolios](https://arxiv.org/abs/2308.06525)__: The article proposes a new loan portfolio model for banks that takes into account liquidity risk and limited liability, indicating that a haircut constraint reduces liquidity risk. (2023-08-12, shares: 4) · https://www.ml-quant.com/papers/arxiv/2308.06525/
- __[Reinforcement Learning for Insurance Pricing on Comparison Sites](https://arxiv.org/abs/2308.06935)__: The paper presents a new reinforcement learning framework for insurers to develop better pricing strategies on price comparison websites, proving its effectiveness over existing methods in terms of sample efficiency and cumulative reward. (2023-08-14, shares: 3) · https://www.ml-quant.com/papers/arxiv/2308.06935/

### Economics

- __[Verhulst-Solow Model for Population and Economic Growth](https://arxiv.org/abs/2308.08315)__: The research modifies Verhulst and Solow models to examine the link between human population growth and economic dynamics, using real-world data for verification. (2023-08-16, shares: 4) · https://www.ml-quant.com/papers/arxiv/2308.08315/
- __[Overcoming Zero Trade in Gravity Models](https://arxiv.org/abs/2308.06303)__: The paper suggests a two-step method involving linear regression and machine learning to calculate gravity parameters in global trade, tackling the issue of zero flow trades. (2023-08-11, shares: 3) · https://www.ml-quant.com/papers/arxiv/2308.06303/
- __[Extending Cross-Impact Balance Method in Multiple Timespans](https://arxiv.org/abs/2308.06223)__: The article presents a simplified method to apply Cross-Impact Balance Analysis over various time periods, aiming to aid policy creation in management and social sciences. (2023-08-11, shares: 2) · https://www.ml-quant.com/papers/arxiv/2308.06223/
- __[Silicon Valley Bank Run Contagion](https://arxiv.org/abs/2308.06642)__: The study analyzes the impact of Silicon Valley Bank's failure on other banks, highlighting the role of uninsured deposits and bank size, with mid-sized banks being most affected. (2023-08-12, shares: 2) · https://www.ml-quant.com/papers/arxiv/2308.06642/
- __[Economic Complexity and Sustainability Transition](https://arxiv.org/abs/2308.07172)__: The article discusses the use of Economic Complexity methods in researching the transition to a greener economy, summarizing relevant data and literature, and suggesting areas for future research. (2023-08-14, shares: 2) · https://www.ml-quant.com/papers/arxiv/2308.07172/

### Miscellaneous

- __[Zero Cost Majority Attacks on Blockchains](https://arxiv.org/abs/2308.06568?utm_source=dlvr.it&utm_medium=twitter)__: Research shows that permissionless blockchains can be attacked at a negative cost by a majority attacker, indicating the need for external security measures beyond protocol mechanisms. (2023-08-12, shares: 4) · https://www.ml-quant.com/papers/arxiv/2308.06568/
- __[UAMM: UBET Market Maker](https://arxiv.org/abs/2308.06375)__: UBET Market Maker: A new method, UBET AMM (UAMM), is proposed for decentralized exchanges, which calculates prices based on external market prices and liquidity pool loss, effectively removing arbitrage opportunities when external market prices are efficient. (2023-08-11, shares: 3) · https://www.ml-quant.com/papers/arxiv/2308.06375/
- __[Stablecoin Comparative Analysis](https://arxiv.org/abs/2308.07041)__: The study examines the stability of various stablecoins under different conditions, stressing the need to understand their collateral's origin and management to ensure stability and reduce risks. (2023-08-14, shares: 2) · https://www.ml-quant.com/papers/arxiv/2308.07041/

### Historical Trending

- __[Model-free Trading Strategies Analysis](https://arxiv.org/abs/2011.02870)__: A new method for analyzing risk and return in dynamic trading strategies is introduced, using a mathematical framework and a non-parametric scenario simulation method that matches empirical data. (2020-11-05, shares: 61) · https://www.ml-quant.com/papers/arxiv/2011.02870/
- __[Combining ML Classifiers for Stock Trading](http://dx.doi.org/10.1504/ijcse.2023.129152)__: A machine learning model using ensemble learning was developed to make profitable trades in the US stock market, dynamically selecting the top 25 features from 148 before each training session, yielding a 54.35% profit from 2011 to 2019. (2021-07-28, shares: 49) · https://www.ml-quant.com/papers/doi/10-1504-ijcse-2023-129152/
- __[Inflation in Times of Low Attention: Who Cares?](https://arxiv.org/abs/2105.05297)__: Who Cares?: The decrease in public attention to inflation after the Great Inflation period in the U.S. complicates managing inflation expectations and can lead to inflation-attention traps, suggesting a need to increase the inflation target. (2021-05-11, shares: 42) · https://www.ml-quant.com/papers/arxiv/2105.05297/
- __[Convex PCA for Wasserstein geodesic PCA](https://arxiv.org/abs/2211.02990)__: The article introduces Convex PCA, a new data reduction method for convex subsets in a Hilbert space, with practical applications in financial data analysis. (2022-11-05, shares: 38) · https://www.ml-quant.com/papers/arxiv/2211.02990/

## SSRN

### Quantitative

- __[Variance Swaps with Options](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4542475)__: The article explains how three vanilla options can be used to delta hedge a variance swap, assuming the market smile is influenced by a stochastic volatility model. (2023-08-16, shares: 43) · https://www.ml-quant.com/papers/ssrn/4542475/
- __[Price Impacts: Robust Portfolio Choice Model](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4541885)__: Robust Portfolio Choice Model: A study on portfolio choice suggests that investors may not always aim for the 'ideal' portfolio due to factors like price impacts and aversion to model estimation errors. (2023-08-16, shares: 3) · https://www.ml-quant.com/papers/ssrn/4541885/
- __[Corporate Capital Structure: Geopolitical Risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539731)__: Geopolitical Risk: A study using a news-based geopolitical risk index found that such risk has a long-term negative effect on leverage, especially for firms with higher existing leverage, more irreversible investment, and higher exposure to geopolitical risk. (2023-08-13, shares: 3) · https://www.ml-quant.com/papers/ssrn/4539731/
- __[Enhanced Volatility Forecasting with Dual EMD](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4540455)__: The article presents a new method called Dual Empirical Mode Decomposition (DEMD) to improve the accuracy of volatility prediction by extracting more information from raw financial data. (2023-08-14, shares: 2) · https://www.ml-quant.com/papers/ssrn/4540455/
- __[Machine Learning and Modern Econometrics for Tail-Risk Protection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4540499)__: The study introduces a dynamic tail risk protection strategy using machine learning, comparing various methods and suggesting an ensemble classifier for better performance. (2020-12-02, shares: 2) · https://www.ml-quant.com/papers/ssrn/4540499/
- __[Future of Natural Language Processing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539657)__: The paper provides an overview of the latest machine learning algorithms, the significance of large datasets, and emerging trends in NLP. (2023-06-10, shares: 4) · https://www.ml-quant.com/papers/ssrn/4539657/
- __[FIVAR-Ito Model for High-Frequency Data](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4537927)__: The article introduces the FIVARIto model, a new Ito diffusion process for predicting large volatility matrices and portfolio allocation. (2021-09-13, shares: 2) · https://www.ml-quant.com/papers/ssrn/4537927/
- __[Optimal Liquidity Provision in Market Making](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4541822)__: The study proposes an optimal liquidity provision strategy for automated market makers with concentrated liquidity, using stochastic optimal control. (2022-11-22, shares: 2) · https://www.ml-quant.com/papers/ssrn/4541822/
- __[Adaptive Robust Volatility Estimation for High-Frequency Data](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539949)__: The paper presents the ARP estimator, a new tool for handling heterogeneous heavy-tailed distributions in high-frequency financial data. (2021-03-26, shares: 2) · https://www.ml-quant.com/papers/ssrn/4539949/

### Financial

- __[ChatGPT for Portfolio Selection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4538502)__: The research shows AI model ChatGPT is effective in selecting stocks from the SP500 index, but not as efficient in assigning optimal weights to portfolio stocks. (2023-08-11, shares: 3) · https://www.ml-quant.com/papers/ssrn/4538502/
- __[Bond Futures: Delivery & Term Structure Modeling](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4542275)__: Delivery & Term Structure Modeling: The article expands a method for swap rate products to include futures, enabling the study of volatility and correlations on bond futures valuation and risk management. (2023-08-15, shares: 3) · https://www.ml-quant.com/papers/ssrn/4542275/
- __[Quantitative Analysis & Social Media Research](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4540580)__: A study reveals that the introduction of quantitative ratings on Seeking Alpha platform can predict future returns and impact social media research. (2023-08-14, shares: 2) · https://www.ml-quant.com/papers/ssrn/4540580/
- __[Valuation of Barrier Options under Risks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4542370)__: The research offers algorithms to price European and American equity derivatives with barrier features in a market model with correlated equity and interest rate risks. (2023-08-16, shares: 2) · https://www.ml-quant.com/papers/ssrn/4542370/
- __[Decline of Active Mutual Funds: Flows & Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539706)__: Flows & Returns: The research indicates that U.S. active equity mutual funds have seen net outflows since 2006, with the impact of these flows on the annualized alpha for the active funds industry turning negative between 2006 and 2021. (2023-08-13, shares: 2) · https://www.ml-quant.com/papers/ssrn/4539706/
- __[Tax-Efficient Factor Investing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4538712)__: Despite high turnover, factor investing can yield significant pre-tax and post-tax alphas, especially with value, quality, and safety buy-and-hold portfolios, making it a viable option for tax-aware investors. (2023-08-11, shares: 2) · https://www.ml-quant.com/papers/ssrn/4538712/
- __[Portfolio Skewness & Optimization](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4540021)__: A new risk measure, negative quadratic skewness, is introduced to increase portfolio skewness, providing a portfolio optimization model akin to the Markowitz model. (2023-08-14, shares: 2) · https://www.ml-quant.com/papers/ssrn/4540021/
- __[Competition & Active Fund Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4541316)__: Fund managers can mitigate the adverse effects of competition on gross alpha by decreasing active management, with larger funds typically reducing active share more in response to competition. (2023-08-15, shares: 4) · https://www.ml-quant.com/papers/ssrn/4541316/
- __[Return Horizon & Investment Alpha](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539467)__: Mutual funds' performance can greatly differ based on the return measurement period, with many showing negative results over longer periods. (2022-05-05, shares: 2) · https://www.ml-quant.com/papers/ssrn/4539467/
- __[Machine Learning for Lag Selection in Finance Research](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4543446)__: Random Regression Forests (RRF) are more effective than traditional methods and other machine learning techniques in choosing optimal lags for forecasting in various data series. (2022-06-01, shares: 2) · https://www.ml-quant.com/papers/ssrn/4543446/
- __[Volatility Dynamics in Crypto](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4542577)__: The fluctuating nature of cryptocurrencies is closely tied to specific events in the crypto market and wider economic history. (2023-07-29, shares: 20) · https://www.ml-quant.com/papers/ssrn/4542577/
- __[Crypto Quanto & Inverse Options](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4538564)__: The article offers a mathematical breakdown of direct and inverse options in crypto trading, including their pricing, hedging features, and benefits for fiat-based traders. (2021-07-28, shares: 2) · https://www.ml-quant.com/papers/ssrn/4538564/
- __[Retail Investor Reactions to ESG Scores](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4539609)__: A study using Robinhood data found that during the COVID-19 pandemic, retail investors favored securities with high ESG sustainability scores, not for financial return but for their preference. (2023-05-09, shares: 3) · https://www.ml-quant.com/papers/ssrn/4539609/

## RePEc

### Finance

- __[Tail Factor Modeling for Heterogeneous Data](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs42521-023-00083-z%3Bh%3Drepec%3Aspr%3Adigfin%3Av%3A5%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1007_s42521-023-00083-z)__: The article introduces a new distribution model for the joint distribution of financial factors and asset returns, demonstrating its benefits in a comparative study. (2023-08-17, shares: 14) · https://www.ml-quant.com/papers/repec/spr-digfin-v-5-y-2023-i-2-d-10-1007-s42521-023-00083-z/
- __[Volatility & Expected Returns: Then & Now](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1561%2F104.00000125%3Bh%3Drepec%3Anow%3Ajnlcfr%3A104.00000125)__: Then & Now: The research confirms previous findings on the impact of aggregate-volatility risk and idiosyncratic volatility on stock returns, and suggests that recent asset-pricing models don't consistently account for these factors, except for the models by Stambaugh and Yuan, and Barillas and Shanken. (2023-08-17, shares: 22) · https://www.ml-quant.com/papers/repec/now-jnlcfr-104-00000125/
- __[Sectoral & Regional Volatility Connectedness: CDS Spreads & Equities](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F0%2F0%2F48488%2F52185%3Bh%3Drepec%3Aibn%3Aijefaa%3Av%3A15%3Ay%3A2023%3Ai%3A4%3Ap%3A8)__: CDS Spreads & Equities: The research examines the volatility connection between the CDS and equity markets in the US, UK, EU, and Japan, and finds that this connection is generally stronger during crisis periods, with equity being the main transmitter of volatility. (2023-08-17, shares: 17) · https://www.ml-quant.com/papers/repec/ibn-ijefaa-v-15-y-2023-i-4-p-8/
- __[Corporate Capital Structure Modeling](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.vopreco.ru%2Fjour%2Farticle%2FviewFile%2F4201%2F2572%3Bh%3Drepec%3Anos%3Avoprec%3Ay%3A2023%3Aid%3A4201)__: The article presents a method for improving a company's capital structure to increase return on equity and maintain financial stability in fluctuating markets. (2023-08-17, shares: 17) · https://www.ml-quant.com/papers/repec/nos-voprec-y-2023-id-4201/
- __[Idiosyncratic Volatility Trends](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1561%2F104.00000127%3Bh%3Drepec%3Anow%3Ajnlcfr%3A104.00000127)__: The study confirms Campbell et al.'s (2001) findings on aggregate idiosyncratic volatility, suggesting these results are sample-specific and offering more understanding of idiosyncratic volatility trends. (2023-08-17, shares: 16) · https://www.ml-quant.com/papers/repec/now-jnlcfr-104-00000127/

### Machine Learning

- __[Global Economic Policy Uncertainty Index with ML](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F15%2F3268%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A15%3Ap%3A3268-%3Ad%3A1202191)__: A new global economic policy uncertainty index has been developed using the Principal Component Analysis machine learning algorithm and Random Matrix Theory, performing as well as existing indexes without needing extra economic data. (2023-08-17, shares: 25) · https://www.ml-quant.com/papers/repec/gam-jmathe-v-11-y-2023-i-15-p-3268-d-1202191/
- __[Explainable ML Methods in Actuarial Context](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F14%2F3088%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A14%3Ap%3A3088-%3Ad%3A1193020)__: The article discusses the use of explainable artificial intelligence in solving insurance problems, highlighting the need for both accuracy and interpretability in machine-learning approaches. (2023-08-17, shares: 24) · https://www.ml-quant.com/papers/repec/gam-jmathe-v-11-y-2023-i-14-p-3088-d-1193020/
- __[Time Series Clustering with Thick-Pen Transform](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00357-023-09437-z%3Bh%3Drepec%3Aspr%3Ajclass%3Av%3A40%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1007_s00357-023-09437-z)__: A new method for clustering high-dimensional zero-inflated time series data has been developed, using a modified thick-pen transform and an efficient iterative clustering algorithm. (2023-08-17, shares: 15) · https://www.ml-quant.com/papers/repec/spr-jclass-v-40-y-2023-i-2-d-10-1007-s00357-023-09437-z/

### Deep Learning

- __[Forecasting GCC Financial Stress with Neural Networks](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10690-022-09387-3%3Bh%3Drepec%3Akap%3Aapfinm%3Av%3A30%3Ay%3A2023%3Ai%3A3%3Ad%3A10.1007_s10690-022-09387-3)__: The research uses a One-Dimensional Convolutional Neural Network to predict financial stress in the GCC oil, stock, and bond markets, and finds that financial stress indices and oil significantly improve forecasting performance and risk hedging. (2023-08-17, shares: 17) · https://www.ml-quant.com/papers/repec/kap-apfinm-v-30-y-2023-i-3-d-10-1007-s10690-022-09387-3/
- __[Deep Learning for Personality Measurement](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fisre.2022.1111%3Bh%3Drepec%3Ainm%3Aorisre%3Av%3A34%3Ay%3A2023%3Ai%3A1%3Ap%3A194-222)__: The second article introduces DeepPerson, a tool for text-based personality detection that merges psycholinguistic theories with deep learning strategies, with the goal of enhancing the precision of personality assessments for improved predictive analytics in organizations and consumer decision making. (2023-08-17, shares: 14) · https://www.ml-quant.com/papers/repec/inm-orisre-v-34-y-2023-i-1-p-194-222/

### Historical Trending

- __[Econometrics and Analytics for Movie Success Forecasts](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2020.3911%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A68%3Ay%3A2022%3Ai%3A1%3Ap%3A189-210)__: Machine learning and social media data can enhance forecast accuracy in commercial applications, especially when combined with econometrics. (2022-06-23, shares: 22) · https://www.ml-quant.com/papers/repec/inm-ormnsc-v-68-y-2022-i-1-p-189-210/
- __[Machine Learning for Housing Prices](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FIJHMA-02-2022-0033%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Aijhmap%3Aijhma-02-2022-0033)__: Housing price trends can be accurately predicted by machine learning algorithms considering land use-transportation interactions, physical conditions, and socio-economic factors. (2022-04-14, shares: 21) · https://www.ml-quant.com/papers/repec/eme-ijhmap-ijhma-02-2022-0033/
- __[Asset Volatility and Capital Structure in Mergers](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2020.3607%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A67%3Ay%3A2021%3Ai%3A5%3Ap%3A2773-2798)__: Capital structure is significantly affected by changes in asset volatility after corporate acquisitions, with increased leverage corresponding to decreased asset volatility. (2021-08-18, shares: 20) · https://www.ml-quant.com/papers/repec/inm-ormnsc-v-67-y-2021-i-5-p-2773-2798/
- __[Enhancing Return Predictability with ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2021.4189%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A68%3Ay%3A2022%3Ai%3A10%3Ap%3A7701-7741)__: A new prediction model using machine learning can enhance stock return predictability by reclassifying stocks based on predicted financial performance. (2022-06-08, shares: 19) · https://www.ml-quant.com/papers/repec/inm-ormnsc-v-68-y-2022-i-10-p-7701-7741/
- __[Uncertainty Indices in Macroeconomy](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fhermes-ir.lib.hit-u.ac.jp%2Fhermes%2Fir%2Fre%2F71969%2Fkeizaikenkyu07203246.pdf%3Bh%3Drepec%3Ahit%3Aecorev%3Av%3A72%3Ay%3A2021%3Ai%3A3%3Ap%3A246-267)__: The paper reveals significant differences in the properties of major uncertainty indices and their relationship with macroeconomic variables in the U.S. and Japan. (2021-05-22, shares: 17) · https://www.ml-quant.com/papers/repec/hit-ecorev-v-72-y-2021-i-3-p-246-267/
- __[Risk Shifting and Volatility](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2020.3593%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A67%3Ay%3A2021%3Ai%3A5%3Ap%3A2751-2772)__: The research suggests that equity holders invest in high-risk ventures when their firms are struggling, supporting the risk-shifting mechanism's explanation for the negative correlation between idiosyncratic volatility and future stock returns. (2021-11-05, shares: 15) · https://www.ml-quant.com/papers/repec/inm-ormnsc-v-67-y-2021-i-5-p-2751-2772/

## Papers with code

### Trending

- __[Open Source Polyglot LLM](https://github.com/modelscope/modelscope)__: Large Language Models (LLMs) excel in comprehending, reasoning, and creating natural language instructions. (2023-08-15, shares: 3411)
- __[Direct Preference Optimization for LM](https://github.com/hiyouga/llama-efficient-tuning)__: Reinforcement Learning from Human Feedback (RLHF) is a sophisticated method that uses human preferences to create a reward model and refine a large unsupervised language model. (2023-08-12, shares: 2573)
- __[Pretrained Code Gen Model with Multilingual Evaluations](https://github.com/thudm/codegeex2)__: OpenAI Codex, a large pre-trained code generation model, can produce precise syntax and functional code, boosting programmer efficiency and promoting artificial general intelligence. (2023-08-17, shares: 2369)
- __[Efficient Generation for Large Language Models](https://github.com/normal-computing/outlines)__: The article introduces a novel method for neural text generation using transitions between states in a finite-state machine. (2023-08-16, shares: 1956)
- __[Trafilatura: Web Scraping Library and CommandLine Tool](https://github.com/adbar/trafilatura)__: Web Scraping Library and CommandLine Tool: The article highlights a tool that surpasses other open-source alternatives in performance evaluations and benchmarks. (2023-08-16, shares: 1750)
- __[PAL: Programaided Language Models](https://github.com/srush/minichain)__: Programaided Language Models: The article attributes successful problem-solving to prompting techniques such as chain-of-thought, utilizing LLMs to comprehend and resolve each problem step. (2023-08-12, shares: 842)

### Rising

- __[AudioSep: Audio Separation with Natural Language Queries](https://github.com/audio-agi/audiosep)__: Audio Separation with Natural Language Queries: The article discusses AudioSep, a new model that separates audio sources based on natural language queries. (2023-08-11, shares: 226)
- __[ReBRAC: Minimalistic Offline Reinforcement Learning Algorithm](https://github.com/corl-team/CORL)__: Minimalistic Offline Reinforcement Learning Algorithm: The article reviews recent offline RL studies and presents ReBRAC, a simple algorithm based on the TD3BC method. (2023-08-12, shares: 187)
- __[Platypus: Refinement of Large Language Models](https://github.com/arielnlee/Platypus)__: Refinement of Large Language Models: The article presents Platypus, a top-performing set of fine-tuned Large Language Models leading the HuggingFace's Open LLM Leaderboard. (2023-08-17, shares: 186)
- __[Code Instructions for Language Models](https://github.com/bigcode-project/octopack)__: CommitPack surpasses other code instructions such as xP3x SelfInstruct OASST in performance on the 16B parameter StarCoder model, making it the best performing model not trained on OpenAI outputs on the HumanEval Python benchmark. (2023-08-16, shares: 106)
- __[Data Synthesis with Perception Annotations](https://github.com/showlab/datasetdm)__: The suggested method creates datasets with detailed pixelwise labels for multiple tasks, including semantic segmentation, instance segmentation, and depth estimation. (2023-08-15, shares: 72)
- __[Role of Database Administrators in Database Management](https://github.com/tsinghuadatabasegroup/db-gpt)__: Database administrators play a crucial role in managing, maintaining, and optimizing database systems to guarantee data availability, performance, and reliability. (2023-08-15, shares: 55)

## GitHub

### Finance

- __[CNNpredKeras: Stock Market Prediction](https://github.com/hoseinzadeehsan/CNNpred-Keras)__: Stock Market Prediction: CNNpred is a system that uses CNN (Convolutional Neural Networks) to predict stock market trends using various factors. (2020-11-20, shares: 52)
- __[Fast TimeSeries ML](https://github.com/dream-faster/fold)__: The software presents a new machine learning library for TimeSeries that enhances the efficiency of building, deploying, and updating composite models. (2022-12-16, shares: 55)
- __[Kepler.gl: Analysis Tool](https://github.com/keplergl/kepler.gl)__: Analysis Tool: The software features Kepler.gl, an open-source tool for large-scale geospatial data analysis. (2018-02-28, shares: 9556)

### Trending

- __[AgentBench: Benchmark for LLM Agents](https://github.com/THUDM/AgentBench)__: Benchmark for LLM Agents: The article offers a comprehensive benchmark for assessing Language Learning Models (LLMs) as agents. (2023-07-28, shares: 452)
- __[litellm: Lightweight LLM API Package](https://github.com/BerriAI/litellm)__: Lightweight LLM API Package: The article presents a lightweight package that simplifies API calls for Language Learning Models (LLMs) on platforms like Azure, OpenAI, Cohere, Anthropic, and Replicate. (2023-07-27, shares: 373)
- __[FastGPT: QA System](https://github.com/labring/FastGPT)__: QA System: The software explores FastGPT, a system that uses the LLM language model to answer knowledge-based questions through data processing and model invocation. (2023-02-23, shares: 3306)
- __[Python Data Project](https://github.com/mattharrison/sample_nb_code_project)__: The software showcases a project example of using Python for data work in notebooks, including code placement in Python files and testing. (2023-08-14, shares: 71)

## News

- __[AI System Picks Stocks](https://news.google.com/rss/articles/CBMiY2h0dHBzOi8vaW52ZXN0b3JwbGFjZS5jb20vMjAyMy8wOC90aGUtbWFya2V0bWFzdGVyLWFpLXN5c3RlbS11c2luZy1tYWNoaW5lLWxlYXJuaW5nLXRvLXBpY2stc3RvY2tzL9IBAA?oc=5)__: The MarketMaster AI system uses machine learning to choose stocks for investment. (2023-08-14, shares: 8)
- __[AA Systems and AI Drive Mutual Fund Innovation](https://news.google.com/rss/articles/CBMijgFodHRwczovL3d3dy5saXZlbWludC5jb20vbW9uZXkvcGVyc29uYWwtZmluYW5jZS9hY2NvdW50LWFnZ3JlZ2F0b3Itc3lzdGVtcy1hbmQtYWktdGhlLWZ1dHVyZS1vZi1pbm5vdmF0aW9uLWluLW11dHVhbC1mdW5kcy0xMTY5MjAzMjE2ODM3My5odG1s0gGSAWh0dHBzOi8vd3d3LmxpdmVtaW50LmNvbS9tb25leS9wZXJzb25hbC1maW5hbmNlL2FjY291bnQtYWdncmVnYXRvci1zeXN0ZW1zLWFuZC1haS10aGUtZnV0dXJlLW9mLWlubm92YXRpb24taW4tbXV0dWFsLWZ1bmRzL2FtcC0xMTY5MjAzMjE2ODM3My5odG1s?oc=5)__: AI and AA systems may spur innovation in the mutual funds sector. (2023-08-14, shares: 4)
- __[WILMOTT Magazine Sept. 2023 Issue Highlights](https://wilmott.com/wilmott-magazine-september-2023-issue/)__: Wilmott Magazine's 2023 Issue 127 includes unique content from renowned columnists, educators, and researchers. (2023-08-17, shares: 0)

## Podcasts

- __[Impact of Vector Databases on AI and MLPodcast:](https://datascienceathome.podbean.com/e/the-new-dimension-of-ai-vector-databases-ep-236/)__: Podcast: The podcast explores the emerging trend of vector databases and their impact on AI and machine learning, questioning their level of innovation. (2023-08-17, shares: 5)
- __[Poet's Guide to Product Management with Peter BogartJohnsonPodcast:](https://www.youtube.com/watch?v=9cpp9KgxWH4)__: Podcast: The article shares an interview with Peter Bogart Johnson, a pioneer program manager at Jane Street, discussing the challenges of introducing new methods in a company and the role of a competent project manager. (2023-08-15, shares: 7)

## X / Twitter

### Quantitative

- __[ML and Corporate Bonds: Price, Fundamentals, and Macro Data](https://twitter.com/quantseeker/status/1690868419220942849)__: Price, Fundamentals, and Macro Data: The research paper explores the application of machine learning in evaluating corporate bonds using price data, company fundamentals, and macroeconomic data. (2023-08-14, shares: 7)
- __[Predicting Mutual Fund Alphas with ML: Fund Momentum, Flow, and Investor Sentiment](https://twitter.com/quantseeker/status/1690458187319705601)__: Fund Momentum, Flow, and Investor Sentiment: Kaniel's team used machine learning to predict mutual fund alphas, identifying fund momentum, flow, and investor sentiment as main predictors, not the characteristics of the stocks in the funds. (2023-08-12, shares: 5)
- __[Lecture Notes on Asset Pricing](https://twitter.com/quantseeker/status/1689949082909806592)__: The article provides detailed lecture notes on empirical asset pricing, discussing topics such as return predictability and volatility. (2023-08-11, shares: 4)
- __[Benefits of Synthetic Market Data](https://twitter.com/quantseeker/status/1691018936400142336)__: Kinlay's paper investigates the pros and cons of using synthetic market data in trading strategies. (2023-08-14, shares: 2)
- __[Comprehensive Book on Finance](https://twitter.com/quantseeker/status/1690795502021816321)__: Roncalli's book delves into Sustainable Finance, discussing ESG ratings and the impact on portfolio construction. (2023-08-13, shares: 2)
- __[Forecasting Volatility with ML Algorithms](https://twitter.com/quantseeker/status/1690750076308566017)__: The article reviews Li and Tang's paper on forecasting volatility using machine learning and ensemble methods. (2023-08-13, shares: 2)

### Miscellaneous

- __[Decentralized Finance (DeFi) Overview and Risks](https://twitter.com/quantseeker/status/1691071170211098624)__: Harvey and Rabetti's paper explores the advantages and possible dangers of the Decentralized Finance (DeFi) market. (2023-08-14, shares: 1)
- __[Statistical Learning with Python Applications (New Edition)](https://twitter.com/quantseeker/status/1691375995326984192)__: The updated version of An Introduction to Statistical Learning now incorporates Python applications. (2023-08-15, shares: 0)
- __[Boosting Sharpe Ratios with Jump-Only Strategies](https://twitter.com/quantseeker/status/1690778379480383489)__: The article explores the division of currency returns into continuous and jump returns, highlighting the superior performance of jump-only strategies. (2023-08-13, shares: 0)
- __[Earnings Acceleration as a Strong Predictor](https://twitter.com/quantseeker/status/1690458189144190976)__: Insufficient information provided to summarize the article. (2023-08-12, shares: 0)

## Reddit

### Quantitative

- __[Simplified Hypothesis Testing](https://www.reddit.com/r/quant/comments/15p1t9n/hypothesis_testing/)__:  (2023-08-12, shares: 16)
- __[Essential Books on Portfolio Construction](https://www.reddit.com/r/quant/comments/15p9ji7/best_books_on_portfolio_construction/)__:  (2023-08-12, shares: 11)
- __[Advice for Starting Quants](https://www.reddit.com/r/quant/comments/15sex3a/for_quants_in_industry_if_you_had_any_piece_of/)__:  (2023-08-16, shares: 110)
- __[Quant Positions in Eastern Europe](https://www.reddit.com/r/quant/comments/15qb1ws/quant_positions_in_eastern_europe_career_dead_end/)__:  (2023-08-13, shares: 22)