Quant LetterNo. 30
204 items across 7 sections, as sent to readers on 20 December 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
42 items
The article suggests a dual-objective model for portfolio selection that optimizes both diversification and expected return, outperforming strategies focused only on diversification or risk-return.
8 shares4 citations todaySource ↗
The research introduces a new method using Compound Hawkes Process to model Limit Order Book dynamics, taking into account order size and maintaining a positive spread.
5 shares12 citations todaySource ↗
The research examines the robust option pricing issue, discovering that adding more information does not enhance the robust pricing bounds, contrary to popular belief.
4 shares1 citation todaySource ↗
The paper discusses the application of a deep residual U-net with self-attention to solve the continuous time-consistent mean variance optimal trade execution problem for multiple agents and assets, surpassing the constraints of finite difference methods.
4 sharesSource ↗
A new framework for Risk Budgeting in portfolio optimization is introduced, which balances risk from assets and factors using various risk measures.
4 sharesSource ↗
The research identifies the weak convergence of a nearly-unstable Hawkes process with a heavy-tailed kernel, useful for creating a scaling limit for a financial market model.
3 shares16 citations todaySource ↗
A nonlinear optimization model for portfolio selection considering risk, return, and ESG criteria is proposed, resolving discrepancies between different agencies' ESG ratings.
3 shares6 citations todaySource ↗
The study applies reinforcement learning to determine optimal portfolio policies in an incomplete market, showing its efficiency and robustness compared to the traditional plug-in method.
3 shares12 citations todaySource ↗
The article suggests a dual-objective model for portfolio selection that optimizes both diversification and expected return, outperforming strategies focused only on diversification or risk-return.
8 shares4 citations todaySource ↗
The research introduces a new method using Compound Hawkes Process to model Limit Order Book dynamics, taking into account order size and maintaining a positive spread.
5 shares12 citations todaySource ↗
The research examines the robust option pricing issue, discovering that adding more information does not enhance the robust pricing bounds, contrary to popular belief.
4 shares1 citation todaySource ↗
The paper discusses the application of a deep residual U-net with self-attention to solve the continuous time-consistent mean variance optimal trade execution problem for multiple agents and assets, surpassing the constraints of finite difference methods.
4 sharesSource ↗
A new framework for Risk Budgeting in portfolio optimization is introduced, which balances risk from assets and factors using various risk measures.
4 sharesSource ↗
The research identifies the weak convergence of a nearly-unstable Hawkes process with a heavy-tailed kernel, useful for creating a scaling limit for a financial market model.
3 shares16 citations todaySource ↗
A nonlinear optimization model for portfolio selection considering risk, return, and ESG criteria is proposed, resolving discrepancies between different agencies' ESG ratings.
3 shares6 citations todaySource ↗
The study applies reinforcement learning to determine optimal portfolio policies in an incomplete market, showing its efficiency and robustness compared to the traditional plug-in method.
3 shares12 citations todaySource ↗
The article suggests a dual-objective model for portfolio selection that optimizes both diversification and expected return, outperforming strategies focused only on diversification or risk-return.
8 shares4 citations todaySource ↗
The research introduces a new method using Compound Hawkes Process to model Limit Order Book dynamics, taking into account order size and maintaining a positive spread.
5 shares12 citations todaySource ↗
The research examines the robust option pricing issue, discovering that adding more information does not enhance the robust pricing bounds, contrary to popular belief.
4 shares1 citation todaySource ↗
The paper discusses the application of a deep residual U-net with self-attention to solve the continuous time-consistent mean variance optimal trade execution problem for multiple agents and assets, surpassing the constraints of finite difference methods.
4 sharesSource ↗
A new framework for Risk Budgeting in portfolio optimization is introduced, which balances risk from assets and factors using various risk measures.
4 sharesSource ↗
The research identifies the weak convergence of a nearly-unstable Hawkes process with a heavy-tailed kernel, useful for creating a scaling limit for a financial market model.
3 shares16 citations todaySource ↗
A nonlinear optimization model for portfolio selection considering risk, return, and ESG criteria is proposed, resolving discrepancies between different agencies' ESG ratings.
3 shares6 citations todaySource ↗
The study applies reinforcement learning to determine optimal portfolio policies in an incomplete market, showing its efficiency and robustness compared to the traditional plug-in method.
3 shares12 citations todaySource ↗
The study examines the effects of artificial latency in the Ethereum network's Proposer-Builder Separation framework, highlighting increased profits for node operators but also potential network inefficiencies and centralization risks.
6 shares3 citations todaySource ↗
Efficient Investing: Blockchain technology is being utilized to create risk-managed portfolios with three different funds, each inversely related to the asset's risk, giving investors the ability to choose their preferred risk or return level.
3 sharesSource ↗
The study examines the effects of artificial latency in the Ethereum network's Proposer-Builder Separation framework, highlighting increased profits for node operators but also potential network inefficiencies and centralization risks.
6 shares3 citations todaySource ↗
Efficient Investing: Blockchain technology is being utilized to create risk-managed portfolios with three different funds, each inversely related to the asset's risk, giving investors the ability to choose their preferred risk or return level.
3 sharesSource ↗
The study examines the effects of artificial latency in the Ethereum network's Proposer-Builder Separation framework, highlighting increased profits for node operators but also potential network inefficiencies and centralization risks.
6 shares3 citations todaySource ↗
Efficient Investing: Blockchain technology is being utilized to create risk-managed portfolios with three different funds, each inversely related to the asset's risk, giving investors the ability to choose their preferred risk or return level.
3 sharesSource ↗
A new model for predicting futures prices in high-frequency trading, using graph neural networks, outperforms existing models in China's futures market.
23 sharesSource ↗
A study introduces a model for predicting market asset prices based on the correlation between investors' expectations and market price movement.
22 sharesSource ↗
The study examines how periodic reward structures affect long-term portfolio strategies, especially when short-selling is not allowed, by transforming the issue into a single-period optimization problem.
13 shares1 citation todaySource ↗
The research introduces Smart Agent-Based Modeling (SABM), a new framework that combines Large Language Models with Agent-Based Modeling to simulate real-world situations more accurately, as demonstrated in three case studies.
13 shares22 citations todaySource ↗
A new model for predicting futures prices in high-frequency trading, using graph neural networks, outperforms existing models in China's futures market.
23 sharesSource ↗
A study introduces a model for predicting market asset prices based on the correlation between investors' expectations and market price movement.
22 sharesSource ↗
The study examines how periodic reward structures affect long-term portfolio strategies, especially when short-selling is not allowed, by transforming the issue into a single-period optimization problem.
13 shares1 citation todaySource ↗
The research introduces Smart Agent-Based Modeling (SABM), a new framework that combines Large Language Models with Agent-Based Modeling to simulate real-world situations more accurately, as demonstrated in three case studies.
13 shares22 citations todaySource ↗
A new model for predicting futures prices in high-frequency trading, using graph neural networks, outperforms existing models in China's futures market.
23 sharesSource ↗
A study introduces a model for predicting market asset prices based on the correlation between investors' expectations and market price movement.
22 sharesSource ↗
The study examines how periodic reward structures affect long-term portfolio strategies, especially when short-selling is not allowed, by transforming the issue into a single-period optimization problem.
13 shares1 citation todaySource ↗
The research introduces Smart Agent-Based Modeling (SABM), a new framework that combines Large Language Models with Agent-Based Modeling to simulate real-world situations more accurately, as demonstrated in three case studies.
13 shares22 citations todaySource ↗
Working papers in finance and economics from SSRN.
54 items
Volatility Risk Premia & Crisis Alpha Harvesting: The author proposes a systematic method for investing in volatility risk premia via ETFs, utilizing futures contracts for backtesting data.
2 sharesSource ↗
A new algorithm uses machine learning and financial data to improve online portfolio selection and automated trading.
2 sharesSource ↗
The paper introduces a new L1L2 regularized sparse portfolio optimization model using the ADMM method, and discusses an extension of the model to include a more general L1Lq regularization.
3 shares2 citations todaySource ↗
The research finds that economic narratives from Wall Street Journal articles and machine learning algorithms can accurately predict inflation, particularly during economic downturns.
2 sharesSource ↗
The paper investigates the volatilities of nine asset markets from 2013 to 2021, identifying three factors affecting volatility and a strong correlation in the volatility of Iranian stock returns.
3 sharesSource ↗
The study explores trading performances under arbitrage conditions in decentralized exchanges, using a simulation model and deep reinforcement learning to determine optimal arbitrage strategies for eight cryptocurrency pairs.
3 shares1 citation todaySource ↗
Volatility Risk Premia & Crisis Alpha Harvesting: The author proposes a systematic method for investing in volatility risk premia via ETFs, utilizing futures contracts for backtesting data.
2 sharesSource ↗
A new algorithm uses machine learning and financial data to improve online portfolio selection and automated trading.
2 sharesSource ↗
The paper introduces a new L1L2 regularized sparse portfolio optimization model using the ADMM method, and discusses an extension of the model to include a more general L1Lq regularization.
3 shares2 citations todaySource ↗
The research finds that economic narratives from Wall Street Journal articles and machine learning algorithms can accurately predict inflation, particularly during economic downturns.
2 sharesSource ↗
The paper investigates the volatilities of nine asset markets from 2013 to 2021, identifying three factors affecting volatility and a strong correlation in the volatility of Iranian stock returns.
3 sharesSource ↗
The study explores trading performances under arbitrage conditions in decentralized exchanges, using a simulation model and deep reinforcement learning to determine optimal arbitrage strategies for eight cryptocurrency pairs.
3 shares1 citation todaySource ↗
A new algorithm uses machine learning and financial data to improve online portfolio selection and automated trading.
2 sharesSource ↗
Asymmetric variables like return volume and jump components are crucial in forecasting risks, aiding in risk management and investment strategy.
8 sharesSource ↗
The paper introduces a new L1L2 regularized sparse portfolio optimization model using the ADMM method, and discusses an extension of the model to include a more general L1Lq regularization.
3 shares2 citations todaySource ↗
The research finds that economic narratives from Wall Street Journal articles and machine learning algorithms can accurately predict inflation, particularly during economic downturns.
2 sharesSource ↗
The research suggests using advanced components within the Hadoop ecosystem to efficiently ingest data from diverse sources, as Hadoop allows for the concurrent and dispersed handling of large datasets.
2 shares1 citation todaySource ↗
The paper investigates the volatilities of nine asset markets from 2013 to 2021, identifying three factors affecting volatility and a strong correlation in the volatility of Iranian stock returns.
3 sharesSource ↗
The study explores trading performances under arbitrage conditions in decentralized exchanges, using a simulation model and deep reinforcement learning to determine optimal arbitrage strategies for eight cryptocurrency pairs.
3 shares1 citation todaySource ↗
Due to the unique characteristics of the ETF lending market, ETFs are costlier to borrow than stocks, creating profitable opportunities for cross-ETF arbitrage.
2 sharesSource ↗
Risk and Return: Cryptocurrency carry trade provides high returns that are not solely explained by cryptocurrency factors or geopolitical risks, indicating a significant part of returns is a premium for equity market volatility risk.
6 shares1 citation todaySource ↗
The article promotes an adaptive investment strategy that focuses on resilience thinking, active ownership, and moving away from narrow financial models due to fast-paced technological, geopolitical, and environmental changes.
2 sharesSource ↗
The article suggests that an equally-weighted stock portfolio can be improved by avoiding negative exposure to certain factor anomalies, while keeping the portfolio construction process simple.
6 shares1 citation todaySource ↗
Machine learning is used to estimate the average daily bid-ask spread in the US and Chinese stock markets, enhancing performance by capturing more raw data and utilizing learned nonlinear relationships.
2 sharesSource ↗
A new performance ratio is created to address the limitations of the Sharpe ratio under non-Gaussian returns and systemic risk, showing improved portfolio selection performance in terms of profitability and risk reduction.
2 sharesSource ↗
The study analyzes risk and return expectations on 19 asset classes from 1987 to 2022, highlighting a strong risk-return tradeoff and the predictive power of expected returns.
609 sharesSource ↗
The risk-return balance in the stock market is affected by the investor's attitude towards ambiguity, with increased market volatility causing a decrease in the equity premium's slope when market optimism is high.
2 sharesSource ↗
Revolutionizing Finance: Automated Market Makers (AMMs) can make liquidity provision more accessible and potentially create deeper markets for high-volume, low-volatility assets, resulting in lower trading costs than traditional markets.
2 sharesSource ↗
Unlocking Alpha: Investors can achieve substantial net alpha by combining short-term signals with advanced trading rules in a liquid global universe, which helps to reduce transaction costs.
3 sharesSource ↗
Solving Equity Premium Puzzle: The issue of a high equity premium and a low risk-free rate, known as the equity risk-premium and volatility puzzle, remains unsolved as current economic models fail to provide a consistent explanation.
2 sharesSource ↗
Due to the unique characteristics of the ETF lending market, ETFs are costlier to borrow than stocks, creating profitable opportunities for cross-ETF arbitrage.
2 sharesSource ↗
Risk and Return: Cryptocurrency carry trade provides high returns that are not solely explained by cryptocurrency factors or geopolitical risks, indicating a significant part of returns is a premium for equity market volatility risk.
6 shares1 citation todaySource ↗
The article promotes an adaptive investment strategy that focuses on resilience thinking, active ownership, and moving away from narrow financial models due to fast-paced technological, geopolitical, and environmental changes.
2 sharesSource ↗
The article suggests that an equally-weighted stock portfolio can be improved by avoiding negative exposure to certain factor anomalies, while keeping the portfolio construction process simple.
6 shares1 citation todaySource ↗
Machine learning is used to estimate the average daily bid-ask spread in the US and Chinese stock markets, enhancing performance by capturing more raw data and utilizing learned nonlinear relationships.
2 sharesSource ↗
A new performance ratio is created to address the limitations of the Sharpe ratio under non-Gaussian returns and systemic risk, showing improved portfolio selection performance in terms of profitability and risk reduction.
2 sharesSource ↗
The study analyzes risk and return expectations on 19 asset classes from 1987 to 2022, highlighting a strong risk-return tradeoff and the predictive power of expected returns.
609 sharesSource ↗
The risk-return balance in the stock market is affected by the investor's attitude towards ambiguity, with increased market volatility causing a decrease in the equity premium's slope when market optimism is high.
2 sharesSource ↗
Revolutionizing Finance: Automated Market Makers (AMMs) can make liquidity provision more accessible and potentially create deeper markets for high-volume, low-volatility assets, resulting in lower trading costs than traditional markets.
2 sharesSource ↗
Unlocking Alpha: Investors can achieve substantial net alpha by combining short-term signals with advanced trading rules in a liquid global universe, which helps to reduce transaction costs.
3 sharesSource ↗
Solving Equity Premium Puzzle: The issue of a high equity premium and a low risk-free rate, known as the equity risk-premium and volatility puzzle, remains unsolved as current economic models fail to provide a consistent explanation.
2 sharesSource ↗
Dealers' hedging in options significantly impacts market trends, with a higher sensitivity to implied volatility changes than underlying equity movements.
51 sharesSource ↗
Volatility Risk Premia & Crisis Alpha Harvesting: The author proposes a systematic method for investing in volatility risk premia via ETFs, utilizing futures contracts for backtesting data.
2 sharesSource ↗
Due to the unique characteristics of the ETF lending market, ETFs are costlier to borrow than stocks, creating profitable opportunities for cross-ETF arbitrage.
2 sharesSource ↗
Risk and Return: Cryptocurrency carry trade provides high returns that are not solely explained by cryptocurrency factors or geopolitical risks, indicating a significant part of returns is a premium for equity market volatility risk.
6 shares1 citation todaySource ↗
The article suggests that an equally-weighted stock portfolio can be improved by avoiding negative exposure to certain factor anomalies, while keeping the portfolio construction process simple.
6 shares1 citation todaySource ↗
The article promotes an adaptive investment strategy that focuses on resilience thinking, active ownership, and moving away from narrow financial models due to fast-paced technological, geopolitical, and environmental changes.
2 sharesSource ↗
Machine learning is used to estimate the average daily bid-ask spread in the US and Chinese stock markets, enhancing performance by capturing more raw data and utilizing learned nonlinear relationships.
2 sharesSource ↗
A new performance ratio is created to address the limitations of the Sharpe ratio under non-Gaussian returns and systemic risk, showing improved portfolio selection performance in terms of profitability and risk reduction.
2 sharesSource ↗
The study analyzes risk and return expectations on 19 asset classes from 1987 to 2022, highlighting a strong risk-return tradeoff and the predictive power of expected returns.
609 sharesSource ↗
The risk-return balance in the stock market is affected by the investor's attitude towards ambiguity, with increased market volatility causing a decrease in the equity premium's slope when market optimism is high.
2 sharesSource ↗
Revolutionizing Finance: Automated Market Makers (AMMs) can make liquidity provision more accessible and potentially create deeper markets for high-volume, low-volatility assets, resulting in lower trading costs than traditional markets.
2 sharesSource ↗
Unlocking Alpha: Investors can achieve substantial net alpha by combining short-term signals with advanced trading rules in a liquid global universe, which helps to reduce transaction costs.
3 sharesSource ↗
Solving Equity Premium Puzzle: The issue of a high equity premium and a low risk-free rate, known as the equity risk-premium and volatility puzzle, remains unsolved as current economic models fail to provide a consistent explanation.
2 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
51 items
The study reveals that the choice of machine learning algorithm significantly affects the financial performance of trading systems, with the random forest algorithm proving most effective.
32 sharesSource ↗
Machine-learning methods can help select profitable mutual fund portfolios, with the study indicating that past performance predicts future performance for active funds, benefiting investors with access to advanced prediction methods.
22 sharesSource ↗
The research recommends restricted CART models for predicting corporate credit ratings using machine learning techniques, emphasizing the role of company size in credit rating prediction.
20 sharesSource ↗
The study reveals that the choice of machine learning algorithm significantly affects the financial performance of trading systems, with the random forest algorithm proving most effective.
32 sharesSource ↗
Machine-learning methods can help select profitable mutual fund portfolios, with the study indicating that past performance predicts future performance for active funds, benefiting investors with access to advanced prediction methods.
22 sharesSource ↗
The research recommends restricted CART models for predicting corporate credit ratings using machine learning techniques, emphasizing the role of company size in credit rating prediction.
20 sharesSource ↗
The study reveals that the choice of machine learning algorithm significantly affects the financial performance of trading systems, with the random forest algorithm proving most effective.
32 sharesSource ↗
Machine-learning methods can help select profitable mutual fund portfolios, with the study indicating that past performance predicts future performance for active funds, benefiting investors with access to advanced prediction methods.
22 sharesSource ↗
The research recommends restricted CART models for predicting corporate credit ratings using machine learning techniques, emphasizing the role of company size in credit rating prediction.
20 sharesSource ↗
The chapter discusses trading strategies in a single risky asset market with a price bubble, showing that wealth preserving strategies can outperform simply holding the asset.
37 sharesSource ↗
The study reveals that arbitrage opportunities in financial markets can only be exploited through short selling, emphasizing the bankruptcy risk involved.
29 sharesSource ↗
The book celebrates Peter Carr's contributions to Quantitative Finance, featuring new research results and tributes from family and friends.
26 sharesSource ↗
The paper studies the realized volatility of cryptocurrencies, showing that the best predictors for Bitcoin and Ethereum come from 30-day implied volatility.
24 sharesSource ↗
The study investigates optimal trading strategies for a partially informed trader under Gaussian price dynamics, proving that optimal strategies depend on current price and an exponentially weighted moving average price.
24 sharesSource ↗
The chapter explores total positivity and relative convexity properties in option pricing models, demonstrating that these properties generally hold in time-homogeneous local volatility models.
23 sharesSource ↗
The paper introduces a one-period XVA model for bilateral and centrally cleared trading, illustrating its potential for stress testing a financial network or optimizing a defaulted clearing member's portfolio.
23 sharesSource ↗
The article suggests an improved 4/2 stochastic volatility model with a new formula for derivative prices, enhancing calibration speed and capturing market volatility.
22 sharesSource ↗
The article uses a GARCH-VAR-Spillover Index method to study the two-way volatility relationship between oil and stock markets during financial crises.
20 sharesSource ↗
The article proposes a formal definition of backtestability for a statistical functional of a distribution and explores its connection with elicitability and identifiability.
19 sharesSource ↗
The article assesses the new VIX1D volatility index's effectiveness in predicting short-term market fluctuations and realized volatility.
18 sharesSource ↗
The research reveals that the use of alternative data, specifically online sales data, boosts trade credit financing for firms in China.
18 sharesSource ↗
The article studies the efficiency of the jump component in predicting Chinese gold futures volatility using high-frequency data.
17 sharesSource ↗
The research suggests a quantitative investment approach that includes recommendations from ChatGPT, demonstrating its potential to enhance portfolio efficiency.
16 sharesSource ↗
The chapter discusses trading strategies in a single risky asset market with a price bubble, showing that wealth preserving strategies can outperform simply holding the asset.
37 sharesSource ↗
The study reveals that arbitrage opportunities in financial markets can only be exploited through short selling, emphasizing the bankruptcy risk involved.
29 sharesSource ↗
The book celebrates Peter Carr's contributions to Quantitative Finance, featuring new research results and tributes from family and friends.
26 sharesSource ↗
The paper studies the realized volatility of cryptocurrencies, showing that the best predictors for Bitcoin and Ethereum come from 30-day implied volatility.
24 sharesSource ↗
The study investigates optimal trading strategies for a partially informed trader under Gaussian price dynamics, proving that optimal strategies depend on current price and an exponentially weighted moving average price.
24 sharesSource ↗
The chapter explores total positivity and relative convexity properties in option pricing models, demonstrating that these properties generally hold in time-homogeneous local volatility models.
23 sharesSource ↗
The paper introduces a one-period XVA model for bilateral and centrally cleared trading, illustrating its potential for stress testing a financial network or optimizing a defaulted clearing member's portfolio.
23 sharesSource ↗
The article suggests an improved 4/2 stochastic volatility model with a new formula for derivative prices, enhancing calibration speed and capturing market volatility.
22 sharesSource ↗
The article uses a GARCH-VAR-Spillover Index method to study the two-way volatility relationship between oil and stock markets during financial crises.
20 sharesSource ↗
The article proposes a formal definition of backtestability for a statistical functional of a distribution and explores its connection with elicitability and identifiability.
19 sharesSource ↗
The article assesses the new VIX1D volatility index's effectiveness in predicting short-term market fluctuations and realized volatility.
18 sharesSource ↗
The research reveals that the use of alternative data, specifically online sales data, boosts trade credit financing for firms in China.
18 sharesSource ↗
The article studies the efficiency of the jump component in predicting Chinese gold futures volatility using high-frequency data.
17 sharesSource ↗
The research suggests a quantitative investment approach that includes recommendations from ChatGPT, demonstrating its potential to enhance portfolio efficiency.
16 sharesSource ↗
The chapter discusses trading strategies in a single risky asset market with a price bubble, showing that wealth preserving strategies can outperform simply holding the asset.
37 sharesSource ↗
The study reveals that arbitrage opportunities in financial markets can only be exploited through short selling, emphasizing the bankruptcy risk involved.
29 sharesSource ↗
The book celebrates Peter Carr's contributions to Quantitative Finance, featuring new research results and tributes from family and friends.
26 sharesSource ↗
The paper studies the realized volatility of cryptocurrencies, showing that the best predictors for Bitcoin and Ethereum come from 30-day implied volatility.
24 sharesSource ↗
The study investigates optimal trading strategies for a partially informed trader under Gaussian price dynamics, proving that optimal strategies depend on current price and an exponentially weighted moving average price.
24 sharesSource ↗
The chapter explores total positivity and relative convexity properties in option pricing models, demonstrating that these properties generally hold in time-homogeneous local volatility models.
23 sharesSource ↗
The paper introduces a one-period XVA model for bilateral and centrally cleared trading, illustrating its potential for stress testing a financial network or optimizing a defaulted clearing member's portfolio.
23 sharesSource ↗
The article suggests an improved 4/2 stochastic volatility model with a new formula for derivative prices, enhancing calibration speed and capturing market volatility.
22 sharesSource ↗
The article uses a GARCH-VAR-Spillover Index method to study the two-way volatility relationship between oil and stock markets during financial crises.
20 sharesSource ↗
The article proposes a formal definition of backtestability for a statistical functional of a distribution and explores its connection with elicitability and identifiability.
19 sharesSource ↗
The article assesses the new VIX1D volatility index's effectiveness in predicting short-term market fluctuations and realized volatility.
18 sharesSource ↗
The research reveals that the use of alternative data, specifically online sales data, boosts trade credit financing for firms in China.
18 sharesSource ↗
The article studies the efficiency of the jump component in predicting Chinese gold futures volatility using high-frequency data.
17 sharesSource ↗
The research suggests a quantitative investment approach that includes recommendations from ChatGPT, demonstrating its potential to enhance portfolio efficiency.
16 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
6 items
Learning Topological Invariants in Dynamical Systems: The study proposes a deep-learning framework for classifying dynamical regimes and identifying bifurcation boundaries in different systems, offering insights into large-scale physical and biological systems.
8 shares3 citations todaySource ↗
The guide introduces reinforcement learning as an extension of supervised learning, offering an easy-to-understand method for learning advanced deep reinforcement learning algorithms such as proximal policy optimization.
29 shares13 citations todaySource ↗
Learning Topological Invariants in Dynamical Systems: The study proposes a deep-learning framework for classifying dynamical regimes and identifying bifurcation boundaries in different systems, offering insights into large-scale physical and biological systems.
8 shares3 citations todaySource ↗
The guide introduces reinforcement learning as an extension of supervised learning, offering an easy-to-understand method for learning advanced deep reinforcement learning algorithms such as proximal policy optimization.
29 shares13 citations todaySource ↗
Learning Topological Invariants in Dynamical Systems: The study proposes a deep-learning framework for classifying dynamical regimes and identifying bifurcation boundaries in different systems, offering insights into large-scale physical and biological systems.
8 shares3 citations todaySource ↗
The guide introduces reinforcement learning as an extension of supervised learning, offering an easy-to-understand method for learning advanced deep reinforcement learning algorithms such as proximal policy optimization.
29 shares13 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
9 items
A novel technique has been found that skips the encoder at certain adjacent timesteps and cyclically reuses the encoder features from previous timesteps for the decoder.
140 shares
Simpler, Faster Point Transformer V3: The article does not seek to introduce any new concepts or improvements to the attention mechanism in machine learning.
119 shares
Softmax and Linear Attention Integration: Agent Attention enhances the traditional attention module by incorporating an extra set of agent tokens.
73 shares
A novel technique has been found that skips the encoder at certain adjacent timesteps and cyclically reuses the encoder features from previous timesteps for the decoder.
140 shares
Simpler, Faster Point Transformer V3: The article does not seek to introduce any new concepts or improvements to the attention mechanism in machine learning.
119 shares
Softmax and Linear Attention Integration: Agent Attention enhances the traditional attention module by incorporating an extra set of agent tokens.
73 shares
A novel technique has been found that skips the encoder at certain adjacent timesteps and cyclically reuses the encoder features from previous timesteps for the decoder.
140 shares
Simpler, Faster Point Transformer V3: The article does not seek to introduce any new concepts or improvements to the attention mechanism in machine learning.
119 shares
Softmax and Linear Attention Integration: Agent Attention enhances the traditional attention module by incorporating an extra set of agent tokens.
73 shares
Repositories the letter featured.
27 items
The article explores the application of Polars in large-scale timeseries machine learning, particularly in parallel feature extraction and panel data forecasts.
677 shares
The article provides practical examples of how to implement different machine learning algorithms.
9,779 shares
The article investigates the use of Deep Reinforcement Learning for optimizing investment portfolios.
71 shares
Time Series Library for Python: The article presents an open-source library designed for handling time series data in Python.
2,069 shares
The article introduces a repository focused on safe reinforcement learning baselines.
329 shares
The article explores the application of Polars in large-scale timeseries machine learning, particularly in parallel feature extraction and panel data forecasts.
677 shares
The article provides practical examples of how to implement different machine learning algorithms.
9,779 shares
The article investigates the use of Deep Reinforcement Learning for optimizing investment portfolios.
71 shares
Time Series Library for Python: The article presents an open-source library designed for handling time series data in Python.
2,069 shares
The article introduces a repository focused on safe reinforcement learning baselines.
329 shares
The article explores the application of Polars in large-scale timeseries machine learning, particularly in parallel feature extraction and panel data forecasts.
677 shares
The article provides practical examples of how to implement different machine learning algorithms.
9,779 shares
The article investigates the use of Deep Reinforcement Learning for optimizing investment portfolios.
71 shares
Time Series Library for Python: The article presents an open-source library designed for handling time series data in Python.
2,069 shares
The article introduces a repository focused on safe reinforcement learning baselines.
329 shares
ABIDES is a simulation system that enables interactive, agent-based, discrete event modeling.
332 shares
The repo discusses the need for precise instructions for AI systems to produce accurate results.
46,808 shares
KnowLM: A new open-source framework has been created for handling large language models with extensive knowledge.
821 shares
Sklearngenetic: A new scikitlearn module has been introduced that utilizes genetic algorithms for feature selection.
289 shares
ABIDES is a simulation system that enables interactive, agent-based, discrete event modeling.
332 shares
The repo discusses the need for precise instructions for AI systems to produce accurate results.
46,808 shares
KnowLM: A new open-source framework has been created for handling large language models with extensive knowledge.
821 shares
Sklearngenetic: A new scikitlearn module has been introduced that utilizes genetic algorithms for feature selection.
289 shares
ABIDES is a simulation system that enables interactive, agent-based, discrete event modeling.
332 shares
The repo discusses the need for precise instructions for AI systems to produce accurate results.
46,808 shares
KnowLM: A new open-source framework has been created for handling large language models with extensive knowledge.
821 shares
Sklearngenetic: A new scikitlearn module has been introduced that utilizes genetic algorithms for feature selection.
289 shares
Industry news: funds, hiring, markets and regulation.
15 items
Quant Trading Firms' use of NeurIPS AMDs chips has sparked a dispute with Nvidia, as per The Information.
4 shares
A paper presents a straightforward market making model for high frequency trading in the North American stock market, without including performance analysis.
1 shares
An article delves into quantitative investing and AQR, offering valuable insights into the sector.
1 shares
The article suggests Christmas gift ideas related to quantitative analysis, including books on various financial and mathematical topics and mechanical pencils.
17 shares
The article discusses the importance of matrix multiplication in statistics and AI, highlighting the challenges posed by large matrices and high computational costs.
6 shares
Quant Trading Firms' use of NeurIPS AMDs chips has sparked a dispute with Nvidia, as per The Information.
4 shares
A paper presents a straightforward market making model for high frequency trading in the North American stock market, without including performance analysis.
1 shares
An article delves into quantitative investing and AQR, offering valuable insights into the sector.
1 shares
The article suggests Christmas gift ideas related to quantitative analysis, including books on various financial and mathematical topics and mechanical pencils.
17 shares
The article discusses the importance of matrix multiplication in statistics and AI, highlighting the challenges posed by large matrices and high computational costs.
6 shares
Quant Trading Firms' use of NeurIPS AMDs chips has sparked a dispute with Nvidia, as per The Information.
4 shares
A paper presents a straightforward market making model for high frequency trading in the North American stock market, without including performance analysis.
1 shares
An article delves into quantitative investing and AQR, offering valuable insights into the sector.
1 shares
The article suggests Christmas gift ideas related to quantitative analysis, including books on various financial and mathematical topics and mechanical pencils.
17 shares
The article discusses the importance of matrix multiplication in statistics and AI, highlighting the challenges posed by large matrices and high computational costs.
6 shares