---
title: Quant Letter No. 45: April 2024, Week 3
url: https://www.ml-quant.com/issues/2024-04-17/
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: 2024-04-17
---


# Quant Letter No. 45: April 2024, Week 3

Sent 2024-04-17. 78 items.

## arXiv

### Finance

- __[Artificial Market Simulations](https://arxiv.org/abs/2404.09462)__: The study suggests a new method for deep hedging in finance using artificial market simulations, which performs similarly to traditional models but has certain limitations. (2024-04-15, shares: 6) · https://www.ml-quant.com/papers/arxiv/2404.09462/
- __[Risk Measure Derivatives](https://arxiv.org/abs/2404.09646)__: The paper provides the derivatives of any risk measures, including VaR and ES for portfolio loss variables, and presents asymptotic results for heavy-tailed portfolio loss variables. (2024-04-15, shares: 3) · https://www.ml-quant.com/papers/arxiv/2404.09646/
- __[Informed Trading and Private Information](https://arxiv.org/abs/2404.08757)__: The paper investigates market-clearing equilibrium in a risky financial market, showing that insider welfare increases with signal precision and price impact can both benefit and harm traders. (2024-04-12, shares: 3) · https://www.ml-quant.com/papers/arxiv/2404.08757/
- __[Factor Risk](https://arxiv.org/abs/2404.08475)__: The paper presents factor risk measures to assess risk relative to major factors, discussing their use in regulatory capital requirement and risk-sharing issues. (2024-04-12, shares: 3) · https://www.ml-quant.com/papers/arxiv/2404.08475/
- __[PathIntegral Approximation](https://arxiv.org/abs/2404.08903)__: The paper improves the pricing of fixed income instruments within the Black-Karasinski model using neural networks, showing better results for multiple calibrations over extended periods. (2024-04-13, shares: 2) · https://www.ml-quant.com/papers/arxiv/2404.08903/

### Miscellaneous

- __[Backward Deep Learning for BSDEs](https://arxiv.org/abs/2404.08456)__: The study introduces a new deep learning algorithm for solving complex backward stochastic differential equations, proving its effectiveness with numerous numerical tests. (2024-04-12, shares: 6) · https://www.ml-quant.com/papers/arxiv/2404.08456/
- __[Weighted Moving Models](https://arxiv.org/abs/2404.08136)__: The paper outlines a method for approximating the exponentially weighted moving model using only a set number of past samples and convex optimization. (2024-04-11, shares: 6) · https://www.ml-quant.com/papers/arxiv/2404.08136/
- __[Japanese Financial LLM](https://arxiv.org/abs/2404.10555)__: The research focuses on developing a large language model specifically for Japanese finance, showing its enhanced performance on related benchmarks. (2024-04-16, shares: 3) · https://www.ml-quant.com/papers/arxiv/2404.10555/

## SSRN

### Quantitative

- __[Exposure Hedging Strategy](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4796356)__: The paper presents a model for optimizing a dealer's hedging strategy in foreign exchange fixings, suggesting smaller exposures are fully hedged in the short term, while larger ones are hedged over a longer period. (2024-04-16, shares: 73) · https://www.ml-quant.com/papers/ssrn/4796356/
- __[Implied Volatility in Defi Pools](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4792110)__: The article introduces a breakeven implied volatility for decentralized finance pools, which aligns with a previous definition based on a market impact rule in traditional finance. (2024-04-11, shares: 7) · https://www.ml-quant.com/papers/ssrn/4792110/
- __[EPS Impact on Capital Structure](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4793002)__: The study reveals that firms adjust their capital structures based on earnings per share (EPS) levels, with the impact of EPS becoming more significant after the Sarbanes-Oxley Act in 2002. (2024-04-12, shares: 6) · https://www.ml-quant.com/papers/ssrn/4793002/
- __[Deep Learning for Cryptocurrency Trends](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4796336)__: The research presents a new data preprocessing technique and a Convolutional Neural Networks (CNN) model for predicting Bitcoin market trends using 15-minute candlestick data. (2024-04-16, shares: 2) · https://www.ml-quant.com/papers/ssrn/4796336/
- __[Composite Likelihood Estimation for Gaussian Processes](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4791807)__: The paper outlines a framework for composite likelihood inference of parametric continuous-time stationary Gaussian processes, focusing on the random log-spot variance of financial asset returns. (2024-04-11, shares: 3) · https://www.ml-quant.com/papers/ssrn/4791807/
- __[ETFs Impact on Bond Liquidity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794939)__: The article discusses how a bond's inclusion in a creation or redemption basket improves its liquidity, especially in the case of redemptions. (2021-11-18, shares: 109) · https://www.ml-quant.com/papers/ssrn/4794939/
- __[Analyst Forecast](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4796635)__: A machine learning method categorizes analysts' forecast revisions into five types, improving accuracy and reducing information asymmetry in earnings announcements. (2022-07-29, shares: 2) · https://www.ml-quant.com/papers/ssrn/4796635/
- __[Interpolation Algorithm](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4793595)__: A new trigonometric interpolation algorithm for even periodic functions, implementable via Fast Fourier Transform, optimizes operations and overcomes the classic algorithm's limitations. (2024-02-02, shares: 3) · https://www.ml-quant.com/papers/ssrn/4793595/
- __[Stock Price Prediction with Deep Learning](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794069)__: The piece introduces a hybrid model that combines various AI techniques for predicting stock prices and optimizing trading decisions. (2024-03-05, shares: 2) · https://www.ml-quant.com/papers/ssrn/4794069/
- __[Business Time Modeling for Commodity Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794748)__: The piece presents a model that accurately represents commodity forward curves, useful for pricing exotic derivatives and managing commodity portfolios. (2023-07-28, shares: 2) · https://www.ml-quant.com/papers/ssrn/4794748/
- __[Sentiment Impact](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4795367)__: A new measure of anticipatory sentiment, created using statistical natural language processing, significantly influences macroeconomic and financial variables, including credit market stress indicators. (2022-10-25, shares: 2) · https://www.ml-quant.com/papers/ssrn/4795367/

### Financial

- __[Risk Premia in Commodity Market](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4796343)__: Machine learning methodologies reveal that momentum factors from equity, bonds, and currencies are priced into commodity returns, indicating a connection between commodity and other financial markets. (2024-04-16, shares: 3) · https://www.ml-quant.com/papers/ssrn/4796343/
- __[Owner's Earnings & Stock Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794183)__: Stocks with high owner's earnings tend to predict average stock returns and outperform other factors, providing significant alpha over the FamaFrench 6factor and q5 factor models. (2024-04-14, shares: 14) · https://www.ml-quant.com/papers/ssrn/4794183/
- __[NASDAQ Electronic Trading Stock Dynamics](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4792199)__: NASDAQ stocks exhibit a U-shaped pattern in bid-ask spreads and trading volumes due to aggressive trading at market open and close, especially for smaller stocks and those with larger order imbalances. (2024-04-12, shares: 5) · https://www.ml-quant.com/papers/ssrn/4792199/
- __[Optimal Averaging for Portfolios](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4792535)__: The article suggests a new method for optimizing portfolio weights by combining minimum-variance strategies, which enhances the variance and Sharpe ratio. (2024-02-02, shares: 87) · https://www.ml-quant.com/papers/ssrn/4792535/
- __[Machine Learning in Fund Classification](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794900)__: The paper uses machine learning to categorize hedge funds, finding that those classified as systematic yield higher excess returns. (2021-09-27, shares: 2) · https://www.ml-quant.com/papers/ssrn/4794900/
- __[Investment Sensitivity and Algos](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4794479)__: The article reveals that algorithmic trading that supplies liquidity boosts firms' investment sensitivity to stock price and enhances operating performance, while the opposite is true for liquidity-demanding algorithmic trading. (2021-12-20, shares: 2) · https://www.ml-quant.com/papers/ssrn/4794479/
- __[Cross-Momentum in Financial Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4793814)__: A study shows that equity futures and currency portfolios sorted by cross-momentum perform better than those sorted by normal momentum, especially in commodity exporting countries. (2023-11-29, shares: 2) · https://www.ml-quant.com/papers/ssrn/4793814/
- __[Portfolio Choice with Genetic Programming](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4793204)__: A new method for creating efficient portfolios using genetic programming and economic constraints has been developed, which doubles the out-of-sample Sharpe ratio of existing methods. (2024-01-16, shares: 2) · https://www.ml-quant.com/papers/ssrn/4793204/
- __[Liquidity Provision in Futures](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4795329)__: A study of the Chicago Mercantile Exchange's futures markets shows that aggressive trades and limit orders significantly contribute to price discovery, with most limit orders providing uninformed liquidity. (2024-02-15, shares: 2) · https://www.ml-quant.com/papers/ssrn/4795329/
- __[Downside Risk Estimation](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4796363)__: A new model for estimating risk based on the corrected Cornish-Fisher expansion provides more accurate downside risk forecasts for various equity indices and commodity futures. (2023-02-01, shares: 3) · https://www.ml-quant.com/papers/ssrn/4796363/

## RePEc

### Finance

- __[AI in Finance: Data and Opportunities](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X24000581%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A84%3Ay%3A2024%3Ai%3Ac%3As0927538x24000581)__: Data and Opportunities: The article highlights the significant role of big data and AI in the finance industry, suggesting a blend of financial knowledge and data analytics for improved financial systems. (2024-04-17, shares: 26) · https://www.ml-quant.com/papers/repec/eee-pacfin-v-84-y-2024-i-c-s0927538x24000581/
- __[Efficient Frontiers and Tangent Portfolio](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.worldscientific.com%2Fdoi%2Fabs%2F10.1142%2FS0217595923500124%3Bh%3Drepec%3Awsi%3Aapjorx%3Av%3A41%3Ay%3A2024%3Ai%3A02%3An%3As0217595923500124)__: The paper explores the characteristics of kinks in portfolio optimization, demonstrating their universal existence and the absence of tangency. (2024-04-17, shares: 16) · https://www.ml-quant.com/papers/repec/wsi-apjorx-v-41-y-2024-i-02-n-s0217595923500124/
- __[Sectoral Volatility and Jump Risk](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1566014124000050%3Bh%3Drepec%3Aeee%3Aememar%3Av%3A59%3Ay%3A2024%3Ai%3Ac%3As1566014124000050)__: The study examines the structure of risk contagion across sectors, emphasizing the need for accurate identification of risk contagion structure for effective regulation. (2024-04-17, shares: 14) · https://www.ml-quant.com/papers/repec/eee-ememar-v-59-y-2024-i-c-s1566014124000050/
- __[Memory-Enhanced Momentum in Futures](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1351847X.2023.2220118%3Bh%3Drepec%3Ataf%3Aeurjfi%3Av%3A30%3Ay%3A2024%3Ai%3A8%3Ap%3A773-802)__: The research suggests a memory-enhanced momentum strategy for commodity futures markets, which surpasses traditional momentum in reward and risk, independent of the overall commodity market movement. (2024-04-17, shares: 12) · https://www.ml-quant.com/papers/repec/taf-eurjfi-v-30-y-2024-i-8-p-773-802/
- __[Efficient Portfolios: Pareto-Dirichlet Approach](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10479-023-05507-y%3Bh%3Drepec%3Aspr%3Aannopr%3Av%3A335%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10479-023-05507-y)__: Pareto-Dirichlet Approach: The paper presents a Pareto–Dirichlet method to solve the MVSK portfolio optimization problem, enabling the creation of optimal portfolios efficiently. (2024-04-17, shares: 11) · https://www.ml-quant.com/papers/repec/spr-annopr-v-335-y-2024-i-1-d-10-1007-s10479-023-05507-y/

### Statistical

- __[Predicting Systemic Financial Risk with Interpretable ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1062940824000123%3Bh%3Drepec%3Aeee%3Aecofin%3Av%3A71%3Ay%3A2024%3Ai%3Ac%3As1062940824000123)__: Research suggests that machine learning models and financial stress index can accurately predict systemic financial risk, especially in stock and money markets. (2024-04-17, shares: 26) · https://www.ml-quant.com/papers/repec/eee-ecofin-v-71-y-2024-i-c-s1062940824000123/
- __[Innovative ML Workflow for China's Financial Crisis Prediction](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-023-00574-3%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A10%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1186_s40854-023-00574-3)__: A study outlines a method for predicting China's systemic financial crises using machine learning models and macroeconomic indicators, identifying six high-risk periods from 1990 to 2020. (2024-04-17, shares: 15) · https://www.ml-quant.com/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-023-00574-3/
- __[Gold Price Forecasting](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10479-021-04187-w%3Bh%3Drepec%3Aspr%3Aannopr%3Av%3A334%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10479-021-04187-w)__: The study suggests using the eXtreme Gradient Boosting machine learning model and Shapley additive explanations for precise prediction and understanding of gold price changes. (2024-04-17, shares: 13) · https://www.ml-quant.com/papers/repec/spr-annopr-v-334-y-2024-i-1-d-10-1007-s10479-021-04187-w/
- __[Option-Implied Kurtosis](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X24000374%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A84%3Ay%3A2024%3Ai%3Ac%3As0927538x24000374)__: The research concludes that including risk-neutral volatility skewness and kurtosis in forecasting models does not improve their predictive power and may even lead to less accurate predictions. (2024-04-17, shares: 11) · https://www.ml-quant.com/papers/repec/eee-pacfin-v-84-y-2024-i-c-s0927538x24000374/

### Machine Learning

- __[Predicting Output Trends in China](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X24000763%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A84%3Ay%3A2024%3Ai%3Ac%3As0927538x24000763)__: Machine learning study on Chinese data from 1993-2016 reveals credit is a better output predictor than money, but its effectiveness has lessened post-2007 due to financial development. (2024-04-17, shares: 28) · https://www.ml-quant.com/papers/repec/eee-pacfin-v-84-y-2024-i-c-s0927538x24000763/
- __[Challenges in Reusing ML Applications](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10796-023-10388-4%3Bh%3Drepec%3Aspr%3Ainfosf%3Av%3A26%3Ay%3A2024%3Ai%3A2%3Ad%3A10.1007_s10796-023-10388-4)__: The article categorizes machine learning applications into four types based on reuse strategies and offers insights for their development and deployment. (2024-04-17, shares: 23) · https://www.ml-quant.com/papers/repec/spr-infosf-v-26-y-2024-i-2-d-10-1007-s10796-023-10388-4/
- __[CostSensitive ML for Startup Investments](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Fisaf.1548%3Bh%3Drepec%3Awly%3Aisacfm%3Av%3A31%3Ay%3A2024%3Ai%3A1%3An%3Ae1548)__: Machine learning models used to predict the success of Israeli startups can reduce investment risk, but may also limit potential profits by predicting fewer successful startups. (2024-04-17, shares: 22) · https://www.ml-quant.com/papers/repec/wly-isacfm-v-31-y-2024-i-1-n-e1548/
- __[ML for hierarchical time series forecasting](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169207022001029%3Bh%3Drepec%3Aeee%3Aintfor%3Av%3A40%3Ay%3A2024%3Ai%3A2%3Ap%3A597-615)__: A multi-output regression model is proposed for better supply chain forecasting, using variables from different hierarchical levels to generate reliable predictions. (2024-04-17, shares: 13) · https://www.ml-quant.com/papers/repec/eee-intfor-v-40-y-2024-i-2-p-597-615/

### Historical Trending

- __[FDI in Western Europe](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FJEFAS-05-2021-0069%2Ffull%2Fhtml%3Futm_source%3Drepec%26utm_medium%3Dfeed%26utm_campaign%3Drepec%3Bh%3Drepec%3Aeme%3Ajefasp%3Ajefas-05-2021-0069)__: The study uses machine learning to analyze factors affecting foreign direct investment in Western Europe, offering insights for capital allocation decisions. (2023-07-22, shares: 24) · https://www.ml-quant.com/papers/repec/eme-jefasp-jefas-05-2021-0069/
- __[ML for Stock Market](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.2478%2Fjoim-2023-0019%3Bh%3Drepec%3Avrs%3Ajoinma%3Av%3A15%3Ay%3A2023%3Ai%3A4%3Ap%3A93-104%3An%3A4)__: The article suggests that machine learning could enhance returns on short-term investments in day-trading. (2023-06-28, shares: 23) · https://www.ml-quant.com/papers/repec/vrs-joinma-v-15-y-2023-i-4-p-93-104-n-4/
- __[Factorial Models in Economic Analysis](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Frepec.vavt.ru%2FRePEc%2Falq%2Frufejo%2Frfej_2022_11_17-38.pdf%3Bh%3Drepec%3Aalq%3Arufejo%3Arfej_2022_11_17-38)__: The article proposes new methods for studying time series and building factor models in response to changing trends in macroeconomic and sectoral modelling. (2022-02-26, shares: 9) · https://www.ml-quant.com/papers/repec/alq-rufejo-rfej-2022-11-17-38/
- __[Dragonfly Optimization Algorithm for Feature Selection](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Ftechniumscience.com%2Findex.php%2Ftechnium%2Farticle%2Fview%2F7203%2F2817%3Bh%3Drepec%3Atec%3Atechni%3Av%3A4%3Ay%3A2022%3Ai%3A1%3Ap%3A44-52)__: The Dragonfly algorithm, a Swarm Intelligence method, is being used to improve the classification of breast cancer. (2022-10-06, shares: 7) · https://www.ml-quant.com/papers/repec/tec-techni-v-4-y-2022-i-1-p-44-52/

## Machine learning

### Recently Published

- __[Under-bagging Analysis](https://arxiv.org/abs/2404.09779)__: The under-bagging method improves classifier training from imbalanced data by enlarging the majority class, showing better performance than under-sampling and simple weighting methods. (2024-04-15, shares: 18) · https://www.ml-quant.com/papers/arxiv/2404.09779/
- __[RLHF Dataset Optimization](https://arxiv.org/abs/2404.08495)__: The DR-PO algorithm enhances Reinforcement Learning by incorporating offline preference data into online policy training, outperforming other techniques in summarization and the Anthropic Helpful Harmful dataset. (2024-04-12, shares: 23) · https://www.ml-quant.com/papers/arxiv/2404.08495/

### Historical Trending

- __[mu-Transfer: Neural Network Scaling Rules](https://arxiv.org/abs/2404.05728)__: Neural Network Scaling Rules: A study has found that the μ-Parameterization (μP) is generally effective in determining the best learning rates for large neural network models, although it doesn't work in all situations. (2024-04-08, shares: 109) · https://www.ml-quant.com/papers/arxiv/2404.05728/
- __[The Story of a Rashomon Quartet](https://arxiv.org/abs/2302.13356)__: A paper presents the idea of a Rashomon Quartet, four models with similar predictive performance but different data relationship explanations, emphasizing the need to visualize models beyond their performance metrics. (2023-02-26, shares: 58) · https://www.ml-quant.com/papers/arxiv/2302.13356/
- __[Neural Scaling Laws](https://rss.arxiv.org/abs/2402.01092)__: A study examines a random feature model trained with gradient descent, providing insights into neural scaling laws, including the correlation between performance, training time, model size, and the increasing gap between training and test loss due to repeated data use. (2024-02-02, shares: 69) · https://www.ml-quant.com/papers/arxiv/2402.01092/

## Papers with code

### Rising

- __[LLMs as Regressors](https://github.com/robertvacareanu/llm4regression)__: The performance of large language models like Llama2, GPT4, Claude 3, etc., in linear and nonlinear regression is examined without any extra training or gradient updates. (2024-04-15, shares: 75)
- __[PolicyGuided Diffusion](https://github.com/emptyjackson/policy-guided-diffusion)__: The article presents a new method as an alternative to autoregressive offline world models, which allows for the controlled generation of synthetic training data. (2024-04-12, shares: 42)

## GitHub

### Finance

- __[Machine Learning Trading](https://github.com/stefan-jansen/machine-learning-for-trading)__: The article shares the coding for the updated version of Machine Learning for Algorithmic Trading. (2018-05-09, shares: 11746)
- __[Microsoft Qlib](https://github.com/microsoft/qlib)__: Qlib is an AI platform that uses machine learning models for investment research and execution. (2020-08-14, shares: 14117)
- __[Superalgos](https://github.com/Superalgos/Superalgos)__: The article introduces a free, open-source bot for automated trading of bitcoin and other cryptocurrencies. (2019-08-12, shares: 3846)

## News

### Quantitative

- __[Citadel Securities Tech Head Joins HFT Firm](https://www.hedgeweek.com/ex-citadel-securities-tech-head-joins-high-frequency-trading-firm/)__: Former Citadel Securities executive, Joshua Fisher, has joined high-frequency trading firm Hudson River Trading. (2024-04-16, shares: 5)
- __[Interactive Brokers Launches PB Service](https://www.hedgeweek.com/interactive-brokers-launches-high-touch-pb-service-and-global-outsourced-trading/)__: Interactive Brokers has broadened its services for hedge funds, introducing a high touch prime brokerage and global outsourced trading services. (2024-04-12, shares: 4)
- __[Trader Accused of Selling Secret](https://www.efinancialcareers.com/news/jane-street-millennium)__: Jane Street has adopted a unique and highly effective trading strategy. (2024-04-15, shares: 1)

## Podcasts

### Quantitative

- __[Pension Wars](https://resolve-gestalt-university.captivate.fm/episode/jeff-weniger-pension-wars-why-everything-is-about-to-change-and-how-to-profit)__: Jeff Weniger discusses the potential effects of the pension wars concept on global equity markets and the role of financial engineering in investment strategies. (2024-04-12, shares: 11)
- __[EM Fixed Income](https://atanyrate.podbean.com/e/em-fixed-income-stepping-aside-again/)__: Jonny Goulden and Saad Siddiqui analyze the latest market developments and their impacts on the EM fixed income asset class. (2024-04-11, shares: 8)
- __[Signs of the Time](https://strategistscornermfs.podbean.com/e/signs-of-the-time-making-sense-of-a-world-in-transition/)__: Rob Almeida and Bill Gevov discuss the future of interest rates, changing global dynamics, and the potential influence of AI on the investment community. (2024-04-12, shares: 7)
- __[Geopolitical Risk](https://garpcast.libsyn.com/geopolitical-risk-trends-challenges-and-prognostications)__: Daniel Wagner talks about the complexities of the global geopolitical risk landscape and provides strategies for financial risk managers to better measure and mitigate geopolitical threats. (2024-04-14, shares: 6)

### Related

- __[Samara Cohen Insights](https://omny.fm/shows/masters-in-business/samara-cohen-on-global-markets-insights-with-black)__: Barry Ritholtz of Bloomberg Radio interviews Samara Cohen from BlackRock Inc., discussing her career and roles at the company. (2024-04-11, shares: 5)
- __[Goldilocks Danger](https://audioboom.com/posts/8489201)__: LGIM's CIO Sonja Laud and experts discuss the potential impact of political and geopolitical factors on 2024's asset rally in their first official CIO call. (2024-04-11, shares: 4)
- __[Global Commodities Update](https://atanyrate.podbean.com/e/global-commodities-natural-gas-and-agriculture-markets-catch-up/)__: A JPMorgan podcast discusses the potential impact of a court ruling on future LNG demand and the effects of the USDA's April WASDE report on South American corn and soybean production. (2024-04-12, shares: 4)
- __[US Rates PostCPI](https://atanyrate.podbean.com/e/us-rates-post-cpi-view-on-us-fixed-income-and-gold-with-phoebe-white-head-of-us-inflation-strategy-and-greg-shearer-head-of-metals-research/)__: A JPMorgan podcast explores the impact of a high CPI print and strong jobs print on the 'high for long' narrative, and the reasons for the recent gold rally. (2024-04-12, shares: 4)
- __[Uranium Market Growth](https://macrovoices.podbean.com/e/macrovoices-423-justin-huhn-accelerated-demand-growth-in-supply-driven-bull-market/)__: MacroVoices hosts interview Justin Huhn, founder of Uranium Insider, discussing the uranium bull market and the future of the uranium mining sector. (2024-04-11, shares: 4)

## X / Twitter

### Quantitative

- __[Algorithmic Trading: Portfolio Construction](https://twitter.com/__paleologo/status/1779934331068142029)__: Portfolio Construction: Article 1: The lecture discusses the estimation of idiosyncratic covariance matrix, off-diagonal cluster analysis, and updating of short-term idiovol in portfolio construction. (2024-04-15, shares: 9)
- __[Machine Learning for Corporate Bond Return Prediction](https://twitter.com/quantseeker/status/1780293968518922580)__: The study finds machine learning models can predict corporate bond returns with significant accuracy, even after accounting for transaction costs. (2024-04-16, shares: 4)
- __[Stocks and High Inflation](https://twitter.com/paradoxinvestor/status/1778748471094256127)__: The study shows that stocks and bonds perform poorly during high inflation periods, offering weak protection for investment portfolios. (2024-04-12, shares: 3)
- __[Financial History Analysis](https://twitter.com/quantseeker/status/1780370918897770970)__: The article analyzes the historical trends of stocks and bonds over the last 800 years using data from GlobalFinData. (2024-04-17, shares: 2)
- __[Equity Factors Debate](https://twitter.com/quantseeker/status/1762852981844660532)__: Frey's research suggests that around 40% of recorded equity factors are due to mispricing, with most factors indicating a return to fundamental values. (2024-04-13, shares: 2)
- __[AI Index Report](https://twitter.com/carlcarrie/status/1780310491069587517)__: Stanford University has published the 2024 AI Index report. (2024-04-16, shares: 1)

### Miscellaneous

- __[Tiny Time Mixers](https://twitter.com/carlcarrie/status/1780553403057258502)__: Tiny Time Mixers (TTMs) provide quick pretrained models for forecasting multivariate time series with zero or few shots, with the pretraining process being efficient, taking only 36 hours using 6 A100 GPUs. (2024-04-17, shares: 0)
- __[Bookshelf Recs](https://twitter.com/quantseeker/status/1779949005134233973)__: The author suggests six books from their personal library for readers to enjoy. (2024-04-15, shares: 0)
- __[Linear Models Notes](https://twitter.com/quantseeker/status/1778699279088890310)__: The author commends Peng Ding's comprehensive lecture notes on Linear models and extensions from Berkeley. (2024-04-12, shares: 0)
- __[WILMOTT May 2024](https://wilmott.com/wilmott-magazine-may-2024-issue/)__: The 2024 Wilmott magazine edition contains exclusive articles from renowned columnists, educators, and researchers. (2024-04-16, shares: 0)