---
title: Quant Letter No. 39: March 2024, Week 1
url: https://www.ml-quant.com/issues/2024-03-06/
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: 2024-03-06
---


# Quant Letter No. 39: March 2024, Week 1

Sent 2024-03-06. 98 items.

## arXiv

### Finance

- __[Optimal Hedging](https://arxiv.org/abs/2403.00139)__: The study investigates the best way to hedge risk using derivatives in incomplete markets, focusing on an investor exposed to two assets and using vanilla options as hedging tools. (2024-02-29, shares: 8) · https://www.ml-quant.com/papers/arxiv/2403.00139/
- __[Insider Trading Detection](https://arxiv.org/abs/2403.00707)__: The article introduces an unsupervised machine learning technique for detecting potential insider trading by analyzing large datasets, using principal component analysis and autoencoders. (2024-03-01, shares: 7) · https://www.ml-quant.com/papers/arxiv/2403.00707/
- __[Volatility Strategy](https://arxiv.org/abs/2403.00474)__: The research looks at the growth of derivative markets in China, focusing on a short-volatility strategy using ETF options data, and suggests model improvements based on volatility forecasts. (2024-03-01, shares: 7) · https://www.ml-quant.com/papers/arxiv/2403.00474/
- __[Deep Learning for Pricing](https://arxiv.org/abs/2403.00746)__: The study introduces a new deep learning method for pricing European options in diffusion models, transforming the option pricing equation into an energy minimization problem and using deep artificial neural networks. (2024-03-01, shares: 6) · https://www.ml-quant.com/papers/arxiv/2403.00746/
- __[Digitwashing](http://arxiv.org/abs/2403.01360v1)__: The discrepancy between a company's digital transformation promises and actual results can lead to a stock price crash, worsened by economic policy uncertainty and unprofitable firms. (2024-03-03, shares: 4) · https://www.ml-quant.com/papers/arxiv/2403.01360/
- __[Fourier Pricing of Multi-Asset Options](https://arxiv.org/abs/2403.02832)__: The RQMC quadrature enhances the scalability of Fourier methods in pricing multi-asset options, surpassing traditional methods and offering practical error estimates. (2024-03-05, shares: 4) · https://www.ml-quant.com/papers/arxiv/2403.02832/
- __[Properties of EVaR](https://arxiv.org/abs/2403.01468)__: The Lambert function has been used to successfully calculate the Entropic Value-at-Risk (EVaR) measure for various distributions like Poisson, Gamma, and Laplace. (2024-03-03, shares: 3) · https://www.ml-quant.com/papers/arxiv/2403.01468/
- __[Fill Probabilities in Order Book](https://arxiv.org/abs/2403.02572)__: A new stochastic model accurately calculates fill probabilities for limit orders at different price levels in the order book, effectively capturing its dynamics. (2024-03-05, shares: 2) · https://www.ml-quant.com/papers/arxiv/2403.02572/

### Miscellaneous

- __[Robust Utility Valuation](https://arxiv.org/abs/2402.18872)__: The article presents a new method for maximizing utility with semistatic strategies for exotic options, introducing a robust form of convex integral functionals and establishing key results, which provide a solution for the robust utility maximization problem and a representation of associated indifference prices. (2024-02-29, shares: 9) · https://www.ml-quant.com/papers/arxiv/2402.18872/
- __[MambaStock Prediction](https://arxiv.org/abs/2402.18959)__: The paper presents MambaStock, a new Mamba-based model for predicting stock prices using historical market data, which outperforms previous methods in accuracy, aiding investors in making informed decisions. (2024-02-29, shares: 7) · https://www.ml-quant.com/papers/arxiv/2402.18959/
- __[Bandit Profit-maximization](http://arxiv.org/abs/2403.01361v1)__: The study explores a sequential profit-maximization problem, optimizing price and marketing expenditures across multiple markets with different demand curves, and introduces near-optimal algorithms for this problem in an adversarial bandit setting, proving an upper and lower regret bound for monotonic demand curves. (2024-03-03, shares: 6) · https://www.ml-quant.com/papers/arxiv/2403.01361/
- __[ARED](https://arxiv.org/abs/2403.00273)__: The article presents ARED, the first comprehensive Argentinian real estate price prediction dataset, featuring descriptive details and images for each listing. (2024-03-01, shares: 3) · https://www.ml-quant.com/papers/arxiv/2403.00273/
- __[Transformer for Time Series](https://arxiv.org/abs/2403.02523)__: The research investigates the use of transformer models in financial time series prediction, showing promising results with synthetic data and insightful findings on S&P500 data volatility prediction. (2024-03-04, shares: 3) · https://www.ml-quant.com/papers/arxiv/2403.02523/

### Historical Trending

- __[Two-Way-Fixed-Effects Regression for Panel Data](https://arxiv.org/abs/2107.13737)__: The article introduces a new estimator for calculating average causal effects of binary treatment with panel data, offering better performance and robustness than traditional two-way estimators. (2021-07-29, shares: 361) · https://www.ml-quant.com/papers/arxiv/2107.13737/
- __[Market Microstructure and Pricing](https://arxiv.org/abs/2304.02356)__: The paper presents a discrete binary tree for pricing contingent claims, which is arbitrage-free, market-complete, and maintains all parameters controlling the historical price dynamics. (2023-04-05, shares: 57) · https://www.ml-quant.com/papers/arxiv/2304.02356/
- __[Local Volatility in Rate Models](https://arxiv.org/abs/2301.13595)__: The article explains the implementation of Local Volatility in market modeling to replicate most swaption prices within a single model, but short-term swaption volatility cannot be accurately generated due to the use of a normal distribution. (2023-01-31, shares: 37) · https://www.ml-quant.com/papers/arxiv/2301.13595/
- __[Gamma Hedging](https://arxiv.org/abs/2309.05054)__: The research uses rough path theory to show that a specific hedging strategy can replicate other European options, even without a specific pricing model. (2023-09-10, shares: 32) · https://www.ml-quant.com/papers/arxiv/2309.05054/
- __[Talent Hoarding](https://arxiv.org/abs/2206.15098)__: The study reveals that talent hoarding by managers in companies discourages employees from seeking new roles, affecting career growth and talent distribution within the organization. (2022-06-30, shares: 30) · https://www.ml-quant.com/papers/arxiv/2206.15098/

## SSRN

### Quantitative

- __[Inflation Model](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742904)__: The article presents a new model for predicting inflation volatility, claiming superior performance over traditional methods. (2024-02-29, shares: 20) · https://www.ml-quant.com/papers/ssrn/4742904/
- __[Intraday Volatility Prediction](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4747889)__: The paper proposes a new method for predicting intraday volatility in financial data using Ito semimartingale models and a Two-side Projected-PCA procedure. (2024-03-05, shares: 2) · https://www.ml-quant.com/papers/ssrn/4747889/
- __[Banking Stability Prediction](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4747568)__: The research uses the CAMELS framework and machine learning to assess the performance of major banks in top GDP countries, with the aim of predicting future performance. (2024-03-04, shares: 2) · https://www.ml-quant.com/papers/ssrn/4747568/
- __[Seeking Alpha: Investment Advice](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4747465)__: Investment Advice: Investment advice from Seeking Alpha offers timely and relevant information for savvy investors, impacting immediate market returns and 90-day drift returns. (2024-03-04, shares: 3) · https://www.ml-quant.com/papers/ssrn/4747465/
- __[AI Risk Package](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744576)__: The authors introduce a Python package and metrics for managing risks in Artificial Intelligence applications, emphasizing their interpretability and reproducibility. (2024-03-01, shares: 14) · https://www.ml-quant.com/papers/ssrn/4744576/
- __[Asset Pricing Frictions](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742370)__: The notes detail a Big Data Asset Pricing course, covering asset pricing basics, transaction costs, market liquidity risk, and machine learning. (2022-04-29, shares: 2) · https://www.ml-quant.com/papers/ssrn/4742370/
- __[Barycentric ML Optimization](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742189)__: The study suggests a method to decrease machine learning algorithms' execution time in high-dimensional spaces using the barycentric correction procedure. (2024-02-24, shares: 3) · https://www.ml-quant.com/papers/ssrn/4742189/
- __[Robust Inference for Financial Portfolios](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742088)__: The article highlights the importance of accurately modeling asset dependence in financial portfolios, emphasizing the significance of correlation-concordance matrices during market stress. (2022-04-20, shares: 2) · https://www.ml-quant.com/papers/ssrn/4742088/
- __[Corporate Refinancing and Bond Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4749327)__: The paper reveals that the urgency of refinancing maturing debt can increase future corporate bond returns, particularly during periods of high default and liquidity risk. (2024-02-08, shares: 2) · https://www.ml-quant.com/papers/ssrn/4749327/

### Financial

- __[Local Edgeworth](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4747434)__: The article introduces a mathematical model to estimate changes in level-volatility in a Brownian semimartingale, incorporating skewness and kurtosis through fluctuating correlations and volatility changes. (2024-03-04, shares: 3) · https://www.ml-quant.com/papers/ssrn/4747434/
- __[Greenium Search](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744608)__: The study introduces a robust green score and expected returns to calculate the greenium, the expected return of green securities compared to brown, which is found to be more negative in greener countries and over time. (2024-03-01, shares: 25) · https://www.ml-quant.com/papers/ssrn/4744608/
- __[Commodity Pricing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4746258)__: The article shows that a latent-factor model using the Instrumented Principal Component Analysis methodology surpasses existing models in explaining variations in commodity futures returns, with momentum, expected shortfall, and idiosyncratic volatility as key factors. (2024-03-03, shares: 3) · https://www.ml-quant.com/papers/ssrn/4746258/
- __[Asset Ratio](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4746302)__: The article proposes a strategy to improve weak medium-term returns in retirement portfolios by adjusting the stock percentage based on the earnings yield of stock and the current yield of bonds, with caution needed when stock prices exceed sustainable levels. (2024-03-03, shares: 3) · https://www.ml-quant.com/papers/ssrn/4746302/
- __[Toxicity Trade-off](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4746270)__: The paper investigates the liquidity provision game in decentralized exchanges, revealing a tradeoff between toxicity and competitiveness in liquidity provision and offering a new guideline for liquidity provision in the decentralized financial market. (2024-03-03, shares: 6) · https://www.ml-quant.com/papers/ssrn/4746270/
- __[Insider Trading Detection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4746851)__: The article proposes a machine learning method for detecting potential insider trading by analyzing large datasets of trading positions. (2024-03-01, shares: 2) · https://www.ml-quant.com/papers/ssrn/4746851/
- __[Limit Order Book Simulations Review](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4745587)__: The piece reviews models of Limit Order Books simulations, emphasizing the role of AI in improving these models and the significance of price impacts in algorithmic trading. (2024-03-01, shares: 2) · https://www.ml-quant.com/papers/ssrn/4745587/
- __[Overcoming Markowitz's Instability](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4748151)__: The paper shows the hierarchical risk parity (HRP) approach is superior to the traditional Markowitz portfolio allocation method in terms of noise reduction and robustness. (2024-03-05, shares: 4) · https://www.ml-quant.com/papers/ssrn/4748151/
- __[Dispelling Myths in Optimization](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4747461)__: The article discusses the widespread use of mean-variance optimization in quantitative finance, dispels associated myths, and introduces the concept of mean-variance-equivalent distributions. (2024-03-03, shares: 7) · https://www.ml-quant.com/papers/ssrn/4747461/
- __[Global FX Ambiguity in Portfolios](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742236)__: The study examines the effect of global foreign exchange ambiguity on currency portfolios, finding that high ambiguity leads to high currency carry returns and uncovers uncertainty not captured by FX volatility. (2024-02-29, shares: 3) · https://www.ml-quant.com/papers/ssrn/4742236/
- __[Volatility Risk Premiums in Swaption Market](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744144)__: The study investigates how unconventional monetary policies and pandemics affect volatility risk premiums in the USD interest rate swaption market from 2007 to 2022. (2023-11-07, shares: 118) · https://www.ml-quant.com/papers/ssrn/4744144/
- __[Mutual Fund Efficiency & Internationalization](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4743637)__: The research finds that UK equity mutual funds with a domestic focus perform better than those with an international focus. (2022-03-14, shares: 86) · https://www.ml-quant.com/papers/ssrn/4743637/
- __[Robust Stochastic Volatility Models](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742141)__: The paper suggests four principles to evaluate the suitability of a Stochastic Volatility model for valuing derivative securities across various asset classes. (2023-12-27, shares: 2) · https://www.ml-quant.com/papers/ssrn/4742141/
- __[Procyclicality of Risk-based Initial Margin Models](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4747312)__: The study presents a method to evaluate the responsiveness of initial margin calculation models during periods of high market volatility. (2024-02-15, shares: 64) · https://www.ml-quant.com/papers/ssrn/4747312/
- __[Mutual Fund Outflows and First-Mover Advantage](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4749211)__: The study suggests that mutual fund outflows after poor performance are due to a firstmover advantage in the asset market, not investor behavior, affecting mutual fund industry regulation and understanding. (2021-11-18, shares: 4) · https://www.ml-quant.com/papers/ssrn/4749211/
- __[Cryptocurrency Factor Diversification](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4744431)__: Research indicates that adding size and momentum-based cryptocurrency factors to a stock-bond portfolio can significantly diversify it, with machine-learning asset allocation strategies enhancing these benefits. (2023-01-09, shares: 3) · https://www.ml-quant.com/papers/ssrn/4744431/
- __[Global Mutual Fund Flows Study](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742625)__: The research shows that investors' decisions are influenced by the performance of mutual funds, with variations based on the fund's size and market position. (2022-04-03, shares: 2) · https://www.ml-quant.com/papers/ssrn/4742625/
- __[Volatility, Leverage, and Skewness in Stock Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4742615)__: The study finds that variance shocks strongly influence the conditional skewness of index returns, impacting asset pricing, portfolio selection, and risk management applications. (2022-04-27, shares: 2) · https://www.ml-quant.com/papers/ssrn/4742615/

## RePEc

### Finance

- __[Investment Strategies Development](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-023-00573-4%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A10%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1186_s40854-023-00573-4)__: A new scale for assessing short and long-term investment strategies was developed and proven reliable for understanding investment decision-making processes. (2024-03-06, shares: 16) · https://www.ml-quant.com/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-023-00573-4/
- __[Estr OIS Market Efficiency](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS104244312400009X%3Bh%3Drepec%3Aeee%3Aintfin%3Av%3A91%3Ay%3A2024%3Ai%3Ac%3As104244312400009x)__: The study finds that only investors skilled in navigating the bid-ask spread can profit from mispricing in Euro Short Term Rate Overnight Index Swaps. (2024-03-06, shares: 16) · https://www.ml-quant.com/papers/repec/eee-intfin-v-91-y-2024-i-c-s104244312400009x/
- __[Mutual Funds Active Investors](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F00036846.2023.2176455%3Bh%3Drepec%3Ataf%3Aapplec%3Av%3A56%3Ay%3A2024%3Ai%3A13%3Ap%3A1489-1508)__: The study reveals that individual investors in mutual funds act as momentum buyers and contrarian sellers, with older and larger transaction investors more likely to be momentum buyers. (2024-03-06, shares: 14) · https://www.ml-quant.com/papers/repec/taf-applec-v-56-y-2024-i-13-p-1489-1508/
- __[Credit Rating Announcements Trading Responses](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Fjbfa.12686%3Bh%3Drepec%3Abla%3Ajbfnac%3Av%3A51%3Ay%3A2024%3Ai%3A1-2%3Ap%3A84-112)__: The research finds that investors react differently to changes in credit ratings from issuer-paid and investor-paid agencies, and can earn significant abnormal returns by using information from both. (2024-03-06, shares: 11) · https://www.ml-quant.com/papers/repec/bla-jbfnac-v-51-y-2024-i-1-2-p-84-112/

### Statistical

- __[AI Assets Connectedness with Traditional Classes](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS104244312300197X%3Bh%3Drepec%3Aeee%3Aintfin%3Av%3A91%3Ay%3A2024%3Ai%3Ac%3As104244312300197x)__: Research suggests AI tokens can diversify traditional assets under normal market conditions, but fail to do so during extreme market shocks. (2024-03-06, shares: 16) · https://www.ml-quant.com/papers/repec/eee-intfin-v-91-y-2024-i-c-s104244312300197x/
- __[Spatial Autocorrelation in Hedonic Models](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11146-022-09915-y%3Bh%3Drepec%3Akap%3Ajrefec%3Av%3A68%3Ay%3A2024%3Ai%3A2%3Ad%3A10.1007_s11146-022-09915-y)__: The study proposes a spatial cross-validation strategy to correct bias in error estimates of tree-based algorithms in real estate data due to spatial autocorrelation. (2024-03-06, shares: 23) · https://www.ml-quant.com/papers/repec/kap-jrefec-v-68-y-2024-i-2-d-10-1007-s11146-022-09915-y/
- __[Financial Machine Learning with R](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fmpra.ub.uni-muenchen.de%2F119998%2F1%2FMPRA_paper_119998.pdf%3Bh%3Drepec%3Apra%3Amprapa%3A119998)__: The paper explores the limitations of machine learning in finance, offering advice on method selection and referencing R libraries for computation. (2024-03-06, shares: 20) · https://www.ml-quant.com/papers/repec/pra-mprapa-119998/
- __[AI for human learning](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fonlinesciencepublishing.com%2Findex.php%2Fajel%2Farticle%2Fview%2F1024%2F1597%3Bh%3Drepec%3Aonl%3Aajoeal%3Av%3A9%3Ay%3A2024%3Ai%3A1%3Ap%3A1-21%3Aid%3A1024)__: The paper presents a framework for integrating AI into education and proposes a learning design model for AI-based learning support systems. (2024-03-06, shares: 12) · https://www.ml-quant.com/papers/repec/onl-ajoeal-v-9-y-2024-i-1-p-1-21-id-1024/
- __[Machine learning in supply chain](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D136856%3Bh%3Drepec%3Aids%3Aijlsma%3Av%3A47%3Ay%3A2024%3Ai%3A3%3Ap%3A327-355)__: The research reviews the use of machine learning in supply chain management, providing insights for future studies in this area. (2024-03-06, shares: 12) · https://www.ml-quant.com/papers/repec/ids-ijlsma-v-47-y-2024-i-3-p-327-355/
- __[Stock Return Forecasting with Machine Learning](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0304405X2400014X%3Bh%3Drepec%3Aeee%3Ajfinec%3Av%3A153%3Ay%3A2024%3Ai%3Ac%3As0304405x2400014x)__: The article uses machine learning to predict stock returns, challenging the efficient market hypothesis due to its strong predictive power. It also shows that machine learning models are effective in out-of-sample performance. (2024-03-06, shares: 17) · https://www.ml-quant.com/papers/repec/eee-jfinec-v-153-y-2024-i-c-s0304405x2400014x/

### Historical Trending

- __[Adaptive Portfolio Selection with Transaction Costs](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2023.2287134%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A24%3Ay%3A2023%3Ai%3A1%3Ap%3A59-82)__: The paper introduces an adaptive moving average method with peer impact for online portfolio selection, which considers the influence of other risky assets for accurate return predictions, and an adaptive mean-variance model for risk measurement. (2023-02-18, shares: 23) · https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2023-i-1-p-59-82/
- __[Volatility, Growth Options, and Returns](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Frapstu%2Fraad006%3Bh%3Drepec%3Aoup%3Arasset%3Av%3A13%3Ay%3A2023%3Ai%3A4%3Ap%3A653-690.)__: The study reveals that growth firms and high idiosyncratic volatility firms outperform the CAPM during periods of high aggregate volatility, thus lowering their risk. (2023-10-01, shares: 21) · https://www.ml-quant.com/papers/repec/oup-rasset-v-13-y-2023-i-4-p-653-690/
- __[Robust Testing of Risk Premia](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fjjfinec%2Fnbac010%3Bh%3Drepec%3Aoup%3Ajfinec%3Av%3A21%3Ay%3A2023%3Ai%3A2%3Ap%3A263-297.)__: The article introduces new tests for risk premia in linear factor models that are robust to small sample sizes and weak identification of risk premia, and revisits two empirical applications to show differences from traditional tests. (2023-02-05, shares: 11) · https://www.ml-quant.com/papers/repec/oup-jfinec-v-21-y-2023-i-2-p-263-297/
- __[Independent Directors and Financial Fraud](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F21697213.2023.2239670%3Bh%3Drepec%3Ataf%3Arcjaxx%3Av%3A11%3Ay%3A2023%3Ai%3A3%3Ap%3A465-492)__: The research finds that companies with dissenting independent directors, identified through machine learning predictions and Chinese board voting data, have a lower future risk of financial fraud. (2023-05-20, shares: 9) · https://www.ml-quant.com/papers/repec/taf-rcjaxx-v-11-y-2023-i-3-p-465-492/
- __[Adaptive Portfolio Selection](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.worldscientific.com%2Fdoi%2Fabs%2F10.1142%2FS0219024923500243%3Bh%3Drepec%3Awsi%3Aijtafx%3Av%3A26%3Ay%3A2023%3Ai%3A06n07%3An%3As0219024923500243)__: The paper discusses the use of polynomial series, specifically Taylor and Bernstein series, to solve dynamic portfolio optimization problems. (2023-05-25, shares: 9) · https://www.ml-quant.com/papers/repec/wsi-ijtafx-v-26-y-2023-i-06n07-n-s0219024923500243/
- __[Volatility, Growth, and Returns](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fjjfinec%2Fnbab011%3Bh%3Drepec%3Aoup%3Ajfinec%3Av%3A21%3Ay%3A2023%3Ai%3A3%3Ap%3A616-650.)__: The research suggests using the characteristic function to estimate linear models with errors in financial econometrics, with applications to the capital asset pricing model. (2023-10-20, shares: 8) · https://www.ml-quant.com/papers/repec/oup-jfinec-v-21-y-2023-i-3-p-616-650/
- __[Robust Testing](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fjjfinec%2Fnbaa046%3Bh%3Drepec%3Aoup%3Ajfinec%3Av%3A21%3Ay%3A2023%3Ai%3A2%3Ap%3A316-367.)__: A new test is introduced for identifying changes in risk exposures of large financial asset portfolios, revealing portfolio weight dynamics across different regimes. (2023-05-20, shares: 8) · https://www.ml-quant.com/papers/repec/oup-jfinec-v-21-y-2023-i-2-p-316-367/
- __[Independent Directors](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10037-023-00198-1%3Bh%3Drepec%3Aspr%3Ajahrfr%3Av%3A43%3Ay%3A2023%3Ai%3A3%3Ad%3A10.1007_s10037-023-00198-1)__: The research uses micro-scale job-household data and machine learning to analyze spatiotemporal patterns in Tokyo, highlighting urbanization and suburbanization trends. (2023-05-12, shares: 7) · https://www.ml-quant.com/papers/repec/spr-jahrfr-v-43-y-2023-i-3-d-10-1007-s10037-023-00198-1/

## Machine learning

### Recently Published

- __[Active Inference for Stat Analysis](https://arxiv.org/abs/2403.03208)__: A new method called Active inference, which uses machine learning to collect data and focuses on areas where the model is uncertain, achieves the same accuracy with fewer samples than previous methods. (2024-03-05, shares: 32) · https://www.ml-quant.com/papers/arxiv/2403.03208/
- __[Behavior Generation with VQ-BeT](https://arxiv.org/abs/2403.03181)__: The Vector-Quantized Behavior Transformer (VQ-BeT), a new model for behavior generation, improves multimodal action prediction, conditional generation, and partial observations, and speeds up inference. (2024-03-05, shares: 32) · https://www.ml-quant.com/papers/arxiv/2403.03181/
- __[Preventing Reward Hacking](https://arxiv.org/abs/2403.03185)__: A novel method to prevent reward hacking in AI systems uses state occupancy measure instead of action distribution, effectively avoiding significant drops in true reward. (2024-03-05, shares: 10) · https://www.ml-quant.com/papers/arxiv/2403.03185/
- __[Variance in Fair Classification](https://arxiv.org/abs/2301.11562)__: Variance in predictions across different models is a major source of error in fair binary classification, and a new metric, self-consistency, is proposed to measure and reduce randomness, challenging the effectiveness of common algorithmic fairness methods. (2023-01-27, shares: 65) · https://www.ml-quant.com/papers/arxiv/2301.11562/

## GitHub

### Finance

- __[HSBC Order Book Project](https://github.com/mlwynne24/HSBC-Limit-Order-Book-Data-Project-)__: University students and HSBC's AI team have developed a profitable trading strategy using machine learning algorithms on level2 limit order book data. (2023-10-20, shares: 9)
- __[Math Finance Analysis](https://github.com/qarix3/mathematical-finance)__: The article explores the application of financial modeling and quantitative analysis in finance. (2021-03-30, shares: 3)
- __[ML USU Repository](https://github.com/PJalgotrader/Machine_Learning-USU)__: The piece details a Github repository for a machine learning course managed by a professor. (2022-01-05, shares: 28)
- __[Real-time Data Pipelines](https://github.com/Paulescu/real-time-data-pipelines-in-python)__: The article provides a guide on creating real-time feature pipelines using Python. (2024-01-09, shares: 80)
- __[DataDreamer Data Generation](https://github.com/datadreamer-dev/DataDreamer)__: The article explores the creation of synthetic data and training of align models using DataDreamer Prompt. (2023-06-02, shares: 524)
- __[Python Made Easy](https://github.com/astral-sh/rye)__: The article offers tips for a smoother experience with Python programming. (2023-04-22, shares: 10260)
- __[Smart Contract Empowerment](https://github.com/FuelLabs/sway)__: The piece provides guidance on creating reliable and efficient smart contracts. (2021-01-19, shares: 57633)

## Podcasts

### Quantitative

- __[Mastering Quant Finance Interviews](https://www.cqfinstitute.org/content/master-quant-finance-interview-strategies-success)__: Katherina DuongBernet provides tips for successful interviews in the quant finance sector, covering employer expectations, common questions, and preparation strategies. (2024-03-04, shares: 19)
- __[Specialty Finance and Data Investing](https://omny.fm/shows/masters-in-business/david-snyderman-on-specialty-finance-and-data-in-i)__: Barry Ritholtz of Bloomberg Radio interviews David Snyderman from Magnetar Capital LLC, discussing his career and role in the company. (2024-03-01, shares: 13)
- __[Spreadbites High Yield Conference Takeaways](https://atanyrate.podbean.com/e/spreadbites-high-yield-leveraged-finance-2024-conference-takeaways/)__: Stephen Dulake and Samantha Azzarello discuss global credit market trends following J.P. Morgan’s 2024 High Yield & Leveraged Finance Conference. (2024-03-04, shares: 8)

## X / Twitter

### Quantitative

- __[Machine Learning for Stock Prediction](https://twitter.com/quantseeker/status/1765100789603942504)__: Machine learning algorithms have proven to be more effective than traditional models in predicting weekly stock performance. (2024-03-05, shares: 8)
- __[Weekly Research Topic Recap](https://twitter.com/quantseeker/status/1765002033197691127)__: A weekly summary of research on topics like ESG investing, Macro Machine Learning, Volatility, etc. has been released. (2024-03-05, shares: 6)
- __[Empirical Asset Pricing Review](https://twitter.com/quantseeker/status/1765372679492620499)__: A recent paper offers an extensive review of empirical asset pricing and machine learning studies. (2024-03-06, shares: 5)
- __[Multipletesting for FX Trading Signals](https://twitter.com/quantseeker/status/1765332758719500549)__: A new framework for testing predictive signals surpasses current frameworks when used on over 20,000 FX trading rules across 30 currencies. (2024-03-06, shares: 3)
- __[Quant Trading Books](https://twitter.com/quantseeker/status/1764219918390526153)__: The author suggests three beneficial books on quant trading, two of which were instrumental in their early hedge fund career. (2024-03-03, shares: 3)
- __[Private Equity](https://twitter.com/quantseeker/status/1764936099405689236)__: The article offers an in-depth analysis and discussion on the subject of private equity. (2024-03-05, shares: 2)
- __[International Finance Notes](https://twitter.com/quantseeker/status/1764708964694966274)__: The article provides detailed lecture notes covering a range of topics within international finance. (2024-03-04, shares: 1)
- __[Equity Risk Factor Paper](https://twitter.com/carlcarrie/status/1763042255495934422)__: The article introduces a research paper focused on Equity Risk Factor Regimes. (2024-02-29, shares: 1)

### Miscellaneous

- __[Stock Forecasting Comparison](https://twitter.com/quantseeker/status/1763141572948885665)__: The article explores the prediction of a stock's performance over the market, noting that simple models can be as effective as complex ones. (2024-02-29, shares: 0)
- __[Profiting Trends](https://twitter.com/quantseeker/status/1763643562032234905)__: Transtrend provides strategies on how to gain profits from market trends. (2024-03-01, shares: 0)
- __[Weirdness Prompting AIs](https://twitter.com/carlcarrie/status/1765126139050176893)__: The article delves into the complexities and unpredictability associated with prompting artificial intelligence. (2024-03-05, shares: 0)
- __[PyRIT AI Red Team Tool](https://twitter.com/carlcarrie/status/1764986745311895800)__: Microsoft employs PyRIT, a Python-based tool, for risk identification in generative AI. (2024-03-05, shares: 0)
- __[Highyield spreads and momentum factor performance](https://twitter.com/quantseeker/status/1764657275954684295)__: Momentum factor performance is strongly predicted by high-yield spreads and often crashes after significant drawdowns in market reversals. (2024-03-04, shares: 0)
- __[Current regime compared to late 90s boom](https://twitter.com/quantseeker/status/1764958604862300581)__: The current economic climate closely resembles the late 90s boom, particularly June 1997, suggesting potential for further growth. (2024-03-05, shares: 0)
- __[Microsoft's AI Red Team and PyRIT tool](https://twitter.com/carlcarrie/status/1764989001759371560)__: Microsoft's AI Red Team utilizes the PyRIT Python Risk Identification Tool to manage AI risks and ensure compliance. (2024-03-05, shares: 0)
- __[Modality Aware Transformers for TimeSeries on FRED Data](https://twitter.com/carlcarrie/status/1763040798591823881)__: Modality Aware Transformers (MAT) are employed for TimeSeries on FRED Data, with corresponding R code used to measure sentiment from texts. (2024-02-29, shares: 0)

### Related

- __[High Frequency Arbitrage](http://jonathankinlay.com/2024/03/high-frequency-statistical-arbitrage/)__: The article explains how high-frequency statistical arbitrage uses advanced tech and models to take advantage of brief market inefficiencies. (2024-03-04, shares: 14)
- __[Stop Loss Strategy Pros And Cons](http://jonathankinlay.com/2024/03/high-frequency-statistical-arbitrage/)__: The piece highlights how hedge funds have led the way in using high-frequency statistical arbitrage to profit from tiny, fleeting price differences in various assets. (2024-03-04, shares: 14)
- __[Responsible Investing Path](https://research-center.amundi.com/article/outerblue-convictions-global-investment-views-market-debate-rages)__: The piece discusses how changes in investors' expectations of central bank actions have recently affected financial markets. (2024-03-01, shares: 4)
- __[High Frequency Arbitrage](https://www.man.com/maninstitute/investing-responsibly-commodities)__: The article explores the inclusion of a frequently ignored asset class into a sustainable multi-asset portfolio. (2024-03-06, shares: 3)
- __[Benchmark for RL](https://github.com/michaeltmatthews/craftax)__: The article criticizes some research tools for being either too slow without significant computational resources or not challenging enough. (2024-03-01, shares: 60)