---
title: Quant Letter No. 23: October 2023, Week 4
url: https://www.ml-quant.com/issues/2023-10-25/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
issue_date: 2023-10-25
---


# Quant Letter No. 23: October 2023, Week 4

Sent 2023-10-25. 87 items.

## arXiv

### Finance

- __[Topological Risk Measures in Derivative Markets](https://arxiv.org/abs/2310.14604)__: The article presents a new method for financial risk assessment called Density Change Under Stress (DCUS), which uses topological data analysis for a better understanding of market risks. (2023-10-23, shares: 10) · https://www.ml-quant.com/papers/arxiv/2310.14604/
- __[Topological Portfolio Optimization with Filtering Networks](https://arxiv.org/abs/2310.14881)__: The paper suggests the use of Statistically Robust Information Filtering Network (SR-IFN) to minimize noise in empirical covariance estimation, improving portfolio optimization by aiding in the selection of diversified, high-performing portfolios. (2023-10-23, shares: 9) · https://www.ml-quant.com/papers/arxiv/2310.14881/
- __[American Option Pricing with Self-Attention GRU and Shapley Value](https://arxiv.org/abs/2310.12500)__: The study introduces a machine learning technique for predicting SPY option prices using a self-attention gated recurrent unit (GRU) model, which performs better than other models due to its ability to utilize complex temporal dependencies and historical data. (2023-10-19, shares: 5) · https://www.ml-quant.com/papers/arxiv/2310.12500/
- __[Martingale Sinkhorn Algorithm](https://arxiv.org/abs/2310.13797)__: The article presents a method for interpolating between measures using a system similar to the Sinkhorn system in Entropic Optimal Transport. (2023-10-20, shares: 4) · https://www.ml-quant.com/papers/arxiv/2310.13797/
- __[Stochastic Order Flow Unwinding](https://arxiv.org/abs/2310.14144)__: The study investigates strategies for managing stochastic order flow with minimal transaction costs in a central risk book within a financial institution. (2023-10-22, shares: 4) · https://www.ml-quant.com/papers/arxiv/2310.14144/
- __[Portfolio Optimization Methods Study](https://arxiv.org/abs/2310.14748)__: The chapter evaluates and compares the MVP, HRP, and HERC portfolio optimization methods using data from 15 sectors of the Indian stock market. (2023-10-23, shares: 3) · https://www.ml-quant.com/papers/arxiv/2310.14748/
- __[Energy Cost of Informed Decisions](https://arxiv.org/abs/2310.15082)__: The paper introduces a measure of cognitive energy cost in belief dynamics, based on Landauer's principle, and examines its relationship with optimal decision-making in a two-armed bandit game. (2023-10-23, shares: 2) · https://www.ml-quant.com/papers/arxiv/2310.15082/

### Miscellaneous

- __[Blending Boosted Trees and Neural Networks for Forecasting](http://dx.doi.org/10.1016/j.ijforecast.2022.01.001)__: The paper outlines a successful method for point and probabilistic forecasting using a mix of machine learning models, as demonstrated in the M5 Competition, highlighting the significance of diverse models and careful validation example selection. (2023-10-19, shares: 5) · https://www.ml-quant.com/papers/doi/10-1016-j-ijforecast-2022-01-001/
- __[Wireless Traffic Evolution: Devices and Policy Impact](http://dx.doi.org/10.1016/j.telpol.2023.102595)__: Devices and Policy Impact: The article explores the growth of urban wireless traffic due to IoT devices and suggests mobile network operators should collaborate to create a shared small cell network for cost and capacity management. (2023-10-22, shares: 12) · https://www.ml-quant.com/papers/doi/10-1016-j-telpol-2023-102595/
- __[Local Explainability of Random Forests](https://arxiv.org/abs/2310.12428)__: The study introduces a new method to explain the performance of random forest models by viewing them as adaptive weighted K nearest-neighbors models, offering a localized understanding of model predictions. (2023-10-19, shares: 7) · https://www.ml-quant.com/papers/arxiv/2310.12428/
- __[Co-Training Volatility Prediction Model with NN](http://dx.doi.org/10.1145/3604237.3626870)__: The paper presents a machine learning model that uses an invertible neural network to predict stock volatility, outperforming other methods on a dataset of 100 stocks. (2023-10-23, shares: 4) · https://www.ml-quant.com/papers/doi/10-1145-3604237-3626870/
- __[Neural Networks for Insurance Pricing with Frequency and Severity Data](https://arxiv.org/abs/2310.12671)__: The research evaluates different models for insurance claim frequency and severity data, presenting combined actuarial neural networks (CANNs) that merge baseline predictions with a neural network correction, and recommends using global surrogate models for practical deployment. (2023-10-19, shares: 4) · https://www.ml-quant.com/papers/arxiv/2310.12671/
- __[Realized Min. Variance Portfolio Models](https://arxiv.org/abs/2310.13511)__: The article introduces a new model for predicting future portfolios using high-frequency financial data, which minimizes variance using the least absolute shrinkage and selection operator. (2023-10-20, shares: 4) · https://www.ml-quant.com/papers/arxiv/2310.13511/
- __[Quantum Tortoise & Classical Hare: Assessing Quantum Computing Benefits](https://arxiv.org/abs/2310.15505)__: Assessing Quantum Computing Benefits: The research suggests that only larger problems or those with significant algorithmic gains will benefit from near-term quantum computing, providing a framework to identify such problems. (2023-10-24, shares: 4) · https://www.ml-quant.com/papers/arxiv/2310.15505/

### Crypto & Blockchain

- __[Misquoted Bitcoin Perpetual Swaps](https://arxiv.org/abs/2310.14973?utm_source=dlvr.it&utm_medium=twitter)__: Research shows that major derivatives exchanges often inaccurately report open interest in bitcoin perpetual swaps, with issues ranging from unlikely open interest figures to late notifications of compulsory trades. (2023-10-23, shares: 11) · https://www.ml-quant.com/papers/arxiv/2310.14973/
- __[Analysis of RMM-01 Market Maker](https://arxiv.org/abs/2310.14320)__: The study investigates a time-dependent Constant Function Market Maker (CFMM) named RMM-01, analyzing its pricing characteristics, price manipulation costs, and arbitrage effects, and proposes integrating lending protocols with RMM-01 to attain various option payoffs. (2023-10-22, shares: 8) · https://www.ml-quant.com/papers/arxiv/2310.14320/
- __[Bitcoin's Economic Value: Volatility Timing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606787)__: Volatility Timing: A study suggests Bitcoin outperforms gold in a portfolio during dovish monetary policy periods, but loses value during rapid rate hikes. (2023-10-19, shares: 6) · https://www.ml-quant.com/papers/ssrn/4606787/
- __[Efficiency of NFT Markets: Wash Trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610162)__: Wash Trading: The study reveals a complex interplay between wash trading, real trading volumes, and prices in the non-fungible token (NFT) markets, questioning their efficiency. (2023-10-22, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610162/
- __[Bitcoin Sentiment Analysis & Efficient Market Hypothesis](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610497)__: Machine learning models used to predict Bitcoin trends support the Efficient Market Hypothesis and can yield higher returns than traditional strategies. (2023-01-28, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610497/

### Historical Trending

- __[Retail Concentration in the US](https://arxiv.org/abs/2202.07609)__: Retail concentration in the U.S. has grown from 1992 to 2012 due to multi-market firms expanding, contributing to a third of the increase in retail gross margins. (2022-02-15, shares: 39) · https://www.ml-quant.com/papers/arxiv/2202.07609/
- __[Extreme Measures in Finance](https://arxiv.org/abs/2210.13671)__: Dynamic spectral risk measures, determining a claim's value limits, are affected by market scenarios with lower than expected losses for upper limits and slightly lower gains for lower limits. (2022-10-25, shares: 37) · https://www.ml-quant.com/papers/arxiv/2210.13671/
- __[Static Hedging of European Options](https://arxiv.org/abs/2310.01104)__: The study expands the hedging of European options to include options over multiple short maturities, comparing the performance of the Black-Scholes and Merton Jump Diffusion models. (2023-10-02, shares: 12) · https://www.ml-quant.com/papers/arxiv/2310.01104/
- __[Deep Learning for Financial Trading](https://arxiv.org/abs/2309.16679)__: The article reviews different Deep Learning techniques for financial trading, addressing their efficiency, training issues, and potential solutions. (2023-07-23, shares: 11) · https://www.ml-quant.com/papers/arxiv/2309.16679/
- __[Black-Litterman Asset Allocation](https://arxiv.org/abs/2310.12333)__: The study investigates the Black-Litterman asset allocation model under a specific distribution, revealing that the resulting optimal portfolio has reduced risk and a negative correlation between volatility and skewness. (2023-10-18, shares: 7) · https://www.ml-quant.com/papers/arxiv/2310.12333/

## SSRN

### Quantitative

- __[Dynamic KGs for Global Finance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608445)__: The study presents a new use of dynamic Knowledge Graphs in modeling global financial systems, incorporating deep learning and a large language model, and introduces an open-source system for financial analytics. (2023-10-20, shares: 7) · https://www.ml-quant.com/papers/ssrn/4608445/
- __[Cluster-Enhanced IV Portfolios](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610075)__: The paper proposes a new method for portfolio construction, cluster-enhanced inverse volatility, which improves upon traditional inverse volatility portfolios, especially in large-asset portfolios. (2023-10-23, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610075/
- __[Optimizing Levenberg-Marquardt Hyperparameters](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610057)__: The effectiveness of the Levenberg-Marquardt algorithm in solving Complex Nonlinear Least Squares problems can be enhanced by using machine learning tools and adjusting hyperparameter values. (2023-10-23, shares: 3) · https://www.ml-quant.com/papers/ssrn/4610057/
- __[Dynamic Minimum Variance Portfolio Models](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608270)__: A new dynamic minimum variance portfolio model is presented, using nonlinear volatility dynamic models and the least absolute shrinkage and selection operator for parameter estimation. (2023-10-20, shares: 2) · https://www.ml-quant.com/papers/ssrn/4608270/
- __[Forecasting Transportation Demand in the U.S. Market](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4611277)__: The study forecasts U.S. domestic transportation demand using machine learning and econometric methods, with machine learning models providing more accurate predictions. (2023-10-24, shares: 2) · https://www.ml-quant.com/papers/ssrn/4611277/
- __[Machine Learning-based News Recommendation Generator](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4609841)__: A machine learning-based recommendation system can improve news consumption by simplifying the process of discovering news articles. (2023-10-23, shares: 3) · https://www.ml-quant.com/papers/ssrn/4609841/
- __[Closed-Form Option Pricing Model with Stochastic Volatility](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4607397)__: A new option pricing model with stochastic volatility is introduced, outperforming existing models and providing realistic risk premiums and pricing kernels. (2023-10-19, shares: 2) · https://www.ml-quant.com/papers/ssrn/4607397/
- __[Machine Learning in Insurance Claims Forecasting](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610457)__: A novel approach to insurance claims forecasting is introduced, using weather conditions and car sales as variables and machine learning algorithms for prediction. (2023-10-23, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610457/
- __[Smart Beta Performance: US vs. EM](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608423)__: US vs. EM: Research shows that US large-cap equity Smart Beta funds don't outperform active or passive strategies in risk-adjusted returns, but those in emerging markets do. (2021-06-29, shares: 2) · https://www.ml-quant.com/papers/ssrn/4608423/
- __[Forecasting Inflation Spikes with ML](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610424)__: A study using machine learning predicts spikes in the U.S. inflation rate with an accuracy of 87.27%. (2023-09-01, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610424/
- __[Asset Allocation with Clustered EF Coefficients](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4609682)__: A new asset allocation model using a Markov process has been proposed to characterize market states and optimize portfolios. (2023-05-01, shares: 3) · https://www.ml-quant.com/papers/ssrn/4609682/

### Financial

- __[Dynamics of US Economy's Frequency and Connectivity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4607668)__: A study indicates that the US economy's connectivity is greatly affected by sectoral indices volatility, with the DJIA, Wilshire 5000, and S&P 500 as the main influencers. (2023-10-20, shares: 2) · https://www.ml-quant.com/papers/ssrn/4607668/
- __[Assessing Network Risk with FRM in Cryptos](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606863)__: The Financial Risk Meter uses quantile-LASSO regression to identify systemic financial risk and dependencies in the crypto market, showing strong predictive abilities for future systemic risk. (2023-10-19, shares: 4) · https://www.ml-quant.com/papers/ssrn/4606863/
- __[Heterogeneity in Risk Aversion and Equity TS](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608133)__: The study explores a two-agent model's ability to explain equity term structure, suggesting more research is needed to align preference-heterogeneous agent models in asset pricing with empirical evidence. (2023-10-20, shares: 2) · https://www.ml-quant.com/papers/ssrn/4608133/
- __[Risk Premia in EU Sovereign Bonds](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606828)__: The research uses a stochastic volatility model to estimate risk premia for Germany, France, Italy, and the UK, finding that risk premia depend on stochastic volatility, not the yield curve's level and slope. (2023-10-19, shares: 2) · https://www.ml-quant.com/papers/ssrn/4606828/
- __[Beliefs and Price Formation](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4611162)__: A new study suggests investors often overestimate their knowledge and ignore price information, challenging the theory of rational expectations. (2023-10-24, shares: 3) · https://www.ml-quant.com/papers/ssrn/4611162/
- __[Liquidity and Risk in Search Economy](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610562)__: The paper indicates that liquidity can affect a firm's decision to default and cause price spirals, highlighting the link between liquidity and fundamental risks in asset pricing. (2023-10-23, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610562/
- __[Long-Term Yields & Short-Term Risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606821)__: Long-term interest rates can enhance the precision of risk premium and future rate predictions, with yield volatility being crucial. (2021-02-22, shares: 275) · https://www.ml-quant.com/papers/ssrn/4606821/
- __[Carbon Risk Management & Credit Default Swaps](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4611661)__: Firms with robust carbon risk management have lower credit default swap spreads, suggesting a positive impact on their credit risk evaluation. (2022-01-10, shares: 2) · https://www.ml-quant.com/papers/ssrn/4611661/
- __[Valuation & Hedging of Crypto Inverse Options](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606748)__: Inverse options, highly traded in the crypto market, perform equally well in USD and Coin units, and don't require fiat cash accounts. (2023-10-15, shares: 2) · https://www.ml-quant.com/papers/ssrn/4606748/
- __[Mutual Fund Redemptions & Stock Liquidity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4609248)__: Mutual fund investor withdrawals can adversely affect the liquidity of stock holdings, influenced by investor sentiment and stock returns. (2023-01-01, shares: 2) · https://www.ml-quant.com/papers/ssrn/4609248/
- __[Segmented Market Strategic Arbitrage](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610554)__: The article suggests a model where arbitrageurs selectively enter markets with entry costs, with evidence showing that not all arbitrage opportunities are pursued. (2022-12-11, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610554/
- __[Economics of Voluntary Portfolio Disclosure](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610422)__: Mutual funds often willingly reveal their portfolio holdings, with more frequent disclosure linked to higher institutional ownership and load fees, and lower investment risk and portfolio illiquidity. (2023-09-07, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610422/
- __[COVID Equity Tail Protection Strategies](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610153)__: The article discusses the pros and cons of three risk mitigation strategies during COVID, concluding that each carries its own set of challenges and unpredictability. (2023-05-10, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610153/
- __[Volatility & Pricing Kernel](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4610387)__: The study shows that negative stock market returns have a greater impact during low volatility periods, challenging some asset pricing theories. (2021-11-30, shares: 2) · https://www.ml-quant.com/papers/ssrn/4610387/
- __[Sustainability Bonds & Market Reaction](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4608606)__: The research finds that the market reaction to sustainability bonds is stronger than traditional bonds and that ESG scores significantly affect corporate performance. (2022-08-09, shares: 2) · https://www.ml-quant.com/papers/ssrn/4608606/
- __[Bank Failures & Optimization Errors](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4609719)__: The paper attributes the three largest bank failures since 2008 to improper hedging for interest rate risk during Federal Reserve rate hikes from 2022 to 2023. (2023-09-26, shares: 2) · https://www.ml-quant.com/papers/ssrn/4609719/

## Machine learning

### Recently Published

- __[Transformers' Length Generalization Abilities](https://arxiv.org/abs/2310.16028)__: The study suggests that Transformers can demonstrate strong length generalization on tasks that can be solved by a short RASP program applicable to all input lengths. (2023-10-24, shares: 93) · https://www.ml-quant.com/papers/arxiv/2310.16028/
- __[ManifoldNeRF: View-dependent Feature Supervision](https://arxiv.org/pdf/2310.13670.pdf)__: View-dependent Feature Supervision: ManifoldNeRF is a proposed method that uses interpolated features from known viewpoints to supervise feature vectors at unknown viewpoints, enhancing novel view synthesis in Neural Radiance Fields. (2023-10-20, shares: 18) · https://www.ml-quant.com/papers/arxiv/2310.13670/
- __[Iterative Dataset Synthesis with Language Models](https://arxiv.org/abs/2310.13671#)__: Synthesis Step by Step (S3) is a new data synthesis framework designed to minimize the distribution gap between synthesized and real task data, improving the performance of small models trained on the synthesized dataset. (2023-10-20, shares: 17) · https://www.ml-quant.com/papers/arxiv/2310.13671/
- __[Variational Inference for SDEs with Fractional Noise](https://arxiv.org/abs/2310.12975)__: A new variational framework for inference in stochastic differential equations driven by fractional Brownian motion has been introduced, providing a unique architecture for latent video prediction. (2023-10-19, shares: 10) · https://www.ml-quant.com/papers/arxiv/2310.12975/
- __[Unit Test Data Generation and Reinforcement Learning for Code Synthesis](https://arxiv.org/abs/2310.13669)__: A new method for automatically gathering data for reinforcement learning training of Code Synthesis models has been introduced, enhancing the performance of a pre-trained code language model. (2023-10-20, shares: 9) · https://www.ml-quant.com/papers/arxiv/2310.13669/
- __[Efficient Data Selection for Language Models](https://arxiv.org/abs/2302.03169)__: The Data Selection with Importance Resampling (DSIR) framework is a new method for selecting subsets of large raw unlabeled datasets, surpassing manual curation and heuristic filtering methods in both specific and general language models. (2023-02-06, shares: 279) · https://www.ml-quant.com/papers/arxiv/2302.03169/
- __[Valid and Diverse Mutations for DNN Testing](https://arxiv.org/abs/2112.01956)__: The study reexamines two main objectives in media input mutation - perception diversity and validity - based on manifold, offering a unified solution for mutating media data in various formats, surpassing previous methods in testing comprehensiveness. (2021-12-03, shares: 19) · https://www.ml-quant.com/papers/arxiv/2112.01956/

## Papers with code

### Trending

- __[Inverted Transformers for Time Series Forecast](https://github.com/thuml/Time-Series-Library)__: Transformers are being used by forecasters to model global dependencies over time series temporal tokens, each token comprising multiple variates of the same timestamp. (2023-10-21, shares: 1906)
- __[Enhancing LLM Agent Abilities](https://github.com/thudm/agenttuning)__: The article emphasizes the importance of research in enhancing the performance of Large Language Models (LLMs) without compromising their overall functionality. (2023-10-22, shares: 212)
- __[EleutherAI Llemma: Math Language Model](https://github.com/EleutherAI/math-lm)__: Math Language Model: The second article presents Llemma, a new large language model developed specifically for mathematical applications. (2023-10-19, shares: 160)
- __[Foundation Models for Graph Reasoning](https://github.com/DeepGraphLearning/ULTRA)__: The article underscores the difficulties in creating foundation models for Knowledge Graphs that can make inferences on any graph, regardless of entity and relation vocabularies. (2023-10-25, shares: 56)
- __[OpenAgents: Open Platform for Language Agents](https://github.com/xlang-ai/openagents)__: Open Platform for Language Agents: Large Language Models (LLMs) demonstrate potential in using natural language for intricate tasks in various environments. (2023-10-19, shares: 484)
- __[Human-Level Reward Design for LM](https://github.com/eureka-research/Eureka)__: Eureka's versatility enables a new gradient-free in-context learning method for reinforcement learning from human feedback, enhancing the quality and safety of generated rewards without model updating. (2023-10-23, shares: 614)

## GitHub

### Finance

- __[PIN Informed Trading Estimation](https://github.com/shuangology/Probability-of-Informed-Trading)__: A new method for calculating the likelihood of informed trading has been applied to AShare's daily public data. (2020-08-18, shares: 24)
- __[Greenplum DB: Parallel PostgreSQL for Analytics](https://github.com/greenplum-db/gpdb)__: Parallel PostgreSQL for Analytics: Greenplum Database, an open-source platform, utilizes parallel data for analytics, machine learning, and AI applications. (2015-10-23, shares: 5992)
- __[Replication Crisis in Finance: Study Code](https://github.com/bkelly-lab/ReplicationCrisis)__: Study Code: The code for the 2022 study 'Is There a Replication Crisis in Finance' by Jensen, Kelly, and Pedersen is made available. (2021-01-15, shares: 168)
- __[GymTradingEnv: Customizable Trading Gym Environment](https://github.com/ClementPerroud/Gym-Trading-Env)__: Customizable Trading Gym Environment: A flexible Gymnasium environment is offered for use in trading activities. (2023-03-24, shares: 129)
- __[RL Stock Trader](https://github.com/timeolord/Reinforcement-Learning-Stock-Trader)__: A revised version of Werner Duvaud's MuZero implementation is employed to train an AI to trade stocks using data from Yahoo Finance. (2021-04-18, shares: 15)

## News

### Quantitative

- __[US regulators may curb leveraged hedge fund trading](https://www.hedgeweek.com/us-regulators-may-lean-on-banks-to-curb-leveraged-hedge-fund-trading/)__: US regulators, including the SEC, are contemplating measures to limit highly leveraged trading by hedge funds due to potential systemic risks. (2023-10-20, shares: 4)
- __[Hedge Fund Tech Head on Gen AI -> Tech Head on Gen AI](https://www.efinancialcareers.com/news/2023/10/hedge-fund-man-group-generative-ai)__: The use of generative AI technology is becoming more prevalent in the operations of hedge funds. (2023-10-25, shares: 2)
- __[Simplified Finance Optimization for Quant Dev](https://www.youtube.com/watch?v=tKl73IFlBcI)__: A quant dev's role, different from ML Ops or Dev Ops, involves using advanced math to enhance financial models, while quants are responsible for creating these models. (2023-10-22, shares: 0)
- __[Risk Analysis of Central Counterparties with Skin in the Game](https://www.youtube.com/watch?v=lBv4FXGRPP8)__: Samim Ghamami, an economist at the U.S. Securities and Exchange Commission, delivered a lecture on risk analysis of central counterparties at the Department of Finance and Risk Engineering. (2023-10-19, shares: 0)
- __[Trade Surveillance Complexity Rises with Record Volumes](https://www.hedgeweek.com/complexity-of-trade-surveillance-soars-amid-record-volumes-and-volatility/)__: Acuiti's report highlights increased trade surveillance complexity due to high volatility, large volumes, and fragmented markets. (2023-10-19, shares: 6)
- __[AIMA Hedge Fund Managers Gain Confidence](https://www.hedgeweek.com/hedge-fund-managers-growing-in-confidence-says-aima/)__: The Hedge Fund Confidence Index by the Alternative Investment Management Association indicates growing confidence among global hedge fund managers. (2023-10-19, shares: 5)
- __[Balyasny: Buy and Build for hedge fund success](https://www.hedgeweek.com/buy-and-build-is-key-to-success-in-the-hedge-fund-talent-war-says-balyasny/)__: Buy and Build for hedge fund success: Dimitry Balyasny, founder of Balyasny Asset Manager, believes rising costs require managers to invest in talent for business growth, according to Bloomberg. (2023-10-20, shares: 3)

## Podcasts

### Quantitative

- __[Trading Secrets: Mastering Systematic Approach](https://ibkrcampus.com/podcasts/ibkr-podcasts/unearthing-trading-secrets-adrian-reid-on-mastering-the-systematic-approach/)__: Mastering Systematic Approach: Adrian Reid provides insights into the benefits and drawbacks of manual and automated trading systems. (2023-10-19, shares: 2)
- __[Anna Coulling on Volume Price Analysis](https://rss.com/podcasts/confessionsmm/1187769)__: Experienced trader Anna Coulling shares her financial market insights on the Confessions of a Market Maker podcast, offering listeners a chance to become funded prop traders. (2023-10-24, shares: 8)
- __[David Rosenberg on Interest Rate's Economic Impact](https://pdcn.co/e/www.buzzsprout.com/2034153/13815372-david-rosenberg-sheds-light-on-the-economic-impact-of-interest-rates.mp3)__: Financial consultant David Rosenberg discusses the impact of interest rates on long-duration assets and the bond market, and the potential effects of increased capital costs on the corporate sector. (2023-10-20, shares: 4)

## X / Twitter

### Quantitative

- __[Factors for Capturing Alpha](https://twitter.com/quantseeker/status/1715009035500433462)__: The study indicates that approximately 15 elements are required to capture the alpha of the factor zoo, with more alpha discovered in equal-weighted and international factors than in value-weighted and US factors. (2023-10-19, shares: 4)
- __[TimeSplines: Sketching Temporal Axes](https://twitter.com/carlcarrie/status/1717162983896297523)__: Sketching Temporal Axes: Article 2: The TimeSplines project enables users to draw multiple freeform temporal axes and fill them with diverse time-oriented data using incremental and lazy data binding. (2023-10-25, shares: 2)

## Reddit

### Quantitative

- __[ML for MidPrice Forecasting](https://www.reddit.com/r/quant/comments/17dxjim/mldl_for_midprice_forecasting_w_limit_order_book/)__:  (2023-10-22, shares: 3)
- __[Library/API for TA Patterns](https://www.reddit.com/r/algotrading/comments/17ehr9c/libraryapi_for_ta_patterns/)__:  (2023-10-23, shares: 6)
- __[Salary Guidance](https://www.reddit.com/r/quant/comments/17brp4w/2023_salary_guidance/)__:  (2023-10-19, shares: 195)

### Rising

- __[Quant Interns: An Insider's Perspective](https://www.reddit.com/r/quant/comments/17c4q7e/what_are_quant_interns_actually_doing/)__:  (2023-10-20, shares: 75)
- __[Decoding High Sharpe Ratios in HFT](https://www.reddit.com/r/quant/comments/17decv4/how_are_hft_sharpe_ratios_so_high/)__:  (2023-10-21, shares: 87)
- __[Exploring Unlikely Outcomes: Strategy Name Inquiry](https://www.reddit.com/r/quant/comments/17cfxn0/whats_the_name_for_a_strategy_that_focuses_on/)__:  (2023-10-20, shares: 47)