Quant LetterNo. 23
87 items across 9 sections, as sent to readers on 25 October 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
24 items
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.
10 sharesSource ↗
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.
9 shares8 citations todaySource ↗
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.
5 shares3 citations todaySource ↗
The article presents a method for interpolating between measures using a system similar to the Sinkhorn system in Entropic Optimal Transport.
4 shares12 citations todaySource ↗
The study investigates strategies for managing stochastic order flow with minimal transaction costs in a central risk book within a financial institution.
4 shares9 citations todaySource ↗
The chapter evaluates and compares the MVP, HRP, and HERC portfolio optimization methods using data from 15 sectors of the Indian stock market.
3 shares1 citation todaySource ↗
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.
2 sharesSource ↗
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.
5 shares17 citations todaySource ↗
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.
12 shares5 citations todaySource ↗
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.
7 shares5 citations todaySource ↗
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.
4 sharesSource ↗
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.
4 shares16 citations todaySource ↗
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.
4 shares2 citations todaySource ↗
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.
4 shares9 citations todaySource ↗
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.
11 sharesSource ↗
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.
8 sharesSource ↗
Volatility Timing: A study suggests Bitcoin outperforms gold in a portfolio during dovish monetary policy periods, but loses value during rapid rate hikes.
6 sharesSource ↗
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.
2 shares2 citations todaySource ↗
Machine learning models used to predict Bitcoin trends support the Efficient Market Hypothesis and can yield higher returns than traditional strategies.
2 sharesSource ↗
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.
39 shares17 citations todaySource ↗
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.
37 sharesSource ↗
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.
12 shares1 citation todaySource ↗
The article reviews different Deep Learning techniques for financial trading, addressing their efficiency, training issues, and potential solutions.
11 shares3 citations todaySource ↗
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.
7 sharesSource ↗
Working papers in finance and economics from SSRN.
27 items
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.
7 shares20 citations todaySource ↗
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.
2 sharesSource ↗
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.
3 sharesSource ↗
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.
2 sharesSource ↗
The study forecasts U.S. domestic transportation demand using machine learning and econometric methods, with machine learning models providing more accurate predictions.
2 sharesSource ↗
A machine learning-based recommendation system can improve news consumption by simplifying the process of discovering news articles.
3 sharesSource ↗
A new option pricing model with stochastic volatility is introduced, outperforming existing models and providing realistic risk premiums and pricing kernels.
2 shares1 citation todaySource ↗
A novel approach to insurance claims forecasting is introduced, using weather conditions and car sales as variables and machine learning algorithms for prediction.
2 sharesSource ↗
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.
2 sharesSource ↗
A study using machine learning predicts spikes in the U.S. inflation rate with an accuracy of 87.27%.
2 shares1 citation todaySource ↗
A new asset allocation model using a Markov process has been proposed to characterize market states and optimize portfolios.
3 sharesSource ↗
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.
2 sharesSource ↗
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.
4 sharesSource ↗
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.
2 sharesSource ↗
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.
2 sharesSource ↗
A new study suggests investors often overestimate their knowledge and ignore price information, challenging the theory of rational expectations.
3 sharesSource ↗
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.
2 sharesSource ↗
Long-term interest rates can enhance the precision of risk premium and future rate predictions, with yield volatility being crucial.
275 sharesSource ↗
Firms with robust carbon risk management have lower credit default swap spreads, suggesting a positive impact on their credit risk evaluation.
2 sharesSource ↗
Inverse options, highly traded in the crypto market, perform equally well in USD and Coin units, and don't require fiat cash accounts.
2 shares2 citations todaySource ↗
Mutual fund investor withdrawals can adversely affect the liquidity of stock holdings, influenced by investor sentiment and stock returns.
2 sharesSource ↗
The article suggests a model where arbitrageurs selectively enter markets with entry costs, with evidence showing that not all arbitrage opportunities are pursued.
2 sharesSource ↗
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.
2 sharesSource ↗
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.
2 sharesSource ↗
The study shows that negative stock market returns have a greater impact during low volatility periods, challenging some asset pricing theories.
2 sharesSource ↗
The research finds that the market reaction to sustainability bonds is stronger than traditional bonds and that ESG scores significantly affect corporate performance.
2 sharesSource ↗
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.
2 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
7 items
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.
93 shares218 citations todaySource ↗
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.
18 shares6 citations todaySource ↗
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.
17 shares46 citations todaySource ↗
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.
10 shares13 citations todaySource ↗
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.
9 shares8 citations todaySource ↗
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.
279 shares371 citations todaySource ↗
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.
19 shares13 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
6 items
Transformers are being used by forecasters to model global dependencies over time series temporal tokens, each token comprising multiple variates of the same timestamp.
1,906 shares
The article emphasizes the importance of research in enhancing the performance of Large Language Models (LLMs) without compromising their overall functionality.
212 shares
Math Language Model: The second article presents Llemma, a new large language model developed specifically for mathematical applications.
160 shares
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.
56 shares
Open Platform for Language Agents: Large Language Models (LLMs) demonstrate potential in using natural language for intricate tasks in various environments.
484 shares
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.
614 shares
Repositories the letter featured.
5 items
A new method for calculating the likelihood of informed trading has been applied to AShare's daily public data.
24 shares
Parallel PostgreSQL for Analytics: Greenplum Database, an open-source platform, utilizes parallel data for analytics, machine learning, and AI applications.
5,992 shares
Study Code: The code for the 2022 study 'Is There a Replication Crisis in Finance' by Jensen, Kelly, and Pedersen is made available.
168 shares
Customizable Trading Gym Environment: A flexible Gymnasium environment is offered for use in trading activities.
129 shares
A revised version of Werner Duvaud's MuZero implementation is employed to train an AI to trade stocks using data from Yahoo Finance.
15 shares
Industry news: funds, hiring, markets and regulation.
7 items
US regulators, including the SEC, are contemplating measures to limit highly leveraged trading by hedge funds due to potential systemic risks.
4 shares
The use of generative AI technology is becoming more prevalent in the operations of hedge funds.
2 shares
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.
0 shares
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.
0 shares
Acuiti's report highlights increased trade surveillance complexity due to high volatility, large volumes, and fragmented markets.
6 shares
The Hedge Fund Confidence Index by the Alternative Investment Management Association indicates growing confidence among global hedge fund managers.
5 shares
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.
3 shares
Episodes on markets, quant methods and economics.
3 items
Mastering Systematic Approach: Adrian Reid provides insights into the benefits and drawbacks of manual and automated trading systems.
2 shares
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.
8 shares
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.
4 shares
Posts from quant researchers on X.
2 items
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.
4 shares
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.
2 shares
Threads from r/quant, r/algotrading and friends.
6 items
3 shares
6 shares
195 shares