ML-QuantSubscribe

Quant LetterNo. 24

November 2023, Week 1

80 items across 9 sections, as sent to readers on 2 November 2023. Paper titles open their ML-Quant page; ↗ goes to the source.

arXiv

Quantitative-finance and ML-for-finance preprints from arXiv.

17 items

Finance8

02

Agent-based Model for Deep Hedging

The Chiarella-Heston model, an advanced agent-based model, enhances deep hedging strategies by incorporating different types of traders, and performs better in creating realistic financial time series than three other models.

9 shares13 citations todaySource ↗

03

Estimating Systemic Risk in Networks

The article proposes a two-step nonparametric estimation method for measuring financial systemic risk, showing that only the second step's estimation error affects the results.

4 sharesSource ↗

04

Optimal Fees in Hedge Funds with Compensation

The research suggests alternative fee schemes for hedge funds, arguing that traditional management and performance fees are suboptimal and that the recommended schemes reduce the fund's volatility.

3 shares3 citations todaySource ↗

05

Visibility Graph Analysis of Oil Futures Markets

A study using visibility graph methodology examines the effects of the Russia-Ukraine conflict and COVID-19 on crude oil futures markets, uncovering distinct market reactions to global disturbances.

5 shares1 citation todaySource ↗

07

Corruption's Impact on Performance

The study investigates the effect of managerial corruption on company performance, emphasizing the need for ethical corporate governance and careful manager selection.

4 shares2 citations todaySource ↗

08

Characterizing Law-Invariant Measures

The paper introduces new characterizations for law-invariant star-shaped functionals, demonstrating their wide use in finance, insurance, and probability scenarios.

2 shares16 citations todaySource ↗

Crypto & Blockchain2

01

NFT Market Fluctuations: Statistical Properties

Statistical Properties: The study shows that the Non-fungible token (NFT) market, although new and unique in its trading methods, has many statistical similarities with traditional financial markets, with some variations in certain quantitative measures.

7 shares15 citations todaySource ↗

Historical Trending7

01

Optimal Execution with Machine Learning

A study introduces a numerical algorithm using dynamic programming and deep learning for optimal order execution, highlighting the convenience of using neural-network substitutes in stochastic control issues.

52 shares4 citations todaySource ↗

02

Analysis of Nonlinear Pricing

A paper proposes a method to calculate the best price schedule considering consumer diversity in continuous-choice situations, demonstrating that optimal price discrimination can boost a firm's profit by at least 5.5% compared to linear pricing.

43 shares1 citation todaySource ↗

06

TabR: Tabular DL Meets Nearest Neighbors

Tabular DL Meets Nearest Neighbors: TabR, a new deep learning model for tabular data, outperforms existing models by using a k-Nearest-Neighbors-like component for better predictions.

68 shares117 citations todaySource ↗

SSRN

Working papers in finance and economics from SSRN.

24 items

Quantitative10

01

VolGAN: Realistic Volatility Surfaces

Realistic Volatility Surfaces: VolGAN, a new model that can generate realistic scenarios for the joint dynamics of implied volatility surfaces and underlying assets, is introduced.

173 sharesSource ↗

02

Machine Learning for Earnings Forecasts

Using machine learning models and comprehensive Compustat financial statement data for earnings forecasting can yield predictions that are up to 13% more accurate than traditional linear approaches.

7 shares2 citations todaySource ↗

05

Projected Fuzzy C-Means Algorithm

The article proposes a new algorithm for high-dimensional data clustering in machine learning, aiming to improve performance and manage anomalous instances.

2 sharesSource ↗

06

KMeans Initialization

The article highlights the role of clustering in data mining and machine learning, focusing on the Kmeans algorithm and the challenge of selecting optimal cluster centroids.

2 sharesSource ↗

07

The Common Factor in Volatility Risk Premia

Firm-level volatility risk premium has a strong factor structure, with stocks with the weakest exposures to the common bad volatility risk premium factor earning higher average returns, and the common factor in total bad volatility risk premium predicting stock market returns.

3 sharesSource ↗

09

US. Treasuries: Liquidity Premiums and Results

Liquidity Premiums and Results: A new model of U.S. Treasuries suggests that liquidity factors are more significant than others, Federal Reserve asset purchases impact expected rates and term premiums, and inflation expectations are less stable than previously thought.

2 sharesSource ↗

Financial14

03

Green Derivatives & Climate Risk

The EU Green Deal aims to make Europe carbon-neutral by 2050, requiring 1 trillion euro in sustainable investments, with derivatives markets and 'green derivatives' crucial for managing climate risk.

4 shares1 citation todaySource ↗

04

Time & Frequency Analysis of Oil Futures Market

A study of the oil futures market from 1986 to 2020 reveals patterns and relationships between inventory, basis, hedging pressure, and futures risk premium, emphasizing the importance of the data measurement period.

4 sharesSource ↗

06

Indian Mutual Funds Performance Analysis

The study examines the performance and risk characteristics of Indian mutual funds across market capitalization groups, offering insights for investors and financial professionals.

2 sharesSource ↗

08

Bond Funds and Liquidity Provision

Changes in regulations have moved profits from liquidity provision in the corporate bond market to mutual funds, increasing volatility and vulnerability to market disruptions like the COVID-19 pandemic.

23 sharesSource ↗

09

ETFs and Market Efficiency

Capital constraints on intermediaries can affect the pricing efficiency of assets they manage, as seen in ETFs and their lead market makers during the COVID-19 debt market disruptions.

369 sharesSource ↗

10

ETF Closures: Inaction for Investors?

Inaction for Investors?: Research indicates smaller ExchangeTraded Funds (ETFs) often yield higher daily returns and typically close after positive returns. Investors usually fare better by not reacting to closure announcements.

60 sharesSource ↗

11

Investor Returns: Market-Based Statistics

Market-Based Statistics: The study presents three market-based approximations of actual return from market trades, which deviate from traditional evaluations based on time series analysis of investors' returns.

25 sharesSource ↗

12

Cost of Capital: Cross-Sectional Analysis

Cross-Sectional Analysis: Research spanning 20 years across multiple countries shows that most variations in perceived capital cost are not supported by subsequent returns, questioning the production-based asset pricing model.

2 sharesSource ↗

14

Levered ETF Rebalancing: Market Volatility Impact

Market Volatility Impact: The study reveals that the interaction between investor behavior, ETFs fund flows, and index return autocorrelation can either temper or intensify market volatility, as observed during the COVID-19 pandemic onset.

2 sharesSource ↗

RePEc

Economics working papers from RePEc's NEP field reports.

12 items

Machine Learning3

Finance9

04

Performance of U.S. ESG ETFs

A study finds that ESG equity ETFs in the U.S. generally outperform the S&P 500 Index, challenging the notion that ESG investing compromises financial returns.

15 sharesSource ↗

09

Performance of Actively Managed ETFs

A study from 2018-2021 reveals that actively managed Exchange Traded Funds (ETFs) in the U.S. did not yield significant above-market returns, indicating managers lacked superior market timing skills.

18 sharesSource ↗

Papers with code

Papers that shipped their code, from the Papers with Code feed (2023-25).

3 items

Rising3

01

Natural Language Graphs

ChatGPT, a large-scale pretrained language model, has significantly advanced various fields of artificial intelligence research.

117 shares

GitHub

Repositories the letter featured.

3 items

Trending3

03

SolidGPT: Code Collaboration

Code Collaboration: The article explores a platform that facilitates interaction with your code repository and discussion of coding needs.

1,369 shares

Podcasts

Episodes on markets, quant methods and economics.

6 items

Quantitative6

01

Scariest Options Strategies Revealed

The Options Insider Media Group talks about the current market situation, the forthcoming earnings season, and the five most daunting options strategies.

8 shares

Videos

Talks, lectures and tutorials.

2 items

Quantitative2

02

Where Did All the Quants Go?

A LinkedIn comment criticizes quant programs for lacking intuition and rigor, stressing the need for continuous learning and understanding of financial market logic and mathematics.

52 shares

X / Twitter

Posts from quant researchers on X.

9 items

Quantitative5

02

Decoding the Volatility Puzzle

Swedroe's article investigates the idiosyncratic volatility puzzle by studying the fundamental aspects.

2 shares

03

SciPhi ΨΦ: Custom Data Generation with LLMs

Custom Data Generation with LLMs: The article introduces SciPhi ΨΦ, a system for creating synthetic data to meet specific requirements using LLM-based OpenAI Anthropic Llama.

2 shares

05

Langchain Extensions for Coordinated Computation

The article presents Permchain and Langchain extensions, tools that enable multiple agents to coordinate over several computation steps using LangChain Expression Language and Pregel.

0 shares

Miscellaneous4

04

Python and R Time Series Library

Pytimetk is a high-performance timeseries library, compatible with Python and R, that utilizes Polaris dataframes for simplicity.

0 shares

Reddit

Threads from r/quant, r/algotrading and friends.

4 items

Quantitative4

    Type to search. Try rough volatility, LLM agents or FinGPT.

    ↑↓ move↵ openesc closeFull search page