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Quant LetterNo. 6

July 2023, Week 1

107 items across 11 sections, as sent to readers on 5 July 2023. Paper titles open their ML-Quant page; ↗ goes to the source.

arXiv

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

14 items

Finance4

Crypto & Blockchain2

Economics2

02

Barriers to Learning in Game Theory

Agents' accuracy in decision-making can cause instability in certain games, and the stability of best-response dynamics depends on the learning barrier.

2 shares2 citations todaySource ↗

Historical Trending6

04

Growth Dynamics and Supply Chain Correlation

Firm growth rates are correlated through a common factor, but supply chain-linked firms have a stronger correlation, allowing for reconstruction of the supply chain network using Gaussian Markov Models.

16 shares6 citations todaySource ↗

05

Efficiency for Equity Autocallables

The Orthogonal Chebyshev Sliding Technique reduces computational costs for calculating ES values in FRTB-IMA for equity autocallable portfolios, improving efficiency.

8 shares1 citation todaySource ↗

06

Bounded Regret Recommendation Learning

Bounded regret can be achieved in recommender systems modeled as linear contextual bandits, even without exact knowledge of the linear model, using Synthetic Control Methods.

7 shares2 citations todaySource ↗

SSRN

Working papers in finance and economics from SSRN.

19 items

Quantitative9

06

Stablecoin Arbitrage

Stablecoin issuers face risk due to illiquid assets and fixed redemption values, worsened by efficient arbitrage.

240 sharesSource ↗

Financial10

RePEc

Economics working papers from RePEc's NEP field reports.

19 items

Finance5

Statistical4

Machine Learning3

Deep Learning2

Historical Trending5

Papers with code

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

8 items

Trending4

Rising4

01

NN Voice Conversion

Anytoany voice conversion transforms speech into a different voice using a few examples.

158 shares

GitHub

Repositories the letter featured.

6 items

04

Prepping Tables for ML

Insights into the steps involved in preparing tables for machine learning applications are provided in this article.

755 shares

News

Industry news: funds, hiring, markets and regulation.

8 items

Quantitative5

Miscellaneous3

01

Is EPS Enough for Measuring Investment Success?

EPS is being questioned as the best measure for investment success: There are doubts about whether earnings per share (EPS) is the most reliable metric for determining investment success.

2 shares

02

BofA Singapore IT Head Joins 206bn Tech Firm

A prominent tech manager in Singapore is leaving the finance industry: A well-known technology manager in Singapore is stepping away from the finance sector.

2 shares

Podcasts

Episodes on markets, quant methods and economics.

9 items

Quantitative4

Related5

Blogs

Posts from quant and economics blogs and newsletters.

5 items

Quantitative5

Videos

Talks, lectures and tutorials.

2 items

Quantitative2

X / Twitter

Posts from quant researchers on X.

9 items

Quantitative4

02

Currency Risk Premia Academic Review

This tweet reviews academic research on currency risk premia, looking at multihorizon results and the bond-currency relationship.

4 shares

03

Low Volatility Investing Dive

The tweet explores low volatility investing, discussing selection vs allocation effects, pandemic performance, and currency in global low vol portfolios.

3 shares

04

AI Predicting PE Fund Performance

A paper suggests that machine learning can predict private equity fund performance by analyzing fundraising prospectuses and comparing limited partners to unlimited machines.

2 shares

Miscellaneous5

01

MAPE: The Worst Metric

The Worst Metric: MAPE is not a good metric for forecasting, use other metrics instead.

1 shares

Reddit

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

8 items

Rising4

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