트레이딩과 관련한 2020년 논문들…

1.
2021년의 시작입니다. 올해 어떤 사건들이 자본시장을 채울까요? 흔히 하는 말처럼 다사다난할 듯 합니다. 그럼에도 불고하여 지난 십여년간 이어진 흐름이 바뀌지 않을 듯 합니다. 기계와 알고리즘을 이용한 매매…

개인적으로 보면 2010년 HFT가 시작으로 보입니다. 개인투자자 – 물론 넓은 의미의 개인입니다 – 들이 논문을 읽고 분석하고 프로그래밍하면서 매매를 하기 시작하였습니다. 증권사가 제공하는 데이타에서 벗어나서 나만의 데이타와 나만의 방법을 찾기 시작하였습니다. 그리고 금융공학을 체계적으로 공부하는 분들이 늘어났고 관련한 과정도 많아졌습니다. 제가 오래 전에 기획했던 “알고리즘트레이딩교육”도 그 중 하나입니다. 최근의 모습을 보면 이전보다 더 깊어지고 더 넓어진 듯 합니다. 몇 가지 이유가 있을 듯 합니다.

첫째는 매매와 관련한 논문에 접근하기 쉬워졌습니다. SSRN과 같은 사이트들이나 논문을 소개하는 글들이 늘었기때문입니다. 물론 논문을 익히고 내 것으로 만들 능력자들이 많아진 듯 합니다.
둘째는 Python이나 R과 같은 언어가 보편화하였습니다. 논문을 코드화하여 분석하거나 자신의 전략모형을 코드화하여 공개하는 곳들때문입니다.

첫째와 관련한 정보를 제공하는 수많은 곳이 있습니다. Open Access를 지향하는 Arxiv를 이용하면 충분히 접근가능하지만 너무 많은 논문이라 하나하나 살펴보기 힘듭니다. 제가 가끔씩 살벼보는 Quantopia가 추천한 Top Ten Blog Posts on Quantpedia in 2020처럼 2020년 10대 논문같이 엄선한 정보가 좋습니다.

Nbr. 10: What is the Bitcoin’s Risk-Free Interest Rate – cryptocurrency themes are usually popular, so it’s not a surprise, that our analysis of Bitcoin’s risk-free rate made it into the top 10
Nbr. 9: Backtesting ESG Factor Investing Strategies – Socially Responsible Investing is more and more popular, so again, no surprise here
Nbr. 8: Trend Breaks in Trend-Following Strategies – nice paper showing the way how to improve trend-following strategies during unfavourable periods
Nbr. 7: Multi-Asset Skewness Trading Strategy – trading strategies based on higher moments of statistical distributions (volatility, skewness, kurtosis) are among our favourite
Nbr. 6: YTD Performance of Crisis-Hedge Strategies – 2020 was a coronavirus crisis year, and a lot of people were interested to see how some of the crisis hedge strategies performed in the middle of the crisis
Nbr. 5: How Do Investment Strategies Perform After Publication – one of the most common questions we encounter is related to the performance of trading strategies post-publication
Nbr. 4: Trading Index – TRIN Formula Calculation & Trading Strategy in Python – a short QuantInsti paper related to TRIN trading indicator
Nbr. 3: Cryptocurrency Volatility Index – an interesting research paper with a methodology to build “Cryptocurrency VIX Index”
Nbr. 2: Hierarchical Risk Parity – a summary of popular research paper on a risk parity enhancement
Nbr. 1: Working With High-Frequency Tick Data – Cleaning the Data – it seems that HFT is and always will be one of the most popular topics

Alpha Architect도 유명한 회사입니다.The 2021 Annual Finance Research Geek Fest: Top 5 Most Interesting Papers

Forest through the Trees: Building Cross-Sections of Stock Returns: I’m still trying to understand what is going on in this paper, but one thing is clear — their figures/graphics are incredible!
In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis: Have you ever wondered how fund flows might affect asset prices?
What Drives the Size and Value Factors?: I was convinced that there was nothing left to be said on this topic. I was wrong. Flows matter.
Do Women Receive Worse Financial Advice? This is a remarkable study that uses undercover agents in Hong Kong. Fascinating.
The Great Divorce Between Investment and Profitability: More information on the dynamics and foundations for the profitability factor, which we cover often on this blog.

2.
좀더 금융공학 논문을 잘 정리해놓은 곳은 없을까요? 이런 의문에 답을 소개한 분이 계십니다. 페이스북에서 보았습니다.

Hundreds of quant papers from #QuantLinkADay in 2020라는 글입니다. Cuemacro는 생소합니다. 회사 소개를 보니까 이런 지향을 가지고 있네요.

Seeking the cues in macro markets

소개한 논문중 Trading을 주제로 한 논문들의 목록입니다.

January 2020

01-Jan / Trading / Design of High-Frequency Trading Algorithm Based on Machine Learning
15-Jan / Trading / Path-dependent volatility models
16-Jan / Trading / A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data
18-Jan / Trading / Exponential moving average versus moving exponential average
27-Jan / Trading / Trading on the Floor after Sweeping the Book
28-Jan / Trading / Corporate Governance, Noise Trading and Liquidity of Stocks

February 2020

09-Feb / Trading / PCA for Implied Volatility Surfaces
11-Feb / Trading / NAPLES;Mining the lead-lag Relationship from Non-synchronous and High-frequency Data

March 2020

02-Mar / Trading / SHIFT: A Highly Realistic Financial Market Simulation Platform
03-Mar / Trading / G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning
04-Mar / Trading / Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
06-Mar / Trading / Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact
18-Mar / Trading / Optimal market making with persistent order flow
21-Mar / Trading / Informed trading, limit order book and implementation shortfall: equilibrium and asymptotics
22-Mar / Trading / Application of Deep Q-Network in Portfolio Management

April 2020

01-Apr / Trading / Optimal Execution of Foreign Securities: A Double-Execution Problem with Signatures and Machine Learning
03-Apr / Trading / Market structure dynamics during COVID-19 outbreak
12-Apr / Trading / How to build a cross-impact model from first principles: Theoretical requirements and empirical results
13-Apr / Trading / Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
14-Apr / Trading / Slow-Moving Capital and Execution Costs: Evidence from a Major Trading Glitch
16-Apr / Trading / Effects of MiFID II on Stock Price Formation
18-Apr / Trading / Financial Markets and News about the Coronavirus

May 2020

03-May / Coding / Springer has released 65 Machine Learning and Data books for free
08-May / Trading / Investment Factor Timing: Harvesting The Low-Risk Anomaly Using Artificial Neural Networks

June 2020

20-Jun / Trading / Learning a functional control for high-frequency finance

July 2020

19-Jul / Trading / Price Discovery in Two-Tier Markets
20-Jul / Trading / Transaction Costs in Execution Trading
21-Jul / Trading / Deep Learning modeling of Limit Order Book: a comparative perspective

August 2020

05-Aug / Trading / Investment sizing with deep learning prediction uncertainties for high-frequency Eurodollar futures trading
08-Aug / Trading / Insider Trading with Temporary Price Impact
09-Aug / Trading / Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models
24-Aug / Trading / Integrating Alternative Data (Also Known as ESG Data) in Investment Decision Making

September 2020

03-Sep / Trading / Market-making with reinforcement-learning (SAC)
04-Sep / Trading / DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
21-Sep / Trading / How to build a cross-impact model from first principles: Theoretical requirements and empirical results
22-Sep / Trading / Machine Learning for Temporal Data in Finance: Challenges and Opportunities
24-Sep / Trading / Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories
29-Sep / Trading / CoVaR with volatility clustering, heavy tails and non-linear dependence

October 2020

01-Oct / Code library / PyTorch Forcasting – Python library for time series forecasting
02-Oct / Code library / FinancePy – Python based option pricing library (inc FX options)
10-Oct / Trading / On Detecting Spoofing Strategies in High Frequency Trading
12-Oct / Trading / Qlib: An AI-oriented Quantitative Investment Platform
13-Oct / Trading / Tail-risk protection: Machine Learning meets modern Econometrics
15-Oct / Trading / Hierarchical PCA and Modeling Asset Correlations
29-Oct / Trading / Robust Optimization Approaches for Portfolio Selection: A Computational and Comparative Analysis
30-Oct / Trading / Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity
31-Oct / Trading / On the impact of publicly available news and information transfer to financial markets

November 2020

03-Nov / Trading / Machine Learning Treasury Yields
07-Nov / Trading / Excursion Risk
14-Nov / Trading / Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features
21-Nov / Trading / Gamma Fragility
25-Nov / Trading / Price Impact on Term Structure
26-Nov / Trading / FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
29-Nov / Trading / Tail Risk and Expectations

December 2020

01-Dec / Trading / A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection
04-Dec / Trading / Information and the Arrival Rate of Option Trading Volume
05-Dec / Trading / A Frequency-Specific Factorization to Identify Commonalities with an Application to the European Bond Markets
10-Dec / Trading / Learning (Not) to Trade: Lindy’s Law in Retail Traders
11-Dec / Trading / News Shocks Under Financial Frictions
12-Dec / Trading / The Impact of Trustees’ Age and Representation on Strategic Asset Allocations
16-Dec / Trading / Deep Portfolio Optimization via Distributional Prediction of Residual Factors
17-Dec / Trading / Building Cross-Sectional Systematic Strategies By Learning to Rank

위에서 소개한 논문들은 Cuemacro의 설립자인 Saeed가 트위터에 #QuantLinkADay로 해시태그를 붙여서 소개한 논문들입니다. 예를 들면 아래와 같습니다.

Redrawing the Map of Global Capital Flows: The Role of Cross-Border Financing and Tax Havens https://t.co/vOsleNpWOZ #QuantLinkADay pic.twitter.com/NFfGXSHWKY

— Saeed (@saeedamenfx) January 3, 2021

2021년 모두 건강하고 행복한 해로 기억되시길 기도드립니다.