Quant LetterNo. 73
167 items across 10 sections, as sent to readers on 6 November 2024. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
15 items
The first article explores the calculation of short-term predictions for the prices of options on realized variance in local-stochastic volatility models, offering solutions for both out-of-the-money and in-the-money options.
7 sharesSource ↗
The second article proposes a quantitative method for risk-based compliance in data protection, highlighting the need for a shift towards using data protection analytics and quantitative risk analysis.
6 sharesSource ↗
The article presents a new method for detecting uncertainty shocks in financial markets by modifying the Volatility Index (VIX), resulting in a more precise volatility measure.
9 shares3 citations todaySource ↗
The paper investigates the gender wage gap in Brazil, revealing that 17% of the gap is due to worker-firm interactions and women's lower likelihood of employment in high-return firms.
2 sharesSource ↗
The study explores the effect of generative AI tools on security operations center productivity, noting a 30.13% decrease in security incident resolution time.
2 shares4 citations todaySource ↗
A study found that the involvement of 16 emerging economies in the Global Value Chain from 1995-2018 has resulted in higher carbon emissions, supporting the Pollution Haven Hypothesis.
2 shares1 citation todaySource ↗
Research indicates that greater involvement in China's three-pillar pension system from 2012-2020 has helped decrease multidimensional poverty, emphasizing the significance of state social insurance, enterprise annuity, and individual commercial insurance.
2 sharesSource ↗
An evaluation of Canada's financial regulatory frameworks for climate risk disclosure identifies areas for improvement to meet energy net zero targets, comparing it to international standards.
2 sharesSource ↗
The article discusses a time-causal variational autoencoder (TC-VAE) that generates reliable financial time series data. This data closely mirrors real market distribution and is beneficial for financial optimization tasks.
6 shares13 citations todaySource ↗
The article introduces a Genetic Algorithm-generated Alpha Sentiment (GAS) model that uses advanced learning techniques and sentiment analysis to predict Bitcoin market trends and price changes.
5 shares2 citations todaySource ↗
The study investigates the use of Generative Adversarial Networks (GANs) in understanding financial time series, emphasizing the need for careful model selection due to varying performance.
13 shares7 citations todaySource ↗
The research shows that statistical arbitrage in rank space outperforms name space in equity market dynamics, particularly when using neural networks.
13 shares1 citation todaySource ↗
The paper uses Deep Reinforcement Learning (DRL) to address the volatility fitting issue in equity derivatives, demonstrating DRL's effectiveness and suitability for complex functions and online learning.
5 shares4 citations todaySource ↗
The paper offers a comprehensive solution to finding equilibrium strategies in trading competition, revealing the benefits for firms that strategically centralize their trades.
5 shares2 citations todaySource ↗
The paper introduces a new model for simulating sparse limit order books in illiquid markets, utilizing an inhomogeneous Poisson process to represent the irregular nature of order arrivals and cancellations.
5 shares2 citations todaySource ↗
Working papers in finance and economics from SSRN.
40 items
A study finds that large language models like ChatGPT4o can mimic human responses in surveys and predict election results, but struggle with sensitive topics.
20 sharesSource ↗
Large language models (LLMs) in AI are expected to ease the load on financial institutions in managing complex regulatory projects, especially in software development.
10 sharesSource ↗
A study suggests that increasing the number of ICT engineers in firms not using AI could boost their likelihood to adopt AI, but significant investment is needed to bridge the current ICT skills gap.
6 sharesSource ↗
The FDA is considering Predetermined Change Control Plans (PCCP) to ease the administrative burden of AI algorithms learning from new data, but its effect on Good Machine Learning Practices (GMLP) compliance is unclear.
6 sharesSource ↗
A new method, LDPPPA, offers privacy protection for computing attribute means and user covariance matrices in local differential privacy-preserving principal component analysis, but struggles with attribute heterogeneity.
3 sharesSource ↗
A new Chinese program uses tax data and fintech to enhance loan accessibility for small businesses, resulting in increased sales and return on equity.
5 sharesSource ↗
The Quantum Finance Optimization Qubit model accelerates financial model testing by managing the complex, noisy nature of financial data.
2 sharesSource ↗
Causal machine learning can transform ineffective marketing campaigns into profitable ones by targeting individual treatment effects, correlating with measures of loss aversion.
3 sharesSource ↗
Data-driven modelling, especially through machine learning, is a promising alternative in catalysis due to the limitations of traditional models.
3 sharesSource ↗
An economic belief indicator, developed using a data-rich environment and machine learning, corrects macroeconomic expectation errors and predicts bond returns.
3 sharesSource ↗
The article highlights Geoffrey Hinton's key contributions to neural networks, including backpropagation, deep learning, and Capsule Networks, and their role in current AI applications.
224 sharesSource ↗
The study reveals a lack of transparency in predictive machine learning studies in top business and economic journals, leading to fewer citations due to inadequate benchmarking against traditional statistical models.
15 sharesSource ↗
The article discusses the potential of Big Data analytics in enhancing risk management in IT service delivery through real-time risk identification, assessment, and mitigation.
2 sharesSource ↗
The study proposes a semi-parametric data-driven decision-making framework for inventory and financial hedging of new products, using proxy return factors to infer derivatives returns and demand-return relationships.
4 sharesSource ↗
The paper presents a method for identifying similar assets for statistical arbitrage strategies using high-frequency intraday data and evaluates the effectiveness of single-view and multi-view spectral clustering algorithms.
7 sharesSource ↗
The research assesses machine learning methods for predicting multiphase flow in oil and gas production, aiming to enhance accuracy and reduce uncertainty.
6 sharesSource ↗
The study uses a mixed-method approach to identify key success factors in risk management within the Nigerian banking system from 1960 to now.
6 sharesSource ↗
The research explores alpha-attractor quintessential inflation, finding it provides a better fit than Lambda-CDM based on various cosmic measurements.
5 sharesSource ↗
The study reveals that machine learning can predict post-deal returns of M&A deals, but these returns are not directly tied to immediate post-deal earnings.
3 sharesSource ↗
The research examines arbitrage opportunities in the liquidity tokens market, proposing new Automated Market Maker models to update the pricing of these tokens.
3 sharesSource ↗
Research indicates that household consumption surplus and firms' investment to capital ratio can forecast stock and investment returns in a production economy, supporting the investment-based model.
3 sharesSource ↗
Private video game companies receiving private equity investment generally outperform other markets, making them appealing investment opportunities.
2 sharesSource ↗
Asset managers increase their demand for green stocks after significant climate events, indicating their role in converting environmental concerns into asset prices.
4 sharesSource ↗
Investors with larger dealer trading networks in over-the-counter markets make better trading decisions and achieve superior performance, emphasizing the significance of trading relationships for private information acquisition.
2 sharesSource ↗
A machine learning-based study successfully predicts short-term stock market trends in the Korean market, showcasing the potential of deep learning techniques in financial market predictability.
2 sharesSource ↗
The expansion and updated classification of firms in the Indian equity market have little significant impact on factor returns, affirming the reliability of the Data Library FamaFrench Factors Momentum and LowRisk Factors.
3 sharesSource ↗
A model combining kernel methods and principal component analysis offers superior analysis of asset return variations within a trading day, especially during information flow periods.
2 sharesSource ↗
A suggested framework indicates that US quantitative easing programs have positively contributed to welfare by balancing output stimulation benefits against taxpayer rollover risk costs.
5 sharesSource ↗
The greenium, or yield difference between green and conventional bonds, is primarily paid by investment funds, banks, and insurance companies, with their decisions influenced by various motivations and financial frictions.
3 sharesSource ↗
US biodiversity protection land use restrictions result in a 45% land value decrease and a 30% development rate decline, underscoring the economic impact of conservation policies on real asset values.
3 sharesSource ↗
The article discusses the risks of trendfollowing investment strategies and suggests mitigating them by replicating a broad index of such funds.
205 sharesSource ↗
The paper reviews machine learning methods in portfolio decision-making, discussing their limitations and potential future applications in systematic trading and trade execution.
8 sharesSource ↗
The research indicates that substantial hedge fund allocations can aid diversification, even without alpha generation, but these allocations are highly dependent on alpha assumptions.
7 sharesSource ↗
The study reveals that US banks are reluctant to sell underwater bonds at a discount, especially those that do not recognize unrealized losses in regulatory capital.
6 sharesSource ↗
The article evaluates the performance of Large Language Models in forecasting stock market data, showing varying effectiveness in stable and complex data scenarios.
21 sharesSource ↗
Prospectus vs. Ratings: Retail and institutional fund flows are primarily driven by sustainability statements in fund prospectuses, not external sustainability ratings, as per an analysis of over 23,000 equity mutual funds and ETFs.
22 sharesSource ↗
Sentiment Spillover: A study's dynamic model of index investing indicates that index stocks have higher prices, more volatility, greater comovement with other index stocks, and increased trading volume due to sentiment spillover from index investors.
4 sharesSource ↗
Despite the aim for transparent trading, hidden orders account for up to 75% of the dollar volume traded for high-priced stocks on U.S. equity exchanges, and an AI model can predict where these orders will likely appear.
4 sharesSource ↗
A new multivariate GARCH model can identify shocks and volatility spillovers in speculative return systems, using proxy variables and Riemannian optimization techniques, as shown in an analysis of daily S&P 500 returns, 10-year Treasury yields, and the U.S. Dollar Index.
7 sharesSource ↗
High foreign exchange volatility results in higher currency carry returns when foreign exchange ambiguity is high, indicating that investors avoid trading during these periods, as per a study examining the relationships between currency portfolios and market conditions.
7 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
25 items
The article suggests a new investment strategy using clustering techniques to minimize assets in a portfolio, potentially outperforming traditional equal weight portfolios.
21 sharesSource ↗
The paper investigates the use of Online Gradient Update and Online Newton Update meta-algorithms in online portfolio selection to decrease risk and enhance price prediction.
19 sharesSource ↗
The study introduces a multi-index model-based portfolio optimization method that includes environmental, social responsibility, and corporate governance factors, providing a flexible alternative to large-scale covariance matrix estimation.
18 sharesSource ↗
The research studies the effects of high-frequency and algorithmic trading on China's market quality, showing improvements in contract continuity, liquidity diversification, and lower slippage costs for investors.
17 sharesSource ↗
Machine learning, particularly the random forest model, is more effective in predicting economic growth in Russia's Sverdlovsk region than traditional models, according to a study.
28 sharesSource ↗
Machine learning techniques like extreme gradient boosting and random forest are more accurate in predicting aggregated stock market risk premium based on online investor sentiment than traditional linear models.
22 sharesSource ↗
A study found that adding nonfinancial disclosures to financial predictive models greatly improves the prediction of company performance among nonfinancial firms in Pakistan.
18 sharesSource ↗
The article investigates the impact of enterprise risk management on the performance and value of ten Turkish banking firms from 2019 to 2022.
12 sharesSource ↗
The study applies an operational employability model to analyze the job transition of Croatian graduates, highlighting the positive influence of cultural, human, and social capital.
11 sharesSource ↗
The research introduces a multi-stage stochastic model for predicting green bond issuance, demonstrating its effectiveness in determining the most cost-efficient conditions amidst various risks.
10 sharesSource ↗
The paper presents a mixed-integer quadratic optimization model for classification problems, using an iterative clustering approach to enhance computational efficiency.
10 sharesSource ↗
Machine learning was used to predict default risk in financial institutions, with bailout probability, market share, and market-to-book ratio being key variables.
24 sharesSource ↗
A study found that only 20.31% of machine learning manuscripts reported hyperparameters and their tuning, indicating a need for more transparency in machine learning models.
21 sharesSource ↗
A new algorithm was developed to detect collusion in auctions using public procurement data, successfully identifying a significant number of contracts with high collusion probability.
18 sharesSource ↗
A model was used to study the effect of public debt on macroeconomic equilibrium and wealth distribution, revealing that income channel has the most significant impact on welfare changes due to public debt.
17 sharesSource ↗
The research uses the Naive Bayes Classifier algorithm to categorize emails as spam or not using the Kaggle spam mails dataset and R software.
16 sharesSource ↗
The paper introduces a new stopping criterion for gender identification in biographical texts using support vector machine classifiers, with enhanced results in inflected languages.
13 sharesSource ↗
The study explores the impact and potential of AI, machine learning, and big data in fostering innovation, using bibliometric analyses and citation counts.
13 sharesSource ↗
The research employs Large Language Models to enhance predictions of National Hockey League draft outcomes by analyzing scouting report texts and on-ice statistics.
11 sharesSource ↗
The study analyzes the use of Facebook by French political parties during the 2022 election, finding campaign themes are more influenced by traditional strategies than user engagement.
9 sharesSource ↗
A predictive model using news text from The Wall Street Journal is more effective at forecasting investment opportunities than traditional models.
6 sharesSource ↗
Machine learning analysis shows a significant rise in partisanship among SEC Commissioners from 2010-2019, based on language used in Congress and SEC rules.
4 sharesSource ↗
Regulatory intensity, as determined by administrative data and machine-learning models, increases costs and prompts companies to cut investment and employment, especially in financially constrained firms.
4 sharesSource ↗
A study of social media interaction with EU news shows that negative content increases reactions and shares, while emotional content prompts more comments, influencing citizen engagement with information.
4 sharesSource ↗
The regulation of collusion, including detection, prosecution, and negotiation, is explored with a focus on the unique aspects of the legal system, with the goal of improving collusion regulation.
1 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
20 items
A study shows that FP8 weight and activation quantization in large language models (LLMs) is lossless across all scales, while INT8 and INT4 quantizations also perform well with proper tuning, offering guidelines for deploying quantized LLMs.
50 shares44 citations todaySource ↗
A novel method creates photorealistic, relightable head avatars from a phone scan with unknown illumination, surpassing existing methods and maintaining real-time rendering.
35 shares54 citations todaySource ↗
SelfCodeAlign, a new pipeline, enhances the ability of LLMs to follow human instructions without extensive human annotations, outperforming previous methods and creating a top-performing coding model.
14 shares78 citations todaySource ↗
EgoMimic, a new framework, improves manipulation tasks performance using human embodiment data, proving more effective than existing imitation learning methods and showing that human data is more valuable than robot data.
13 shares245 citations todaySource ↗
GeoSplatting, a new hybrid representation combining 3D Gaussian Splatting with geometric guidance and differentiable physically-based rendering equations, enhances the accuracy of physically-based inverse rendering, outperforming existing methods.
11 shares24 citations todaySource ↗
The article introduces a Machine Learning-enhanced Ray Tracing method for efficient radio propagation modeling, reducing computational effort while maintaining accuracy.
11 shares2 citations todaySource ↗
The paper explores the use of machine learning to analyze Maternal Inflammatory Response (MIR) from whole slide images, achieving up to 88.5% balanced accuracy.
10 shares2 citations todaySource ↗
The research proposes strategies to defend against undetectable backdoors in machine learning models, using random self-reducibility-inspired techniques.
10 shares7 citations todaySource ↗
The study shows that autonomous agents using large vision and language models can be significantly disrupted by strategically designed adversarial pop-ups.
9 shares124 citations todaySource ↗
A Physical Law Perspective: The paper assesses the capability of video generation models to identify physical laws from visual data, indicating that mere scaling is not enough for these models to discover fundamental physical laws.
9 shares230 citations todaySource ↗
CAdvUL, a new adversarial training algorithm, enhances the resilience of large language models against adversarial attacks by efficiently calculating attacks in the continuous embedding space.
82 shares148 citations todaySource ↗
Autonomous Vehicle Simulation: NAVSIM, a non-reactive simulator, allows for large-scale real-world testing of vision-based driving policies using extensive datasets and simulation-based metrics, bridging the gap between open-loop and closed-loop evaluations.
66 shares425 citations todaySource ↗
By optimizing recurrent weights and individual neuron properties, Recurrent Neural Networks (RNNs) can extend timescales to solve memory-dependent tasks, with the multi-head curriculum enhancing training speed, network stability, and task adaptability.
63 shares13 citations todaySource ↗
Task Ambiguity in NLG: AmbigNLG, a new task and dataset, tackles task ambiguity in instructions for Natural Language Generation, improving the alignment of generated text with user expectations and boosting the performance of Large Language Models.
46 shares16 citations todaySource ↗
A theoretical study reveals that a clipped version of the Adam optimization algorithm, combined with the model exponential moving average, achieves optimal convergence rates in various nonconvex optimization scenarios, highlighting the benefits of coordinate-wise adaptivity.
36 shares36 citations todaySource ↗
Coding Task SE Agents: HyperAgent, a multi-agent system for software engineering tasks, has achieved new benchmarks in tasks like GitHub issue resolution and code generation across different programming languages.
29 shares66 citations todaySource ↗
Discontinuity Image Rendering: A new framework improves Gaussian Splatting's image rendering by introducing a Bézier-boundary gradient approximation strategy for better handling of discontinuities and boundaries.
26 shares15 citations todaySource ↗
Outlier Management for LLMs: DuQuant, a new method for quantizing large language models, uses rotation and permutation transformations to manage outliers, surpassing performance of existing baselines in various tasks.
18 shares194 citations todaySource ↗
Argumentation Research in ECHR Hearings: LaCour!, the first corpus of textual oral arguments from the European Court of Human Rights, provides transcribed multilingual oral hearings linked to final judgments, enhancing legal research.
16 shares2 citations todaySource ↗
A novel method for causal inference uses ancestral graph sampling and expert feedback to refine causal discovery, providing uncertainty estimates and accounting for unobserved confounders.
15 shares9 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
9 items
The article discusses the complexity of applying Large Language Models (LLMs) to speech tasks due to the difficulty in merging audio and text modalities.
1,378 shares
The piece introduces ServerlessLLM, a system designed for quick serverless inference in Large Language Models (LLMs).
311 shares
The article suggests substituting linear projections in Transformers with a token-parameter attention layer, using input tokens as queries and model parameters as keys and values.
99 shares
The article presents a benchmark to measure the time required to pretrain models on specific GPUs and find the best settings for maximum training speed.
76 shares
ShadowKV has proven to support larger batch sizes and increase throughput on an A100 GPU without compromising accuracy, even outperforming infinite batch size under infinite GPU memory assumption.
71 shares
The discussed framework is task-specific in data tuning but task-agnostic in architecture, providing a useful tool for the community and insights for future research on task-agnostic generation systems.
69 shares
The article emphasizes the need for a standard benchmark to effectively evaluate the performance of offline GCRL algorithms.
56 shares
The article explores the issue of pretraining data detection, a type of membership inference attack (MIA), as a potential solution to a current research need.
52 shares
The article presents lightweight learnable modules that refine depth and pose estimates, improving the quality of 3D reconstruction and novel view synthesis.
37 shares
Repositories the letter featured.
10 items
The article presents a tool that predicts stock market trends using Elasticsearch, Twitter news, and Python for sentiment analysis.
2,149 shares
The piece discusses the application of advanced machine learning methods in web applications.
11,807 shares
The article introduces a package that facilitates working with SEC data through datamule endpoints.
53 shares
The article explores a study on stock trading simulation using a large language model.
56 shares
The piece offers a beginner's guide to implementing algorithms and data structures in JavaScript following best practices.
32,478 shares
The article offers a detailed guide on resources for mathematical optimization, including courses, books, software, and more.
285 shares
The article provides a step-by-step guide on setting up a fly server on a VPS quickly and efficiently.
5,255 shares
The article introduces AutoJobsApplierAIHawk, an AI tool designed to automate and personalize the job application process.
21,742 shares
The article showcases a tool that can transform screenshots into clean HTML, Tailwind, React, and Vue code.
939 shares
The article explores xlwings, a Python library that enables interaction between Python and Excel on multiple platforms.
2,979 shares
Industry news: funds, hiring, markets and regulation.
20 items
Hedge funds are increasingly using options on banks' short volatility quantitative investment strategies to limit losses, with BNP Paribas, Citi, and UBS launching call options.
13 shares
Digital asset derivatives exchange Deribit has introduced a hybrid custody model to simplify access for institutional custodians and brokerages.
6 shares
London-based quantitative hedge fund MPP&E Capital plans to launch later this year with single managed account allocations, followed by a fund launch in 2025.
6 shares
A report by Hedge Fund Research suggests that hedge funds perform better under Democratic presidencies.
5 shares
Hong Kong-based hedge fund Zeal Asset Management will close next year and return over $1.6bn to investors as its founders retire.
5 shares
Hedge funds are exploring currency options that could profit from a potential dollar decline if Kamala Harris becomes president, says Bloomberg.
4 shares
The Securities and Futures Commission in Hong Kong has updated its market sounding practices guidelines following feedback from hedge funds and banks.
4 shares
CoinShares, a European digital assets firm, has named Lisa Avellini as Group General Counsel, effective from November 4, 2024.
4 shares
Digital assets and AI are transforming finance by enhancing transparency and efficiency, but they also present challenges for asset managers due to potential data overload and complex workflows.
3 shares
China's quantitative hedge funds are finding it difficult to sustain performance due to a surge in local stocks triggered by the government's recent economic stimulus, Bloomberg reports.
3 shares
GCEX Group has partnered with RULEMATCH to provide institutional clients with a fast cryptocurrency trading platform.
3 shares
Walleye Capital has let go of five portfolio managers, including Global Macro Head Anuraj Dua, in a recent round of layoffs.
3 shares
Coatue Management, a tech-focused hedge fund, is seeking to raise $1bn for its main fund to boost its artificial intelligence investments.
2 shares
Quantitative analysts at Quadratures are currently earning a low salary of £2.
2 shares
Analysts Neil Campling and Toby Clothier, who identified potential fraud at Wirecard, have shut down their hedge fund, Chameleon Global Master Fund, due to its small size.
2 shares
The ex-distribution head of Lombard Odier has been appointed to the board of a quantitative adviser firm in Switzerland.
2 shares
CoinShares' report reveals a record $29.2bn inflow into digital asset investment products this year, with $2.2bn in the last week alone.
2 shares
The article explores the advantages of self-employment.
2 shares
Pyth contributors have launched Crypto Redemption Rate Feeds, introducing two new asset classes - liquid staking tokens and yield-bearing stablecoins - key trends in the DeFi sector recently.
1 shares
The article examines the hiring and firing trends in the London job market.
1 shares
Episodes on markets, quant methods and economics.
10 items
Mapping the Global Story of Money by Dr. Dariusz Wójcik is a book that uses visuals to simplify and explain the complex history and world of finance.
11 shares
Marty Bergin, President of DUNN Capital Management, highlights the firm's 50-year history in trend following, emphasizing resilience, adaptability, and core principles.
10 shares
David Keller of Sierra Alpha Research shares insights on understanding market trends, the role of emotions and biases in investing, and the need for strong exit strategies.
9 shares
Sam Stovall of CFRA Research discusses the influence of Federal Reserve policies and election years on market trends, and the potential for increased volatility and strategic investment opportunities in 2024.
8 shares
Jonny Goulden and Saad Siddiqui discuss the future of emerging market assets ahead of the US election and key points from recent Washington conferences.
7 shares
Jay Barry and Afonso Borges discuss the Treasury's refunding announcement and future issuance expectations in a podcast recorded on 31 October 2024.
7 shares
A podcast recorded on 1 November 2024 explores the impact of potential new administration policies on commodities markets, particularly energy security and price control.
7 shares
Eric Balchunas draws parallels between the ETF industry and the biotech innovation boom, emphasizing a shift towards riskier investment strategies.
6 shares
Ken Mahoney discusses his finance and entertainment experience, investment strategies, fiscal and monetary policies, and the potential impact of political shifts on market dynamics.
6 shares
Meb Faber's 2nd edition of Shareholder Yield: A Better Approach to Dividend Investing advocates for a market strategy focusing on total shareholder value.
4 shares
Posts from quant researchers on X.
8 items
The article suggests that blending trend-following with long-short quality strategies can enhance portfolio resilience against abrupt market fluctuations.
3 shares
Longrun Risk Premia and More: The piece provides a summary of recent research on various topics including long-term risk premiums, corporate bonds, and commodity strategies.
2 shares
The article argues that individual risk factors, such as variance and left tail risks, are more crucial in predicting equity returns than systematic factors.
2 shares
Momentum Anomaly: The article discusses a unique dataset of corporate bonds from 1868 to 1914, highlighting momentum as the most potent and consistent anomaly.
1 shares
The article provides an in-depth study on the long-term returns of different asset classes.
1 shares
The paper explores liquidity and the prevalence of wash trading in digital assets of varying sizes.
1 shares
The article reviews Peter Muller's 2001 piece on proprietary trading and statistical arbitrage, with input from Campbell Harvey at Duke.
0 shares
The article proposes that using a correlation filter in commodity momentum strategies can greatly improve Sharpe ratios.
0 shares
Threads from r/quant, r/algotrading and friends.
10 items
158 shares
31 shares
98 shares
100 shares
25 shares
18 shares
146 shares
49 shares
20 shares
90 shares