Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 03/06/2024

This is a summary of links featured on Quantocracy on Wednesday, 03/06/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Getting Started with the Interactive Brokers Native API [Robot Wealth]

    Here at Robot Wealth, we trade with Interactive Brokers (IB) primarily because they offer access to global markets at a reasonable price. In recent times, IB has put some time and effort into upping its tech game, including development of an API for interacting with its desktop trading applications. An application that interacts with IBs desktop trading applications via the API is essentially a
  • Fitting with: exponential weighting, alpha and the kitchen sink [Investment Idiocy]

    I've talked at some length before about the question of fitting forecast weights, the weights you use to allocate risk amongst different signals used to trade a particular instrument. Generally I've concluded that there isn't much point wasting time on this, for example consider my previous post on the subject here. However it's an itch I keep wanting to scratch, and in
  • Value vs Quality: More Correlated than Ever? [Finominal]

    P/E and ROE long-short factors have become highly correlated During certain periods investors favor expensive and unprofitable stocks However, it is difficult explaining the positive correlation outside of bubbles INTRODUCTION The older I get, the less I seem to know for certain about investing. The confidence in my knowledge has been steadily eroded over the years and much of the curriculum

Quantocracy’s Daily Wrap for 03/02/2024

This is a summary of links featured on Quantocracy on Saturday, 03/02/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Navigating Tradeoffs with Convex Optimisation [Robot Wealth]

    This is the final article in our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to manage turnover and not get killed by costs How to model features as expected returns Building
  • Stochastic Volatility for Factor Heath-Jarrow-Morton Framework [Artur Sepp]

    Let me present our recent research paper with Parviz Rakhmonov on the stochastic volatility model for Factor Heath-Jarrow-Morton (HJM) interest rate framework (available on SSRN: Stochastic Volatility for Factor Heath-Jarrow-Morton Framework). Factor Heath-Jarrow-Morton (HJM) model Under the risk-neutral measure, the interest rate curve can be conveniently modeled using the forward curve f_t(tau)
  • Matlab vs. Python [Jonathan Kinlay]

    In a previous article I made a detailed comparison of Mathematica and Python and tried to identify areas where the former excels. Despite the many advantages of the Python technology stack, I was able to pinpoint a few areas in which I think Mathematica holds the upper hand. Whether those are sufficient to warrant the investment of time and money required to master the Wolfram Language is another
  • Backtest powerful intraday trading strategies [PyQuant News]

    Multi-timeframe (MTF) analysis lets traders build powerful intraday trading strategies. It does this by analyzing asset prices during different timeframes throughout the trading day. The problem is most people get MTF wrong. It requires a vector-based backtest to speed up the operations making it easy to introduce look-ahead bias. When a backtest introduces look-ahead bias, it will overstate
  • Cut your losses: is it a good strategy? [Alpha Architect]

    Conventional wisdom can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two examples are that millions of people once believed the conventional wisdom that the Earth is flat, and millions also believed that the Earth is the center of the universe. An example of conventional wisdom in investing is: Dont just stand

Quantocracy’s Daily Wrap for 02/27/2024

This is a summary of links featured on Quantocracy on Tuesday, 02/27/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replacing the 40 with qrvx, in R [Babbage9010]

    Select portions of quant_rv can be combined to craft a new strategy (qrvx) that provides positive returns, negative correlation to SPY and crisis alpha, making it nice for combining with SPY (like a 60/40 combo) to create strong returns with low drawdowns. In my last Replacing the 40 post, we coded a trend strategy based on the blog post by Elliot Rozner of the same title, and replaced the

Quantocracy’s Daily Wrap for 02/26/2024

This is a summary of links featured on Quantocracy on Monday, 02/26/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How Much Bitcoin Should We Allocate To the Portfolio? [Quantpedia]

    After years of waiting, the recent launch of spot Bitcoin ETFs marked a significant milestone in the cryptocurrency market, making Bitcoin even more accessible for investors. Spot ETFs provide a convenient and regulated way to gain exposure to Bitcoin without the need to hold the digital asset directly, potentially attracting a broader range of market participants. Many investors are waiting to
  • Hedging Bear Markets & Crashes with Tail Risk ETFs [Finominal]

    Tail risk ETFs have achieved similar return profiles despite different portfolios TAIL represents a traditional tail risk strategy, but offers limited diversification benefits BTAL is more diversifying, but not more than CTAs INTRODUCTION Although more than 3000 ETFs are trading on U.S. exchanges, the majority simply provide exposure to the stock market in various shades. Naturally, there are

Quantocracy’s Daily Wrap for 02/24/2024

This is a summary of links featured on Quantocracy on Saturday, 02/24/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building Intuition for Trading with Convex Optimisation with CVXR [Robot Wealth]

    This article continues our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to manage turnover and not get killed by costs How to model features as expected returns Next, well
  • Build state-of-the-art portfolios with machine learning [PyQuant News]

    Portfolio optimization usually requires an estimate of the future returns of the assets in the portfolio. This is hard because we cant see into the future. Traditional risk parity uses a quadratic optimizer A cutting edge technique called Hierarchical Risk Parity (HRP) uses graph theory and machine learning to build a hierarchical structure of the investments. By the end of todays
  • Regression-based macro trading signals [SR SV]

    Regression is one method for combining macro indicators into a single trading signal. Specifically, statistical learning based on regression can optimize model parameters and hyperparameters sequentially and produce signals based on whatever model has predicted returns best up to a point in time. This method learns from growing datasets and produces valid point-in-time signals for backtesting.
  • Biotech stocks – is making a bet on them a lottery ticket? [Alpha Architect]

    The academic research, including the 2023 studies Lottery Preference and Anomalies and Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households, the 2022 study Lottery Demand and the Asset Growth Anomaly, and the 2014 study Do Investors Overpay for Stocks with Lottery-like Payoffs? An Examination of the Returns on OTC Stocks, has found that there

Quantocracy’s Daily Wrap for 02/20/2024

This is a summary of links featured on Quantocracy on Tuesday, 02/20/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Robustness Testing of Country and Asset ETF Momentum Strategies [Quantpedia]

    The investment world witnessed a paradigm shift with the introduction of momentum strategies, a concept pioneered by Jagadeesh and Titman in their landmark 1993 study Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Their groundbreaking approach, hinged on the concept of buying stocks with a strong past performance (over 3- to 12-month periods) and
  • Python vs. Wolfram Language [Jonathan Kinlay]

    As an avid user of both Python and Wolfram Language for technical computing, Im often asked how they compare. Pythons strengths as an open-source language are clear: Ubiquity With millions of users, Python has become ubiquitous across fields like data science, ML engineering, web development, and scientific research. This massive adoption fuels continuous enhancement of its tools.
  • Absolute versus Relative Momentum Across Asset Classes [Finominal]

    Absolute and relative momentum can be used as simple asset allocation frameworks Both would have generated a higher return than an equal-weighted portfolio across asset classes However, risk-adjusted returns were lower and drawdowns higher INTRODUCTION Investing is often overwhelming given the enormous number of strategies and asset classes that are available to investors. Deciding on how to
  • Benchmark selection: addressing strategic distortions [Alpha Architect]

    The paper aims to provide insights into the dynamics of benchmark selection, the effectiveness of Relative Performance Evaluation ( RPE ) incentivization, and the broader implications for fund performance and market competition. Self-Declared Benchmarks and Fund Manager Intent: Cheating or Competing? Chen, Evans and Sun FMA working, 2024 A version of this paper can be found here Want to read

Quantocracy’s Daily Wrap for 02/18/2024

This is a summary of links featured on Quantocracy on Sunday, 02/18/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Model Features as Expected Returns [Robot Wealth]

    Modeling features as expected returns can be a useful way to develop trading strategies, but it requires some care. The main advantage is that it directly aligns with the objective of predicting and capitalising on future returns. This can make optimisation and implementation more intuitive. It also facilitates direct comparison between features and provides a common framework for incorporating

Quantocracy’s Daily Wrap for 02/17/2024

This is a summary of links featured on Quantocracy on Saturday, 02/17/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Gauging Existing Technical Fundamental Features through Mutual Information [Quantpedia]

    Investing truly is an intense intellectual undertaking. For a Portfolio Manager (PM) to execute an investment, they must first convince themselves, then others, that the rationale behind the investment is sound. The variables they utilize in developing their rationale are of the upmost importance; These variables inevitably serve as a foundation in the evaluation of a given Asset, and therefore
  • How to download more fundamental data to power trading [PyQuant News]

    Quants, financial analysis, and traders use fundamental data for investing and trading. These data are derived from quarterly and annual statements that companies file with the U.S. Securities Exchange Commission (SEC). These statements are rich with data that can be used to build predictive factor models for investment portfolios. The problem? We cant download all these documents, parse them,
  • On the Persistence of Growth and Value Stocks [Alpha Architect]

    Expectations of future earnings growth matter a great deal to valuations because investors, in their collective wisdom, assign higher valuations to companies they expect will grow more quickly in the future (growth stocks). In contrast, firms expected to show slower growth (value stocks) are assigned lower valuations. An implicit assumption in most forecasts is that growth is persistent. While

Quantocracy’s Daily Wrap for 02/14/2024

This is a summary of links featured on Quantocracy on Wednesday, 02/14/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Defensive Trend [Return Sources]

    Like the Federal Reserve, trend following is often said to have a dual mandate. One mandate is to earn a positive return, and the other is to provide some sort of crisis alpha, or an offset to drawdowns in traditional, 60/40 type portfolios. There could be tension between these two goals; for example, should trend followers take long positions in equity indices? This will likely improve

Quantocracy’s Daily Wrap for 02/12/2024

This is a summary of links featured on Quantocracy on Monday, 02/12/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • European Investors and TAA Strategies: Four Approaches [Allocate Smartly]

    We track 80+ Tactical Asset Allocation (TAA) strategies, most of which were designed from the perspective of a US investor trading US ETFs. Most European investors cant access US ETFs, instead trading UCITS funds listed on non-US exchanges, often denominated in currencies other than USD. In this post well provide data analyzing four approaches a European investor might take in trading US TAA
  • Prompting is Programming with LMQL [Gautier Marti]

    In this blog, I just toy around with a relatively new framework for querying (large) language models: LMQL, a SQL-like for LLMs. It is a first step toward a novel programming paradigm: Language Model Programming (LMP). These ideas are described in the very interesting paper Prompting Is Programming: A Query Language for Large Language Models. From time to time, Machine Learners revisit the concept
  • ChatGPT – can it be used to select investments? [Alpha Architect]

    One use of the NLP (natural language processing) features of ChatGPT is to search out patterns in the immense amounts of news, data and other sources of information about specific stocks, and then efficiently convert them into summaries valuable for all types of investors. Can this be accomplished with useful results? The authors use the Q2_2023 period to test performance around earnings
  • Duration of U.S. Equities – II [Finominal]

    There are multiple ways to measure interest rate sensitivities High-duration stocks like tech and biotech were not more sensitive to rising rates The relationship between interest rates and stocks is weak INTRODUCTION In our first article on the duration of U.S. equities (read Duration of U.S. Equities) we concluded that the interest rate sensitivity of the stock market ranged significantly