Quantocracy

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Quantocracy’s Daily Wrap for 05/10/2021

This is a summary of links featured on Quantocracy on Monday, 05/10/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Copula for Statistical Arbitrage: C-Vine Copula Trading [Hudson and Thames]

    This is the sixth article of the copula-based statistical arbitrage series. You can read all the articles in chronological order below. In this series, we dedicate articles 1-3 to pairs-trading using bivariate copulas and 4-6 to multi-assets statistical arbitrage using vine copulas. Copula for Pairs Trading: A Detailed, But Practical Introduction. Copula for Pairs Trading: Sampling and Fitting to
  • Kalman Filter Techniques And Statistical Arbitrage In China’s Futures Market In Python [Quant Insti]

    Contrary to a more developed market, arbitrage opportunities are not readily realised which suggests there might be opportunities for those looking and able to take advantage of them. This project focuses on China's futures market using Statistical Arbitrage and Pair trading techniques. This article is the final project submitted by the author as a part of his coursework in Executive
  • Improving the Odds of Value Investing [Factor Research]

    The stock market volatility, skewness, and yield curve influence the performance of the value factor Investors require a certain market environment to buy troubled companies The key performance driver of the value factor is risk sentiment INTRODUCTION Ted Theodore first wrote about value versus momentum stocks way back in 1984, but almost 40 years later, there still is no real consensus among

Quantocracy’s Daily Wrap for 05/09/2021

This is a summary of links featured on Quantocracy on Sunday, 05/09/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 05/07/2021

This is a summary of links featured on Quantocracy on Friday, 05/07/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How I learned to stopped worrying and love the Bitcoin (future) [Investment Idiocy]

    For the last seven years since I started trading my own account I've pretty much kept the same set of futures markets: around 40 or so, with very occasional changes. The number is limited, as to trade more markets I'd need more capital. The set of markets I have is a compromise between getting a diversified portfolio, avoiding low volatility, not paying too much in trading costs, not
  • Accelerating Dual Momentum Redux: Longer History, Tempered Expectations [Allocate Smartly]

    This is a follow up to a strategy weve covered previously: Accelerating Dual Momentum (ADM) from EngineeredPortfolio.com. See our first test of ADM, which includes a description of the strategy rules and our own analysis of the strategy. Here weve extended our test by 20 years to include a less effective era for this strategy. Results from 1970 net of transaction costs follow. Read more
  • Risk Parity Asset Allocation [Quantpedia]

    This article is a primer into the methodology we use for the Portfolio Risk Parity report, which is a part of our Quantpedia Pro offering. We explain three risk parity methodologies Naive Risk Parity (inverse volatility weighted), Equal Risk Contribution and Maximum Diversification. Quantpedia Pro allows the design of model risk parity portfolios built not just from the passive market factors

Quantocracy’s Daily Wrap for 05/06/2021

This is a summary of links featured on Quantocracy on Thursday, 05/06/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • U.K. Value Factor – The 200+ Year View [Two Centuries Investments]

    One year ago, I wrote about the U.S. Value factor and what I found by extending its history back in time before 1926. In summary, I wrote that Values drawdown in March 2020 was normal and likely close to its bottom. Without the insights from the extended history, Value had appeared dead given it had crossed the previous all-time maximum drawdown. As far as factor timing goes, I was close
  • Text-Based Factor Investing [Alpha Architect]

    This is the first part of a series of guest posts by Kai Wu, the CIO & Founder of Sparkline Capital. The Factor Zoo As readers of Alpha Architects blog, youre certainly familiar with factor investing. Factors are quantifiable firm characteristics that explain cross-sectional stock returns. While some factors merely explain risk (e.g., industry), others are also associated with positive

Quantocracy’s Daily Wrap for 05/05/2021

This is a summary of links featured on Quantocracy on Wednesday, 05/05/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Resurrecting the Value Premium [Alpha Architect]

    The dramatic underperformance of value stocks as defined by the HmL (the return on high book-to-market stocks minus the return on low book-to-market stocks) since 2017 has led many to question the existence of the value premium. The recent drawdown has been by far the largest ever experienced. From January 2017 through August 2020, the Fama-French small value research index produced a total return

Quantocracy’s Daily Wrap for 05/03/2021

This is a summary of links featured on Quantocracy on Monday, 05/03/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quant Minds Online Virtual Conference, May 24-28. Save 10% with this link. [Quant Minds]

    Mid-year learning and knowledge sharing for the quant finance community A week of 5 precision-engineered digital summits, laser-focused on the most innovative research. Choose the days that matter to you. Meet the quants finding solutions to the same problems you face.
  • 60/40 Portfolios Without Bonds [Factor Research]

    Bonds have become less useful in asset allocation given low to negative expected returns Liquid alternative strategies can be used to replace bonds From a historic perspective, long volatility strategies would have been especially attractive INTRODUCTION John Maynard Keynes famously asked, when the facts change, I change my mind what do you do, sir?. If this question was directed at
  • Macro information waste and the quantamental solution [SR SV]

    Financial markets are not macro information efficient. This means that investment decisions miss out on ample relevant macroeconomic data and facts. Information goes to waste due to research costs, trading restrictions, and external effects. Evidence of macro information inefficiency includes sluggishness of position changes, the popularity of simple investment rules, and the prevalence of

Quantocracy’s Daily Wrap for 05/01/2021

This is a summary of links featured on Quantocracy on Saturday, 05/01/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Timing Using Aggregate Equity Allocation Signals [Alpha Architect]

    When it comes to predicting long-term equity returns, several well-known indicators come to mindfor example, the CAPE ratio, Tobins Q, and Market Cap to GDP, to name a few. Yet there is another indicator without nearly as high of a profile that has outperformed the aforementioned indicators significantly when it comes to both forecasting and tactical asset allocation. That indicator, known
  • Research Review | 30 April 2021 | Interest Rates & Yield Curves [Capital Spectator]

    Forecasting Bond Risk Premia using Stationary Yield Factors Tobias Hoogteijling (Robeco Asset Management), et al. April 12, 2021 The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which

Quantocracy’s Daily Wrap for 04/28/2021

This is a summary of links featured on Quantocracy on Wednesday, 04/28/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Copula for Statistical Arbitrage: Stocks Selection [Hudson and Thames]

    This is the fifth article of the copula-based statistical arbitrage series. You can read the previous four articles with the first three focusing on pairs-trading: Copula for Pairs Trading: A Detailed, But Practical Introduction. Copula for Pairs Trading: Sampling and Fitting to Data. Copula for Pairs Trading: A Unified Overview of Common Strategies. Copula for Statistical Arbitrage: A Practical
  • Reducing data dimensionality using PCA [Quant Dare]

    One common problem when looking at financial data is the enormous number of dimensions we have to deal with. For instance, if we are looking at data from the S&P 500 index, we will have around 500 dimensions to work with! If we have enough computing power, we will be able to process so much data, but that will not always be the case. Sometimes, we need to reduce the dimensionality of the

Quantocracy’s Daily Wrap for 04/27/2021

This is a summary of links featured on Quantocracy on Tuesday, 04/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Site: GANs and Synthetic Market Data (h/t @thodoha) [Mark Best]

    I have been thinking a lot about risk lately. The liquidity injections from the FED are pushing risk assets higher and higher. There seem to be bubbles in nearly every speculative assets. The main concern long term would be rising rates at the same time as a falling dollar suggesting there is no longer a market for US debt. Recently however rates have been rising which is causing concern that the
  • Learning the Exit (part 2) [Tr8dr]

    As described in my prior post Learning the Exit (part 1), I have a model that indicates mean reversion entries with ~81% accuracy, however I did not have a good approach in handling the exit. While 81% of MR signals had a minimum profit of 25% (of prior amplitude), the mean profit available was 150%, pointing to a larger profit opportunity to be had if can better handle the exit. I have found it

Quantocracy’s Daily Wrap for 04/26/2021

This is a summary of links featured on Quantocracy on Monday, 04/26/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Investing: The Truth Has Many Shades [Factor Research]

    The data from Professor French has laid the foundation for factor investing However, over time factor portfolio construction grew complex and with many nuances Returns may look more or less attractive, which makes a weak foundation INTRODUCTION When I was growing up one of my favourite TV shows was The X-Files, which followed the lives of FBI Special Agents Fox Mulder (David Duchovny) and Dana
  • Top rated contributor @Robot_Wealth teaches you how to trade part-time like a quant. Enroll til Friday.

    How do you make money trading in the highest probability, most effective way? Most trading advice does not address this question seriously enough. Either it smothers you in meaningless platitudes (don't fight the trend, don't risk more than 2% of your account on any trade), or it misses the mark in the other direction – being something that only a full-time professional trader and coding
  • Building a Zipline bundle for Yahoo CSV files [Quant Insti]

    Zipline is a fantastic tool for backtesting and data is the main raw material for doing this kind of analysis. In this post, we are going to focus on how to load our own data files. Through an example, we will create a bundle to load data from csv files downloaded from Yahoo finance.
  • Building a Better q-Factor Asset Pricing Model [Alpha Architect]

    Since the development of the first asset pricing model, the Capital Asset Pricing Model (CAPM), academic research has attempted to develop models that increase the explanatory power of the cross-section of stock returns. We moved from the single-factor CAPM (market beta), to the three-factor Fama-French model (adding size and value), to the Carhart four-factor model (adding momentum), to Hou, Xue,