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Quantocracy’s Daily Wrap for 02/06/2020

This is a summary of links featured on Quantocracy on Thursday, 02/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case Against REIT’s [Alpha Architect]

    Surveys often reveal investor behavior that is challenging to understand. For example, Preqins Alternative Investor Outlook for H2 2019 highlighted the following: 65% of institutional investors believe that real estate is overvalued and a correction likely to occur in 2019, 2020, or beyond. However, 45% want to allocate the same amount of capital to real estate and 28% want to allocate even
  • What is the right way to set stop losses? [Investment Idiocy]

    Stop losses are the most common method used by traders to control risk. However, they're often used inappropriately. In this post I'll quickly bust some of the myths around them, and explain how to use them properly. This is the first of three posts aimed at answering three fundamental questions in trading: How should we control risk (this post) How much risk should we take? How fast

Quantocracy’s Daily Wrap for 02/05/2020

This is a summary of links featured on Quantocracy on Wednesday, 02/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Inverse Volatility Sizing Index [Alvarez Quant Trading]

    In my last post, Inverse Volatility Position Sizing, I tested inverse volatility sizing on a monthly rotation strategy. I saw very little difference in the rest results versus equal position sizing. I was talking to a trading friend about the research and how I was surprised at how there was not any difference in the results. He suggested that made creating an index using this method. Now, this
  • Factor Risk and Return [Falkenblog]

    Factor returns should reflect risk, in that they have traditionally been interpreted as proxies for some kind of risk not measured by beta. The idea is that perhaps what people really care about is whether there will be another oil shock, and nothing matters as much. Stocks that have a high dependence on cheap oil would have more risk than other stocks. In the early 1980s, this was a common
  • Visualising ETFs with UMAP [Quant Dare]

    In previous posts (Visualising Fixed Income ETFs with T-SNE) we have talked about dimensionality reduction algorithms to visualize financial assets and find recognizable patterns. The conclusions were that it didnt perform well compared to PCA, which is a more classical approach. Can we do any better? T-SNE was from 2008, but more dimensionality reduction algorithms have been released since

Quantocracy’s Daily Wrap for 02/04/2020

This is a summary of links featured on Quantocracy on Tuesday, 02/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • QuantMinds International Conference in Hamburg, Germany, May 11-15 [Quant Minds]

    The world's leading quant finance conference 450+ experts from banks, buy-side, regulators, Silicon Valley, academia and beyond examine every facet of quant in five amazing days Key themeslatest agenda SPECIALIST FOCUS. SPECIALIST KNOWLEDGE. Maximise your experience with our full-day summit or technical workshops. Dig deeper into the topics that matter most to you. Learn more The world's
  • Book Review: Smart(er) Investing by Elisabetta and Tommi [Alpha Architect]

    Its not often I get the opportunity to write a book review for our fellow teammates and the best authors on our website Elisabetta Basilico and Tommi Johnsen! If you havent read Elisabetta and Tommis mountain of blog posts on our site youve been hiding under a rock somewhere (or clearly not spending enough time on the Alpha Architect website). I believe that rigorous academic
  • How to Learn Advanced Mathematics Without Heading to University – Part 4 [Quant Start]

    It has been some time since wrote Parts I, II and III of our popular series of articles on How to Learn Advanced Mathematics Without Heading to University. Many of you have contacted us asking for the final Part IV of the series. We have now completed our internal research and can present our view on the most appropriate modules to self-study in lieu of carrying out a structured fourth year of a

Quantocracy’s Daily Wrap for 02/03/2020

This is a summary of links featured on Quantocracy on Monday, 02/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sneak Peak: Robustness to Noise [Allocate Smartly]

    This is an early preview of a new analytical tool well be adding to our platform later this month. Learn more about what we do. Broadly speaking, the goal of tactical asset allocation is to take advantage of broad market trends via trend-following and/or momentum. Those trends can be difficult to identify because of noise; short-term price fluctuations that confuse/distort the underlying
  • Can Managed Futures Offset Equity Losses? [Flirting with Models]

    Managed futures strategies have historically provided meaningful positive returns during left-tail equity events. Yet as a trading strategy, this outcome is by no means guaranteed. While trend following is mechanically convex, the diverse nature of managed futures programs may actually prevent the strategy from offsetting equity market losses. We generate a large number of random managed
  • Machine learning and macro trading strategies [SR SV]

    Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously learning from new data, thereby challenging or refining prevalent beliefs. Machine learning and expert
  • Sentiment and Factor Performance [Factor Research]

    Stock sentiment can be aggregated from public sources using a big data approach Results indicate that sentiment has some predictability for short-term factor performance Positive sentiment resulted in higher subsequent returns than negative sentiment INTRODUCTION Albert Einstein famously stated that information is not knowledge, which is more relevant than ever as the amount of available

Quantocracy’s Daily Wrap for 01/31/2020

This is a summary of links featured on Quantocracy on Friday, 01/31/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is AI coming after your job? [Mathematical Investor]

    It is no secret that artificial intelligence (AI) systems have made enormous strides in recent years, partly due to the adoption of Bayesian (probability-based) machine learning techniques rather than the rule-based techniques used until about 20 years ago. AI systems have advanced in lockstep with advances in robotics, even though in recent years AI systems have also addressed rather different
  • Low Volatility-Momentum Factor Investing Portfolios [Alpha Architect]

    Factor investing is hard and some factors make it harder than others. A value strategy results in a portfolio of stocks that exhibit temporary or structural issues and are usually rated Sell by brokers, which makes these emotionally challenging to hold. Small caps are companies that are unknown to most investors and lack the prestige associated with investing in firms like Apple or Amazon.
  • Generating OHLC bars with Generative Adversarial Networks [Quant Dare]

    Open-High-Low-Close (OHLC) bars are a type of financial data typically used to represent daily movements in the price of a financial instrument. They give us more information about certain characteristics of the series than line charts, such as intraday volatility or daily momentum. Could Generative Adversarial Networks learn to generate series with the underlying structure of OHLC bars? If its

Quantocracy’s Daily Wrap for 01/29/2020

This is a summary of links featured on Quantocracy on Wednesday, 01/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantitative Analytics: Optimal Portfolio Allocation [R Shenanigans]

    The literature in portfolio optimisation has been around for decades. In this post I cover a number of traditional portfolio optimisation models. The general aim is to select a portfolio of assets out of a set of all possible portfolios being considered with a defined objective function. The data: The data is collected using the tidyquant() packages tq_get() function. I then convert the daily
  • Is the Fama-French Model Dead? [Falkenblog]

    When I was in graduate school at Northwestern in the early 90s the hot financial topics were all related to finding and estimating risk factors: Arbitrage Pricing Theory via latent factors (Connor and Koraczyk 1986), Kalman filter state-space models (eg, Stock and Watson 1989), and method of moment estimators (Lars Hansen 1982). These appealed to central limit theorem proofs, which is the academic
  • The predictability of crowding on factor strategy performance [Alpha Architect]

    The focus of this study is on the response of typical or systematic risk premia to crowding (large inflows of capital). In particular, the paper focused on documenting the response of commonly recognized systematic risk premia strategies to periods, following the identification of crowded conditions. What the focus is not: the impact of a broad-based unwinding such as the quant meltdown of 2007,

Quantocracy’s Daily Wrap for 01/27/2020

This is a summary of links featured on Quantocracy on Monday, 01/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Blending Buy & Hold with Tactical, A “Lethargic” Approach to Asset Allocation [Allocate Smartly]

    This is a test of a new paper from Dr. Wouter Keller titled Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA). This is primarily a buy & hold strategy thats roughly based on the classic Permanent Portfolio, but it includes an element of tactical asset allocation. This blending of buy & hold with tactical can be less stressful to trade, especially for
  • Understanding Pointwise Mutual Information [Eran Raviv]

    The term mutual information is drawn from the field of information theory. Information theory is busy with the quantification of information. For example, a central concept in this field is entropy, which we have discussed before. If you google the term mutual information you will land at some page which if you understand it, there would probably be no need for you to google it in the first
  • Fighting U.S. FOMO [Flirting with Models]

    U.S. equities have out-performed international equities for 8 of the past 10 years, but this trend has tended to flip-flop historically and persist for multi-year stretches. Home country bias is a real phenomenon that investors have to deal with, especially during these streaks where U.S. equities are favored. Balancing broad market expectations with the tendency to have a behavioral tie to home
  • Liquidity and Factor Performance [Factor Research]

    Most institutional investors can only trade the largest, most liquid stocks Introducing minimum liquidity requirements impacts factors differently Factor portfolio construction with liquidity constraints is especially challenging in small stock markets INTRODUCTION Index funds have breached $11 trillion assets under management in late 2019 to the detriment of active managers according to data from

Quantocracy’s Daily Wrap for 01/26/2020

This is a summary of links featured on Quantocracy on Sunday, 01/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio starter kit [OSM]

    Say youve built a little nest egg thanks to some discipline and frugality. And now you realize that you should probably invest that money so that youve got something to live off of in retirement. Or perhaps you simply want to earn a better return than stashing your cash underneath your bed, I mean your savings account. How do you choose the assets? What amount of money should you put into
  • How to Turn Cross-Sectional into Time-Series Momentum [Alpha Architect]

    A point of confusion for many new quant momentum investors is the difference between Time- Series Momentum and Cross-Sectional Momentum: Time-series (TS) looks at each individual stocks momentum and owns assets with positive momentum while shorting those with negative momentum; Cross-sectional (CS) observes a universe of stocks and chooses those with the best momentum relative to the universe
  • The q-factor model for equity returns [SR SV]

    Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital and the net present value rule of corporate finance. The q-factor model is an implementation of investment capital asset pricing that explains many empirical features of relative equity returns. In particular, the model proposes that the following

Quantocracy’s Daily Wrap for 01/23/2020

This is a summary of links featured on Quantocracy on Thursday, 01/23/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Hidden Risk FIRE Investors Miss [Movement Capital]

    The financial independence, retire early (FIRE) movement has gained a lot of traction. We retired at 30 headlines get clicks and have made people question the typical retirement timetable: A main goal for those pursuing FIRE is to reach a portfolio balance that can reasonably fund their retirement. This is found by dividing estimated expenses by an initial withdrawal rate that (hopefully)
  • Visualization Sector Trends with R Code [Alpha Architect]

    Welcome to a year-end installment of Reproducible Finance with R, a series posts that will be a little bit different from the norm on Alpha Architect (see here for my last post). We will search for and hopefully unearth some interesting market conditions, but well primarily focus on the code that we use for telling stories with data. Todays project is to visualize market sector
  • Pre-Election Drift in the Stock Market [Quantpedia]

    There are many calendar / seasonal anomalies by which we can enhance our overall investment strategy. One of the least frequent but still very interesting anomalies is for sure the Pre-Election Drift in the stock market in the United States. This year is the election year, and public discussion is getting more heated. The current president of the United States and candidate for re-election, Donald
  • Correlations Profile | Major Asset Classes | 23 January 2020 [Capital Spectator]

    Return correlations for the major asset classes have edged down in recent years, which implies that diversification opportunities have increased, if only marginally. The correlation readings are only modestly softer overall and for several asset class pairings its fair to say that nothing much has changed. But reviewing all the key slices of global markets by way of pairwise return correlations

Quantocracy’s Daily Wrap for 01/22/2020

This is a summary of links featured on Quantocracy on Wednesday, 01/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Persistency Beyond Almost All Other Rallies [Quantifiable Edges]

    Last week I noted the current rally was reaching historical extremes for persistency. Here I will look at another study from the subscriber letter, and then update last weeks study. In last nights letter I looked at all times back to the inception of the NASDAQ in 1971 in which both SPX and the NASDAQ Composite closed above their 10ma for at least 30 days in a row. The short list is below.
  • Calculating a VIX3M Style Index Back to 1990 Reveals Surprising Trends [Six Figure Investing]

    The Cboes VIX (30-day) and VIX3M (93-day) indexes enable us to quantify volatility term structures but until now, historical analyses between VIX style indexes have been limited to dates after December 2001. This post introduces the results of VIX3M style calculations back to 1990, and reviews issues and trends that were revealed. In November 2007, the Cboe introduced VIX3M, a volatility