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Quantocracy’s Daily Wrap for 04/05/2020

This is a summary of links featured on Quantocracy on Sunday, 04/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • GARCHery [OSM]

    In our last post, we discussed using the historical average return as one method for setting capital market expectations prior to constructing a satisfactory portfolio. We glossed over setting expectations for future volatility, mainly because it is such a thorny issue. However, we read an excellent tutorial on GARCH models that inspired us at least to take a stab at it. The tutorial hails from

Quantocracy’s Daily Wrap for 04/04/2020

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

  • Pandemics and Factor Investing: A Glimpse into the Past [Alpha Architect]

    When I was in the Marines we were voluntold to read a lot on the history of warfare. This mandate came from General Mattis desire that we lean on the 5,000+ years of fighting experience amongst us illustrious humans. Of course, history never tells you exactly what will happen in the future, but the perspective of history can be useful for preparing ourselves for the future. In a similar
  • Accelerating Python for Exotic Option Pricing (h/t @PyQuantNews) [Nvidia Developer]

    In finance, computation efficiency can be directly converted to trading profits sometimes. Quants are facing the challenges of trading off research efficiency with computation efficiency. Using Python can produce succinct research codes, which improves research efficiency. However, vanilla Python code is known to be slow and not suitable for production. In this post, I explore how to use Python
  • A statistical learning workflow for macro trading strategies [SR SV]

    Statistical learning for macro trading involves model training, model validation and learning method testing. A simple workflow [1] determines form and parameters of trading models, [2] chooses the best of these models based on past out-of-sample performance, and [3] assesses the value of the deployed learning method based on further out-of-sample results. A convenient technology is the

Quantocracy’s Daily Wrap for 04/03/2020

This is a summary of links featured on Quantocracy on Friday, 04/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Portfolio Optimization for Efficient Stock Portfolios [Invest Resolve]

    Its time to rethink passive stock investing. While capitalization weighted U.S. stock indices have delivered good performance over the past decade and the long-term, many investors dont realize that they can achieve similar returns with much less risk by employing risk-efficient portfolio construction. Risk-efficient portfolios avoid active stock picking and instead focus on achieving
  • Managing Expectations: Comparing S&P 500 s Deepest Drawdowns [Capital Spectator]

    In a previous post, I simulated S&P 500 drawdowns for perspective on what the current market correction may dispense in the weeks and months ahead. Lets supplement that analysis by visually comparing the current and ongoing peak-to-market decline with the ten deepest drawdowns since 1950. History doesnt repeat, at least not exactly when it comes to stock market trends. But you can still

Quantocracy’s Daily Wrap for 04/02/2020

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

  • How to Predict Bitcoin Price with Deep Learning LSTM Network – Part 1 [Quant at Risk]

    You cant predict the future unless you have a crystal ball but you can predict an assets trading price in next time step if you have a right tool and enough confidence in your model. With the development of a new class of forecasting models employing Deep Learning neural networks, we gained new opportunities in foreseeing near future. A rebirth of Long Short Term Memory (LSTM) artificial
  • How fast should we trade? [Investment Idiocy]

    This is the final post in a series aimed at answering three fundamental questions in trading: How should we control risk (first post) How much risk should we take? (previous post) How fast should we trade? (this post) Understanding these questions will allow you to avoid the two main mistakes made when trading: taking on too much risk and trading too frequently. Incidentally, systematic traders
  • Volatility Expectations and Returns [Alpha Architect]

    A large body of research, including the 2017 study Tail Risk Mitigation with Managed Volatility Strategies by Anna Dreyer and Stefan Hubrich, demonstrates that while past returns do not predict future returns, past volatility largely predicts future near-term volatility, i.e., volatility is persistent (it clusters). High (low) volatility over the recent past tends to be followed by high

Quantocracy’s Daily Wrap for 04/01/2020

This is a summary of links featured on Quantocracy on Wednesday, 04/01/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation: Surveying the Damage in March [Allocate Smartly]

    Tactical Asset Allocation (TAA) weathered the storm in March well, significantly paring down losses versus conventional buy & hold. We track 50+ TAA strategies sourced from books, papers, etc., allowing us to draw some broad conclusions about TAA as a style. In the table below we show the March and YTD returns of these 50+ strategies: 20 out of 54 strategies were up for the month, and 16 up
  • Predicting the fall: Revisiting the Forecasting VIX peaks experiment [Quant Dare]

    We are living through unprecedented times. Due to the ongoing global health pandemic, the international markets have plummeted with speeds never seen before, reminiscent of the 1930s and the Great Depression. On February 19, 2020, the SP500 Index closed at an all-time high price and then proceeded to decline sharply over the following days, accumulating 7 consecutive daily losses. Then after a

Quantocracy’s Daily Wrap for 03/31/2020

This is a summary of links featured on Quantocracy on Tuesday, 03/31/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Real Corporate Bond Puzzle [Falkenblog]

    The conventional academic corporate bond puzzle has been that 'risky' bonds generate too high a return premium (see here). The most conspicuous credit metric captures US BBB and AAA bond yields going back to 1919 (Moody's calls them Baa and Aaa). This generates enough data to make it the corporate spread measure, especially when looking at correlations with business cycles. Yet BBB

Quantocracy’s Daily Wrap for 03/30/2020

This is a summary of links featured on Quantocracy on Monday, 03/30/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Revenge of the Stock Pickers [Robot Wealth]

    To say were living through extraordinary times would be an understatement. We saw the best part of 40% wiped off stock indexes in a matter of weeks, unprecedented co-ordinated central bank intervention on a global scale, and an unfolding health crisis that for many has already turned into a tragedy. As an investor or trader, what do you do? You manage your exposures the best you can, dial
  • Range Bound Trading Strategy [Milton FMR]

    The following system helps you identify range bound formations and when to enter and exit such trades. Range bound formations occur when prices bounce back and forth establishing a nearly identical pattern of highs and lows. An upper resistance and lower support level is created. A key point to observe is the recognition of these range bound patterns which have the common characteristic that
  • An Empirical Challenge for Trend-Following [Alpha Architect]

    There is ample evidence in the literature that stock past returns predict future returns. One of the most comprehensive studies is Moskowitz et al. (2012), which shows that time-series momentum (TSM) is everywhere (they test it on 55 assets). 1 Later studies confirmed the results on even a broader set of asset classes and time periods (here and here). At the same time, other studies question the
  • Get Every Learning Resource and Trading Strategy Robot Wealth Has Ever Released – Offer Ends Friday

    The World Has Changed Massively. In these uncertain times, NOW is the time to build the skills and network you need to profit from home. Level up in Lock down! For one time only we're inviting you to GET EVERY LEARNING RESOURCE AND SYSTEMATIC TRADING STRATEGY ROBOT WEALTH HAS EVER RELEASED! Are you going to binge on another Netflix show, or are you going to future-proof your earning capacity
  • Thou Shall Not Short the VIX [Factor Research]

    The VIX has not remained at high levels for long in recent times, theoretically making a mean-reversion strategy attractive However, there were periods historically where volatility stayed elevated for years Furthermore, the VIX is not a tradeable index and related products should be viewed with caution INTRODUCTION Scratching the surface of most peoples knowledge often does not reveal depth,
  • One Hedge to Rule Them All [Flirting with Models]

    About two years ago, we compared and contrasted different approaches to risk managing equity exposure; including fixed income, risk parity, managed futures, tactical equity, and options-based strategies. Given the recent market events as the world navigates through the COVID-19 crisis, we revisit this analysis to see how these strategies would have fared over the past two years. We find that all

Quantocracy’s Daily Wrap for 03/29/2020

This is a summary of links featured on Quantocracy on Sunday, 03/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean expectations [OSM]

    Were taking a break from our extended analysis of rebalancing to get back to the other salient parts of portfolio construction. We havent given up on the deep dive into the merits or drawbacks of rebalancing, but we feel we need to move the discussion along to keep the momentum. This should ultimately tie back to rebalancing, but from a different angle. Well now start to examine capital
  • Some Basic Code Housekeeping [Dekalog Blog]

    Since my last post, back in late November last year, I have been doing a few disparate things such as: improving the coding of some functions in R to use the Oanda API to automatically download data using cronjobs coding some Octave functions to plot/visualise the above data more work on Random Vector Functional Link networks trying my hand at some discretionary day trading to take advantage of
  • Corporate Governance, ESG, and Stock Returns around the World [Alpha Architect]

    Figuring out exactly how to score companies on social issues isnt as simple as tossing around a universal ESG Ratio that works for all. Instead, we have to dig into the details and find the nuanced answer to discover which companies are performing and delivering on social issues. This paper takes on the challenge of discovering methods that may work in deciphering ESG performance.
  • The basics of low-risk strategies [SR SV]

    Low-risk investment strategies prefer leveraged low-risk assets over high-risk assets. The measure of risk can be based on price statistics, such as volatility and market correlation, or fundamental features. The rationale for low-risk strategies is that leverage is not available for all investors (but required to increase the weight of low-risk longs) and that many investors pay over the odds for

Quantocracy’s Daily Wrap for 03/26/2020

This is a summary of links featured on Quantocracy on Thursday, 03/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlations go to One [Alvarez Quant Trading]

    There is a saying: in bear markets correlations go to one. I wanted to see how true that is for both stocks and a basket of ETFs. Now they dont go to exactly one, not that I expected that, but they take some large steps towards one. Definitions When calculating correlation, I am using the daily percentage move of the stock. When I give a date range, the last day of the range is the day
  • Correlation Between the VVIX and VIX indices [Relative Value Arbitrage]

    The VIX index is an important market indicator that everyone is watching. VVIX, on the other hand, receives less attention. In this post, we are going to take a look at the relationship between the VIX and VVIX indices. While the VIX index measures the volatility risks, VVIX measures the volatility-of-volatility risks. Its calculation methodology is similar to the VIXs except that instead of

Quantocracy’s Daily Wrap for 03/25/2020

This is a summary of links featured on Quantocracy on Wednesday, 03/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • This Crisis Has Not Been Kind to Tactical ETFs [Allocate Smartly]

    DIY tactical asset allocation (i.e. the types of public strategies that we cover on this site) has been strong through this crisis. Tactical ETFs on the other hand have struggled badly. We track 50+ DIY TAA strategies, allowing us to draw some broad conclusions about TAA as a style. In the graph below we show the YTD performance of the average strategy that we track in orange, versus the 60/40
  • Econometric GDP Models Struggle With Coronavirus Fallout [Capital Spectator]

    The widespread disruption from the coronavirus pandemic is obvious to everyone, but economic nowcasting and forecasting models are only just beginning to reflect the damage to what had been a moderately expanding US economy. Thanks to the lag in economic data, which can arrive with as long as two to three months after the fact, formerly robust methodologies for tracking the US macro trend have