Quantocracy

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Quantocracy’s Daily Wrap for 02/25/2018

This is a summary of links featured on Quantocracy on Sunday, 02/25/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithm design and correctness [Quantum Financier]

    Giving software you wrote access to your or your firms cash account is a scary thing. Making a mistake when manually executing a trade is bad enough when it happens (you can take my word for it if you havent yet), but when unintended transactions are made by a piece of software in a tight loop it has the potential to be an extinction event. In other words, there is no faster way to

Quantocracy’s Daily Wrap for 02/23/2018

This is a summary of links featured on Quantocracy on Friday, 02/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Allocation Roundup [Allocate Smartly]

    Recent asset allocation articles (tactical or otherwise) that you might have missed: 40 Shades of Tactical Asset Allocation Across Bull And Bear Markets (Earl Adamy) Earl walks readers though his own approach to finding the best TAA strategies using the data available in our members area. We try not to define the best strategies for members, as the best is highly dependent on each
  • Are Factors Better and More Diversifying Than Asset Classes? [Alpha Architect]

    Factor investing promises outperformance at low cost. But to add value in a portfolio, it must deliver positive risk-adjusted returns and with low correlation to existing holdings. Historically, pure factor exposures have earned similar risk-adjusted returns to buying and holding plain vanilla asset classes like stocks and bonds. Therefore, factors such as Value and Momentum should only be

Quantocracy’s Daily Wrap for 02/22/2018

This is a summary of links featured on Quantocracy on Thursday, 02/22/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algotopian [Backtrader]

    Following ideas, proposals, pushes and comments similar and disimilar to those, for example, in this post Community – What is the direction of backtrader, the last weeks have been used to craft an idea about the potential future of backtrader. It has been named Algotopian and its being shared today, for review, comments, feedback. First in this community (for which a Category will be opened),
  • Podcast: Managed Futures and Trend-Following with Dr. Kaminski [Alpha Architect]

    Here is a link to our podcast on Behind the Markets Wes and Jeremy chat with Katy Kaminski, Visiting Lecturer in Finance at the MIT Sloan School of Management and co-author of the book, Trend Following with Managed Futures: The Search for Crisis Alpha. They discuss trend following and managed futures. Katy also delves into going long and short based on the direction of the market and

Quantocracy’s Daily Wrap for 02/20/2018

This is a summary of links featured on Quantocracy on Tuesday, 02/20/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Creating a Table of Monthly Returns With R and a Volatility Trading Interview [QuantStrat TradeR]

    This post will cover two aspects: the first will be a function to convert daily returns into a table of monthly returns, complete with drawdowns and annual returns. The second will be an interview I had with David Lincoln (now on youtube) to talk about the events of Feb. 5, 2018, and my philosophy on volatility trading. So, to start off with, a function that I wrote thats supposed to mimic
  • How to Evaluate Multi-Asset Strategies [Alpha Architect]

    What are the research questions? Institutional Investors are increasingly allocating to multi-asset strategies (p.17 ) as they seek to access greater diversity, liquidity, and reduced volatility (survey results from Greenwich Associates, 2015). The main research questions of the paper are as follows: Is there ONE correct way to evaluate multi-asset strategies? Which are the most appropriate
  • Stress Testing an Intraday Strategy Through Monte Carlo Methods [Flare 9x]

    This is an intraday ES strategy that I was testing for a client. The client was not interested in it due to the low frequency of trades, hence I may post it for others to view. It shows how a strategy was proved through stress testing and looking for optimal conditions to apply the strategy. The steps for strategy development are below: Strategy Development Example Andrew Bannerman

Quantocracy’s Daily Wrap for 02/19/2018

This is a summary of links featured on Quantocracy on Monday, 02/19/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mixture Model Trading (Part 5 – Algorithm Evaluation with pymc3) [Black Arbs]

    See . This research demonstrates a systematic trading strategy development workflow from theory to implementation to testing. It focuses on the concept of using Gaussian Mixture Models as a method for return distribution prediction and then using a simple market timing strategy to take advantage of the predicted asset return outliers. Chapter Goals Demonstrate how to extract algorithm portfolio
  • Sequential Model: Sorting by 5 Factors [Factor Research]

    The sequential model ranks stocks by factors sequentially Allows investors to prioritise factors and results in concentrated portfolios However, the factor sequence matters and only a few factors can be considered INTRODUCTION In a recent research report we showed how investors can combine factors into a multi-factor portfolio by ranking for several factors simultaneously (please see

Quantocracy’s Daily Wrap for 02/18/2018

This is a summary of links featured on Quantocracy on Sunday, 02/18/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sunday Marks the Quantifiable Edges Subscriber Letter s 10th Anniversary [Quantifiable Edges]

    Sunday Feb 18th marks the 10th anniversary of the Quantifiable Edges Subscriber Letter. I can hardly believe I have been writing it for 10 years, but it is true. A few highlights and anecdotes from the last 10 years When the letter began, there was not even a website just a blogspot blog and a Paypal button. My 1st subscriber was Mr. Norwood. I remember distinctly, because my wife
  • A Closer Look At The Links For Stocks, Interest Rates, And Inflation [Capital Spectator]

    Does history offer a reason to be cautious on the outlook for stocks if inflation and interest rates are rising? Yes, sort of, according to a New York Times article published on Thursday. Hedging just a bit, the Times piece relates that its long been a truism that higher inflation and its close cousin, higher interest rates, are deadly for stock prices. But in the wake of this months
  • Spx Low Vol Streak, Update [Voodoo Markets]

    Spx had a rather long streak of low volatility. There is no predictive value or signal here, i just wanted to eyeball & visualize how long the low vol streak lasted and how it compared to other low vol streaks. Here are all the Spx low vol streaks (close to close change > +/-1%) that lasted more than 42 days, since the 50s

Quantocracy’s Daily Wrap for 02/14/2018

This is a summary of links featured on Quantocracy on Wednesday, 02/14/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading the Equity Curve [Alvarez Quant Trading]

    A popular method for determining if a strategy should be kept trading is trading the equity curve. What this means we apply an indicator, say 200-day moving average, to the equity curve. When the equity curve falls below this value we stop trading. We then continue to paper trade the strategy until it gets above the moving average and then trade it live again. The general idea being that you get
  • The Kelly Criterion [Quant Dare]

    Forecasting the market or the outcome of a gamble is important. Deciding how much to invest or bet based on how confident you are about the prediction is similarly as important. But dont let the pressure get to you; the Kelly criterion is here to help us make this decision. Betting with the Kelly criterion Imagine you are invited to place bets on an indefinite sequence of coin tosses with fair
  • Price Overreactions in the Cryptocurrency Market [Quantpedia]

    This paper examines price overreactions in the case of the following cryptocurrencies: BitCoin, LiteCoin, Ripple and Dash. A number of parametric (t-test, ANOVA, regression analysis with dummy variables) and non-parametric (MannWhitney U test) tests confirm the presence of price patterns after overreactions: the next-day price changes in both directions are bigger than after normal days.

Quantocracy’s Daily Wrap for 02/12/2018

This is a summary of links featured on Quantocracy on Monday, 02/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Should You Dollar-Cost Average? [Flirting with Models]

    Dollar-cost averaging (DCA) versus lump sum investing (LSI) is often a difficult decision fraught with emotion. The historical and theoretical evidence contradicts the notion that DCA leads to better results from a return perspective, and only some measures of risk point to benefits in DCA. Rather than holding cash while implementing DCA, employing a risk managed strategy can lead to better DCA
  • Value Factor – Intra vs Cross-Sector [Factor Research]

    Intra versus cross-sector Value portfolios share the major trends Neutralising the sector exposure increases the risk-return ratio of the Value factor However, the benefits are marginal and come with higher operational complexity INTRODUCTION 2018 started almost identical to 2017 in terms of factor performance in the US Momentum, Growth and Quality gained while Value lost. Investors with a
  • What SPY s Gap Up, Reverse Down & Rebound Back Up From Friday Suggest For This Week [Quantifiable Edges]

    The sizable gap up, pullback, and then move back higher on Friday triggered an old Quantifinder study for the 1st time in a long time. Below is the full list of trades with a 5-day holding period. 2018-02-11 All 8 instances saw run-ups of at least 1%, and they all closed positive. While instances are low, the initial inclination appears quite bullish. This study may be worth some consideration
  • Short Sellers Profitably Trade Prior to Credit Rating Agency Announcements [Alpha Architect]

    What are the research questions? This research focuses on the relationship between the frequency of unexpected short selling behavior and abnormal returns surrounding credit watch and rating change announcements in the equity market. It is notable that it employs a unique database that affords the authors the opportunity to study the behavior of unexpected short selling around credit rating agency
  • Low Risk Anomaly in Banking Industry and Its Implications [Quantpedia]

    Traditional capital structure theory in frictionless and efficient markets predicts that reducing banks leverage reduces the risk and cost of equity but does not change the overall weighted average cost of capital (and thus the rates for borrowers). We test these two predictions. We confirm that the equity of better-capitalized banks has lower beta and idiosyncratic risk. However, over the last

Quantocracy’s Daily Wrap for 02/09/2018

This is a summary of links featured on Quantocracy on Friday, 02/09/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Thoughts On Dealing With Historically Abnormal Markets [Quantifiable Edges]

    I have discussed some lately that the market is acting outside of historical norms. Thursdays action reinforced that. The pullback has come so fast and been so extreme that it is going beyond even many of the most extreme moves in similar situations. For instance, I looked back to 1960 with the SPX for to find other times SPX closed down > 10% from a 250-day closing high within 2 weeks of
  • Time Series Momentum (aka Trend-Following): A Good Time for a Refresh [Alpha Architect]

    Similar to some better-known factors like size and value, time-series momentum is a factor which has historically demonstrated above-average excess returns. Time-series momentum, also called trend momentum or trend-following, is measured by a portfolio which is long assets which have had recent positive returns and short assets which have had recent negative returns.(1) Compare this to the

Quantocracy’s Daily Wrap for 02/08/2018

This is a summary of links featured on Quantocracy on Thursday, 02/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Examining Short Term Reversals in Stocks Part 1 (Returns Data) [Sober Quant]

    Short-term reversals, including intraday and monthly reversals, are well-known in academic literature and are observable in the markets every day. This phenomenon persists across many different asset classes, especially stocks. There are many theories to explain this phenomenon. Some say the reversions are news-driven, in that when there is new information (e.g. Earnings or Clinical Trials),
  • VIX vs Stock Market Volatility: Similar But Different [Capital Spectator]

    The recent plunge in the US stock market ended the extended run of tranquility in equity returns. The medias metric of choice to cite this change is the CBOE Volatility Index, or VIX, which surged earlier this week to the highest level in nearly three years, based on daily data. The upward explosion was even sharper on an intraday basis. As useful as the VIX is for quantifying market
  • Correlation with prices or returns: that is the question [Quant Dare]

    Thought you knew everything about correlation? Think theres no fooling you with the question of correlation with financial prices or returns? Well maybe, just maybe, this post will enlighten you. Correlation: the debate is on Correlation can be a controversial topic. Things can go awry when two seemingly unrelated variables appear to move in a similar pattern and are found to be correlated.
  • Chess, Jeopardy, Poker, Go and Investing? [CXO Advisory]

    How can machine investors beat humans? In the introductory chapter of his January 2018 book entitled Financial Machine Learning as a Distinct Subject, Marcos Lopez de Prado prescribes success factors for machine learning as applied to finance. He intends that the book: (1) bridge the divide between academia and industry by sharing experience-based knowledge in a rigorous manner; (2) promote