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Quantocracy’s Daily Wrap for 10/01/2017

This is a summary of links featured on Quantocracy on Sunday, 10/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in September (Now Adjusted for Timing Luck) [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Learn more about what we do or let AllocateSmartly help you follow these strategies in
  • Smart Portfolios from @InvestingIdiocy [Reading the Markets]

    Robert Carver, author of Systematic Trading, has turned his attention to the thorny problem of portfolio construction. In Smart Portfolios: A Practical Guide to Building and Maintaining Intelligent Investment Portfolios (Harriman House, 2017) he deals with such topics as how to blend assets with different levels of risk, the reasons that forecasting returns is so difficult, and how to calculate

Quantocracy’s Daily Wrap for 09/30/2017

This is a summary of links featured on Quantocracy on Saturday, 09/30/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Calibrating Financial Models using a Non-Parametric Technique [Top of The Bell Curve]

    Traditionally, asset returns have been modeled using diffusion processes. Diffusion processes assume that the sample path of the process being modeled is continuous. However, empirical evidence suggests that there are jumps that occur in asset returns, such as those that occurred during the financial crisis of 2008. The presence of jumps has implications in derivative pricing and asset allocations

Quantocracy’s Daily Wrap for 09/29/2017

This is a summary of links featured on Quantocracy on Friday, 09/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Kelly Criterion Does It Work? [QuantStrat TradeR]

    This post will be about implementing and investigating the running Kelly Criterion that is, a constantly adjusted Kelly Criterion that changes as a strategy realizes returns. For those not familiar with the Kelly Criterion, its the idea of adjusting a bet size to maximize a strategys long term growth rate. Both https://en.wikipedia.org/wiki/Kelly_criterionWikipedia and Investopedia have

Quantocracy’s Daily Wrap for 09/28/2017

This is a summary of links featured on Quantocracy on Thursday, 09/28/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • VIX and Trend-Following, the Killer Combo? [Alpha Architect]

    Some things in life are naturally made for each other. Some examples include the following: Peanut Butter and Jelly Starsky & Hutch Value and Momentum So my ears perked up when the idea of combining VIX levels and Trend Following started making the rounds on finance twitter. Like any geek, I was eager to start testing the idea, knowing ahead of time that there is a major overfitting hurdle to
  • Are Short Out-of-the-Money Put Options Risky? Part 2: Dynamic Case [Relative Value Arbitrage]

    This post is the continuation of the previous one on the riskiness of OTM vs. ATM short put options and the effect of leverage on the risk measures. In this installment were going to perform similar studies with the only exception that from inception until maturity the short options are dynamically hedged. The simulation methodology and parameters are the same as in the previous study. As a
  • Craftsmanship Alpha [Quantpedia]

    Successful investing requires translating sound investment concepts into actual trading strategies. We study many of the implementation details that portfolio managers need to pay attention to; such choices range from portfolio construction to execution. While these kinds of decisions apply to any type of investment strategy, they are particularly important in the context of style investing.
  • What Kind of Asset Is Bitcoin? [CXO Advisory]

    Does Bitcoin behave like some other asset class? To investigate, we calculate daily and monthly return correlations between Bitcoin and each of 34 exchange-traded products encompassing eight used in Simple Asset Class ETF Momentum Strategy (SACEMS), 24 considered in SACEMS Portfolio-Asset Addition Testing plus SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL) and Powershares DB US Dollar

Quantocracy’s Daily Wrap for 09/27/2017

This is a summary of links featured on Quantocracy on Wednesday, 09/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • High Frequency Trading III: Optimal Execution [Quant Start]

    In this article series Imanol Prez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to QuantStart outlines the basics of high-frequency trading. In this article Imanol uses the theory of stochastic optimal control to optimally execute a large trade order. It is well known that when a large order is trying to be executedeither a sell or buy orderthe
  • Calculate monthly returns with Pandas [Quant Dare]

    Calculating returns on a price series is one of the most basic calculations in finance, but it can become a headache when we want to do aggregations for weeks, months, years, etc. In python the Pandas library makes this aggregation is very easy to do, but if we dont pay attention we could still make mistakes. Assuming that we want the return of the whole month, and we are not interested, for

Quantocracy’s Daily Wrap for 09/25/2017

This is a summary of links featured on Quantocracy on Monday, 09/25/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Addressing Low Return Forecasts in Retirement with Tactical Allocation [Flirting with Models]

    The current return expectations for core U.S. equities and bonds paint a grim picture for the success of the 4% rule in retirement portfolios. While varying the allocation to equities throughout the retirement horizon can provide better results, employing tactical strategies to systematically allocate to equities can more effectively reduce the risk that the sequence of market returns is
  • Quality Factor: Zero Alpha for Most Investors? [Factor Research]

    SUMMARY Its difficult to rationalise why there should be excess returns from high quality stocks The Quality factor needs to be constructed beta-neutral to achieve positive returns Exposure to the Quality factor is an attractive hedge for an equity-centric portfolio INTRODUCTION The concept of investing into the Quality factor is an odd one as its difficult to rationalise why investors
  • Global Diversification Works for Multi-Factor Portfolios [Quantpedia]

    The benefits of country diversification are well established. This article shows that the same benefits extend to equity factors, such as value, size, momentum, investment, and profitability. Specifically, country factor portfolios reflect both common variation, which we define as the global factor, and local variation. On average, a US investor could enjoy a 30% reduction in portfolio volatility

Quantocracy’s Daily Wrap for 09/22/2017

This is a summary of links featured on Quantocracy on Friday, 09/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Timing Investigation: Interest Rates, Value Spreads, and Factor Premiums [Alpha Architect]

    Now that the Federal Reserve has begun the process of raising interest rates, and has announced their intention to begin to unwind their policy of quantitative easing (reducing the amount of bonds in their portfolio, either by selling holdings or allowing holdings to mature), investors may be concerned about the impact of rising interest rates on factor premiums. Wei Dai, senior researcher at
  • Downloading Historical Data Using Oanda’s API and R [Dekalog Blog]

    It has been about 5 months since my last blog post and in this time I have been working away from home, been on summer holiday and spent some time mucking about on boats, so I have not been able to devote as much time to my blog as I would have liked. However, that has now changed, and this blog post is about obtaining historical data. Many moons ago I used to download free, EOD data from

Quantocracy’s Daily Wrap for 09/21/2017

This is a summary of links featured on Quantocracy on Thursday, 09/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Option Chain Extraction For NSE Stocks Using Python [Quant Insti]

    We are back again with another post on Python. Our last post, Basic Operations on Stock data using Python was well received and we are glad to see the number of likes & shares for the post on various quant trading and Python forums. Keep them coming! Financial market data is a very critical element of a trading system. Be it historical or live data, you need data for various purposes
  • Trinity Portfolio (Lite) from @MebFaber [Allocate Smartly]

    This is a test of the Trinity Portfolio from Meb Faber and Cambria Investments, so named for the three key elements of the strategy: (1) a globally diversified mix of assets, (2) a tilt towards the value and momentum factors, and (3) exposure to momentum and trend-following. Weve titled our test Trinity Lite because weve made some not insignificant changes to Fabers original model
  • Seven Habits of Highly Ineffective Quants [CXO Advisory]

    Why dont machines rule the financial world? In his September 2017 presentation entitled The 7 Reasons Most Machine Learning Funds Fail, Marcos Lopez de Prado explores causes of the high failure rate of quantitative finance firms, particularly those employing machine learning. He then outlines fixes for those failure modes. Based on more than two decades of experience, he concludes that:

Quantocracy’s Daily Wrap for 09/20/2017

This is a summary of links featured on Quantocracy on Wednesday, 09/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SVM Trend Strategy on Nikkei 225 Mini Futures [Golden Compass]

    Motivation Support Vector Machines (SVM) are among the most popular Supervised Learning techniques for classification and regression, due to their ease in usage to find non-linear patterns. They work by separating data by finding an optimal threshold known as a decision boundary or hyperplane, to classify observations. When new data is presented to the SVM, it can distinguish which side of the
  • Evidence Based Investing is Dead. Long Live Evidence Based Investing! Part 1 [Invest Resolve]

    Michael Edesses article, The Trend that is Ruining Finance Research makes the case that financial research is flawed. In this two-part article series, we will examine the points that Michael raises in some detail. We find his arguments have some merit. Importantly however, his article fails to undermine the value of finance research in general. Rather, his points serve to highlight that
  • ETF Sector Trading: The effect of daily, weekly and monthly timeframes [Alvarez Quant Trading]

    I recently gave a presentation on Sector trading using the 200-day moving average at the Northwest Traders and Technical Analysts. Some questions asked were: What if we only trade this monthly? What if we used weekly bars to trade only weekly? Wat if we used weekly bars to trade monthly? The reason for these questions was to reduce the frequency of having to check signals and the total number of

Quantocracy’s Daily Wrap for 09/18/2017

This is a summary of links featured on Quantocracy on Monday, 09/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Lie of Averages [Flirting with Models]

    Averages are often used to summarize data: but sometimes fitting for the average means fitting nothing at all. Expected returns are a meaningful input to portfolio construction, but are unlikely to be the returns actually realized. Reality rarely looks average. The world is dynamic and forecasts can change. Not only should we expect that things will not be average, but we should expect that our
  • Factor Allocation 101: Equal vs Volatility-Weighted [Factor Research]

    Equal-weight and volatility-weighted allocations are two common factor allocation frameworks Risk-return ratios are not higher with volatility-weighted allocations Different reasons can explain the superiority of equal-weight allocations INTRODUCTION In July we published a research report Factors & Volatility-Based Risk Management were we analysed Value, Size and Momentum based on
  • The Weakest Week (Updated) [Quantifiable Edges]

    From a seasonality standpoint, there isnt a more reliable time of the year to have a selloff than this upcoming week. In the past I have referred to is as The Weakest Week. Since 1961 the week following the 3rd Friday in September has produced the most bearish results of any week. Below is a graphic to show how this upcoming week has played out over time. 2017-09-17 image1 As you can see
  • Research Review | Portfolio Management [Capital Spectator]

    Asset Allocation in a Low Yield Environment John Huss (AQR Capital Mgt.), et al. August 17, 2017 The year 2016 saw bond yields fall to unprecedented low levels in major developed markets, with nominal yields on 10-year German and Japanese government bonds even turning negative. While yields have risen off their lows in 2017, we are still in a very low rate environment. Does this demand exceptional
  • Why Machine Learning Funds Fail [Quantpedia]

    The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms