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Quantocracy’s Daily Wrap for 11/06/2018

This is a summary of links featured on Quantocracy on Tuesday, 11/06/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Today’s Markets. Tomorrow’s Technology. | Trading Show Chicago | 8 – 9 May 2019

    The Trading Show Chicago is the only event that combines quant, automated trading, exchange technology, big data and derivatives. Whether youre focused on new quantitative models, adopting low latency systems or managing risk, The Trading Show Chicago provides unparalleled opportunities to network and ultimately do business with top trading firms, quant funds, international exchanges, end
  • Profiling Factor ETF Correlations [Capital Spectator]

    Slicing and dicing the US equity market into factor buckets is, at its core, an effort to enhance return by engineering more control over risk management. A key part of this framework is recognizing that risk and return for the stock market overall is a byproduct of multiple factors, such as shares trading at low valuations or posting strong price momentum in the recent past. In turn, its
  • Forward Propagation In Neural Networks [Quant Insti]

    In this blog, we will intuitively understand how a neural network functions and the math behind it with the help of an example. In this example, we will be using a 3-layer network (with 2 input units, 2 hidden layer units, and 2 output units). The network and parameters (or weights) can be represented as follows. forward propagation 1 Let us say that we want to train this neural network to predict
  • Trend Following in October [Wisdom Trading]

    October 2018 Trend Following: DOWN -6.13% / YTD: -14.45% Please find this months report of the Wisdom State of Trend Following. Performance is hypothetical. Chart for October: Wisdom State of Trend Following – October 2018 And the 12-month chart: Wisdom State of Trend Following 12 months – October 2018 Below are the summary stats: Horizon Return Ann. Vol. Last month -6.13% 15.5% Year To Date

Quantocracy’s Daily Wrap for 11/05/2018

This is a summary of links featured on Quantocracy on Monday, 11/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Precisely Forecasting Price Ranges with Volatility [Six Figure Investing]

    Using a tool like Bollinger Bands to forecast future price ranges is a time-honored technique but its calculations are simplified and in some situations flawed. Incorporating the log-normal nature of stock prices into the calculations gives better answers. One greed inducing aspect of volatility is that it enables us to make theoretically sound forecasts about the future. It doesnt matter
  • The Problem With Financial Oracles [Mathematical Investor]

    In recent years, machine learning techniques and big-data facilities have become quite popular in the finance and investment world. In the wake of this success, numerous machine learning researchers have decided to found their own asset management companies, hoping to capitalize on this trend. This begs the question: Are large amounts of data and computing power all that is needed to tame the
  • Fund Capacity Analysis: How Much Capital Will a Strategy Handle? [Alpha Architect]

    The article addresses the estimation of capacity for an equity fund that forms portfolios based on a given investment strategy. It fits within three strands of literature: i) theoretical models of optimal trading or portfolio construction under alpha erosion and trade frictions; ii) empirical estimates of capacity for specific equity strategies; and iii) capacity analysis undertaken within the
  • Measuring the Benefit of Diversification [Flirting with Models]

    The benefits of diversification are often touted, but many investors feel disappointed in diversified portfolios because of the dispersion in performance of the individual holdings. In the context of three different unconstrained sleeves, we look at a way to measure and visualize the benefit (or detriment) of diversification based on achieving different objectives. Through this lens, we get a
  • The Odd Factors: Profitability and Investment [Factor Research]

    The Profitability factor generated attractive returns in the US and Europe since 1990 It is difficult to explain why investors should be compensated for holding highly profitable companies The Investment factor was less attractive and is unusual from a financial analysts perspective INTRODUCTION Discretionary and systematic investors tend to have different perspectives on what works in the
  • Midterm Elections Have Not Provided A Reliable Short-Term Market Edge [Quantifiable Edges]

    Today I decided to look at SPX performance following past mid-term elections. I did not find much that suggested a strong edge. Below is a look at results since 1970 following mid-term elections. 2018-11-04-1 The numbers suggest perhaps a mild inclination for the market to celebrate the results on Wednesday. After that there does not appear to be a strong tendency in either direction. Below
  • Historical Returns for US Bonds since 1793 [Quantpedia]

    We have mentioned it several times – we are quants but we love history and we love research papers like this: Author: McQuarrie Title: The First Eighty Years of the US Bond Market: Investor Total Return from 1793, Combining Federal, Municipal, and Corporate Bonds Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3260733 Abstract: US securities markets took root after Alexander Hamiltons

Quantocracy’s Daily Wrap for 11/03/2018

This is a summary of links featured on Quantocracy on Saturday, 11/03/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 11/01/2018

This is a summary of links featured on Quantocracy on Thursday, 11/01/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in October [Allocate Smartly]

    This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies, net of transaction costs. 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
  • Asset Diversification in a Flat World [Alpha Architect]

    Diversification is a fundamental principle of prudent investing due to its ability to mitigate/minimize risks. In fact, it has been called the only free lunch in investing because, done properly, it can reduce risk without reducing expected returns. This led to the conclusion that investors should diversify by including international equities, including emerging markets, in their portfolios,
  • This Incredibly Bullish Seasonal Period Has Just Begun [Quantifiable Edges]

    With the calendar moving from October to November, it has now entered its Best 6 Months. The Best 6 Months tendency was first published by Yale Hirsch, founder of the Stock Traders Almanac, in 1986. The concept behind the Best 6 Months is simple. Seasonality suggests that over the last several decades the market has made a massive portion of its gains between November and
  • The Existence Of A Bubble vs. The Timing Of Its Crash [Alex Chinco]

    Journalists love to talk about bubbles. The Wall Street Journal has hinted at bubbles in both the Chinese stock market and the market for Bitcoin during the past month alone. But, financial economists are much more reluctant to call something a bubble. Theres debate about whether bubbles even exist. And, much of this debate revolves around whether its possible to predict the timing of the
  • Synthetic prices and burgers [Quant Dare]

    If all finance developers around the world were asked to choose the main nightmare they have to face on daily basis, I bet most of them would choose overfitting. Furthermore, imagine you have to develop an algorithm which has only one ingredient to be modelled, only one time-series representing the historical information Yes, in that case, youll need the Synthetic Financial Time

Quantocracy’s Daily Wrap for 10/29/2018

This is a summary of links featured on Quantocracy on Monday, 10/29/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When Simplicity Met Fragility [Flirting with Models]

    Research suggests that simple heuristics are often far more robust than more complicated, theoretically optimal solutions. Taken too far, we believe simplicity can actually introduce significant fragility into an investment process. Using trend equity as an example, we demonstrate how using only a single signal to drive portfolio allocations can make a portfolio highly sensitive to the impact of
  • The Dark Side of Low-Volatility Stocks [Factor Research]

    This research note was originally published by the CFA Institutes Enterprising Investor blog. Here is the link. SUMMARY Low-volatility stocks have outperformed the market over the last 25 years The strategy has reduced equity drawdowns in the US, Europe, and Japan significantly However, low-volatility stocks have partially been bond-proxies, which poses risk when rates rise MARKETING INGENUITY

Quantocracy’s Daily Wrap for 10/28/2018

This is a summary of links featured on Quantocracy on Sunday, 10/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Missing the best or worst market days [Alvarez Quant Trading]

    This morning I saw the chart on Ritholz.com of what happens when you miss the best X days of the market. I see a variation of this chart often and is used to argue why someone should not try and time the market. One concept I like to do is to invert. Meaning try the opposite idea and see what you get. What I rarely see is the chart if you missed the worst X days. Given it is a rainy Sunday morning

Quantocracy’s Daily Wrap for 10/27/2018

This is a summary of links featured on Quantocracy on Saturday, 10/27/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Variance term premia [SR SV]

    Variance term premia are surcharges on traded volatility that compensate for bearing volatility risk in respect to underlying asset prices over different forward horizons. The premia tend to increase in financial market distress and decrease in market expansions. Variance term premia have historically helped predicting returns on various equity volatility derivatives. The premia themselves can be
  • Alpha Architect Weekly Recap: Tracking Error and the Mix Versus Integrate Debate [Alpha Architect]

    You can watch the video via the link below: This week Ryan and Jack discuss several important topics. First, they discuss the tracking error associated with trend-following strategies. Second, they chat about a paper by researchers from Goldman Sachs, Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending.

Quantocracy’s Daily Wrap for 10/26/2018

This is a summary of links featured on Quantocracy on Friday, 10/26/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Explaining The Robot ETF s Bull Run With Factor Analysis [Capital Spectator]

    Bloomberg last week published an intriguing story about a new exchange traded fund (ETF) that uses artificial intelligence (AI) to outperform market indexes and active managers alike. The implication: a new era of AI-driven investing has dawned, putting the standard applications of indexing at a disadvantage. Yet a closer look at the so-called Robot ETFs results via a factor-analysis lens tells

Quantocracy’s Daily Wrap for 10/25/2018

This is a summary of links featured on Quantocracy on Thursday, 10/25/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How large is the tracking error created by trend following? [Alpha Architect]

    A question Ive received in the past is the following: If you could go back in time five years ago and tell yourself something about investing, what would it be? My response is the following: Tracking error. First, what is tracking error?(1) Tracking error is a measure of how much a strategy deviates from a benchmark. If you are a U.S. equity investor, a standard benchmark is the SP500.(2) One
  • Elevated CBI And New SPX Low Carry Bullish Implications [Quantifiable Edges]

    As we approached the close I noted on Twitter (@QuantEdges) that the Quantifiable Edges Capitulative Breadth Index (CBI) was starting to spike. And the closer we got to 4pm EST, the higher it got. At the end of the day, the CBI finished at 10, which is a level I have long considered bullish. The combination of a 10+ CBI and a 50-day closing low is something I have shown in the past to be bullish
  • Creating our own S&P 500 Momentum ETF [Quant Dare]

    Smart Beta ETFs are achieving an increasing popularity, seen as the perfect equilibrium between passive investment and active management. But, whats the difference between them and the traditional ones? Is it possible to create our own ETF with some previous experience and without assuming higher trading costs? Would it be worthwhile? Have you ever heard about Factor ETFs or Smart Beta

Quantocracy’s Daily Wrap for 10/23/2018

This is a summary of links featured on Quantocracy on Tuesday, 10/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stocks, Significance Testing & p-Hacking: How volatile is volatile? [Patrick David]

    Over the past 32 years, October has been the most volatile month on average for the S&P500 and December the least, in this article we will use simulation to assess the statistical significance of this observation and to what extent this observation could occur by chance. All code included! Our goal: Demonstrate how to use Pandas to analyze Time Series Understand how to construct a hypothesis