Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 11/29/2020

This is a summary of links featured on Quantocracy on Sunday, 11/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Analyst Ratings – Return Prediction [Tr8dr]

    I have a couple of equities strategies that I will start trading shortly, and I want to understand the risk from all angles. Towards this end I try to utilize both market signals and exogenous unstructured data to minimize surprise and maximize selection or prediction efficiency. In thinking about single name risks (i.e. the risk associated with selecting and trading a particular stock), I wanted

Quantocracy’s Daily Wrap for 11/28/2020

This is a summary of links featured on Quantocracy on Saturday, 11/28/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: Get Rid of Noise [Financial Hacker]

    A major problem of indicator-based strategies is that most indicators produce more or less noisy output, resulting in false signals. The faster the indicator reacts on market situations, the noisier is it usually. In the S&C December issue, John Ehlers proposed a de-noising technology based on correlation. Compared with a lowpass filter, this method does not delay the signal. As an example, we

Quantocracy’s Daily Wrap for 11/27/2020

This is a summary of links featured on Quantocracy on Friday, 11/27/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Black-Litterman Portfolio Allocation Model in Python [Python For Finance]

    A while ago I posted an article titled INVESTMENT PORTFOLIO OPTIMISATION WITH PYTHON REVISITED which dealt with the process of calculating the optimal asset weightings for a portfolio according to the classic Markowitz mean-variance approach. With this method we aim to maximise our level of return for any given level of risk, in doing so we develop the concept of an efficient

Quantocracy’s Daily Wrap for 11/26/2020

This is a summary of links featured on Quantocracy on Thursday, 11/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Thoughts on Crypto Market Making [Tr8dr]

    In the past have been a HFT market maker for FX and other traditional instruments, however have not investigated exchange-based market making in Crypto. As I have proprietary signals applicable for Crypto, thought it would be worthwhile to investigate the difficulty of market making on crypto exchanges. The crypto ecosystem and microstructure is quite different from FX and equities in the

Quantocracy’s Daily Wrap for 11/24/2020

This is a summary of links featured on Quantocracy on Tuesday, 11/24/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading FX using Autoregressive Models [Robot Wealth]

    Im a big fan of Ernie Chans quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them. Ernie explicitly states that the examples in the books wont be tradable, but theyve certainly
  • Hedge Fund Battle: Discretionary vs Systematic Investing [Factor Research]

    Given alternative data, machine learning, and AI advances, systematic should beat discretionary investing However, the performance of systematic and discretionary equity market neutral hedge funds has largely been the same since 2009 Both were also correlated to the stock market, offered low returns, and featured no performance consistency INTRODUCTION Anyone who has watched the hit TV show
  • Temporal Clustering on Real Prices [Dekalog Blog]

    Having now had time to run the code shown in my previous post, Temporal Clustering, part 3, in this post I want to show the results on real prices. Firstly, I have written two functions in Octave to identify market turning points and each function takes as input an n_bar argument which determines the lookback/lookforward length along price series to determine local relative highs and lows. I ran
  • What Matters to Individual Investors? Evidence from the Horse’s Mouth [Alpha Architect]

    Finance literature is abundant with theories. As academics, we like to think these theories foster behaviors and choices by investors, which in turn translate into asset prices. To test these theories, scholars typically try to infer the validity of these assumptions by examining outcomes. The authors in this study follow a different and more direct approach: they survey a nationally

Quantocracy’s Daily Wrap for 11/22/2020

This is a summary of links featured on Quantocracy on Sunday, 11/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Structural Change in Stock Market Valuations [Light Finance]

    In Stock Market Valuation and the 2020s in R I investigated whether the CAPE ratio could forecast the future trajectory of earnings and/or stock returns over the period 1980-2019. From this study, we made a couple of observations: The CAPE ratio cannot be used to forecast future earnings growth at a 1- or 5-year horizon The CAPE ratio only weakly explains stock returns over the next year.
  • Updating Thanksgiving Week Historical Odds [Quantifiable Edges]

    The time around Thanksgiving has shown some strong tendencies over the years both bullish and bearish. I have discussed them a number of times over the years. In the updated table below I show SPX performance results based on the day of the week around Thanksgiving. The bottom row is the Monday of Thanksgiving week. The top row is the Monday after Thanksgiving. Monday and Tuesday of
  • Estimating the positioning of trend followers [SR SV]

    There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures markets over plausible lookback windows, under consideration of a leverage constraint, and uses estimated assets under management as a scale factor. For optimization and out-of-sample analysis, the approach can be enhanced by sequential estimation

Quantocracy’s Daily Wrap for 11/20/2020

This is a summary of links featured on Quantocracy on Friday, 11/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Primer on Survivorship Bias [Quant Rocket]

    What is survivorship bias, and why should you care about it? This post explains how survivorship bias can trick you into drawing faulty conclusions from your research, and what you need to know to avoid being tricked. What is survivorship bias? Equities datasets are said to have survivorship bias if they do not include stocks that delisted in the past due to bankrupties, mergers and acquisitions,
  • Using Quality to Seperate Good and Bad Value Stocks [Alpha Architect]

    Running a marathon is similar to being a value investor, especially in recent years where it seems more like an ultra-marathon. Both activities are painful experiences that require the ability to suffer and persist through physically and emotionally straining times. Moreover, value investors always need to worry about the perennial dogs in their portfolio, which are stocks that are cheap and

Quantocracy’s Daily Wrap for 11/19/2020

This is a summary of links featured on Quantocracy on Thursday, 11/19/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why complex models are data-hungry? [Eran Raviv]

    If you regularly read this blog then you know I am not one to jump on the AI Bandwagon, being quickly weary of anyone flashing the Its Artificial Intelligence joker card. Dont get me wrong, I understand it is a sexy term I, but to me it always feels a bit like a sales pitch. If the machine does anything (artificially) intelligent it means that the model at the back is complex,
  • Tesla’s inclusion in the S&P 500 – Is there a trade? [Robot Wealth]

    The S&P index committee recently announced that Tesla, already one of the biggest stocks listed in the country, would be included in the S&P 500. Heres the press release: Due to TSLAs size, it was widely expected to have entered the S&P 500 index much earlier but S&P has some discretionary criteria it applies to ensure that the index is an effective measure of the larger
  • Does low volatility anomaly work in funds? [Quant Dare]

    After many years there are many evidences that the low volatility anomaly works in stock markets. We have also mentioned this topic a long time ago to analysis the costs of it. This anomaly says stocks with less price variability deliver higher returns, contrary to everyones belief, which expects that return is supposed to be related to risk. In this post, we want to play and check what would

Quantocracy’s Daily Wrap for 11/17/2020

This is a summary of links featured on Quantocracy on Tuesday, 11/17/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Finding Similar Stocks Via Fast GPU Based Nearest Neighbors with Faiss [Machine Learning Applied]

    There are many ways to find stocks with similar behavior based on how one defines similarity and the data used. In this article we use a 12 period channel where, for each period, we have (current adjusted close price minimum value)/(maximum value minimum value). Maximum and minimum values are computed for the adjusted close prices for the past 21 trading days (representing a trading
  • An Easy Way to Simplify and Improve the Shiller CAPE Ratio as a Prediction Tool [Alpha Architect]

    Shillers (1998) original CAPE ratio (the cyclically adjusted price of an equity index/10 year average of real earnings) used to predict long term equity returns, like every online recipe, has been improved over the years by various reviewers. A number of substitutes for real earnings have been proposed and analyzed including national income + product account earnings; revenue + cash flow; and
  • Exploring Defined Outcome ETFs [Factor Research]

    Defined outcome ETFs have quickly gathered almost $5 billion in assets Not unexpected given their much lower drawdowns when the market crashed in March 2020 However, they are complex and expensive products and there are viable alternatives INTRODUCTION If ETFs had an arch enemy, then it probably would not be mutual funds, but rather structured products. ETFs symbolize simplicity, transparency,

Quantocracy’s Daily Wrap for 11/15/2020

This is a summary of links featured on Quantocracy on Sunday, 11/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 13 November 2020 | Factor Investing [Capital Spectator]

    Resurrecting the Value Premium David Blitz (Robeco) and Matthias X. Hanauer (Technische Universitt Mnchen) October 15, 2020 The prolonged poor performance of the value factor has led to doubts about whether the value premium still exists. Some have noted that the observed returns still fall within statistical confidence intervals, but such arguments do not restore full confidence in the value
  • An Introduction to the NAVA Toolbox [Nava Capital]

    We decided to allow anyone to take advantage of some tools we constantly use at NAVA Capital. Investors and financial managers often need to perform similar tasks, like analyzing financial time series, comparing two investments, adjusting gross performance by management fees, performance fess and so on. Doing it in Excel is time consuming, error prone and unpractical. Doing it in Python is
  • Temporal Clustering, Part 3 [Dekalog Blog]

    Continuing on with the subject matter of my last post, in the code box below there is R code which is a straight forward refactoring of the Octave code contained in the second code box of my last post. This code is my implementation of the cross validation routine described in the paper Cluster Validation by Prediction Strength, but adapted for use in the one dimensional case. I have refactored