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Quantocracy’s Daily Wrap for 11/24/2021

This is a summary of links featured on Quantocracy on Wednesday, 11/24/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Live Algo Trading on the Cloud – AWS [Algo Trading 101]

    Table of contents: What does live algorithmic trading on the Cloud mean? What are the pros and cons of deploying your trading strategies to the Cloud? What is the Cloud Service? What is Cloud used for? What cloud providers are good? What are Amazon Web Services (AWS)? Why should I use AWS? Why shouldnt I use AWS? Kraken Bot How to sign up for an AWS server? How to pick a good AWS server
  • Transformers: is attention all we need in finance? Part I [Quant Dare]

    In recent years weve seen an increase in the accuracy of NLP models through the use of Transformers. These models rely on the attention mechanism to identify key features but, how do they work? And most importantly, can we somehow use them in finance? Transformers The transformer is a relatively new network architecture that is solely based on attention mechanisms, dispensing with recurrence
  • The Value of the Value Factor: Cheaper now than a year ago? [Alpha Architect]

    About a year and a half ago, after one of the worst relative drawdowns the value factor has ever seen, I wrote a piece showing the value factor was cheap relative to history. Since then, value strategies are on a solid run (look at pretty much any type of value strategy and I think youd agree). Today? The valuation spread between the cheapest 10% and the universe of stocks is cheaper. We are at
  • A Consolidation After A New $SPY High [Quantifiable Edges]

    The range over the last week has been very tight. Every SPY close in the 5 days since 11/16 has been within the intraday range of that 11/16/21 bar. It is said that consolidations are often resolved in the direction of the trend. This guideline suggests that were more likely to see another leg up from here than a breakdown. The study below tests this concept. It was last seen in the 11/15/19

Quantocracy’s Daily Wrap for 11/22/2021

This is a summary of links featured on Quantocracy on Monday, 11/22/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Enhancing Portfolio Income With the Equity Volatility Premium [Simplify]

    With more than 118 million Americans in or nearing retirement (age 50 or older)[1], income has become a key component for many portfolios. But as asset allocators reach for yield in a low-rate environment, they must be extra mindful of the additional risks they may be accepting. In this blog we demonstrate that the equity volatility premium is a compelling strategic income holding given its
  • Musing about S&P 500 Valuations [Factor Research]

    The valuation of the S&P 500 depends on portfolio construction Analyst EPS projections are likely overly optimistic PEG ratios of other stock markets are more attractive INTRODUCTION Striking a bargain seems to bring joy to all people, regardless of their location and their beliefs. It is almost as if it was genetically coded into us to feel good about having acquired something below its
  • The Quant Cycle – The Time Variation in Factor Returns [Quantpedia]

    Although the factors in asset pricing models offer a premium in the long run, they are undergoing bull and bear market cycles in the short term. One would expect that it is due to their connection to the business cycles as the factor premium represents a reward for bearing the macroeconomic risks. A novel study by Blitz (2021) finds that traditional business cycle indicators cant explain much

Quantocracy’s Daily Wrap for 11/20/2021

This is a summary of links featured on Quantocracy on Saturday, 11/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mr Greedy and the Tale of the Minimum Tracking Error Variance – Part two [Investment Idiocy]

    My last blog post was about a new method for a daily dynamic optimisation of portfolios with limited capital, to allow them to trade large numbers of instruments. (Although I normally write my blog posts to be self contained, you'll definitely have to read the previous one for this to make any sense!) Subsequent to writing that post I implemented the method, and quickly ran into some
  • Community Alpha of @QuantConnect Part 4 [Quantpedia]

    This blog post is the continuation (and finale) of series about Quantconnects AlphaMarket strategies. This part is related to the multi-factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but the investment universe is reduced based on the insights of the last part. So, without
  • Another Look At Thanksgiving Week [Quantifiable Edges]

    The time around Thanksgiving has shown some strong tendencies 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. SPX performance during Thanksgiving week Monday
  • Chasing Low Beta Loses Alpha [Alpha Architect]

    One of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relationship between risk and return. However, empirical studies have found the actual relationship to be basically flat, or even negative. Over the last 50 years, the most defensive (low-volatility or low-beta, low-risk) stocks have delivered both

Quantocracy’s Daily Wrap for 11/17/2021

This is a summary of links featured on Quantocracy on Wednesday, 11/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diving Deeper: Does the Day of the Month Matter? [Allocate Smartly]

    Most Tactical Asset Allocation (TAA) strategies trade just once a month. Backtests of those strategies usually assume trades are executed on the last trading day of the month. Why? Monthly asset data is often available further back into history than daily data. Assuming trades are executed at month-end allows for longer backtests, showing how the strategy has performed in a wider variety of

Quantocracy’s Daily Wrap for 11/15/2021

This is a summary of links featured on Quantocracy on Monday, 11/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Complete Starter System: Trading with a Forecast [Raposa Trade]

    Lets talk about insider trading. In an imaginary world where you know with certainty that the price of a stock will change on a given date you can place a huge investment for enormous gains. Why wouldnt you? There is no risk of the trade going bad because you have complete certainty in your buy (or sell) signal. In the real world, an obvious downside is that this is illegal (unless
  • How to Combine Different Momentum Strategies [Quantpedia]

    Today we will again talk more about the portfolio management theory, and we will focus on techniques for combining quantitative strategies into one multi-strategy portfolio. So, lets imagine we already have a set of profitable investment strategies, and we need to combine them. The goal of such strategy allocation usually is to achieve the best risk-adjusted return possible. There is no
  • Inflation-Themed ETFs: As Complicated as Inflation [Factor Research]

    Given the importance of inflation as a topic, there are surprisingly few inflation-themed ETFs The few available pursue differentiated strategies that result in heterogeneous portfolios The correlation of these ETFs to inflation has been relatively low INTRODUCTION Creating an investment framework is challenging as investing is complex and complicated. We need to educate ourselves about various

Quantocracy’s Daily Wrap for 11/14/2021

This is a summary of links featured on Quantocracy on Sunday, 11/14/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Top2Vec: Distributed Representations of Topics [Gautier Marti]

    Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis were the most widely used methods for topic modeling for the past 20 years. However, they rely on heavy pre-processing of the text content (custom stop-word lists, stemming, and lemmatization), and require the number of topics to be known. As a result, results of these approaches are often unstable. Moreover, they rely on
  • Fundamental value strategies [SR SV]

    Value opportunities arise when market prices deviate from contracts present values of all associated entitlements or obligations. However, this theoretical concept is difficult and expensive to apply. Instead, simple valuation ratios, such as real interest rates or equity earnings yields with varying enhancements, have remained popular. Moreover, value strategies can take a long time to pay off
  • Pairs Trading An Advanced Strategy: CAD – Crude Oil [Milton FMR]

    Now before we dive into testing a strategy we must first define what makes a good pair to test in the first place. This is a question that has not one answer but several depending on the approach you want to take. We will discuss some of the possibilities and the weapons of choice if you want to code a good pairs trading strategy. Testing for Cointegration The idea: While it may be difficult to
  • Factor Investing Deep Dive with Jack Vogel [Alpha Architect]

    Ben and Cameron, which host the excellent Rational Reminder podcast, sit down with Jack Vogel and go through a laundry list of factor investing questions. The topics discussed: 0:27 Do long-only factor premiums survive transaction costs? 2:28 Would the market impact of rebalancing a fund like MTUM also show up in the index? 3:45 What are the capacity constraints for factors? 5:32 What would AA do
  • How News Move Markets? [Quantpedia]

    Nobody would argue that nowadays, we live in an information-rich society the amount of available information (data) is constantly rising, and news is becoming more accessible and frequent. It is indisputable that this evolvement has also affected financial markets. Machine learning algorithms can chew up big chunks of data. We can analyze the sentiment (which is frequently related to the

Quantocracy’s Daily Wrap for 11/11/2021

This is a summary of links featured on Quantocracy on Thursday, 11/11/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reddit for Fun and Profit (part 2) [Alpha Scientist]

    In the prior post Tracking Posts on WallStreetBets – Part I, we demonstrated how relatively easy it is to extract reddit activities related to a given stock ticker – in their raw form. If you haven't already read that post, you may want to take a moment to skim that article. In this post, we are going to take the next obvious step: aggregating the raw results into a meaningful timeseries
  • Portfolio Diversification Via Hierarchical Clustering [Machine Learning Applied]

    In this article, we cluster stock price time series with hierarchical clustering and Euclidean, correlation, and Jensen-Shannon distances to answer two questions regarding portfolio diversification. How diversified is a given portfolio? How can a diversified portfolio be constructed? Procedure For the Euclidean distance, we follow the first 3 steps articulated in Portfolio Diversification Via
  • The Vanishing Illiquidity Premium [Alpha Architect]

    Liquiditythe ability to buy and sell significant quantities of a given asset quickly, at low cost, and without a major price concessionis valuable to investors. Therefore, they demand a premium as compensation for the greater risks and costs of investing in less-liquid securities. For example, liquidity risk partly explains the equity risk premiumthe average transaction costs on stock
  • Webinar: Considerations For Combining Models [Quantifiable Edges]

    Date and time: Thursday 11/11/2021 at 4:15pm EST & Saturday 11/13/2021 at 11:00am EST Duration: 30-40 minutes + Q&A At Capital Advisors 360, I manage some composite portfolios that include several different models I have developed over the years. Using a couple of the models I trade as examples, I will share several of the factors I consider when determining what models are likely to work
  • Rolling Returns for the SP-500 [Alvarez Quant Trading]

    I just got back from a long vacation in Iceland (highly recommend visiting). As usual, when people discover what I do, they ask me about the markets. Several people were worried that the markets are too high. Then I read that the 20-year return of the SPX from 2001 to 2020 was way below the average 20-year return. My thinking was how could the massive run since 2009 not have gotten us above the
  • How Crazy is the Current Market? Not that Crazy. [Alpha Architect]

    Eric Balchunas had a recent tweet that I found fascinating. Erics tweet merely captures the tip of the iceberg with respect to the current market environment, which certainly feels bubbly. 1 The gist of the tweet is that $META, which is an ETF from our friends over at Roundhill Investments, has gained a substantial level of new assets because investors are confusing the ETF with the name

Quantocracy’s Daily Wrap for 11/10/2021

This is a summary of links featured on Quantocracy on Wednesday, 11/10/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding Equities Data [Quant Start]

    In this brief tutorial we will take a look at the different aspects of end-of-day equities data. We will develop an understanding of what the Open, High, Low and Close (OHLC) prices mean, as well as discuss the traded Volume. We will look at how a typical Adjusted Close price is calculated and the effects that stock splits, dividends and rights offerings have on our data and why they are

Quantocracy’s Daily Wrap for 11/08/2021

This is a summary of links featured on Quantocracy on Monday, 11/08/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Mirage of Direct Indexing [Factor Research]

    Direct indexing is one of the growth areas in the asset management industry However, direct indexing represents active management, specifically an inferior approach to it Given the poor track record of active management, most investors should avoid pursuing this INTRODUCTION Direct indexing is hot. In October 2020, Morgan Stanley bought the asset manager Eaton Vance primarily for its direct
  • A Complete System for New Traders: Trading without a Stop Loss [Raposa Trade]

    Risk control is absolutely crucial for traders. The first rule of trading is to stay in the game. The humble stop loss is the bread and butter for traders trying to control their risk. But what if I told you the stop loss wasnt needed? What if you could control your risk while also increasing your returns? To do this, well introduce a new concept: continuous trading. This will be added to
  • US Funds Have Surprisingly Large International Exposure [Alpha Architect]

    Did you know that the percentage of foreign sales of the FTSE 100 is 76% and 43% for the SP500? This study investigates the power of indirect international exposure, that is international exposure through holdings of domestic stocks. The authors ask the following: What is the indirect international exposure of domestic mutual funds? Is the indirect international exposure associated with fund

Quantocracy’s Daily Wrap for 11/04/2021

This is a summary of links featured on Quantocracy on Thursday, 11/04/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Longest Winning Streak for Bitcoin [Quant at Risk]

    In the previous article Estimating Probability of Bitcoin Pullback in its Bullish Market we touched an interesting point worth exploring a bit further. Namely, the probability of Bitcoin close-price closing each day higher than a day ago days in a row. We had seen that in July 2021 Bitcoin moved and closed higher 10x in a row. We showed how to calculate the probability of a pullback in Bitcoin
  • New Site: Portfolio Diversification Via K-means [Machine Learning Applied]

    We use the K-means algorithm to answer two questions regarding portfolio diversification. How diversified is a given portfolio? How can a diversified portfolio be constructed? Additionally, we use the multidimensional scaling (MDS) algorithm to visualize results. Procedure Take the last 120 days of adjusted close data. Zscore the data (substract the mean and divide by the standard deviation).
  • Using Machine Learning to Predict Options Returns [Alpha Architect]

    Though classical option pricing models assume that options are redundant assets, more recent research rejects this idea. However, research on cross-sectional predictors of option returns is relatively scarce and not very well understood. Contrarily, extensive literature examines cross-sectional determinants of stocks, bonds, currencies, mutual funds, and hedge funds. In this paper, the authors use