Quant Mashup Using Absolute Momentum to Positively Skew Calendar Year Returns [EconomPic]There are instances where I "borrow" an idea from someone (actually... most of my posts were at a minimum inspired by someone else). In this case, I am stealing the initial concept from Ryan Detrick who posted the following chart of annual U.S. stock returns going back ~200 years as there(...) Time Series Momentum, Volatility Scaling, and Crisis Alpha [Alpha Architect]If you couldn’t tell from our recent monster commodity futures post, we’ve been thinking a lot about futures recently. The futures research area is relatively “fresh,” and a lot more exciting than hacking through equity stock selection research where we already understand the basic answer(...) Applying Genetic Algorithms to define a Trading System [Quant Dare]When talking about quantitative trading, there are a large number of indicators and operators we can use as a buy/sell rule. But apart from deciding what indicator we will follow, the most important part would be setting the correct parameters. So, one method we can use to find adequate parameters(...) An Effect of Monetary Conditions on Carry Trades [Quantpedia]This paper investigates the relation between monetary conditions and the excess returns arising from an investment strategy that consists of borrowing low-interest rate currencies and investing in currencies with high interest rates, so-called "carry trade". The results indicate that carry(...) Sorting Through The Factor Zoo [Larry Swedroe]As Professor John Cochrane observed, the literature on investment factors now fills a veritable “factor zoo” with hundreds of options. How do investors select from among this huge array of possibilities? Noah Beck, Jason Hsu, Vitali Kalesnik and Helge Kostka, authors of the paper “Will Your(...) 38 DTE Iron Condor Results Summary [DTR Trading]The introduction to this series, here, described the different variations of SPX iron condors (IC) and exits that were tested at 38 days to expiration (DTE). Recall, the tests covered 9 IC variations, with short strike deltas at four locations, utilizing 12 exits. In all, there were 432 test runs (9(...) Volatility Trading Strategies, A Comparison of VRP and RY Strategies [Relative Value Arbitrage]In previous posts, we presented 2 volatility trading strategies: one strategy is based on the volatility risk premium (VRP) and the other on the volatility term structure, or roll yield (RY). In this post we present a detailed comparison of these 2 strategies and analyze their recent performance.(...) Ex-ante and Ex-post Risk Model – An Empirical Test [Alphaism]Whenever constructing a quant portfolio or managing portfolio risk, the risk model is at the heart of the process. A risk model, usually estimated with a sample covariance matrix, has 3 typical issues. Not positive-definite, which means…[not invertible] Exposed to extreme values in the sample,(...) Factor Rotation: Possible, but Worth It? [Flirting with Models]With significant research into factor rotation in 2016, we expect to see more factor rotation strategies in the market in 2017. Using six popular factors (Value, Size, Reinvestment, Operating Profitability, Momentum and Beta), we explore both switching and rotation based strategies. We find(...) Vix single day spikes & their historical returns [Voodoo Markets]Taking a look at Vix single day spikes, since there have been two rather significant and rare single day spikes of 39 and 49% in 2016. Note im not talking about Vix daily swings, but single day spikes from close to close. The intention here is to gauge how Vix has historically behaved after(...) Protective Asset Allocation [Allocate Smartly]This is a test of two variations of the Protective Asset Allocation (PAA) strategy from Wouter Keller and JW Keuning’s paper: PAA: A Simple Momentum-based Alternative to Term Deposits. The strategy is notable for its aggressive use of a “crash protection” asset that has resulted in extremely(...) An Interesting Analysis of Shiller's CAPE Ratio [Quantpedia]Robert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long-term stock returns. This result has often been interpreted as evidence of market inefficiency. We present two findings that are contrary to such an interpretation. First, if markets(...) The Price Is Wrong [Basis Pointing]In this piece, we compare U.S. equity mutual funds’ annual expenses to our estimate of their potential future pre-fee excess returns. We demonstrate that many funds are priced to fail—their fees approach or exceed their potential future pre-fee excess returns. Whereas investors might have(...) TAA Exposure to Rising Interest Rates [Allocate Smartly]Some of the tactical asset allocation strategies that we track have significant exposure to rising interest rates, or more specifically, to the types of assets that are most negatively affected by rising rates. While we don’t (yet) track every published TAA model, the strategies that we do track(...) Asset Pricing using Extreme Liquidity with Python (Part-2) [Black Arbs]POST OUTLINE Part-1 Recap Part-1 Error Corrections Part-2 Implementation Details, Deviations, Goals Prepare Data Setup PYMC3 Generalized Linear Models (GLM) Evaluate and Interprate Models Conclusions References part-1 recap In part 1 We discussed the theorized underpinnings of Ying Wu of Stevens(...) Betting on Perfection [EconomPic]Just how perfect do circumstances need to be going forward for an investor in the S&P 500 to make money? Let's take a look at one measure. The first chart plots forward 10-year returns for the S&P 500 at various 5 point "CAPE" valuation buckets (i.e. less than 10x P/E all the(...) Machine Learning for Stock Market Prediction: Global Indices [Keith Selover]When applying Machine Learning tools to market prediction, the internet is saturated with academic papers and lacking in practical code examples. In this post, it’s my goal to translate one such paper from text to code. Mark Dunne’s Undergraduate Thesis, “Stock Market Prediction“, approaches(...) Timing the Stock Market with the Shiller CAPE [iMarketSignals]The Shiller CAPE (cyclically adjusted price-earnings ratio) is typically regarded as a stock market valuation measure. When the CAPE is high stocks are supposed to be expensive, and vice-versa. The CAPE itself is not a good stock market timer. However, the CAPE can indirectly be used for market(...) A Dynamic Approach to Factor Allocation [EconomPic]ETF Trends (hat tip Josh) showed the following "quilt" of large cap factor calendar year returns in the post Low Volatility is Not a Buy and Hold Strategy. Author John Lunt's takeaway (bold mine): It is reasonable to conclude that low volatility is not a buy and hold strategy. This is(...) The Ghost of GDP Past [Flirting with Models]Summary Economic growth is a key driver of long-term stock and bond returns. Economic growth comes from two main sources: demographic changes (i.e. increases in the number of workers) and productivity growth (i.e. each worker producing more output). Historically, approximately 55% of growth has(...) Interest Rates and Value Investing [Alpha Architect]There is still no value in bonds today. Many readers just had a knee-jerk reaction and they’ve determined that I fall into one of two categories: A total idiot A total genius But let’s dig a bit deeper into the claim that bonds lack “value,” even with this quarter’s 85 basis point back-up(...) Hacking True Random Numbers in Python: Blockchain Miners [Quant at Risk]The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted(...) The… Most… Wonderful… Weeeeek… Of…The… Yeeeaaaarrrrr!! [Quantifiable Edges]Over several time horizons op-ex week in December has been the most bullish week of the year for the SPX. The positive seasonality actually has persisted for up to 3 weeks. I’ve shown the study below in the blog many times since 2008. It looks back to 1984, which was the first year that SPX(...) Cryptocurrencies and Machine Learning with @BMouler [Better System Trader]As markets become more mature and more efficient, it can be become increasingly difficult to find sustainable edges. Many traders are looking at the same data and using the same techniques, so what are our options here? 2 of the obvious options we have are: Try to find a unique approach to the(...) Sources of Return for CTAs - A Brief Survey of Relevant Research [Quantpedia]This survey paper will discuss the (potential) structural sources of return for both CTAs and commodity indices based on a review of empirical research articles from both academics and practitioners. The paper specifically covers (a) the long-term return sources for both managed futures programs and(...) Reading Fundamental Data from Yahoo Finance [Copula.de]Recently I read a blogpost and someone was recommending the book "DIY Financial Advisor "by Wesley R. Gray, Jack Vogel and David Foulke. I believe it was the QuantStrat blog but I might be wrong. The book is a good read and also suggest a couple of simple systems any investor can implement(...) Research Review | 8 Dec 2016 | Volatility & Risk Management [Capital Spectator]How Should Investors Respond to Increases in Volatility? Alan Moreira (Yale University) andn Tyler Muir (UCLA) December 2, 2016 They should reduce their equity position. We study the portfolio problem of a long-horizon investor that allocates between a risk-less and a risky asset in an environment(...) You Probably Can't Lose [Cantab Capital]What can an interesting and surprising experiment with finance students and finance professionals tell us about financial decisions and how to maximise extracting returns from low information content systems? Introduction It is well known that humans are bad at estimating probabilities. We(...) Pairs Trading on ETF - EPAT Project Work [Quant Insti]This article is the final project submitted by the author as part of his coursework in Executive Programme in Algorithmic Trading (EPAT™) at QuantInsti. You can check out our Projects page and have a look at what our students are building after reading this article. About the AuthorEPAT student(...) Conditional Value-at-Risk in the Normal and Student t Linear VaR Model [Quant at Risk]Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a(...) Replicating CRSP Volatility Decile Portfolios in R [Propfolio Management]In this post, I provide R code that enables the replication of the Center for Research in Security Prices (CRSP) Volatiliy Deciles using Yahoo! Finance data. This post is related to my last blog post in that it will generate the CRSP low volatility decile portfolio, thereby facilitating the(...) Using recent returns for Mean Reversion [Alvarez Quant Trading]In most of my mean reversion posts, I use RSI(2) to determine if a stock has sold off. In this post, I will explore how to use a stock’s recent return to determine if it has sold off. This will be done in way to normalize the return between low and high volatile stocks. This basic strategy has(...) Ranking the top and bottom TAA strategies [Investing For A Living]Following up on my last post, I’d like to take a deeper dive into the performance of TAA strategies. In particular, I’ll take a look at the differences between the top performing TAA strategies and the bottom performing ones. There are some important points that come out of this analysis which I(...) State of Trend Following Drawdown Levels Comparison [Wisdom Trading]A couple of months ago, we published a study on the performance of trend following after drawdowns, as the State of Trend Following index was hitting high levels of drawdown (about 2/3 of the historical maximum). We showed that in 80% of cases, the post-drawdown performance is positive, showing that(...) Testing Popular Portfolio Optimization Techniques [Allocate Smartly]This is a test of a number of popular approaches to portfolio optimization. Each seeks to answer the question: given a universe of assets, how much should we allocate to each? We’ve intentionally made these tests as simple and fair (read: unoptimized) as possible in order to best represent each(...) TRINdicators [Throwing Good Money]When I start to write a blog post, usually my process is this: Come up with a really bad pun for the title. Write the rest of it. Bad puns are an important part of finance, and life in general. A blog reader contacted me recently to chat about various technical analysis indicators, and one he(...) The Look of a Winner is a Loser (h/t SystematicRelativeStrength.com) [Basis Pointing]Investors tend to have some pretty engrained misconceptions of what “winning” funds look like. For instance, winning funds lay waste to the index and category peers; they do so over the short- and long-term; they corner really well, deftly avoiding big drawdowns and rocking during rallies; they(...) Is dividend investing dangerous? [Flirting with Models]Summary In a persistent, low interest rate environment, dividend strategies have rapidly increased in popularity. In theory, investors should be indifferent to dividends. In practice, they are not. As a strategy, a focus on high dividend yield may simply be a (poor) value strategy in drag. A focus(...) K-Means Clustering of Daily OHLC Bar Data [Quant Start]In this article the concept of unsupervised clustering will be considered. In quantitative finance finding groups of similar assets, or regimes in asset price series is extremely useful. It can aid in the development of filters, or entry and exit rules. This helps improve profitability for certain(...) Optimism of the Training Error Rate [Eran Raviv]We all use models. We all continuously working to improve and validate our models. Constant effort is made trying to estimate: how good our model actually is? A general term for this estimate is error rate. Low error rate is better than high error rate, it means our model is more accurate. By far(...) Sentiment Analysis on News Articles using Python for traders [Quant Insti]In our previous post on sentiment analysis we briefly explained sentiment analysis within the context of trading, and also provided a model code in R. The R model was applied on an earnings call conference transcript of an NSE listed company, and the output of the model was compared with the(...) You Would Have Missed 780% In Gains Using The CAPE Ratio, And That's A Good Thing [Meb Faber]780%. That’s the amount of gains you would have missed had you followed the market timing strategy I’m going to describe in the following article that utilizes the CAPE ratio. Yes, that’s significant. But there’s far more to this story, and I suspect that had you acted on this strategy,(...) November Fall for Trend Following [Wisdom Trading]Every month of this second half of the year seems to have a recurring theme and/or unilateral direction, rendering the YTD performance quite clearly negative. November was no different and produced a variation on the same theme, as you can see below. Below is the full State of Trend Following report(...) Tactical Asset Allocation in November [Allocate Smartly]This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we don’t (yet) include every published TAA model, these strategies are broadly representative of the(...) TAA strategy performance over time [Investing For A Living]In this post I’m going to take a look at performance as a whole of a group of TAA strategies and how that performance has varied over time. I’ll then compare it to the classic 60 40 US stock US bond portfolio and a more globally diversified and modern portfolio, the All Weather Portfolio.(...) Looking Forward Not Backward When Estimating Volatility [Blue Sky AM]When you drive a car, you need to look out your front window and not the rear-view mirror. The same should be true for estimating risk in financial markets. Ironically, most of the “low volatility” products use backward looking information regardless of whether they emphasize low beta or low(...) Common Mistakes of Momentum Investors [Dual Momentum]Like most investors, those using momentum are often guilty of chasing performance. In fact, momentum requires that we do this. But it should be done in a disciplined and systematic way. Performance chasing should not be due to myopia, irrational loss aversion, or other psychological biases.(...) An Impact of Correlation and Volatility on a Pairs Trading Strategy [Quantpedia]This paper explains the idiosyncratic risk puzzle in a novel test setting with a combination of arbitrage risk and arbitrage asymmetry as in Stambaugh/Yu/Yuan (2015). We utilize the popular investment strategy pairs trading to identify a different kind of mispricing and find a dominant negative(...) Chicago Python Workshop [Portfolio Effect]You will learn why the use of high frequency market data is necessary to be able to measure correctly the risk and rebalance your portfolio adequately. You will also learn how to build strategies to generate alpha. You will study how to build your own portfolio, create a strategy, backtest it,(...) Non-Linear Cross-Bicorrelations between Oil Prices and Stock Fundamentals [Quant at Risk]When we talk about correlations in finance, by default, we assume linear relationships between two time-series “co-moving”. In other words, if one time-series changes its values over a give time period, we seek for a tight correlation reflected within the other time-series. If found, we say they(...)