Quant Mashup Crash Sensitivity Explains the Momentum Effect in Stocks [Quantpedia]This paper proposes a risk-based explanation of the momentum anomaly on equity markets. Regressing the momentum strategy return on the return of a self-financing portfolio going long (short) in stocks with high (low) crash sensitivity in the USA from 1963 to 2012 reduces the momentum effect from a(...) Highly Unusual Behavior Between SPX and VIX [Quantifiable Edges]Wednesday saw both SPX and VIX close at 40-day highs (about 2 months). Since they commonly trade opposite each other, to have them both be extended up like this is very rare. In fact, it has only happened 4 other times. Below is a list of those instances along with their 4-day results. 2018-01-18(...) Mixture Model Trading (Part 2 - Gaussian Mixtures) [Black Arbs]This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic(...) Covered Call Options Strategy using Machine Learning [Quant Insti]A covered call is used by an investor to make some small profit while holding the stock. Mostly the reason why a trader would want to create a covered call is because the trader is bullish on the underlying stock and wants to hold for long-term, but the stock doesn’t pay any dividend.The stock is(...) Cointegration in Economy: a long-term relationship [Quant Dare]The relationship between series can be measured by different methods. The most common is to check if both series move in the same way. We’d like to go further, and see if the difference between them is always the same. We call it cointegration. In many cases, we are interested in expressing one(...) Mixture Model Trading (Part 1 - Motivation) [Black Arbs]This is the beginning of a three part series that I completed towards the end of 2017 as a learning module for Quantinsti.com. The purpose of the series is to demonstrate a research workflow focused around the theory and application of mixture models as the core framework behind a algorithmic(...) Surprise! Seeking Alpha Opinions Have Investment Value [Alpha Architect]What are the research questions? According to Datamonitor (2010), the influence of peer-based advice, such as user-generated ratings on Amazon.com or Yelp.com, is increasing while traditional advice (e.g., from Consumer Reports or the Michelin guide) is decreasing. This trend is starting to emerge(...) Factor Investing and The Bets You Didn't Mean to Make [Flirting with Models]Factor investing seeks to balance specificity with generality: specific enough to have meaning, but general enough to be applied broadly. Diversification is a key tool to managing risk in factor portfolios. Imprecision in the factor definitions means that unintended bets are necessarily introduced.(...) January Opex A Weak Week [Quantifiable Edges]Opex week in January is one that the market has seen some struggles over the last 19 years. Below is the list of January op-ex weeks from 1999 – 2017 with their full week performance results. There have been 8 years in which January op-ex week occurred in conjunction with Martin Luther King Day.(...) Factor Investing: Gross to Net Returns [Factor Research]Long-short multi-factor portfolios generate attractive returns before fees Returns are much less attractive post fees charged historically However, some fees in the long-short space are likely justified given higher complexity INTRODUCTION Reality is the murder of a beautiful theory by a gang of(...) Replicating Volatiltiy ETN Returns From CBOE Futures [QuantStrat TradeR]This post will demonstrate how to replicate the volatility ETNs (XIV, VXX, ZIV, VXZ) from CBOE futures, thereby allowing any individual to create synthetic ETF returns from before their inception, free of cost. So, before I get to the actual algorithm, it depends on an update to the term structure(...) Long-Short Equity Strategy using Ranking: Simple Trading Strategies Part 4 [Auquan]In the last post, we covered Pairs trading strategy and demonstrated how to leverage data and mathematical analysis to create and automate a trading strategy. Long-Short Equity Strategy is a natural extension of Pairs Trading applied to a basket of stocks. Download Ipython Notebook here. Underlying(...) A Down Day After A Persistent Upmove To New Highs [Quantifiable Edges]One compelling study from last night’s Quantifinder suggested the recent persistent upmove is unlikely to abruptly end. (This is a theme we have seen many times over the years.) It considers what happens after the market moves up at least 5 days in a row to a 50-day high, and then pulls back. I(...) Plotting Volatility Surface for Options [AAA Quants]This blog post is a revised edition of Tom’s original blog post with a newer data set. More information, source code & inspiration can be found here. Code for this blog post is in our Github repository. Options are complex instruments with many moving parts. Specifically, options are contracts(...) How to turn a losing strategy to a winning strategy with commissions [Alvarez Quant Trading]A mean reversion strategy I trade was developed with another researcher. This strategy enters on a further intraday weakness with a limit order and typically exits a few days later when the stock bounces. Recently this researcher sent me and email saying “Try the strategy as a day trade. Enter at(...) Why You Need Independent Verification of Strategy Results [Allocate Smartly]Our site serves a lot of purposes for tactical asset allocation (TAA) investors: curating the best published strategies, testing those strategies with superior historical data, providing the ability to combine strategies into custom portfolios, and tracking even the most complex strategies in near(...) How Bad Are False Positives, Really? [Alex Chinco]Imagine you’re looking for variables that predict the cross-section of expected returns. No search process is perfect. So, as you work, you will inevitably uncover both tradable anomalies as well as spurious correlations. To figure out which are which, you regress returns on each variables that(...) Big Data and Machine Learning Conference in London [Raven Pack]On the back of our recent event in New York, we are bringing the big data & machine learning revolution to London this April 24th. Register to receive updates on the agenda! Register Now The London Revolution More than 750 finance professionals registered to attend the New York Revolution but we(...) R/Finance 2018: Call for Papers [Foss Trading]The tenth annual R/Finance conference for applied finance using R will be held June 1 and 2, 2018 in Chicago, IL, USA at the University of Illinois at Chicago. The conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing,(...) The Value Effect and Macroeconomic Risk [Alpha Architect]It has been well-documented that value stocks have provided higher expected returns than growth stocks. However, there is a great debate about the source of that premium: Is it risk-based or is it related to behavioral errors that create persistent mispricings? There are many papers presenting(...) State of Trend Following in December [Au Tra Sy]Near-perfect neutral month for the State of Trend Following index to close the year just in negative double-digit territory. 2017 was not the best year for the strategy. Let’s see what 2018 has in store. Happy new year to all readers and best wishes for profitable trading. Please check below for(...) Yes, Departing Outside Directors Are Aware of Fraud Before They Resign [Alpha Architect]What are the research questions? Is the rate of turnover for outside directors unusually high either before fraud is discovered by the firm, or during its commission? Are there regularities in the characteristics of outside directors who depart during the period in which the financial fraud is(...) Levered ETFs for the Long Run? [Flirting with Models]We believe that capital efficiency should remain a paramount objective for investors. The prudent use of leverage can help investors employ more risk efficient portfolios without necessarily sacrificing potential returns. Many investors, however, do not have access to leverage (be it via borrowing(...) Multi-Factor Models 101 [Factor Research]FactorResearch publishes a white paper on building multi-factor models. SUMMARY Three common approaches for creating multi-factor portfolios are the Combination, the Intersectional and the Sequential models The results from the Combination and Intersectional models are comparable in terms of trend(...) Historical Results Following 4 Up Days To Begin A New Year [Quantifiable Edges]The simple fact that the SPX posted a gain on the first 4 days of the year is a pretty rare occurrence, with 2018 only being the 9th instance since 1961. While instances have been low, the intermediate-term performance following such strong starts to the year has been impressive. And looking at most(...) Deep Learning for Trading Part 2: Configuring TensorFlow and Keras to run on GPU [Robot Wealth]This is the second in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems,(...) Academic Research Papers and Presentations Galore! [Alpha Architect]It is that time of year again, the American Finance Association Annual Meeting is underway. The conference is in Philadelphia, starting today (January 5) and running through Sunday (January 7). This 3-day conference has 73 sessions, 246 papers and 12 presentations with no papers (general(...) Beyond Excess Returns: How to Enhance Sentiment Strategies using MSCI Barra Risk Models [Raven Pack]We have just published a white paper showcasing the benefits of hedging a sentiment signal using risk factors from several MSCI Barra Risk Models. In this post, I provide some details on the methodology used for the strategy and on the achieved results. Excess returns: Ignores several risk factors(...) Predicting Stock Returns Using Firm Characteristics [Alpha Architect]A few weeks ago, we did a deep dive into the factors versus characteristics debate. One of the reasons we’ve brought up this debate is due to the fact that “factor” loadings (from regressions) are arguably not as helpful as portfolio characteristics. In other words, knowing a portfolio P/E(...) A novel capital booster: Sports Arbitrage [EP Chan]As traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less. This article describes how one can take a small amount of capital and multiply it as much as 10 fold in one(...) All About the Exits…Revisited [Throwing Good Money]Back in June of 2016, I wrote this post about random entries and trailing exits. It turns out (on average) that you can beat buy-and-hold of the S&P 500 by simply buying members of the S&P 100 randomly, as long as you a) have a market-timing filter, and 2) have a trailing stop of 20%. Yes(...) Can the January effect be exploited in the market? [Mathematical Investor]The “January effect,” in common with the “Halloween indicator” and “sell in May and go away”, is a catchy, get-rich-quick investment idea adored by financial commentators because it is so easy to explain to unsophisticated readers. It rests on the claim that the U.S. stock market(...) When A New Year Starts On A Positive Note [Quantifiable Edges]Last night’s subscriber letter featured (an expanded version of) the following study, which looks at performance in the 1st couple of days following a positive 1st day of a new year. 2018-01-03 The stats and curve all suggest some immediate follow-through has been typical. There have now been 9(...) Deep Learning Insights for Factor Investing [Quantpedia]Deep learning is an active area of research in machine learning. I train deep feedforward neural networks (DFN) based on a set of 68 firm characteristics (FC) to predict the US cross-section of stock returns. After applying a network optimization strategy, I find that DFN long-short portfolios can(...) Tactical Asset Allocation in December [Allocate Smartly]Blogging was light in December. We spent the month working on the launch of a new fintech project that many of our readers will be excited about. We’ll be sharing details in the coming month and getting back to our regular blogging and site development schedule. — Allocate Smartly This is a(...) A Null Hypothesis for the New Year [Flirting with Models]In statistics, the null hypothesis is the default statement that you test with data. From this test, you can either reject the null hypothesis in support of an alternative or assert that there is not enough evidence to believe anything other than the null hypothesis with a certain degree of(...) Factor Olympics 2017 [Factor Research]2017 was a positive year for most factors Quality, Growth and Momentum showed the strongest performance Value, Dividend Yield and Size generated negative returns INTRODUCTION We present the performance of seven well-known factors on an annual basis for the last 10 years and the full-year 2017. It is(...) Deep Learning for Trading: Part 1 [Robot Wealth]In the last few years, deep learning has gone from being an interesting but impractical academic pursuit to an ubiquitous technology that touches many aspects of our lives on a daily basis – including in the world of trading. This meteoric rise has been fuelled by a perfect storm of: Frequent(...) Mean Reverting and Trending Properties of SPX and VIX [Relative Value Arbitrage]In the previous post, we looked at some statistical properties of the empirical distributions of spot SPX and VIX. In this post, we are going to investigate the mean reverting and trending properties of these indices. To do so, we are going to calculate their Hurst exponents. There exist a variety(...) Best of Research Review 2017 [Capital Spectator]So many research papers, so little time. How do you separate the wheat from the chaff? You might start with the following five economic and financial papers that appeared in The Capital Spectator’s Research Review column in 2017. In a sea of newly minted studies over the past 12 months, these(...) The Tax Efficiency of Long-Short Strategies [Alpha Architect]Conventional wisdom can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two great examples are that millions of people once believed the conventional wisdom that the Earth is flat, and millions also believed that the Earth is the(...) Persistance in Cryptocurrencies [Quantpedia]This paper examines persistence in the cryptocurrency market. Two different longmemory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013-2017. The findings indicate that(...) Deep Learning Systems for Bitcoins – Part 1 [Financial Hacker]Since December, bitcoins can not only be traded at more or less dubious exchanges, but also as futures at the CME and CBOE. And already several trading systems popped up for bitcoins and other cryptocurrencies. None of them can claim big success, with one exception. There is a strategy that easily(...) Predicting Long Run Stock Returns? It's All About the Payouts and the Real Economy [Alpha Architect]What are the research questions? Given the prevalence of buybacks as a form of corporate payouts, should they be explicitly included in supply-side models such as the dividend discount model (DDM) used to forecast of stock returns? Does the same superior performance extend to the prediction of(...) A Not-so Merry VIX-mas Part 2 [Quantifiable Edges]Yesterday I decided to examine performance of XIV during the last few days of the year. The thought was that we are now in a time period that is generally regarded as seasonally bullish. Additionally, volume and volatility are often light this week with many traders on vacation. So I thought with(...) Research Compendium 2017 [Factor Research]An investment in knowledge pays the best interest. (Benjamin Franklin) December 2017. Reading Time: Several hours. Author: FactorResearch. SUMMARY Contains 34 research papers that we published on FactorResearch.com in 2017 Focus on factor investing and quantitative strategies from an investor’s(...) Podcast: 2017 roundup: the year in review [Better System Trader]Well here we are, another year gone (and so fast too!). I’m glad you could join me for this final episode for 2017, where we’ll be reviewing all of the special guests we had on the show this year, the topics and insights they’ve shared plus their top trading lessons. I think this is a great(...) A Not-So Merry Vix-mas [Quantifiable Edges]During a time of year that is renowned for its low volatility and bullish seasonality, one might think XIV would have some strong historical returns. Well… 2017-12-25 …one would be wrong. Happy Holidays anyway! Machine learning is for closers [Quantum Financier]Put that machine learning tutorial down. Machine learning is for closers only. As some of you that were around back in the early of this blog may know, I always held high hopes for the application of machine learning (ml) to generate trading edges. I think like many people first coming across(...) The Art of War: How to beat a strategist in the futures market? [No Noise Only Alpha]Strategy: core directional choices that best best moves you into your desired future Tactics: specific actions that will best implement your strategies Without a core strategy to anchor all tactics suggestions to see which best FIT (feasible, impactful, timely) the strategy, one could randomly(...)