Quant Mashup State of Trend Following in September [Au Tra Sy]Another down month for the State of Trend Following report, taking the year in the red a bit further for 2016. Please check below for more details. Detailed Results The figures for the month are: September return: -1.64% YTD return: -7.07% Below is the chart displaying individual system results(...) Value Investing Got Crushed During the Internet Bubble - Here's Why... [Alpha Architect]The dot-com bubble of the late 90s was a wild time in the stock market. Internet stocks were trading through the roof, tech IPOs were a practically daily experience, and people quit their jobs to make millions day trading. And why not? Even a day trading chimp could make money in a market that went(...) Presenting in Dallas and Austin, Texas [Alvarez Quant Trading]I will be in Texas next week giving presentations. Click the links below for more details. I hope to see some readers there. October 17, 2016 Austin Market Technicians Association For more information see https://www.mta.org/event-registration/austin-chapter-meeting-featuring-cesar-alvarez/ October(...) More Reasons To Diversify Factors [Larry Swedroe]Since the publication in 1992 of Eugene Fama and Kenneth French’s paper “The Cross-Section of Expected Stock Returns,” the traditional way to think about diversification has been to view portfolios as a collection of asset classes. However, we now have a nontraditional way to think about(...) Is That Leverage in My Multi-Factor ETF? [Flirting with Models]The debate for the best way to build a multi-factor portfolio – mixed or integrated – rages on. FTSE Russell published a video supporting their choice of an integrated approach, arguing that by using the same dollar to target multiple factors at once, their portfolio makes more efficient use of(...) Want to Learn Way Too Much About Stock Market Factors? Read This Paper [Alpha Architect]During the past few decades, newly discovered stock anomalies have been embarrassing existing factor models, such as the Fama-French 3-factor. As many readers know, each long or short “leg” of these popular long/short factor portfolios is generally constructed by ranking stocks on one specific(...) Implementing Predictive Modeling in R for Algorithmic Trading [Quant Insti]Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends [1] The predictive modeling in trading is a modeling process wherein we predict the(...) Cointegration and Pairs Trading in Stocks [Quantpedia]We examine a new method for identifying close economic substitutes in the context of relative value arbitrage. We show that close economic substitutes correspond to a special case of cointegration whereby individual prices have approximately the same exposure to a common nonstationary factor. A(...) Aggressive Global Tactical Asset Allocation [Allocate Smartly]A number of readers have asked us to test the two aggressive versions of Meb Faber’s GTAA strategy from his seminal paper: A Quantitative Approach to Tactical Asset Allocation. I can’t understate the importance that Faber’s work has had in popularizing the idea of TAA with the general(...) Modeling Stock-Market Regime Shift… Carefully And Selectively [Capital Spectator]Ilya Kipnis at QuantStrat TradeR reminds us that the Hidden Markov Model (HMM), which can be a powerful tool for detecting regime change in markets and macro, has its limitations and pitfalls. In particular, Kipnis reports that HMM’s value as a prediction tool for the stock market is dubious.(...) September Trend Following: Down Streak [Wisdom Trading]A second successive strong down month for the State of Trend Following index, bringing the Year-To-Date performance further in the red as we enter the last quarter of the year. The current drawdown is getting closer to the max Drawdown (24% vs. 32%) which, as we highlighted last month, can be a good(...) Low Vol Strategies In A More Nuanced Light [Larry Swedroe]One of the biggest problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicted a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50 years the most(...) The Problem With Depmix For Online Regime Prediction [QuantStrat TradeR]This post will be about attempting to use the Depmix package for online state prediction. While the depmix package performs admirably when it comes to describing the states of the past, when used for one-step-ahead prediction, under the assumption that tomorrow's state will be identical to(...) Quantitative Momentum: A Guide to Momentum-Based Stock Selection [Alpha Architect]The long wait is over. Our newest book–Quantitative Momentum–is finally here. After 2 years of research review, results replication, reverse engineering, internal idea generation, writing, editing, and final publication, we have a final product. We think the book will help fulfill our firm(...) Ask Me Anything Video for October 5, 2016 [Alvarez Quant Trading]In this short five minute video I will answer the following questions: Have you used HV10/HV100 ratio? Have you found any value in it? When trading multiple strategies, how do you decide what percentage to allocate to each. What do you think about asset allocation ETF strategies, like Ray Dalio’s(...) Lower Volatility Smart Beta Funds - A Safe Haven in Turbulent Times? [Markov Processes]Smart Beta funds are hot. According to ETF.com, more than half of the 150 funds launched in 2016 implemented smart beta strategies. For the year to June 30, 2016, ETFGI’s most recent data show that assets in smart beta funds have a five-year annual compound growth rate of 31.3 percent. And, low(...) Market Timing Using Performance of Hi-Beta and Lo-Beta Stocks [iMarketSignals]This market timing model compares the performance of two different types of stock groups over time and provides signals when to invest or not to invest in the stock market. When the performance of the Hi-Beta stocks becomes lower than, or equal to Lo-Beta stocks the model exits the stock market and(...) The Case for Put Writing in an Expensive Market [EconomPic]Pensions and Investments wrote about the interest pension plans have shown in put writing (seemingly one of the more misunderstood investment strategies out there) in a recent article Funds Go Exotic with Put-write Options to Stem Volatility. I thought the article did a nice job of outlining the(...) Prospecting Dual Momentum With GEM [TrendXplorer]Gary Antonacci popularized dual momentum with an effective and simple approach for dynamic asset allocation: Global Equities Momentum (GEM). Using simulated ETF data series, GEM’s performance over past market conditions can be approximated. For longer investment horizons GEM’s implementation(...) The Perils of Backtesting with Unrealistic Data [Allocate Smartly]As readers hear us repeat often, our results tend to be less optimistic than you’ll find elsewhere. We do our best to show backtested results that are as realistic as possible (even though showing results that are as good as possible would probably be better for business). That’s partially a(...) Hidden Markov Models for Regime Detection using R [Quant Start]In the previous article in the series Hidden Markov Models were introduced. They were discussed in the context of the broader class of Markov Models. They were motivated by the need for quantitative traders to have the ability to detect market regimes in order to adjust how their quant strategies(...) Index Front Running: What Happens When a Stock is Added to an Index? [Signal Plot]This post documents some of my research on index front running. This trading strategy is simply buying stocks before they are added to indexes that passively managed funds are designed to track. I initially came across this idea through a Bloomberg article, The Hugely Profitable, Wholly Legal Way to(...) A shock to the covariance system [Flirting with Models]Mean-variance optimization assumes that you can fully describe the risks and returns of assets in a few simple numbers. Extreme market events often cause volatilities and correlations to spike dramatically, but stress testing on an individual asset basis can allow our own biases and oversights to(...) Implementing Python in Interactive Brokers C++ API [Quant Insti]In the previous article on IBPy Tutorial to implement Python in Interactive Brokers API, I talked about Interactive Brokers, its API and implementing Python codes using IBPy. In this article, I will be talking about implementing python in IB’s C++ API using a wrapper, written by Dr. Hui Liu. About(...) Tactical Asset Allocation in September [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(...) Podcast: Reducing Drawdown with Scott Phillips [Better System Trader]Who wants a steadily rising equity curve with little or no drawdown? I'm sure most traders do, but unfortunately it doesn't usually end up that way. Drawdown is a big part of trading and can be one of the the biggest challenges traders face, so what techniques can we use to potentially(...) Buyback Bulls and Bears [Investing Research]A lot of attention has been paid to share repurchases recently, which makes sense given the amount of money involved. As of June 30th, 2016, there had been almost $450 billion net transferred from companies to shareholders over the trailing twelve months through repurchase programs, very close to(...) Really, Beware of Low Frequency Data [EP Chan]I wrote in a previous article about why we should backtest even end-of-day (daily) strategies with intraday quote data. Otherwise, the performance of such strategies can be inflated. Here is another brilliant example that I came across recently. Consider the oil futures ETF USO and its evil twin,(...) Research Review | 30 Sep 2016 | Managing Volatility Risk [Capital Spectator]Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in(...) Option Pricing Methods in the Late 19th Century [Quantpedia]This paper examines option pricing methods used by investors in the late 19th century. Based on the book called “PUT-AND-CALL” written by Leonard R. Higgins in 1896 and published in 1906 it is shown that investors in that period used routinely the put-call parity for option conversion and static(...) Trading with different time frames [Milton FMR]Time frames are used in order to forecast future price trends. Many traders are missing out on this important aspect of trading by only looking at one time frame when trying to define a trend. Therefore its important to categorize trends as primary, intermediate and short-term trends. As a rule of(...) Profitable Market Timing with the Unemployment Rate [iMarketSignals]If the unemployment rate is higher than three months ago the model exits the stock market and enters the bond market, and re-enters the market when the unemployment rate is equal or lower than where it was three months ago. From 2001 to 2016 switching between bonds and stocks provided significant(...) Introducing the Global Earnings Announcement Premium [Alpha Architect]How do stock prices react to earnings announcements? Sometimes prices go up, and sometimes they go down. But here is a potentially more interesting question: What is the average performance across all stocks that have an announcement? The question of whether stocks earn excess returns in(...) The Optimal ETFs For Each Market Segment [Signal Plot]In one of my previous posts, I wrote about my framework for choosing ETFs since I wanted a method of choosing the optimal ETFs for expressing my market views. Generally, the framework is to choose an ETF with a low expense ratio, low trading costs, low tracking error, and a tax-advantaged structure.(...) Taming High Return and High Risk [Alvarez Quant Trading]I was at a recent talk of the Northwest Traders and Technical Analysts group where they presented a VXX strategy with some huge return and drawdown numbers. Trading this would be very difficult. This got me thinking. If I had a strategy like this, how could I tame the numbers? Through the years, I(...) Learning with kernels: an introductory approach [Quant Dare]Time series pervade financial markets and, although some embrace the so-called efficient market hypothesis, stating that current market prices reflect all available information about a security into its price, I am more inclined to think they provide us with a lot of information that we rarely know(...) Momentum Rotation System AmiBroker Code [DTR Trading]I've received several requests for details on the AmiBroker (AB) code and settings used for the backtest shown in my April post: Momentum Rotation 60 Day ROC System Results. That post used the AmiBroker Formula Language (AFL) code from my article in March 2015. That was a long time ago, so here(...) QuantStart Events in October and November 2016 [Quant Start]This is a short post to let QuantStart readers know that I'll be speaking at some events in New York and Singapore over the next couple of months: Wednesday, October 5th 2016, NYC - I'll be talking about "The Quest for Profitability", along with Seong Lee who will be talking(...) The Cost of Contango—It’s Not the Daily Roll [Six Figure Investing]It’s well known that long volatility Exchange Traded Products (ETPs) like VXX, UVXY, and TVIX often experience devastating losses during market quiet spells—even when the value of the VIX is staying relatively stable. These heavy losses occur when the VIX futures that underlie these funds are in(...) Predicting Booms and Busts in Low Volatility Strategies [Alpha Architect]Low volatility funds are some of the best performers in the market these days. As such, they have attracted renewed attention in addition to significant asset flows. (note: a refresher on low volatility investing is here, h.t. Eric Falkenstein). But a question remains — What does the future hold(...) TAA Alpha and The Greatest Trick the Devil Ever Pulled [GestaltU]The investment industry has investors convinced that the only path to better performance is through stock selection. As a result, most investors approach the challenge of portfolio construction exactly backward, and miss out on the most important opportunities to produce differentiated performance.(...) How to Scrape Data for Over 1,900 ETFs [Signal Plot]You can download the ETF metadata I scraped here and the price history here. You can also take a look at the code I used to scrape this data at my Github repository. Why Scrape ETFs? At the investment management firm I worked at, we had teams of people that devoted lots of time just trying to(...) Alternate Trading Days: An Important Analytical Tool [Allocate Smartly]Many of the tactical asset allocation strategies that we track are designed to only trade at the end of the month. When tracking these strategies for members however, we show the results of trading on other days of the month as well. We don’t do this to show off our backtesting prowess; it’s an(...) Webinar: Contemporary Portfolio Optimization Modeling with R [Interactive Brokers]In the first part of this webinar, we will review the most common ways to conduct the task of portfolio optimization with R. After this introduction, we will address some remarks on the modeling of portfolio problems. In the second part, we will demonstrate a revolutionary way to model and solve(...) Is the Value Premium Disappearing (h/t @AbnormalReturns)? [A Wealth of Common Sense]The value premium has been talked about in investment circles going all the way back to the 1934 release of Benjamin Graham’s Security Analysis. At its core value investing relies on buying undervalued securities, something every investor can or should be able to intuitively understand. You buy(...) High Yield Bond ETFs: Liquidity Time Bombs? [Flirting with Models]Many articles expounding upon the risks of ETFs that invest in illiquid assets (high yield bonds, bank loans, emerging markets, etc.) have been published in recent years. While there are additional risks inherent to these ETFs, the ETF structure provides an additional layer of liquidity that is not(...) Kalman Filter-Based Pairs Trading Strategy In QSTrader [Quant Start]Previously on QuantStart we have considered the mathematical underpinnings of State Space Models and Kalman Filters, as well as the application of the pykalman library to a pair of ETFs to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy. In this article we will(...) Does Interest Rate Exposure Explain the Low Volatility Anomaly? [Quantpedia]We show that part of the outperformance of low volatility stocks can be explained by a premium for interest rate exposure. Low volatile portfolios have a positive exposure to interest rates, whereas the more volatile stocks have a negative exposure. Incorporating an interest rate premium explains(...) Intuition Behind the Bayesian LASSO [Alex Chinco]Imagine you’ve just seen Apple’s most recent return, r, which is Apple’s long-run expected return, \mu^\star, plus some random noise, \epsilon \overset{\scriptscriptstyle \mathrm{iid}}{\sim} \mathrm{N}(0, \, 1): (1) \begin{align*} r &= \mu^\star + \epsilon. \end{align*} You want to use(...) Analyzing Risk-Managed Funds With R [Capital Spectator]Morningstar tells us that efforts at taming volatility in a multi-asset class framework generally turns up mixed results among publicly traded funds. Studying 60 products that are labeled “multiasset volatility-protection funds,” a recent Morningstar article reports that “as a group,(...)