Tag: Quant

Blackarbs Retirement Strategy Algorithm Debut (Part 1)

Join the growing Blackarbs Research Group Discord community here Get access to the strategy that has returned 48% in live trading since this article was written here (Updated: 2024-Mar-02) Mission Recap Blackarbs current mission is to create automated strategies with the goal of beating the market with superior risk adjusted returns. Originally, I wanted to illuminate some of the more hidden aspects of markets and investing that I found interesting and of value. Over time, that goal crystall

READ MORE →

Is it Possible to Know the Daily High or Low Intraday with 80% Accuracy?

Introduction This is an old concept concerning the opening range. The idea is that the opening range often sets the day’s high or low within the first hour of cash equities trading (9:30 am - 10:30 am EST). Recently a trader on Youtube made the claim that you can know with 88% probability the high or low of the day after the first hour of trading. He managed to successfully repopularize the idea of using the opening range in a a more specific way than other methods. In this article I set out t

READ MORE →

The Secret to Shorting Stocks

This post contains affiliate links. An affiliate link means Blackarbs may receive compensation if you make a purchase through the link, without any extra cost to you. Blackarbs strives to promote only products and services which provide value to my business and those which I believe could help you, the reader. Misinformation is everywhere. Many people believe the key to successful short selling is simply the inversion of a successful long strategy. I also used to believe this, among other short

READ MORE →

Synthetic ETF Data Generation (Part-2) - Gaussian Mixture Models

This post is a summary of a more detailed Jupyter (IPython) notebook where I demonstrate a method of using Python, Scikit-Learn and Gaussian Mixture Models to generate realistic looking return series. In this post we will compare real ETF returns versus synthetic realizations. To evaluate the similarity of the real and synthetic returns we will compare the following: * visual inspection * histogram comparisons * descriptive statistics * correlations * autocorrelations The data set we will

READ MORE →

A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 4)

For newsletter subscribers this post is best viewed directly on my blog. Recap In Part 3 of the series we reviewed the relationship between returns and correlation of the 2-asset portfolio UPRO and TMF. The basic equal weight strategy was very compelling in terms of total return and CAGR. However, the strategy is susceptible to large drawdowns, especially in situations where US equities and long term bonds are out favor, for example in the 2015 and 2018 periods. We also went over some prototy

READ MORE →

A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 3)

Recap This is an update to the original blog series that explored a simple strategy of being long UPRO and TMF in equal weight, inverse volatility and inverse-inverse volatility. This strategy crushed the cumulative and risk-adjusted returns of the benchmark SPY etf. However through our research we determined that this strategy is heavily dependent on the correlation between the two assets. This strategy works best when correlations are positive and prices are trending positively, however, theo

READ MORE →

Synthetic Data Generation (Part-1) - Block Bootstrapping

Outline * Introduction * An Alternative Solution? * Notebook Description and Links * Conclusions * Future Work * Resources and Links Introduction Data is at the core of quantitative research. The problem is history only has one path. Thus we are limited in our studies by the single historical path that a particular asset has taken. In order to gather more data, more asset data is collected and at higher and higher resolutions, however the main problem still exists; one historical path

READ MORE →

Labeling and Meta-Labeling Returns for ML Prediction

Post Outline * Introduction * Links * Embedded Notebook INTRODUCTION This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado. In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period information, and also meta-labeli

READ MORE →

Exploring Alternative Price Bars

Post Outline * Introduction * Links * Embedded Notebook Introduction This post explores a concept at the heart of quantitative financial research. Most qfin researchers utilize statistical techniques that require varying degrees of stationarity. As many of you are aware financial time series violate pretty much all the rules of stationarity and yet many researchers, including me, have applied or will apply techniques when not appropriate thereby calling into question many of the resulting

READ MORE →

Mixture Model Trading (Part 5 - Algorithm Evaluation with pymc3)

Post Outline * Recap * Chapter Goals and Outline * Links * Embedded Jupyter Notebook Recap See <Mixture Model Trading (Part 1, Part 2, Part 3, Part 4, Part 5, Github Repo)>. This research demonstrates a systematic trading strategy development workflow from theory to implementation to testing. It focuses on the concept of using Gaussian Mixture Models as a method for return distribution prediction and then using a simple market timing strategy to take advantage of the predicted asset retu

READ MORE →

Mixture Model Trading (Part 4 - Strategy Implementation)

Post Outline * Chapter Goals and Outline * Links * Introduction * Mixture Model Trading Algorithm Outline * GMM Algorithm Implementation * Next Steps Chapter Goals and Outline * Use Part 3 - strategy research as a basis for algorithmic trading strategy. * Implement strategy using the Quantconnect platform. Links * Mixture Model Trading Github Repo * Part 4 Jupyter Notebook Link * Full Algorithm Python Script Introduction This notebook will walkthrough the algorithm implement

READ MORE →

Mixture Model Trading (Part 3 - Strategy Research)

Post Outline * Introduction * Links * Notebook * Next Steps Introduction 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 trading strategy. Below is a quote taken from the README of the github repo: “The primary goal of this repo is to demonstrat

READ MORE →

Mixture Model Trading (Part 2 - Gaussian Mixtures)

Post Outline * Introduction * Links * Notebook * Next Steps Introduction 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 trading strategy. Below is a quote taken from the README of the github repo: “The primary goal of this repo is to demonstrat

READ MORE →

Mixture Model Trading (Part 1 - Motivation)

Post Outline * Introduction * Links * Notebook * Next Steps Introduction 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 trading strategy. Below is a quote taken from the README of the github repo: I will be presenting each of the notebooks

READ MORE →

Computing Option Skews with Dask

Post Outline * Introduction * Links + Datasets * Notebook * Next Steps Introduction This article series provides an opportunity to move towards more interactive analysis. My plan is to integrate more Jupyter notebooks and Github repos into my research/publishing workflow. For datasets that are too big to share through github I will provide a download link both here and in the github readme. I will be posting the notebooks into this blog using iframes. If you experience any issues with f

READ MORE →

Can We Use Mixture Models to Predict Market Bottoms? (Part 3)

Post Outline * Recap * Webinar Hypothesis * Anaylsis/Conclusions * Jupyter (IPython) Notebook * Github Links and Resources Recap Thus far in the series we've explored the idea of using Gaussian mixture models (GMM) to predict outlier returns. Specifically, we were measuring two things: 1. The accuracy of the strategy implementation in predicting return distributions. 2. The return pattern after an outlier event. During the exploratory phase of this project there were some interestin

READ MORE →

Can We Use Mixture Models to Predict Market Bottoms? (Part 2)

Post Outline * Recap * Model Update * Model Testing * Model Results * Conclusions * Code Recap In the previous post I gave a basic "proof" of concept, where we designed a trading strategy using Sklearn's implementation of Gaussian mixture models. The strategy attempts to predict an asset's return distribution such that returns that fall outside the predicted distribution are considered outliers and likely to mean revert. It showed some promise but had many areas in need of improvement.

READ MORE →

Can We Use Mixture Models to Predict Market Bottoms?

Post Outline * Recap * Hypothesis * Strategy * Conclusion * Caveats and Areas of Exploration * References Recap In Part 1 we learned about Hidden Markov Models and their application using a toy example involving a lazy pet dog. In Part 2 we learned about the expectation-maximization algorithm, K-Means, and how Mixture Models improve on K-Means weaknesses. If you still have some questions or fuzzy understanding about these topics, I would recommend reviewing the prior posts. In those po

READ MORE →

Intro to Expectation-Maximization, K-Means, Gaussian Mixture Models with Python, Sklearn

Post Outline * Part 1 Recap * Part 2 Goals * Jupyter (IPython) Notebook * References part 1 recap In part 1 of this series we got a feel for Markov Models, Hidden Markov Models, and their applications. We went through the process of using a hidden Markov model to solve a toy problem involving a pet dog. We concluded the article by going through a high level quant finance application of Gaussian mixture models to detect historical regimes. part 2 goals In this post, my goal is to impar

READ MORE →

Introduction to Hidden Markov Models with Python Networkx and Sklearn

Post Outline * Who is Andrey Markov? * What is the Markov Property? * What is a Markov Model? * What makes a Markov Model Hidden? * A Hidden Markov Model for Regime Detection * Conclusion * References Who is Andrey Markov? Markov was a Russian mathematician best known for his work on stochastic processes. The focus of his early work was number theory but after 1900 he focused on probability theory, so much so that he taught courses after his official retirement in 1905 until his death

READ MORE →

Understanding Hidden Variables with Python - Research Roadmap

Post Outline * Motivating the Journey * Where Do Edges Come From? * The Problem with Traditional Research * The Hidden Side * A Brief Description: * Part 1 - A Visual Introduction to Hidden Markov Models with Python * Part 2 - Exploring Mixture Models with Scikit-Learn and Python * Part 3 - Predicting Market Bottoms with Scikit-Learn and Python Motivating the Journey Where do Edges Come From? Edges come from superior ability to identify and execute profitable strategies

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (12/31/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT Stocks exhi

READ MORE →

Asset Pricing using Extreme Liquidity with Python (Part-2)

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 Institute of Technology - School's asset pricing model. Theory links the catalyst of systemic risk events to the funding difficulties of major financial intermediaries. Thus c

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (12/06/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT Stocks exhi

READ MORE →

Asset Pricing using Extreme Liquidity Risk with Python (Part-1)

Post Outline * Introduction * Get Data * Calculate Cross-Sectional Extreme Liquidity Risk * Quick and Dirty Observations * Next Steps * References iNTRODUCTION One of the primary goals of quantitative investing is effectively managing tail risk. Failure to do so can result in crushing drawdowns or a total blowup of your fund/portfolio. Commonly known tools for estimating tail risk, e.g. Value-at-Risk, often underestimate the likelihood and magnitude of risk-off events. Furthermore, tai

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (11/16/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT Stocks exhi

READ MORE →

Time Series Analysis (TSA) in Python - Linear Models to GARCH

If you’re interested in automating your strategy or process using AWS Cloud click here. Post Outline * Motivation * The Basics * Stationarity * Serial Correlation (Autocorrelation) * Why do we care about Serial Correlation? * White Noise and Random Walks * Linear Models * Log-Linear Models * Autoregressive Models - AR(p) * Moving Average Models - MA(q) * Autoregressive Moving Average Models - ARMA(p, q) * Autoregressive Integrated Moving Average Models -

READ MORE →

Does Factor Rank Matter for the Implied Volatility Skew Strategy?

Post Outline * Strategy Summary * Results * Conclusions/Analysis Strategy Summary First, if you're unfamiliar with the Implied Volatility Skew Strategy you can find a recent deep dive into the strategy and its performance here. In this short post, I look at the effect of using only the top N ranked ETFs from each Long/Short portfolio. In this case, N is equal to 3. This is an arbitrary selection and this study could be done with the top 1, 2, 4, etc. This differs from the original strate

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (10/18/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT Stocks exhi

READ MORE →

Backtesting the Implied Volatility Strategy - A Deeper Dive (10/03/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) * Deep Dive into the Weekly Strategy using Quantopian's Pyfolio * Strategy Concerns Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (9/27/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT Stocks exhi

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (9/20/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT Stocks exhi

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (9/14/16)

Post Outline * Strategy Restart * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Restart After a ~2 month pause, the implied volatility long/short strategy has returned! If you were previously unaware this strategy relied on aggregating free options data via the now defunct Yahoo Finance Options API. After some time I was able to track down another free, reliable, source for options data via Bar

READ MORE →

Aggregating Free Options Data with Python

Post Outline * Motivation * Code Requirements * Creating our Scraper Class * Aggregating the Data * Github Gist Code * Disclaimers Motivation This year I implemented a simulated trading strategy based on the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor has predictive power for equity returns for up to 6 months. Because historical options data

READ MORE →

Institutional Level Long/Short Strategies in Quantopian (OCF/EV)

Post Outline * Background * Strategy Summary * Implementation Details * Strategy Results and Analysis * References (Notes) Background Recently I was blessed with an introduction to S&P Global-Market Intelligence's Director of Business Development, Matt Morrissy and gifted a trial membership to the S&P Capital IQ platform [1]. Specifically, I was given an opportunity to review and provide feedback for their "Alpha Factor Library." Their team, the Quantamental Group [2], has researched an

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (7/12/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Update (Target Leverage=1) * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts ind

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (7/6/16)

Post Outline * Strategy Summary * References * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Update (Target Leverage=1) * 1-Week Holding Period Strategy Updated (Target Leverage=2) Strategy Summary This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts ind

READ MORE →

Backtesting the Implied Volatility Long/Short Strategy (6/28/16)

Post Outline * 4-Week Holding Period Strategy Update * 1-Week Holding Period Strategy Update (Target Leverage=1) * 1-Week Holding Period Strategy Updated (Target Leverage=2) 4-Week Holding Period Strategy Update Download the spreadsheet here. 1-Week Holding Period Strategy Update (Target Leverage=1) Results simulated using quantopian platform 1-Week Holding Period Strategy Update (Target Leverage=2) RESULTS SIMULATED USING QUANTOPIAN PLATFORM I'll post the components of each of the

READ MORE →

Updating the Implied Volatility L/S Strategy (6/21/2016)

There are two versions of this strategy that I have previously tracked and reported. The original strategy consists of forming weekly portfolios each held for a period of four (4) weeks before being liquidated. The second strategy is a higher frequency version which forms weekly portfolios with a holding period of one (1) week. Thus far, both strategies have exceeded expectations. Their apparent theoretical success is so far beyond expectations it required much deeper investigation and better si

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (6/04/16)

To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name e

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (5/31/16)

To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name e

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (5/21/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

BACKTESTING THE IMPLIED VOLATILITY STRATEGY WITH QUANTOPIAN (5/20/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here. To summarize, the strategy calculates a SKEW measure using ATM calls and OTM puts for a collection of ETF symbols. It then sorts the symbols into quintiles based on the SKEW factor. Using daily close/close log return calculations for this strategy has shown exceptional performance as can be seen here. However, translating a successful dail

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (5/14/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (5/14/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot (best vs worst vs benchmark) Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends an

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (5/08/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot (best vs worst vs benchmark) Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends an

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (5/08/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/30/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/30/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot (best vs worst vs benchmark) Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends an

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/24/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

Exploring the Relationship Between SPY and TLT

In this post I examine the relationship between the SPY and TLT ETFs. This can be considered Part 2.5 of my series exploring the 2-Asset Leveraged ETF portfolio of UPRO and TMF. Thus far I've posted results of the strategy using two implementations: "Inverse Risk-Parity" and "Risk-Parity". I've also covered some key concepts behind investing in leveraged ETFs including convexity, and beta-slippage/decay. Now we can explore the strengths and weaknesses of the strategy. The strategy works because

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/17/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/17/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot (best vs worst vs benchmark) Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends an

READ MORE →

BACKTESTING THE IMPLIED VOLATILITY STRATEGY WITH QUANTOPIAN (4/09/16)

To see the origin of this series click here. To summarize, the strategy calculates a SKEW measure using ATM calls and OTM puts for a collection of ETF symbols. It then sorts the symbols into quintiles based on the SKEW factor. Using daily close/close log return calculations for this strategy has shown exceptional performance as can be seen here. However, translating a successful daily strategy with no transaction costs and perfect trading fills into a robust strategy that can execute and perfo

READ MORE →

A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 2)

In Part 1, and Part 1.5 I introduced a simple 2-asset portfolio that substantially outperformed the SPY ETF since 2009. In Part 1 I examined the performance of an "inverse risk-parity" approach where the ETF with the largest volatility contribution to the portfolio was weighted more heavily. In Part 1.5 I examined the performance of the actual "risk-parity" approach, where the ETF with the smallest volatility contribution is weighted more heavily. In this post I will examine some of the conceptu

READ MORE →

A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 1.5)

In Part 1 of this series I shared a simple strategy which showed outsized performance relative to the SPY ETF since 2009. I made a small error in the implementation. The previous portfolio was not rebalanced according to a risk-parity framework. It was actually the inverse. The strategy was rebalanced such that the ETF responsible for the highest percentage of the portfolio's volatility was weighted more heavily! Surprisingly this error did nothing to substantially alter the performance of the p

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/09/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/09/2016)

Blog RSS FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends and Observati

READ MORE →

A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 1)

I'm going to share a portfolio with you that has absolutely annihilated the performance of the market (as proxied by SPY) since the recovery began in 2009*. The strategy has not had a down year since. This portfolio maintains constant exposure, has 1 un-optimized parameter and wins on a risk-adjusted basis even after considering reasonable transaction costs. I can't claim credit for the general idea. I found it in the comments section while reading a SeekingAlpha article written by Jonathan Kin

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/02/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by fol

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/02/2016)

Blog RSS FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends and Observat

READ MORE →

Using ETF Internal Analytics to Identify Mean Reversion Opportunities (python)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE Since I started producing the following graphic for the ETF Internal Analytics product, I found the weekly return bin information compelling. I became curious about whether there was an opportunity to be exploited in the distribution patterns. Blackarbs ETF Internal Analytics Sample for SPY I distilled all the questions I had into two: 1. Does the percentage of ETF component stocks

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (3/29/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by f

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (3/19/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends and Observations COMP

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (3/19/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by f

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (3/14/16)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by f

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (3/05/16)

#block-yui_3_17_2_2_1457211500463_15952 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smir

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (3/05/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends and Observations COMP

READ MORE →

BACKTESTING THE IMPLIED VOLATILITY STRATEGY WITH QUANTOPIAN (2/27/16)

#block-yui_3_17_2_5_1456621692348_14798 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here. To summarize, the strategy calculates a SKEW measure using ATM calls and OTM puts for a collection of ETF symbols. It then sorts the symbols into quintiles based on the SKEW factor. Using daily close/cl

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (2/27/16)

#block-yui_3_17_2_1_1456604859059_182762 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smi

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (2/27/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Notable Trends and Observations Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables COMP

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (2/20/16)

#block-yui_3_17_2_3_1456020426677_22579 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smir

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (2/20/2016)

#block-yui_3_17_2_2_1456020426677_173171 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations

READ MORE →

Backtesting the Implied Volatility Strategy with Quantopian (2/17/16)

#block-yui_3_17_2_3_1455744353210_9420 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } To see the origin of this series click here. I've been tracking this strategy for ~7 weeks now and it appears to have substance. To summarize, the strategy calculates a SKEW measure using ATM calls and OTM puts for a collection of ETF symbols. It then sorts the symbols into quintiles based on the SKEW metric. Using daily close/close return calculations for th

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (2/13/16)

#block-yui_3_17_2_4_1455397888451_24737 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smir

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (2/13/2016)

#block-yui_3_17_2_4_1455394346404_17847 .social-icons-style-border .sqs-svg-icon--wrapper { box-shadow: 0 0 0 2px inset; border: none; } FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (2/06/16)

To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (2/06/2016)

FOR A DEEPER DIVE INTO ETF PERFORMANCE AND RELATIVE VALUE SUBSCRIBE TO THE ETF INTERNAL ANALYTICS PACKAGE HERE LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Implied Cost of Capital Estimates Composite ETF Cumulative Return Tables Notable Trends and Observations COMP

READ MORE →

Implied Volatility Skew Strategy Mid-Week Update Using Python (2/03/2016)

To see this weekend's prediction click here. Overall this strategy has been impressive in its trial run over the last 4.5 weeks. I figured, given the volatility and uncertainty in the broad markets this week I'd like to see a mid-week update of the strategy using Python and the BarChart OnDemand API. To see my original article on the basics of using the BarChart OnDemand API click here. First I import the basic modules needed to execute the script: From there I define a couple convenience fun

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (1/30/16)

To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (1/30/16)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/30/2016)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (1/23/16)

To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (1/23/16)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/23/2016)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (1/17/16)

To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (1/16/16)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/16/2016)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

USING IMPLIED VOLATILITY TO PREDICT EQUITY/ETF RETURNS (1/11/16)

To see the origin of this series click here In the paper that inspired this series ("What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns") the authors research shows that Option Volatility Smirk they calculate is predictive up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. I will then track the results of the Long/Short portfolio, in equity returns, cumulatively fo

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (1/09/16)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/09/2016)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

Using Implied Volatility to Predict Equity/ETF Returns

During a discussion with an knowledgeable options trader, I was told the significance of interpreting the "Implied Volatility Skew" for stocks and given a paper to read for homework. To get a basic understanding of Implied Volatility Skew see this link here. The paper I was told to read was "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. In their paper they show empirically, using their SKEW measure, allowed one to p

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (1/02/16)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/02/2016)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (12/26/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (12/26/2015)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (12/19/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (12/19/2015)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: LAST 252 TRADING DAYS Click here for help understanding this chart Click here for help understanding this chart Click here

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (12/13/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (12/13/2015)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Cumulative Return Tables Notable Trends and Observations COMPOSITE ETF COMPONENTS: YEAR-TO-DATE LAST 247 TRADING DAYS Click here for help understanding this Chart Click here for help understanding this Char

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (12/06/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (12/06/2015)

LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot and associated Tables Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) COMPOSITE ETF COMPONENTS: YEAR-TO-DATE LAST 242 TRADING DAYS Click here for help understanding this Chart Click here for help understanding this Chart Click here for help understanding this Chart Clic

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (11/28/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (11/28/2015)

NEW LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot and associated Tables Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) COMPOSITE ETF COMPONENTS: YEAR-TO-DATE LAST 237 TRADING DAYS Click here for help understanding this Chart Click here for help understanding this Chart Click here for help understanding this Chart

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (11/21/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (11/21/2015)

NEW LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot and associated Tables Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) COMPOSITE ETF COMPONENTS: YEAR-TO-DATE LAST 232 TRADING DAYS Click here for help understanding this Chart Click here for help understanding this Chart Click here for help understanding this Chart

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (11/14/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (11/14/2015)

NEW LAYOUT (Organized by Time Period): Composite ETF Cumulative Returns Momentum Bar plot and associated Tables Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) Composite ETF Components: YEAR-TO-DATE LAST 227 TRADING DAYS Click here for help understanding this Chart Click here for help understanding this Chart Click here for help understanding this Chart

READ MORE →

COMPOSITE MACRO ETF WEEKLY IMPLIED COST OF CAPITAL ESTIMATES vs. CUMULATIVE RETURNS (11/07/15)

WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL? “In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ” — CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School The basic concept of the ICC model is

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (11/07/2015)

NEW LAYOUT: Composite ETF Cumulative Returns Momentum Bar plot and associated Tables Composite ETF Cumulative Returns Line plot Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean) Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot) YEAR-TO-DATE LAST 222 TRADING DAYS Click here for help understanding this Chart Click here for help understanding this Chart Click here for help understanding this Chart Click here for help understanding this Chart LAST 63

READ MORE →

COMPOSITE MACRO ETF IMPLIED COST OF CAPITAL ESTIMATES

Earlier this year I used to publish a bi-weekly article using the "Implied Cost of Capital" model as an ETF relative value estimation tool. Unfortunately State Street began reporting obvious erroneous data points and eventually stopped providing certain fundamental data altogether. As a result I had to suspend publishing of my ICC estimates. Well thanks to YCharts.com and their excellent site I was able to find the requisite data needed to begin publishing my model estimates again. what is th

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (10/31/2015)

NEW LAYOUT: Going forward I will be testing a new organizational format for the charts. I have created a chart description page which details how each plot type is commonly interpreted (used). I will provide a link to the chart description page in the caption of each chart. The primary change is all the chart types will be grouped by the referenced time period. It is my hope that grouping the data this way will allow for easier analysis. Each time period grouping will display the charts in the

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (10/25/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (10/18/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (10/10/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (10/04/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

Could SPY ETF Component Participation Have Alerted Us to Sell (Hedge) Prior to the Recent Market Downturn?

This is the Python Code version of a guest post presented here on RectitudeMarket.com. If you would like to read the analysis without the Python code please click the link above. To market pundits and casual observers the recent correction in equity markets appeared as a surprise. Overall headline economic data was positive at best and mixed at worst. Domestically, capital markets had been looking ‘ok’ while most of the major volatility was taking place abroad in emerging markets, and commodity

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (9/28/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

How to get Free Intraday Stock Data with Python and BarCharts OnDemand API

To this day the most popular article I have ever written on this blog was "How to get Free Intraday Stock Data with Netfonds". Unfortunately the Netfonds API has really declined in terms of usability, with too many popular stocks missing, and irregular trade and price quotes. Simply put, as the API went down, so did the code. However, all hope is not lost. The wonderful people at BarChart.com have created a well documented, easily accessible API for intraday stock data and even near real-time q

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (9/20/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

COMPOSITE MACRO ETF WEEKLY ANALYTICS (9/14/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

Composite Macro ETF Weekly Analytics (8/30/2015)

COMPOSITE ETF CUMULATIVE RETURN MOMENTUM These charts show the sum (cumulative) of the daily returns of each composite ETF over the specified period. The daily return is calculated as the log of the percent change between daily adjusted close prices. These charts help determine asset class return momentum. This is important because momentum is arguably the strongest and most persistent market anomaly. Poorly performing asset classes are likely to continue under performing while outperforming a

READ MORE →

COMPOSITE MACRO ETF CUMULATIVE RETURN MOMENTUM (08.23.2015)

Here is the updated list of composite ETF components. LAST 1260 TRADING DAYS (5 YEARS) Source: Yahoo Finance LAST 504 TRADING DAYS (2 YEARS) Source: Yahoo Finance LAST 252 TRADING DAYS (1 YEAR) Source: Yahoo Finance LAST 126 TRADING DAYS (6 MONTHS) Source: Yahoo Finance LAST 63 TRADING DAYS (3 MONTHS) Source: Yahoo Finance LAST 21 TRADING DAYS (1 MONTH) Source: Yahoo Finance LAST 10 TRADING DAYS Source: Yahoo Finance Blog RSS #block-yui_3_17_2_2_1440420671029_16964 .social-i

READ MORE →

Composite Macro ETF Cumulative Return Momentum (08.16.2015)

Here are the updated ETF components I'm using to construct the ETF composites. Last 504 Trading Days Composite ETF Cumulative Returns Last 252 Trading Days Composite ETF Cumulative Returns Last 126 Trading Days Composite ETF Cumulative Returns Last 63 Trading Days Composite ETF Cumulative Returns last 21 Trading days Composite ETF Cumulative Returns Last 10 Trading days Composite ETF Cumulative Returns Blog RSS #block-yui_3_17_2_4_1439677863749_22267

READ MORE →

Price Dispersion as a Smart Money Indicator

Before I get into the topic at hand, let me say I have not seen the following stock price data interpreted or studied like I am about to show you. As far as I am aware my approach is unique in that it is not overly complicated, can be generalized across a large cross section of asset class ETFs, and makes intuitive sense regarding market structure. Before I introduce the chart it is important that I clarify some definitions. What is Price Dispersion? I'm sure this may have many meanings amon

READ MORE →

How do Bank Stocks Perform During Periods of Rising Rates? (Python Code Version)

This is the Python version of a guest article that originally appeared on RectitudeMarket.com. In this version I include the Python code used to generate the anaylsis. This subject has garnered a healthy debate among market participants in recent weeks. Conventional wisdom says that banks and the financial sector overall should benefit from a rising rate environment. The story goes that bank profitability is inextricably linked to `Net Interest Margin (NIM)`. If rates are rising, it is assumed

READ MORE →

How Volatile Are US Treasury Bond Yields?

The volatility in global bond markets have generated quite the discussion among market participants. So much so that BlackRock has indicated publicly that their old risk models need to be redone. BlackRock was speaking specifically about the European sovereign bond market but in light of the recent volatility in US treasuries I wanted to contextualize what's happening domestically. Especially considering I went out on a limb regarding the relative-value offered by the US long bond I thought thi

READ MORE →

Composite Sector ETF Valuation Report [6.15.2015]

Check out the updated IPython Notebook where I take a look at changes and trends in ETF valuations using the Implied Cost of Capital model. To learn more about the model and the methodology used see here and here. For reference here is a Table of Contents, but due to some technical issues the TOC is not working properly on the nbviewer.org page. I'll keep working to fix it for the next issue.

READ MORE →

Sector ETF Valuation Using the Implied Cost of Capital (ICC) Model

This post is part of a series examining the ICC model's use as a valuation tool. I first introduced the topic in this post, where I outlined the following: * how I calculate the ICC formula for use in this sector ETF relative valuation model * my assumptions for the model * expected model output and sanity check * why and how I use the model results to enhance my investing Recently I expanded on the subject by detailing the Python code I use to run the analysis along with my interpretation

READ MORE →

How to get Free Intraday Stock Data from Netfonds

Daily stock data is everywhere for free. Yahoo, Google, and Quandl all provide useful daily stock prices for basic number crunching. However computational analysis for intraday stock data is much harder to find. In fact, Intraday stock data can be very expensive. So what is a cost conscious quant supposed to do? The Norwegian website Netfonds.no provides free intraday data on stocks and ETF's on the NYSE, Nasdaq, and Amex exchanges. They provide up to 5 days of trade/bid/offer data. I wrote som

READ MORE →

Project Update_iVC Reporting Engine

Still working industriously behind the scenes I thought to take some time and give a progress report. Good news is the iVC Reporting Engine is almost fully operational. I've been able to automate the following processes: Data Collection: I have two methods to obtain public company filings from the SEC via Python scripts. * The primary method I use leverages the excellent services of the free (for now) data provider, Quandl.com. They aggregate and distribute data from several primary sources i

READ MORE →

Walking the Path takes time...

Readers, I have been away somewhat after my declaration of providing more useful and interesting content. Why is that? Am I being hypocritical or something worse? The answer is neither. The essence of the Blackarbs mission is to continuously find ways to add value to investors anywhere and everywhere they may be. Born out of that mission is the iVC report which has helped a few portfolio managers, and private investors (including myself) make prudent well timed investments in value creating st

READ MORE →

Is Trading the FED's POMO Schedule Profitable?

I often consider the market's distortions that are or can be created by its participants. Arguably, the most important market player is the Federal Reserve. For years the FED as been injecting liquidity into the financial system through its Permanent Open Market Operations (POMO). I'm not going to delve into the purpose of these multibillion dollar transactions as others have covered this extensively. Instead I ask a simple question. The FED makes their tentative POMO schedule public beforehand.

READ MORE →