Quantocracy, Author at Quantocracy

Quantocracy’s Daily Wrap for 03/29/2016

This is a summary of links featured on Quantocracy on Tuesday, 03/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

A Monte Carlo Simulation function for your back-test results in R [Open Source Quant]

In this post on bettersystemtrader.com, Andrew Swanscott interviews Kevin Davey from KJ Trading Systems who discusses why looking at your back-test historical equity curve alone might not give you a true sense of a strategys risk profile. Kevin Davey also writes on the topic here for futuresmag.com.So i wrote a Monte Carlo-type simulation function (in R) to see graphically how my back-test
Trading the index with seasonal strategies [ENNlightenment]

I recently listened to an interesting interview at Better System Trader with Jay Kaeppel on Seasonality, a topic which I hadnt done much backtesting on previously. Jay outlined 3 rules for constructing a seasonal trading strategy on the stock index: – Stay long the last 4 days and first 3 days of the month (S_EOM) – Stay long the middle of the month, business days 9, 10, 11 (S_MOM) – Stay long

Quantocracy’s Daily Wrap for 03/28/2016

This is a summary of links featured on Quantocracy on Monday, 03/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Machine Learning and Its Application in Forex Markets [Quant Insti]

In the last post we covered Machine learning (ML) concept in brief. In this post we explain some more ML terms, and then frame rules for a forex strategy using the SVM algorithm in R. To use ML in trading, we start with historical data (stock price/forex data) and add indicators to build a model in R/Python/Java. We then select the right Machine learning algorithm to make the predictions. First,
Glamour Can Distract Investors [Larry Swedroe]

Theres very strong historical evidence to support the existence of a value premium in equity markets. While theres no dispute over the existence of the value premium (value stocks have provided an annual average return 5% higher than growth stocks over the long term), there is much debate over the cause of the difference in returns. In one camp are financial economists who argue that the
The Internal Bar Strength Indicator [System Trader Success]

The internal bar strength or (IBS) is an oscillating indicator which measures the relative position of the close price with respect to the low to high range for the same period. The calculation for Internal Bar Strength is as follows IBS = (Close Low) / (High Low) * 100; For example, on 13/01/2016 the QQQ etf had a high price of $106.23, a low price of $101.74 and a close price of

Quantocracy’s Daily Wrap for 03/27/2016

This is a summary of links featured on Quantocracy on Sunday, 03/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Best Links of the Week [Quantocracy]

These are the best quant mashup links for the week ending Saturday, 03/26 as voted by our readers: FX: multivariate stochastic volatility part 2 [Predictive Alpha] Predicting Stock Market ReturnsLose the Normal and Switch to Laplace [Six Figure Investing] Momentum for Buy-and-Hold Investors [Dual Momentum] Support Vector Machines classifier combining mean reversion and momentum indicators

Best Links of the Week

These are the best quant mashup links for the week ending Saturday, 03/26 as voted by our readers:

* * *

[click graph to enlarge]

Your votes matter to the quant community.

The graph to the right shows the average number of clickthroughs a link receives from our website (excluding RSS, Twitter and Stocktwits), broken out by the number of votes cast by our readers.

A core goal of Quantocracy is to have a positive impact on our corner of the financial world by rewarding the best work, and encouraging the best minds to keep writing.

As the graph makes clear, the citizens of Quantocracy are doing just that (way to go guys). Links with 11 or more votes receive nearly 6-times as many clickthroughs as a link with no votes (wow).

If you haven’t done so already, we invite you to register to vote and be a part of the effort. Your votes matter to the quant community.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 03/26/2016

This is a summary of links featured on Quantocracy on Saturday, 03/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

No new links posted.

Quantocracy’s Daily Wrap for 03/24/2016

This is a summary of links featured on Quantocracy on Thursday, 03/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

On the 60/40 portfolio mix [Eran Raviv]

Not sure why is that, but traditionally we consider 60% stocks and 40% bonds to be a good portfolio mix. One which strikes decent balance between risk and return. I dont want to blubber here about the notion of risk. However, I do note that I feel uncomfortable interchanging risk with volatility as we most often do. I am not unhappy with volatility, I am unhappy with realized loss, that is
On Backtesting: An All-New Chapter from our Adaptive Asset Allocation Book [GestaltU]

If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times – and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category. We're pretty psyched about that. In our book, we spent a great deal of time summarizing the research
The Comprehensive Guide to Stock Price Calculation [Quandl]

Adjusted stock prices are the foundation for time-series analysis of equity markets. Good analysts insist on properly-adjusted stock data. But the best analysts understand the adjustment process from first principles. This is Quandl's guide to the creation and maintenance of accurate adjusted historical stock prices. Introduction Adjustment Principles 1.Cash Dividends 2.Stock Dividends
Markov Chain Monte Carlo for Bayesian Inference – The Metropolis Algorithm [Quant Start]

In previous discussions of Bayesian Inference we introduced Bayesian Statistics and considered how to infer a binomial proportion using the concept of conjugate priors. We discussed the fact that not all models can make use of conjugate priors and thus calculation of the posterior distribution would need to be approximated numerically. In this article we introduce the main family of algorithms,
Have benchmarks made us bad active investors? [Alpha Architect]

Obsession with short-term performance against market cap benchmarks preordains the dysfunctionality of asset markets. The problems start when trustees hire fund managers to outperform benchmark indexes subject to limits on annual divergence Benchmarking causes, first, the inversion of the relationship between risk and return so that high volatile securities and asset classes offer lower returns
Responding to Your Comments on Our Adaptive Asset Allocation Book [SkewU]

If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times – and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category. We're pretty psyched about that. This has been – and continues to be – a very interesting experience.
The Dynamic Duo Of Risk Factors: Part I [Capital Spectator]

The value and momentum factors have earned high praise in recent years as complementary sources of risk premia for designing and managing equity portfolios. AQRs widely cited paper Value and Momentum Everywhere a few years back helped popularize the idea, pointing to applications in equities and beyond. Theres no shortage of support from the wider world of investment management.
A Few Notes On Adaptive Asset Allocation [CXO Advisory]

In the introductory text for Part I of their 2016 book, Adaptive Asset Allocation: Dynamic Global Porfolios to Profit in Good Times and Bad, Adam Butler, Michael Philbrick and Rodrigo Gordillo state: we have come to stand for something square and real, a true Iron Law of Wealth Management: We would rather lose half our clients during a raging bull market than half of our clients money
Smart Beta Strategies in Australia [Quantpedia]

"Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart is smart beta investing can be empirically examined by testing the performance of those factors
[Academic Paper] Optimal Delta Hedging for Options [@Quantivity]

The practitioner Black-Scholes delta for hedging options is a delta calculated from the Black-Scholes-Merton model (or one of its extensions) with the volatility parameter set equal to the implied volatility. As has been pointed out by a number of researchers, this delta does not minimize the variance of changes in the value of a traders position. This is because there is a non-zero
Slides from Investing in Smart Beta Conference [Flirting with Models]

Justin spoke at the Investing in Smart Beta conference this week in Fort Lauderdale, FL. He spoke alongside Research Affiliates in a session titled "The Smart Beta Checklist: Choosing The Best Strategy & Risk/Return Profile For Your Portfolio." Here's a quick description: Depending on market conditions and clients objectives, an array of disparate smart beta strategies can

Quantocracy’s Daily Wrap for 03/22/2016

This is a summary of links featured on Quantocracy on Tuesday, 03/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Why Investors Should Combine Value and Momentum [Alpha Architect]

In the past we have discussed how to combine value and momentum strategies to improve an equity allocation. In this piece we discuss why an investor should combine use value and momentum.* Many investors recognize that stand-alone value and momentum strategies have historically worked. Of course, these strategies don't work all the time and can have long streaks of terrible performance. But
The More Unique Your Portfolio, The Greater Its Potential [Investor’s Field Guide]

If there is a lot of overlap between your portfolio and the market, there is only so much alpha you can earn. This is obvious. Still, when you visualize this potential it sends a powerful message. Active sharethe preferred measure of how different a portfolio is from its benchmarkis not a predictor of future performance, but it is a good indicator of any strategys potential excess return.

Quantocracy’s Daily Wrap for 03/21/2016

This is a summary of links featured on Quantocracy on Monday, 03/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Best Links of the Last Two Weeks [Quantocracy]

The best quant mashup links for the two weeks ending Saturday, 03/19 as voted by our readers: How to Learn Advanced Mathematics Without Heading to University – Part 1 [Quant Start] When Measures Become Targets: How Index Investing Changes Indexes [Investor's Field Guide] Meet the DIY Quants Who Ditched Wall Street for the Desert (h/t Abnormal Returns) Evolving Neural Networks through
Beware bad multi-factor products [Flirting with Models]

This post is available as a PDF here. Summary Multi-factor portfolios are a great way to diversify across multiple factors that can potentially create excess risk-adjusted returns while simultaneously smoothing out relative performance volatility. There are two ways we've seen manufacturers build multi-factor portfolios: (1) combining independent portfolios of factors, or (2) picking stocks
When Trading Detracts From Alpha [Larry Swedroe]

As explained in my latest book, The Incredible Shrinking Alpha, which I co-authored with Andrew Berkin, accompanying the rapid growth of the actively managed mutual fund industry, the average performance of mutual funds has been trending downward over the past few decades. Teodor Dyakov, Hao Jiang and Marno Verbeekauthors of the study The Trading Performance of Active Mutual Funds,

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 03/19 as voted by our readers:

We also welcome one blog making its first ever appearance on the mashup this week:

Out Over Your Skis: How to Identify a Growth Trap [Factor Investor]