A primer on beta convexity and its applications is given in the following post: The essential idea is to evaluate the beta of stock during down-markets, separately from periods when the market is performing well. Beta convexity is a measure of how stable a stock beta is across market regimes, and by

The paper investigates the following research question: What has the absolute risk behavior of leveraged products been historically? Did they behave as intended by design? Is the leverage multiple a reliable indicator of the volatility multiple? Is the leverage multiple a reliable indicator over

Our whitepaper “The Optimization Machine: A General Framework for Portfolio Choice” presented a logical framework for thinking about portfolio optimization given specific assumptions regarding expected relationships between risk and return. We explored the fundamental roots of common portfolio

Trend-following strategies exploit the fact that investors exhibit behavioral biases that cause trends to persist. While many investment strategies have a concave payoff profile that reaps small rewards at the risk of large losses, trend-following strategies exhibit a convex payoff profile, one that

Short-term momentum persists in common equity factors The persistence is strong in Value and Dividend Yield However, these results conflict with short-term mean-reversion on equity index level INTRODUCTION When Trump won the US presidential election in November 2016, small and cheap stocks started

This post has two goals. I hope to make you think about your graphics, and think about the future of data-visualization. An example is given using some simulated time series data. A very quick read. In visualization, like in programming, presenting or any other skill, there is much to learn. Also

One indicator that has gotten some play in the news lately is the Hindenburg Omen. In last weekend’s subscriber letter I discussed the Hindenburg Omen signal in detail. (Click here for a free trial.) A core premise behind the Hindenburg Omen is that there are a large number of stocks hitting both

A late edition of the State of Trend Following report, showing a strong return for August and a YTD figure back in the black. Please check below for more details. Detailed Results The figures for the month are: August return: 6.74% YTD return: 3.36% Below is the chart displaying individual system

Even if you’ve never heard of it, the Law of Large Numbers is something that you understand intuitively, and probably employ in one form or another on an almost daily basis. But human nature is such that we sometimes apply it poorly, often to great detriment. Interestingly, psychologists found

quity return dispersion is measured as the standard deviation of returns across different stocks or portfolios. Unlike volatility it can be measured even for a single relevant period and, thus, can record changing market conditions fast. Academic literature has shown a clear positive relation

The 2014 study by Andrea Frazzini and Lasse Heje Pedersen, “Betting Against Beta,” found strong support for low-beta strategies. I’ve previously written on low-beta strategies here. This paper finds that, for U.S. stocks, the betting against beta (BAB) factor (a portfolio that holds low-beta

You can watch the video via the link below: Video Summary Ryan and I discuss three articles published on our blog this week. First, we examine a summary by Larry Swedroe that highlights the Betting Against Beta (BAB) factor and dives into two new papers examining when the BAB factor performs well.

Overall the last few months, I’ve had several consulting client’s strategy use SCTR for either a ranking or a filter. I finally got curious about the predictive ability of SCTR. How good is? I could find no information on how each of the ranking buckets did X days later on StockCharts.com. Maybe

Is it possible to build a simple systematic approach that beats investing in complex factor models? The research team here has proposed that a simple formula based on low return volatility, high net payout yield (dividends +/- stock buybacks), and strong price momentum gives investors exposure to

Class imbalance can seriously damage the precision of your binary classifier. In this post you will learn some simple ways of evening the size of your classes before training to prevent your classifier from cheating. The class imbalance problem Binary classification is a very common problem in

In our previous post, we touched on FX rates and their impact on international ETFs like EFA or EEM. In short, because international ETFs trading in the US are denominated in USD, most are affected not just by changes in underlying assets, but also by changes in the exchange rate between USD and

Below I’ve collected links to some of my favorite white papers and presentations on volatility. I’ve organized them in the following categories: Volatility Concepts & Volatility Trading Probability Distributions—Normal and Otherwise The VIX and VIX Futures Volatility Contagion—Will Short

Constructing an asset allocation that never lost money over given rolling periods leads to unsettling allocations: large positions in small-caps, long-term U.S. Treasuries, and precious metals. In many investment analyses, past results may be a downright misleading guide to the future because one

This article examines the magnitude of exposures to a set of systematic factors present in widely accepted Benchmarks (S&P500, the Russells, and MSCI global indices) and how they change over time. The authors use conventional style factors of value, size, quality, momentum, and minimum

In this article, we are going to develop a machine learning technique called Deep learning (Artificial Neural network) by using tensor flow and predicting stock price in python. At the end of this article you will learn how to build artificial neural network by using tensor flow and how to code a

Higher volatility & dispersion imply higher stock market risks The relationship between correlation and risk is not linear However, these market technicals do not behave consistently across time INTRODUCTION Financial reporters frequently comment on stock market technicals like volatility and

Few weeks ago Dr Jonathan Kinlay from Quantitative Research and Trading blog published a series of excellent articles on volatility. I wanted to review and comment on the notes. Forecasting Volatility in the S&P500 Index Modeling Asset Volatility Long Memory and Regime Shifts in Asset Volatility

Forward earnings yields and equity carry are plausible indicators of risk premia embedded in equity index futures prices. Data for a panel of 25 developed and emerging markets from 2000 to 2018 show that index forward earnings yields have been correlated with market uncertainty across countries and

Recent asset allocation articles (tactical or otherwise) that you might have missed: We Are All FX Traders Now (Alpha Scientist) Because international ETFs trading in the US (ex. EFA or EEM) are denominated in USD, most are affected not just by changes in the underlying assets, but also by changes

This study investigates the relation between a comprehensive set of firm-specific attributes and future equity returns for a sample of stocks from 27 emerging markets. Univariate analyses based on equal-weighted portfolio returns reveal that the low beta, firm size, book-to-market ratio, momentum

Can the monetary policy environment be used to predict global equity market returns? Should we overweight/buy countries with expansionary monetary policy regimes and underweight/sell countries with contractionary monetary policy regimes? In twelve of the fourteen countries studied, both nominal and

Mutual funds exhibit momentum when measured by their one-year performance Momentum disappears when more reasonable fund selection criteria are applied Performance does not seem effective for fund selection for a full market cycle CHASING PERFORMANCE Chasing mutual fund performance suffers from a bad

Inflation at the producer level (derived from the Producer Price Index – PPI) is arguably an advance indicator for inflation downstream at the consumer level (derived from the Consumer Price Index – CPI). Do investors therefore reliably react to changes in PPI as an indicator of the future

This is a summary of the recent performance of a wide range of excellent tactical asset allocation strategies, net of transaction costs. These strategies are sourced from books, academic papers, and other publications. While we don’t (yet) include every published TAA model, these strategies are

Beta herding means convergence of market betas of individual stocks that arises from investors’ biased perceptions. Adverse beta herding denotes the dispersion of such betas that arises from a reversal of the bias. A new paper suggests that overconfidence in predictions of overall market direction

Nothing is more frustrating than a long piece of code with no standard way of naming elements, presenting code or organizing files. It’s not only unreadable but more importantly not reusable. Unfortunately, unlike other programming languages, R has no widely accepted coding best practices. Instead

We’re in unique position to analyze the behavior of Tactical Asset Allocation investors. Our platform helps members analyze 40+ published TAA strategies from many angles, including: historical performance, tax efficiency, exposure to rising interest rates, etc. Members can combine those strategies

We dissect the performance of factor-based equity portfolios using a characteristics-based multi-factor expected return model. We show that generic single-factor portfolios, which invest in stocks with high scores on one particular factor, are sub-optimal, because they ignore the possibility that

Past research suggests that the relative change in the volume of Google searches for financial terms such as “debt” or “stocks” can be used to anticipate stock market trends. In this analysis the search term “debt” was used to obtain monthly search volume data from Google Trends. The

Interactive Brokers (IB) just published the second installment in a series I’m writing for the brokerage firm about using R for portfolio analysis: Modeling Tail Risk In R With Value at Risk. Today’s update (part deux) is more or less adapted from my recent book: Quantitative Investment

We’ve begun tracking two new tactical asset allocation strategies: Defensive Asset Allocation (DAA) and Accelerating Dual Momentum (ADM). We’ll be introducing both in more detail on our blog in the coming weeks. Members can review their historical performance and begin tracking them in near

Trade optimization is more technical topic than we usually cover in our published research. Therefore, this note will relies heavily on mathematical notation and assumes readers have a basic understanding of optimization. Accompanying the commentary is code written in Python, meant to provide

The Momentum strategy can be applied to stocks, sectors, countries and factors Factor momentum shows positive excess returns across regions However, single-stock Momentum performance is comparable and less complex to implement INTRODUCTION We recently investigated applying the long-short Momentum

How do the major crypto-assets (Bitcoin, Ripple, and Ethereum) stack up against conventional asset classes? In their August 2018 paper entitled “Risks and Returns of Cryptocurrency”, Yukun Liu and Aleh Tsyvinski apply standard tools of asset pricing to measure crypto-asset exposures to: 160

Do users of social media provide valuable information about liquidity that can be used to predict future liquidity? Does social media provide useful information, over and above that provided by traditional, fundamental news sources? Do positive and negative sentiment have the same effects on

SPX closed at a new all-time high on Friday. But NYSE volume came in at the lowest level since mid-July. Low volume at new highs can sometimes be a negative. Of course August frequently has low volume as many market participants are on vacation and not trading as actively. So I decided to look back

The RavenPack Research Symposium returns to New York on September 12th - register to receive updates on the agenda. RavenPack Research Symposium - Generation AI: The New Data-Driven Investor RavenPack’s events have become global, with attendance exceeding 250 buy-side professionals at the London

The Excel workbook referred to in this post can be downloaded here. Market state models are amongst the most useful analytical techniques that can be helpful in developing alpha-signal generators. That term covers a great deal of ground, with ideas drawn from statistics, econometrics, physics and

Imagine you are an alien. You land on planet earth in 1927 and are given a mission. You are told that you need to solve a problem: compound $1,000,000. The goal: compound your extraterrestrial face off. The options: FF_VAL: Top decile B/M, annually rebalanced, market-cap weighted. FF_MOM: Top decile

If behavioral biases explain asset pricing anomalies, they should also materialize in cryptocurrency markets. I test more than 20 stock return anomalies based on daily cryptocurrency data, and document strong evidence of price momentum. Unlike stock markets, price reversal and risk-based anomalies

In the previous post I described a simple rule to double the returns of a mean reversion strategy. In this post, I show how pre-inclusion bias can take a losing strategy and make it a winning one. Recently I had reader send me the rules for a stock trend following strategy. He knew these are the

Two of the smartest econometricians I know are Prof. Stephen Taylor of Lancaster University, and Prof. James Davidson of Exeter University. I recall spending many profitable hours in the 1980’s with Stephen’s book Modelling Financial Time Series, which I am pleased to see has now been reprinted

In the last post we showed how to use PCA to create Offense and Defense portfolios by focusing on the first principal component or “PC1.” After rotation has been completed it is possible to derive weights or portfolios for each principal component. Another good primer on using PCA for asset

"We have met the enemy, and he is us" - Walt Kelly It has long been noted that investors - individual and institutional - tend to be their own worst enemies. They have an uncanny ability to buy stocks near market tops and sell near market bottoms. As a consequence, investor returns, in