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

During the 1990’s the advent of Neural Networks unleashed a torrent of research on their applications in financial markets, accompanied by some rather extravagant claims about their predicative abilities. Sadly, much of the research proved to be sub-standard and the results illusionary, following

Traders love their performance metrics. Anyone who’s used their platform’s backtesting features has probably come across a few dozen of them, and everyone’s got their favorite. Anybody who’s anybody in the finance world has one named after them: Sharpe, Sortino, Calmar, Treynor, Gartman,

We compare and contrast different approaches to risk managing equity exposure; including fixed income, risk parity, managed futures, tactical equity, and options-based strategies; over the last 20 years. We find that all eight strategies studied successfully reduce risk, while six of the eight

The paper investigates the following research question: Does persistence (out of sample performance) exist for alternative beta strategies sponsored by investment banks? Does adding complexity to a strategy increase the risk of backtesting overfitting? Do the strategies capture the factor exposure

As regards the question of forecasting accuracy discussed in the paper on Forecasting Volatility in the S&P 500 Index, there are two possible misunderstandings here that need to be cleared up. These arise from remarks by one commentator as follows: “An above 50% vol direction forecast looks

The Low Volatility, Low Beta and Low Correlation factors are interrelated Low-risk factors generate attractive risk-adjusted returns, but require beta-neutrality Currently they feature moderate to high interest-rate sensitivity INTRODUCTION Coca-Cola versus Bitcoin Investment Trust, Mattel versus

The research in this post and the related paper on Range Based EGARCH Option pricing Models is focused on the innovative range-based volatility models introduced in Alizadeh, Brandt, and Diebold (2002) (hereafter ABD). We develop new option pricing models using multi-factor diffusion approximations

The average annualized return of local-currency index futures for 25 international markets has been 6% with a standard deviation of just under 20%. All markets recorded much fatter tails of returns than should be expected for normal distributions. Autocorrelation has predominantly been positive in

This is a review of James Picerno’s Quantitative Investment Portfolio Analytics in R. Overall, it’s about as fantastic a book as you can get on portfolio optimization until you start getting into corner cases stemming from large amounts of assets. Here’s a quick summary of what the book

In my last post we explored a momentum strategy applied to the USA markets that was provided to us from the good guys over at www.quantpedia.com. One of my readers set about quantifying the same strategy on the JSE and shared their results with me. With permission and thanks, I pass along their fine

This post covers quite a wide range of concepts in volatility modeling relating to long memory and regime shifts and is based on an article that was published in Wilmott magazine and republished in The Best of Wilmott Vol 1 in 2005. A copy of the article can be downloaded here. One of the defining

Accruals are a part of any company’s financial reporting. For those unfamiliar with accrual accounting, a simple explanation is that accruals are adjustments made for (1) revenue that has been earned but not received and (2) costs that have been incurred but have not been paid. In short, one

In this post we demonstrate ideas to test for parameter sensitivity. Here we have a strategy with 5x parameters. 3x being look back periods for a specific indiactor. The other 2x being an entry level threshold and an exit level threshold. I decided to change the original parameters by up to 50% in

Since the mid-1990s the dollar exchange rate has mostly anticipated the outcome of FOMC meetings: it appreciated in the days before a rate hike and depreciated in the days before a rate cut. This suggests that since fixed income markets usually predict policy rate moves early and correctly their

This paper examines the link between microeconomic uncertainty and the size premium across different frequencies in an investment model with heterogeneous firms. We document that the observed time-varying dispersion in firm-specific productivity can account for a large size premium in the

As more thematic products come to the market, it can be difficult for investors to decide how to allocate to them, even if they believe in their future potential. The sector disruptors are a suite of products that focus on areas of the economy that are heavily influenced by new technologies. Taking

Trading costs are a hot topic these days. The topic has sparked investor attention because of the rise of systematic factor investing strategies available via the ETF structure. It seems as if everyone is a “quant” these days, slinging money around like drunken pirates, destroying the price

One of the most highly desired properties of any financial model or investment strategy, by investors and managers alike, is robustness. I would define robustness as the ability of the strategy to deliver a consistent results across a wide range of market conditions. It, of course, by no means the

Investors can express factor views via smart beta ETFs or mutual funds Some mutual funds offer higher factor exposure than smart beta ETFs Given higher fees, strong views on expected factor performance are required INTRODUCTION Similar to wind and water eroding the strongest mountains over time,

I am planning a series of posts on the subject of asset volatility and option pricing and thought I would begin with a survey of some of the central ideas. The attached presentation on Modeling Asset Volatility sets out the foundation for a number of key concepts and the basis for the research to

Is there a relationship between real yields and short-term market valuation? Is there a relationship between inflation rates and short-term market valuation? Does the predictive power of the Shiller P/E improve by using yields and inflation? What are the Academic Insights? YES. The authors describe

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

Opex week often carries some bullish seasonality. Pullbacks into strong seasonal periods will often offer substantial edges. The study below utilizes this concept and examines pullbacks of at least 3 days just prior to opex week. 2018-08-12-1 Numbers here are strong, and suggest a possible upside

Yield curve models are used to price a wide variety of interest rate-contingent claims. The existence of several different competing methods of curve construction available and there is no single standard method for constructing yield curves and alternate procedures are adopted in different business

In this post, we will learn about the Stereoscopic Portfolio Optimization framework and how it can be used to improve a quantitative trading strategy. We’ll also review concepts such as Gaussian Mixture Models, K-Means Clustering, and Random Forests. Our objective is to determine whether we can

Understanding endogenous market risk (“setback risk”) is critical for timing and risk management of strategic macro trades. Endogenous market risk here means a gap between downside and upside risk to the mark-to-market value that is unrelated to a trade’s fundamental value proposition. Rather

The LNVM model is a mixture of lognormal models and the model density is a linear combination of the underlying densities, for instance, log-normal densities. The resulting density of this mixture is no longer log-normal and the model can thereby better fit skew and smile observed in the market. The

Use of machine learning in the quantitative investment field is, by all indications, skyrocketing. The proliferation of easily accessible data - both traditional and alternative - along with some very approachable frameworks for machine learning models - is encouraging many to explore the arena.

All that began to change around 2000 with the advent of high frequency data and the concept of Realized Volatility developed by Andersen and others (see Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (2000), “The Distribution of Exchange Rate Volatility,” Revised version of NBER

James Paulsen, chief investment strategist at Leuthold Group, sees trouble brewing in the growing disconnect between US equity sectors. He told CNBC earlier this week that correlations among US equities is unusually low and flashing a warning signal. That’s an especially dangerous sign when the

We can decompose the returns process Rt as follows: While the left hand side of the equation is essentially unforecastable, both of the right-hand-side components of returns display persistent dynamics and hence are forecastable. Both the signs of returns and magnitude of returns are conditional