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

We are looking for 3-4 traders (or trading teams) to showcase as Strategy Managers on our Algorithmic Trading Platform. Ideally these would be systematic quant traders, since that is the focus of our fund (although they don’t have to be). So far the platform offers a total of 10 strategies in

Often a Quantitative Researcher will develop trading models in Python or R. These models are then passed off to Quantitative Developers, who implement them in trading systems with Java or C++. Usually, a Quantitative Trader will then execute trades with the help of these systems. I have had the

The carry factor is the tendency for higher-yielding assets to provide higher returns than lower-yielding assets — it is a cousin to the value factor, which is the tendency for relatively cheap assets to outperform relatively expensive ones. A simplified description of carry is the return an

It has been a busy few months. I have been exploring market indicators that John Ehlers has created which he publicly made available in his book: Cycle Analytics for Traders : Advanced Technical Trading Concepts. The key theme of his book is applying digital signal processing filters to better

This post is the first of a series where we will be providing some of our own thoughts and commentary the conversations we had in the first season of our new podcast. This post covers our conversation with Meb Faber, which you can listen to here. 2:09 - Meb hijacks the show to ask a very important

It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so. — attributed to Mark Twain. Mark Twain had some great insights. The quote above can apply to just about every aspect of life, including investing. This axiom is particularly relevant

July 2018 Trend Following: DOWN -1.98% / YTD: -7.85% Please find this month’s report of the Wisdom State of Trend Following. Performance is hypothetical. Chart for July: Wisdom State of Trend Following - July 2018 And the 12-month chart: Wisdom State of Trend Following 12 months - July 2018 Below

Slightly positive month for the State of Trend Following, with the YTD slightly negative. Please check below for more details. Detailed Results The figures for the month are: July return: 0.57% YTD return: -2.19% Below is the chart displaying individual system results throughout July: StateTF July

In July 2016, we argued that bond investors should be quick to celebrate the strong returns they had realized year-to-date. The combination of a defined maturity and known coupon rate creates a gravitational pull for bond returns. Using a global bond ETF universe, we develop a simple model to

The simplicity of the Momentum factor can be intellectually challenging Various alternative Momentum versions highlight remarkable similar return profiles The robustness is an attractive characteristic of the investment strategy INTRODUCTION What do selfies, the Kardashians, Crocs, blue cheese, and

While the literature on gold is dominated by studies on its diversification, hedging, and safe haven properties, the question “When to invest in gold?” is generally not analyzed in much detail. We test more than 4,000 seasonal, technical, and fundamental timing strategies for gold. While we find

In their paper “The Probability of Backtest Overfitting” [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2326253] Bailey et al. introduce a method for detecting overfitting. They refer to the method as CSCV or Combinatorially Symmetric Cross Validation. Bailey et al. proceed to show that

Variance swaps are over-the-counter derivatives that exchange payments related to future realized price variance against fixed rates. Variance swaps help estimating term structures for variance risk premia, i.e. market premia for hedging against volatility risk based in the difference between

As we have noted in previous Mathematical Investor blogs (see this blog for instance), surprisingly few mutual funds beat their respective benchmark (typically some market index). Even fewer consistently beat their benchmarks year after year. A new report from S&P Dow Jones sheds light on this

As everyone who’s been invested for the last ten years knows, post-financial crisis stock returns have been incredible. The chart below highlights the total returns for the S&P 500 Index, the MSCI EAFE Index, the MSCI EEM Index, and the MSCI ACWI Index. The results are hypothetical results and

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

While playing around with a 2 period RSI (Relative Strength Index) mean reversion strategy, I came up with a very simple rule change with a much larger impact on the results than expected. I doubled the compounded annual growth rate and cut the maximum drawdown in half. That never happens. In my