One year ago, I wrote about the U.S. Value factor and what I found by extending its history back in time before 1926. In summary, I wrote that Value’s drawdown in March 2020 was normal and likely close to its bottom. Without the insights from the extended history, Value had appeared ‘dead’

Based on many years of reviewing investor portfolios, I concluded that most end up closely resembling a 60% Stocks / 40% Bonds Allocation. Yes, many portfolios also have alternatives, nuanced sub-asset classes, individual security selection, and perhaps some tactical components. But when you look at

As a quant, I have been obsessed with systematic Value and Momentum since the first day I ran a backtest. Part of me knows that the future for this combo is unlikely to be as good as the past. In my R&D, I moved on to other factors more than a decade ago. But another part of me is still in love

As a risk factor, Value is very much alive. Confusing the risk side and return side of factors creates the misconceived question of whether the value factor is dead. Something that is dead, does not move. A dead factor is a flat horizontal line with random noise. By contrast, value has been moving

In last week’s post we extended the systematic value factor (or at least a pretty good proxy of it) back to 1871. The response from readers was encouraging, perhaps because of the pain that value investing has been causing lately. Long-run history gives some relief. This week we dig deeper,

Value investing is struggling big time! As of March 2020, Value factor is down -51% from the peak reached 14 years ago. It is the longest and largest drawdown in value’s recent history. Many value investors have already rotated into growth. The remaining diehards also want to quit. Even Warren

For the past decade, asset managers have been educating clients about factor investing as it became the new norm. And yet after all these years, portfolios are still composed of one factor: Equity Beta. Among many questionable assertions and assumptions behind factor investing (our thoughts here,

This crisis is bringing out the best in many people. When the stakes become real, alertness is heightened, thinking is crystallized. Here is an eclectic collection of thought pieces we’ve enjoyed over the past two weeks. Bin There Done That by Morgan Stanley Ample research shows that most experts

Given the increasing drawdown in the market, it seems prudent to revisit the notion of volatility vs risk. See original post here. 1) To recap, in case you just got back from a 10 day silent mediation retreat, S&P500 peaked on Feb 19th 2020, and has been in pretty much a free-fall since then,

We are closing 2019 with much gratitude to our clients, collaborators and online visitors. We have launched this blog less than a year ago and have had the pleasure of seeing many visitors from all over the world ranging from buy-side investors, financial advisors, asset owners, thought leaders,

As readers would know, discussing ways to combine quant and fundamental investing has been a topic I care about (see some prior posts here, here and here). Perhaps I’m biased. But I believe that proper collaboration between quant and fundamental approaches is still a largely unexplored area. Yes,

Divergent thinking is what we learn in school when we are paying attention. It allows us to solve hard problems with one right answer. Convergent thinking is what happens when we stop paying attention and start doodling. Convergent thinking produces many answers, none of which are technically

In the past, I shared thoughts about the need to combine creative and organized thinking in order to generate investment alpha. “A robust investment process” and “a strict disciplined application” are concepts that one often hears in typical quant and fundamental investment teams. These are

As the book about the most successful quant, Jim Simons, comes out tomorrow (“The Man Who Solved the Market”), I felt inspired to review the ‘recent press’ on quant investing, but with a focus on the quantamental theme - where quantitative and qualitative ideas can ‘collaborate’ well to

Most asset allocation approaches are more or less static. From 60/40 to Risk Parity, such allocations can be easily replicated with a couple ETF’s, and so the outcomes of static asset allocation portfolios, especially the risks such as drawdowns, are 90%+ pre-determined. There is a strand of

Took a look at my book shelf this morning and decided to share some of my non-finance books that help me grow as a quant investor. It’s an eclectic list, not in the order of importance. “A More Beautiful Question” by Warren Berger “Rework” by Jason Fried and David Heinemeier Hansson

As trading costs have just hit zero, and passive investing overtook active in August, the investment industry is braced for further pressure to deliver alpha after fees. In my view, the potential to build great models today is huge, but constrained by the research cultures of most firms. Here is

Over the last few months, much of the financial press expressed concerns about the impact of inverted yield curves on financial markets, in particular, the stock returns. Some previous academic literature has shown that there exists a link between yield curves and economic growth (see references

In Part I Risk Parity, I discussed the“Chasing Diversifiers” problem that harms investors’ performance. This week, I apply the “Relevance of the Long-Run” concept to Risk Parity. First, let me acknowledge upfront that deep historical data is messy and is not precise. Depending on the

The rise and fall (?) of Risk Parity is a great case study of the frameworks I have been writing about so far. We start with the concept of “Chasing Diversifiers.” Chasing Diversifiers (link) Although Risk Parity is as close as you get to a pure risk diversification play, just like other

One of the most effective performance reporting formats I know is a Cone Chart, popularized by Bridgewater Associates. Here are some reasons why a Cone Chart is so effective: It clearly establishes ex-ante expectations of both return and volatility. When actual outcomes deviate within expectations,

Alternative Title: What to Do if Your Boss is Terrified of New Ideas? Several younger quants have asked this question: “The culture of our quant group is very skeptical about new ideas. They are terrified of data-mining, and random factors. How can we innovate in such environment?” My thoughts

Today’s quantitative investors seem split between innovation on one hand and engineering on the other. The prior group is constantly looking for new factors that predict returns in areas like alternative data and machine learning - yet often fail to find them. The latter camp is focused on

In last week’s post, I made a strong assertion that has caused some great feedback and comments. When I first heard Mark Kritzman make a similar point at a UBS conference a few years ago, I had a similar reaction: “Hey, I’m a quant and I love my factors. They are definitely real!”. I still

The question I get asked the most during the past twelve months is “Why are factors not working?” Here are my top 12 personal thoughts on the topic—informed by 15+ years of successfully “factor investing”. 1. There is no such thing as factor investing. There is only investing. 10 years

1. Crashes and Low Returns (link) Static asset allocation locks in the “Two Risks that Ruin Investing” - crashes and low returns. If you accept a static asset allocation strategy, you accept its history repeating in the future. For example, a 60/40 strategy drawdown of -63% in the 1930’s. 2.

Alternative Title: How to Avoid Bad Manager Timing Let’s look at the two types of risks in most investments: Strategy Risk: If you own a black-box ‘go-anywhere’ hedge fund that invests long and short and uses futures and derivatives at any frequencies, you are mostly exposed to the strategy

Last week’s talk by Edward Altman at the 50-year anniversary of Altman’s Z-score event at the CFA New York inspired me to compile an expanded list of memorable inventions in equity analysis. Each one is a successful blend of quantitative and fundamental thinking - which is increasingly being

Much has been written on this topic, but for what it’s worth, here is my take. Volatility is how much something moves up and down. The stock market is more volatile than the bond market, on average. Yet, a black-box hedge fund might be less volatile than S&P500, but is it less risky? Risk =

Here are my 8-thoughts and 1 solution idea about Campbell Harvey and Yan Liu recently released paper on their influential concept of the factor zoo. To sum it up, it says that there are too many data-mined factors out there and that we should be using much higher t-statistics to accept factors.

A Taxonomy of Anomalies and their Trading Costs 2015, Robert Novy-Marx and Mihail Velikov (with data) …and the Cross-Section of Expected Returns, 2013, Campbell Harvey, Yan Liu, Caroline Zhu (factor list) A Comparison of New Factor Models, 2014, Kewei Hou, Chen Xue, Lu Zhang The Supraview of

Quantitative Portfolio Management by Edward Qian, Ronald Hua, Eric Sorensen Expected Returns by Antti Ilmanen Quantitative Value by Wesley Gray and Tobias Carlisle Quantitative Momentum by Wesley Gray and Jack Vogel Dual Momentum Investing by Gary Antonacci Little Book that Still Beats the Market by

Only showing the latest backtest versions without disclosing their out-of-sample degradation Backtesting today’s static holdings (managers, asset allocations, sub-asset-classes) into the past - filled with look-ahead bias Charging fees that are on par with the tracking error of the strategy Asking

What is the most significant risk in quant (and all active) investing today? The First Moment (the mean) The Second Moment (under-estimating tracking error) The Third Moment (skewness, left tails, crash risk) Mis-specified risk model (hidden factor biases, factors ‘eating’ alphas) Sub-optimal

In addition to market valuation ratios like CAPE, the slope of the yield curve is one of the most talked about signals used to estimate future recessions and market returns. During the second half of last month (March 2019), the yield curve has inverted by about 5 basis points with the 10-year rate

Having just attended a great AI conference in New York, here are some observations: First about AI Most quants prefer the term Machine Learning (ML) instead of AI. Questions still remain of where AI (ML) adds value in a quant investment process. For example, Man’s CIO Sandy Rattray said that it

Can quantitative and fundamental approaches be successfully combined? In my estimate, this has been a top 5 industry question for a long time, including this conference at which I’ll be speaking at tomorrow The short answer is: Yes More-so, I believe quantitative approaches cannot work without

Alternative Title: The Gap Everywhere There exist many flavors of market timing. Some are obvious: In 1929, an influential businessman states that US Equities will return 24% per year for the next 20 years; or in 1999, a stock market forecaster predicts Dow Jones to double On dollar-weighted basis,

Commodity Futures contracts were established in 1865, but commercially available data starts in 1959, leaving an 80+ year period of unstudied history. In our latest academic paper “Two Centuries of Commodity Futures Premia” Chris Geczy and I use hand-collected futures data to extend the

In most decisions including investing, there are two ways to be wrong: Doing something that doesn’t work (false positive, type 1 error) Not doing something that would have worked (false negative, type 2 error) Investors and quants in particular worry more about the type 1 error - accepting a fake

The first risk of investing is the Drawdown Risk - the loss from the peak. The second risk of investing is the Low Return Risk - the under-performance vs. expectations over a stretched period of time. First, a few words about drawdown. Quants measure risk in many ways like Volatility, Skew,

While there exists a well-established (at least a century-old) academic interest in the long-run properties of asset class returns like the U.S. Equity, Fixed Income, Commodity and Real Estate Markets, only during the past decade, has there emerged a branch of literature studying the cross-sectional