Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at return time series alone and to detect “strict local martingales”, i.e. episodes when the risk-neutral return temporarily follows a random

From 2017 through 2019, the Russell 1000 Growth Index returned 20.5 percent per annum, outperforming the Russell 1000 Value Index, which returned 9.7 percent, by 10.8 percentage points a year; and the Russell 2000 Growth Index returned 12.5 percent per year, outperforming the Russell 2000 Value

Last week I summarized the 2019 factor performance for U.S. stocks. A natural follow-up question was the following–“what about International stocks?” A great question. So below I dig into the 2019 performance for International Factor Portfolios. 1 Let’s dig into the results. Factor Investing

You may have seen in different papers or websites, analysis of how a specific active portfolio is exposed to different financial factors (Value, Growth, Size, Quality, etc). This insight is very interesting in order to know what to expect from a strategy and to explain and understand its behaviour,

A long/flat tactical equity strategy with a portable beta bond overlay – a tactical 90/60 portfolio – has many moving parts that can make attribution and analysis difficult. By decomposing the strategy into its passive holdings (a 50/50 stock/bond portfolio and U.S. Treasury futures) and active

Venture capital returns are likely to be overstated Top-performing VC funds generated attractive returns, but are difficult to access Average venture capital returns can be replicated efficiently with public equities WINNERS & LOSERS The further the global financial crisis retreats into history,

In our last post, we looked at one measure of risk-adjusted returns, the Sharpe ratio, to help our hero decide whether he wanted to alter his portfolio allocations. Then, as opposed to finding the maximum return for our hero’s initial level of risk, we broadened the risk parameters and searched

A New Index of the Business Cycle William B. Kinlaw (State Street Global Markets), et al. January 2020 The authors introduce a new index of the business cycle that uses the Mahalanobis distance to measure the statistical similarity of current economic conditions to past episodes of recession and

We will learn how to implement a simple function using TensorFlow 2 and how to obtain the derivatives from it. We will implement a Black-Scholes model for pricing a call option and then we are going to obtain the greeks. Matthias Groncki wrote a very interesting post about how to obtain the greeks

While short-term asset returns are unpredictable, volatility is highly predictable theoretically and practically. The VIX index is a forward-looking estimate of volatility based on index option prices. Though introduced in 1992 it has been calculated back to 1986, because when released they wanted

In case you missed it, 2019 was a good year to be an equity investor. Examining market-cap-weighted indices, the U.S. stock market was up ~ 30%, Developed International Markets were up ~ 22%, and Emerging Markets were up ~ 18%. But how did factors do in 2019? Below I update my post from last year,

In response to a member question: Have you ever looked at a systematic approach that invested only in tactical asset allocation strategies that were experiencing a significant drawdown? We know that performance chasing is a flawed behavioral bias, so an approach like this could exploit it. We always

At Newfound, we adopt a holistic view of diversification that encompasses not only what we invest in, but also how and when we make those investment decisions. In this three-dimensional perspective, what is correlation-based, how is payoff-based, and when is opportunity-based. In this piece, we

Factors can be valued like stocks or markets The Low Volatility factor in the US had the best subsequent returns when cheapest and worst when most expensive However, the perspective is less clear when analyzing European and Japanese stock markets INTRODUCTION Funds flows are frequently analyzed by

German Power prices can be explained by supply and demand, but also by causal correlations to underlying energy future prices. A properly weighted basket of gas, coal and emissions should therefore be able to resemble the moves of the power price. This article will introduce multivariate regression

It would be too easy to kick off the series of lectures supplementing my Python for Quants ebooks starting from Machine Learning (ML) as an innovation. ML-based algorithms pay dividends when your problem is fairly well defined and data allow to capture the patterns where they exist. Deep Learning

In our last post, we started building the intuition around constructing a reasonable portfolio to achieve an acceptable return. The hero of our story had built up a small nest egg and then decided to invest it equally across the three major asset classes: stocks, bonds, and real assets. For that we

Retail investors’ return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate the distribution of such expectations based on actual transactions. A new paper shows how to do this through ETFs that track the

Cryptocurrencies, and most notably Bitcoin, are recognized as decentralized currencies. While some see Bitcoin (BTC) as a payment method of the future, others see it as a speculative asset class. No doubt, many have gained on the skyrocketing prices of BTC, but note that many have lost. Despite the

Surveys often reveal investor behavior that is challenging to understand. For example, Preqin’s Alternative Investor Outlook for H2 2019 highlighted the following: 65% of institutional investors believe that real estate is overvalued and a correction likely to occur in 2019, 2020, or beyond.

Stop losses are the most common method used by traders to control risk. However, they're often used inappropriately. In this post I'll quickly bust some of the myths around them, and explain how to use them properly. This is the first of three posts aimed at answering three fundamental

In my last post, Inverse Volatility Position Sizing, I tested inverse volatility sizing on a monthly rotation strategy. I saw very little difference in the rest results versus equal position sizing. I was talking to a trading friend about the research and how I was surprised at how there was not any

Factor returns should reflect risk, in that they have traditionally been interpreted as proxies for some kind of risk not measured by beta. The idea is that perhaps what people really care about is whether there will be another oil shock, and nothing matters as much. Stocks that have a high

In previous posts (Visualising Fixed Income ETFs with T-SNE) we have talked about dimensionality reduction algorithms to visualize financial assets and find recognizable patterns. The conclusions were that it didn’t perform well compared to PCA, which is a more classical approach. Can we do any

The world's leading quant finance conference 450+ experts from banks, buy-side, regulators, Silicon Valley, academia and beyond examine every facet of quant in five amazing days Key themeslatest agenda SPECIALIST FOCUS. SPECIALIST KNOWLEDGE. Maximise your experience with our full-day summit or

It’s not often I get the opportunity to write a book review for our fellow teammates and the best authors on our website — Elisabetta Basilico and Tommi Johnsen! If you haven’t read Elisabetta and Tommi’s mountain of blog posts on our site you’ve been hiding under a rock somewhere (or

It has been some time since wrote Parts I, II and III of our popular series of articles on How to Learn Advanced Mathematics Without Heading to University. Many of you have contacted us asking for the final Part IV of the series. We have now completed our internal research and can present our view

This is an early preview of a new analytical tool we’ll be adding to our platform later this month. Learn more about what we do. Broadly speaking, the goal of tactical asset allocation is to take advantage of broad market trends via trend-following and/or momentum. Those trends can be difficult to

Managed futures strategies have historically provided meaningful positive returns during left-tail equity events. Yet as a trading strategy, this outcome is by no means guaranteed. While trend following is “mechanically convex,” the diverse nature of managed futures programs may actually prevent

Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously

Stock sentiment can be aggregated from public sources using a big data approach Results indicate that sentiment has some predictability for short-term factor performance Positive sentiment resulted in higher subsequent returns than negative sentiment INTRODUCTION Albert Einstein famously stated that

It is no secret that artificial intelligence (AI) systems have made enormous strides in recent years, partly due to the adoption of Bayesian (probability-based) machine learning techniques rather than the rule-based techniques used until about 20 years ago. AI systems have advanced in lockstep with

Factor investing is hard and some factors make it harder than others. A value strategy results in a portfolio of stocks that exhibit temporary or structural issues and are usually rated “Sell” by brokers, which makes these emotionally challenging to hold. Small caps are companies that are

Open-High-Low-Close (OHLC) bars are a type of financial data typically used to represent daily movements in the price of a financial instrument. They give us more information about certain characteristics of the series than line charts, such as intraday volatility or daily momentum. Could Generative

The literature in portfolio optimisation has been around for decades. In this post I cover a number of traditional portfolio optimisation models. The general aim is to select a portfolio of assets out of a set of all possible portfolios being considered with a defined objective function. The data:

When I was in graduate school at Northwestern in the early 90s the hot financial topics were all related to finding and estimating risk factors: Arbitrage Pricing Theory via latent factors (Connor and Koraczyk 1986), Kalman filter state-space models (eg, Stock and Watson 1989), and method of moment

The focus of this study is on the response of typical or systematic risk premia to crowding (large inflows of capital). In particular, the paper focused on documenting the response of commonly recognized systematic risk premia strategies to periods, following the identification of crowded

This is a test of a new paper from Dr. Wouter Keller titled Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA). This is primarily a buy & hold strategy that’s roughly based on the classic “Permanent Portfolio”, but it includes an element of tactical asset

The term mutual information is drawn from the field of information theory. Information theory is busy with the quantification of information. For example, a central concept in this field is entropy, which we have discussed before. If you google the term “mutual information” you will land at some

U.S. equities have out-performed international equities for 8 of the past 10 years, but this trend has tended to flip-flop historically and persist for multi-year stretches. Home country bias is a real phenomenon that investors have to deal with, especially during these streaks where U.S. equities

Most institutional investors can only trade the largest, most liquid stocks Introducing minimum liquidity requirements impacts factors differently Factor portfolio construction with liquidity constraints is especially challenging in small stock markets INTRODUCTION Index funds have breached $11

Say you’ve built a little nest egg thanks to some discipline and frugality. And now you realize that you should probably invest that money so that you’ve got something to live off of in retirement. Or perhaps you simply want to earn a better return than stashing your cash underneath your bed, I

A point of confusion for many new quant momentum investors is the difference between Time- Series Momentum and Cross-Sectional Momentum: Time-series (TS) looks at each individual stock’s momentum and owns assets with positive momentum while shorting those with negative momentum; Cross-sectional

Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital and the net present value rule of corporate finance. The q-factor model is an implementation of investment capital asset pricing that explains many

The financial independence, retire early (FIRE) movement has gained a lot of traction. “We retired at 30” headlines get clicks and have made people question the typical retirement timetable: A main goal for those pursuing FIRE is to reach a portfolio balance that can reasonably fund their

Welcome to a year-end installment of Reproducible Finance with R, a series posts that will be a little bit different from the norm on Alpha Architect (see here for my last post). We will search for and hopefully unearth some interesting market conditions, but we’ll primarily focus on the code that

There are many calendar / seasonal anomalies by which we can enhance our overall investment strategy. One of the least frequent but still very interesting anomalies is for sure the Pre-Election Drift in the stock market in the United States. This year is the election year, and public discussion is

Return correlations for the major asset classes have edged down in recent years, which implies that diversification opportunities have increased, if only marginally. The correlation readings are only modestly softer overall and for several asset class pairings it’s fair to say that nothing much

Last week I noted the current rally was reaching historical extremes for persistency. Here I will look at another study from the subscriber letter, and then update last week’s study. In last night’s letter I looked at all times back to the inception of the NASDAQ in 1971 in which both SPX and

The Cboe’s VIX® (30-day) and VIX3M (93-day) indexes enable us to quantify volatility term structures but until now, historical analyses between VIX style indexes have been limited to dates after December 2001. This post introduces the results of VIX3M style calculations back to 1990, and reviews