As many investors have experienced, Value investing has underperformed for some time now. For the period following the Global Financial Crisis, Value investing (in general) has underperformed (1) the market and (2) Growth stocks. So while the past decade has been rough for Value investors, it can be

Although we find currency risk particularly interesting, it is not often the case with many investors for whom it is no more than a necessary inconvenience. As such, they tend to neglect it, accepting undesirable non-remunerated risks and missing potential opportunities. To prevent this, in this

This is a test of two variations of the Growth-Trend Timing (GTT) strategy from the always thought-provoking Philosophical Economics. GTT combines trends in both price and key economic indicators to switch between US equities and cash. Like most trend-following strategies, the strength of GTT

Because equities are much riskier than high-quality bonds, the vast majority of the risk of a conventional 60 percent equity/40 percent bond portfolio is equity risk. Here’s the simple math demonstrating the point. Well-diversified equity portfolios have volatility of about 20 percent, and

While the SPX closed up the VIX also rose. Most often they trade opposite each other, so this kind of action is somewhat unusual. But VIX has a tendency to decline going into the weekend (Friday afternoons), and then rise when it returns from the weekend. So to see this action on the first trading

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

Part of the reason the market has rallied over the past few days is an indication that a rate cut is likely coming as soon as the next Fed meeting. It is interesting timing for the Fed to begin cutting rates, since their QT program still remains in place (though it is winding down). By reducing the

Investors would like to maximize upside participation while mitigating losses. This preference is at the base of the growth of the liquid insurance market in the form of equity index options. The author investigates the following research question: Are protective put options an effective tail hedge?

In this post, I would like to quickly introduce what I believe to be an underutilized modelling technique that belongs in most analysts’ toolkit: the quantile regression model. As I am discussing some of the main points, I will be working with R’s quantreg package that is maintained by the

Many retirement planning analyses rely on Monte Carlo simulations with static assumptions for withdrawals. Incorporating dynamic spending rules can more closely align the simulations with how investors would likely behave during times when the plan looked like it was on a path to failure. Even a

The ESG factor generated positive returns since 2011 Strong sector biases (long tech & short discretionary) explain the performance Residual returns from ESG investing are essentially zero INTRODUCTION Investing is complicated as it is simple and complex at the same time. Common advice for new

One of the most successful investment strategies since the turn of the century has been the risk-parity “long-long” of combined equity, credit and duration derivatives. In a simple form this trade takes continuous joint equal mark-to-market exposure in equity or credit and duration risk. A

In “Your Complete Guide to Factor-Based Investing,” Andy Berkin and I presented the evidence demonstrating that momentum, both cross-sectional (or relative) momentum and time-series (or absolute, trend following) momentum, not only increases the explanatory power of asset pricing models while

Welcome to the first installment of Reproducible Finance by way of Alpha Architect. For the uninitiated, this series is a bit different than the other stuff on AA – we’ll focus on writing clean, reproducible code, mostly R (but some python too), applied to different ideas from the world of

Yield Curve and Financial Uncertainty: Evidence Based on Us Data Efrem Castelnuovo (University of Melbourne) June 2019 How do short and long term interest rates respond to a jump in financial uncertainty? We address this question by conducting a local projections analysis with US monthly data,

Some price series are mean reverting some of the time, but it is also possible to create portfolios which are specifically constructed to have mean-reverting properties. Series that can be combined to create stationary portfolios are called cointegrating, and there are a bunch of statistical tests

We often hear that the market is 5% off its highs or that it is down 5% from the high of the year. This alone does not tell us much. The questions I want to answer are as follows: “How often does that 5% loss become a 10% loss? Or worse yet — a 20% loss?” In other words, what are the

In my previous post, Market Timing with a Canary, Gold, Copper, LQD, IEF and much more, I tested several market timing methods. The signal was checked on the last day of the month. Now the question is what happens if we check on a different day? How different will the results be? The Test The

In the grand scheme of investing, behavioral risk is second to none on the list of pitfalls that threaten to derail the best-laid plans for investing. The challenge is especially acute in the thankless task of trying to anticipate how you’ll react when a rough patch arrives. The mystery is all the

Both trend-following and absolute momentum are well established methods for managing risk. Another method for managing risk is to use volatility targeting. The former are superior for reducing large drawdowns in bear markets while the latter tends to reduce kurtosis by normalizing the daily bet

The size effect is the phenomenon in which small stocks (i.e., those with lower market capitalizations), on average, outperform large stocks (i.e., those with higher market caps) over time. The size effect was first documented by several academic papers in the early 1980s ( Banz, 1981). However, it

Join top industry experts and practitioners as they debate the future of big data monetization in capital markets. Watch our previous event highlights video for what to expect in NYC! Register today. Industry Leaders For almost a decade, RavenPack Symposiums have consistently provided data-driven

Data corrupts. Absolute Data corrupts absolutely. This is my impression every time I am faced with the amount of data that is available to us in the current times. This is the moment of truth. Today you will learn how to make some predictions in the Forex market. This is probably the Far West of the

In this research note, we continue our exploration of credit. Rather than test a quantitative signal, we explore credit changes through the lens of statistical decomposition. As with the Treasury yield curve, we find that changes in the credit spread curve can be largely explained by Level, Slope,

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

Equal and fundamentally weighted equity indices outperformed market cap weighted in the US since 1990 The higher returns are explained by exposure to Value and Size factors The outperformance is not consistent across time given factor cyclicality THE RISE OF INDICES There are now more than 3.7

A risk spread is a premium for bearing economic risk of an investment, paid over and above the short-term real interest rate. Over the past 30 years, risk spreads in the U.S. have increased significantly and consistently: while real interest rates on ‘safe’ bonds and deposits have collapsed,

How do you make a factor investor more excited? Multi-factors! Terrible half-ass jokes aside, the multi-factor world has been the largest area of growth and discussion in the factor world over the last 5 years. Firms like AQR, MSCI, PIMCO via Research Affiliates, etc. all have multi-factor

It is commonly known that financial returns exhibit characteristics that are not captured by the widely applied normal and log-normal distributions. In a series of posts I want to present some flexible distributions that are well suited to model financial returns. We will work our way through quick

o you want to know how to build a multi-layered neural network? As deep as you want? In the next post, we will use real market data. In this one, we will still use non-trading data, because we are looking for a well-established knowledge of the basic concepts of Tensorflow. But we will use data used

Have you ever thought about the bias of your portfolio to specific countries or asset types? Do you know that high concentration in one region implies a riskier path for your portfolio? If you want to know how to improve your portfolio using Graph Theory, first you’ll need to understand the

This is a summary of the recent performance of a wide range of excellent Tactical Asset Allocation (TAA) 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

In this post I am going to be looking at portfolio optimisation methods, touching on both the use of Monte Carlo, “brute force” style optimisation and then the use of Scipy’s “optimize” function for “minimizing (or maximizing) objective functions, possibly subject to constraints”, as

Researchers often devote a significant amount of time trying to determine the optimal, or best performing, configuration of a trading model. With the prolif­eration of data and advances in high-performance computing, it is trivial to optimize millions, even billions, of trading models and parameter

Most factors generated positive returns in 1H 2019 Low Volatility produced the best and Value the worst performance Factor performance is comparable in the US & Europe, but markedly different in Japan INTRODUCTION We present the performance of five well-known factors on an annual basis for the

What are the research questions? The authors present 4 MYTHs regarding stock buybacks popular in the financial press. MYTH 1: Companies are self-liquidating using share repurchases at a historically high rate. MYTH 2: Share repurchases have come at the expense of profitable investment. MYTH 3: The

We continue our exploration of quantitative signals in fixed income. We use a measure of credit curve steepness as a valuation signal for timing exposure between corporate bonds and U.S. Treasuries. The value signal generates a 0.84% annualized return from 1950 to 2019 but is highly regime dependent

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.

I published a bitcoin swing trading strategy in 2015 over here (German only). Time to review the methodology of swing trading and have a look on the performance. Can a rational strategy get an edge in an irrational market? Have a look and be surprised! Swing Point Trading Technique Swing trading is

Positive month for the Wizards which lifts the YTD performance further up in the positive territory at the halfway mark. Please check below for more details. Detailed Results The figures for the month are: June return: 1.46% YTD return: 4.52% Below is the chart displaying individual system results

Economists use machine learning (ML) to study asset prices in two different ways. Approach #1: use these techniques to predict the cross-section of expected returns—i.e., to predict which stocks are most likely to have high or low future returns. e.g., see here, here, or here. Approach #2: use

Bad beta means market exposure that is expensive to hedge. Good beta is market exposure that is cheap to hedge. Distinguishing between these is crucial for FX trading strategies. The market sensitivity of FX positions can be decomposed into a risk premium beta (‘bad beta’) and a real rate beta

This is part 2 of the Ichimoku Strategy creation and backtest – with part 1 having dealt with the calculation and creation of the individual Ichimoku elements (which can be found here), we now move onto creating the actual trading strategy logic and subsequent backtest. The Ichimoku approach

In the previous post (which should definitely be read first!) we explored how graphs can be used to represent a currency market, and how we might use shortest-path algorithms to discover arbitrage opportunities. Today, we will apply this to real-world data. It should be noted that we are not

One of the recurring themes we see in our research is the concept of “no pain; no gain.” Or as Corey Hoffstein says, “No pain, no premium.” Cliff Asness may put it best when he says that some strategies require that you hold on to them “like grim death.” Bottom line: nothing is easy in

In the great book and series Game of Thrones, the inhabitants of the Iron Islands have a saying “What is Dead May Never Die” which is to be replied with “But rises again harder and stronger.” I am reminded of this saying as more and more market commentators and practitioners declare that

I thought it was about time for another blog post, and this time I have decided to take a look at the “Ichimoku Kinko Hyo” trading strategy, or just “Ichimoku” strategy for short. The Ichimoku system is a Japanese charting and technical analysis method and was published in 1969 by a reporter

TL;DR: We developed a strategy using Augmento sentiment signals, and backtested it on Bitmex XBTUSD to generate a positive return between 2017 and 2019. Creating algorithms to trade Bitcoin is hard, and finding good data that is independent of the price but still correlated with the market is even

This is a follow-up post to a recent post in which we discussed how to generate 1-dimensional financial time series with Generative Adversarial Networks. If you haven’t read that post yet we suggest you to do so, since it introduces the building blocks used in this one. Here we will go over the

With a 5-star rating on iTunes, we are proud to say that Season 1 of our podcast – Flirting with Models – received a tremendously warm welcome. And so we’re happy to announce that Season 2 is now available! You can listen to the new season on: iTunes Stitcher Google Play TuneIn Android The