We built a simple tool recently to review so-called value spreads over time. (1) This tool maps out the median valuations for the top decile and bottom decile “cheap stock” portfolios (as measured by EBIT/TEV). Why might this be useful? This tool allows one to identify the “valuation” spread

As a non-academic finance person, I was never really exposed to academic research until I started working on articles for Alpha Architect. Fortunately (or unfortunately, depending on your perspective), I am now very familiar with the so-called “cross-section of expected returns” debates.

When you build your portfolio you must decide what is your risk profile. A pension fund’s risk profile is different than that of a hedge fund, which is different than that of a family office. Everyone’s goal is to maximize returns given the risk. Sinfully but commonly risk is defined as the

In this commentary we explore the application of several quantitative signals to a broad set of fixed income exposures. Specifically, we explore value, momentum, carry, long-term reversals, and volatility signals. We find that value, 3-month momentum, carry, and 3-year reversals all create

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

The performance of the Size factor in the US was positive since 1926, but not particularly attractive Returns in Europe were more favorable, but not in Japan Alternative metrics to market capitalization would not have resulted in better performance SMALL VERSUS LARGE STOCKS In the David vs. Goliath

Strong result in May for the Trend Following index, taking the Year-to-Date performance in positive territory. Please check below for more details. Detailed Results The figures for the month are: May return: 5.01% YTD return: 2.94% Below is the chart displaying individual system results throughout

Join experts from banks, buy-side, Silicon Valley and academia to meet, network and share ideas at America's leading quant finance event. 3 key themes shaping the agenda: 1. Innovations in machine learning, HFT, AI and data 2. Quant techniques in investment and trading 3. Advances in option

Mean-reverting portfolio construction is an exciting area that involves a wide range of forecasting and optimisation techniques. In Part 1 of the setries, I demonstrate the approach to the construction of optimal mean reverting portfolios satisfying sparsity and volatility constraints.

Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a

In an interview published at the Enterprising Investor blog, Frank Fabozzi, a well-known researcher and author in the mathematical finance field, has sharply criticized the current state of academic economics and finance. Here are some highlights: The “rational models” constructed in economics

An important part of getting a Kalman filter to work well is tuning the process noise covariance matrix Q and the measurement noise covariance matrix R. This post is about obtaining the R matrix, with a post about the Q matrix to come in due course. In my last post about the alternative version

The underperformance of value stocks over the past 10 years has received much attention from the financial media and led at least some investors to conclude that value investing is dead. From 2009 through March 2019, while the S&P 500 Index returned 14.2 percent per annum (total cumulative

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 commentary we explore tactical credit strategies that switch between high yield bonds and core fixed income exposures. We find that short-term momentum signals generate statistically significant annualized excess returns. We use a cross-section of statistically significant 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

This research note was originally published in the Beyond Beta magazine from ETF Stream. Here is the link. SUMMARY Single factor excess returns are attractive over the long-term, less in the short-term Comparing popular asset allocation models does not highlight one superior methodology Multi-factor

The research presented here covers the largest number of factors (65) tested in the academic literature. The most robust and well-cited factors appear in the list of data items, available since the 1960s. A notable exclusion is the IBES dataset, which is available only in the 1980s. Is there

If you’ve ever delved into the world of retail foreign exchange trading, you’ll have come across the MetaTrader platform. Let’s be clear. The platform has its drawbacks. If you’ve traded “grown-up” markets, some of the features will leave you scratching your head. But one thing’s for

The recently updated yfinance added a lot more capabilities to this already popular library. You can now download fundamental data, including company financials, balance sheet and cashflow, as well as option chain data. Here's how... First, import yfinance and create a ticker object: 1 2 import

Below is alternative code for an Extended Kalman filter for a sine wave, which has 4 states: the sine wave value, the phase, the angular frequency and amplitude and measurements thereof. I have found it necessary to implement this version because I couldn't adjust my earlier version code to

In this article I thought I would take a look at and compare the concepts of “Monte Carlo analysis” and “Bootstrapping” in relation to simulating returns series and generating corresponding confidence intervals as to a portfolio’s potential risks and rewards. Both methods are used to

Nothing lasts forever and this definitely stands true for equity markets where volatility can explode and investors can lose a lot of money very quickly. Because of equity market volatility investors often seek so-called “crisis alpha” instruments, or assets that tend to go up when equity

This blog article builds on our first blog article about trade cost optimisation approaches. We discuss some weaknesses of the simple approach presented in the first article and make suggestions for extending and improving the trade cost optimisation towards a more sophisticated and powerful

As a firm, Newfound Research focuses on tactical allocation strategies. However, we also spend time researching other mandates – such as systematic value – in an effort to introduce lateral thinking to our process. Three years ago, we built a systematic value portfolio that seeks to create a

Academic literature has documented a news sentiment effect on equities ( here and here ). The authors investigate the following research question: Does the sentiment derived from media content impact bond market investors? What are the Academic Insights? By studying the sentiment extracted from

Despite Uncle Warren’s understanding of the connection of growth and value, those of us that come to investing through the factor and/or academic world, have always been taught that growth investing is a terrible way to invest. This is simply because we have, in most cases, been taught that growth

I am not a fan of benchmarking against widely available indexes. Most anyone you ask will tell you that you should benchmark against an index because it is an objective measure of performance. It provides you with the “beta” that allows you to figure out if an investment manager delivers

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 =

A global value portfolio on country level features structural country biases Returns were positive since 1990, but lacked consistency Value on country and single stock level exhibit the same trends, highlighting common performance drivers INTRODUCTION Holding Value stocks is emotionally challenging

In the code box below I provide code for an Extended Kalman filter to model a sine wave. This is a mashup of code from a couple of toolboxes I have found online, namely learning-the-extended-kalman-filter and EKF/UKF Tollbox for Matlab/Octave. The modelled states are the phase, angular frequency and

The week of Memorial Day has shown some interesting seasonal tendencies over the years. But it has been less consistent recently. The chart below is one I have shown in the past, and have now updated. It examines SPX performance from the Friday before Memorial Day to the Friday after it. 2019-05-24

Diversification is famously described as the only free lunch in investing and so it’s no surprise that modeling, analyzing and otherwise dissecting the concept is a core part of portfolio design and management. The correlation coefficient is often the go-to metric in this corner of finance. But

In standard cross-sectional asset-pricing models, expected returns are governed by exposure to aggregate risk factors in a market populated by fully rational investors. Here’s how these models work. Because investors are fully rational, they correctly anticipate which assets are most likely to

A new paper proposes to decompose the U.S. government bond yield curve by applying a ‘bootstrapping method’ that resamples observed return differences across maturities. The advantage of this method over the classical principal components approach would be greater robustness to misspecification

In my last post, Avoiding Trades Before Earnings, I mentioned that I used Quantopian to do the research. Several readers asked about my thoughts about Quantopian and how it compares to AmiBroker. Some asked if I had left AmiBroker for Quantopian. What follows are my impressions after using

There’s a large body of research, including the 2017 study “Tail Risk Mitigation with Managed Volatility Strategies” by Anna Dreyer and Stefan Hubrich, that demonstrates that, while past returns do not predict future returns, past volatility largely predicts future near-term

Suppose, in the weather forecast part of a local newscast, the person handling the weather displays a chart of recent temperatures in the local area, pointed out “trends” and “waves,” then mentions a “breakout pattern” from a recent temperature range. Most of us would not have much

Two of the more interesting puzzles in finance are related to volatility—stocks with greater idiosyncratic volatility (IVOL) have produced lower returns and stocks with high uncertainty about risk, as measured by the volatility of expected volatility (vol-of-vol), underperform stocks with low

In Part 3 of the series we reviewed the relationship between returns and correlation of the 2-asset portfolio UPRO and TMF. The basic equal weight strategy was very compelling in terms of total return and CAGR. However, the strategy is susceptible to large drawdowns, especially in situations where

Last week we introduced a signal that appeared to generate statistically significant performance results for performing country rotation. This week, we walk through the steps taken to explore the robustness of the signal. We first explore out-of-sample data with sector and emerging market country

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.

The performance of the Momentum factor in the US has been poor since 2000 Fundamental valuation spreads were ineffective for improving the performance Combinations with other factors and factor volatility filters would have yielded better results INTRODUCTION John H. Cochrane of the Hoover

FDA drug approvals, legal verdicts, mergers, share buybacks, and the occasional CEO podcast appearance, are all examples of events that impact stock prices. Though not as quantifiable as technical indicators, real life events clearly affect prices. In an attempt to further explore the relationship

Many years ago, when I was still trying to beat the market, I used to pair-trade. In principle it is quite straightforward to estimate the correlation between two stocks. The estimator for beta is very important since it determines how much you should long the one and how much you should short the

I released the first version of QTPyLib, my Python library for algo traders, in 2016. If you had told me then that I would still be working on it three years later, I probably wouldn't have believed you. But guess what? That's precisely where I'm doing 🙂 The first release of QTPyLib

Why are expert economic and financial (econometric) forecasters so inaccurate? In his April 2019 presentation package for a graduate course at Cornell entitled “The 7 Reasons Most Econometric Investments Fail”, Marcos Lopez de Prado enumerates shortcomings of standard econometric statistical

In an ideal trading universe, we’d all have a big golden “causation magnifying glass”. Through the lens of this fictional tool, you’d zoom in and understand the fleeting, enigmatic nature of the financial markets, stripping bare all its causes and effects. Knowing exactly what causes

There have been numerous attempts to explain the lackluster performance of value investing so far this decade, which is currently on pace for its worst annualized performance for a decade since the 1930s: Without getting into the arguments made by others, which have been debated elsewhere, I will

In the summer of 2018 we attended a conference organized by Quantopian in which we heard Dr. Marcos Lopez de Prado outlined the challenges of building successful quantitative investment platforms. His book, Advances in Financial Machine Learning provides solutions to many of the problems faced by