If you’ve been a reader of this newsletter for a while, or a student of Getting Started With Python for Quant Finance, you’ll recognize this statement: Backtests are not a way to brute force optimize parameters to maximize a performance metric. Doing that leads to overfitting and losses. But

Bank lending surveys help predict the relative performance of equity and duration positions. Signals of strengthening credit demand and easing lending conditions favor a stronger economy and expanding leverage, benefiting equity positions. Signs of deteriorating credit demand and tightening credit

Traders typically have many ideas for trading strategies – more than they can ever implement in practice! Therefore it’s useful to be able to move quickly in the early research phase. You want to disprove things as quickly as possible so that you can move onto the next thing. Obviously there is

The correlation between stocks and bonds should be a critical component of any asset allocation decision, as it impacts not only the overall risk of a diversified multi-asset class portfolio but also the risk premia one should expect to receive for taking risk in different asset classes. The problem

Are rising interest rates straining balance sheets and increasing the risk of bankruptcies? This article investigates two financial distress factors, the Altman Z-Score and interest coverage ratio, to see if distress is on the rise and how it impacts stock returns. This post is part of the

As we advance into our third year on this blog - it’s dawning upon me that many of the readers are getting left behind…the biggest concern by far is the complexity of the current Russian Doll model and not being sure how to proceed with using the statistical suite presented therein, together

This is a test of the “TrendYCMacro” strategy from the paper Avoid Equity Bear Markets with a Market Timing Strategy from Ďurian and Vojtko of Quantpedia. The strategy combines trends in price, the slope of the yield curve and key economic indicators to switch between US equities and cash.

Short-term return anomalies are generally dismissed in the academic literature “because they seemingly do not survive after accounting for market frictions.” In this research, short-term “factors” are taken seriously, and the authors argue the standard parameters may not apply to short

Over the long term, low-investment firms have outperformed high-investment firms. This finding has led to the investment factor (CMA, or conservative minus aggressive) being incorporated into the leading asset pricing models—the four-factor q-theory model (market beta, size, investment, and

Autocorrelation is a statistical concept that measures the correlation between observations of a time series and its lagged values. It is commonly used in various fields, including trading for technical analysis, to identify patterns, trends, and relationships within data. Autocorrelation helps

If there’s one thing algorithmic traders cannot get enough of, it’s data. The data that fuels our strategies is more than just numbers—it’s the lifeblood of our decision-making processes. And having data available locally—or at least within your control—is a big part of that. In

Predicting Financial Crises: The Role of Asset Prices Tristan Hennig (International Monetary Fund), et al. August 2023 We explore the early warning properties of a composite indicator which summarizes signals from a range of asset price growth and asset price volatility indicators to capture

Today, August 28th, 2023, my company Newfound Research turns 15. It feels kind of absurd saying that. I know I’ve told this story before, but I never actually expected this company to turn into anything. I started the company while I was still in undergrad and I named it Newfound Research after a

The research questions of the article are as follows: How can a Hidden Markov Model be applied to identify regimes of shifting inflation? What are the characteristics and descriptive information of the identified inflation regimes? Which economic variables are the determinants of inflation and how

AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment – Tianping Zhang, Yuanqi Li, Yifei Jin, Jian Li The multi-factor model is a widely used model in quantitative investment. The success of a multi-factor model is largely determined by the

I’m a little embarrassed to admit this, I was recently in a quant interview and the interviewer quickly realised that I didn’t know the Black-Scholes formula! That was definitely a moment when imposter syndrome became reality. To fix the situation, I’ve written up an easy intro to the

Launching a career as a risk quant requires a well-thought-out strategy that combines a strong educational foundation, technical skills, and an understanding of the evolving landscape of risk management. To embark on this journey, aspiring risk quants should start by building a solid educational

Business sentiment is a key driver of inventory dynamics in global industry and, therefore, a powerful indicator of aggregate demand for industrial commodities. Changes in manufacturing business confidence can be aggregated by industry size across all major economies to give a powerful directional

As a general rule of thumb, the more complexity that exists in a Wall Street creation, the faster and farther investors should run. —David Swensen, Unconventional Success Structured products are packages of synthetic investment instruments specifically designed to appeal to needs that investors

In the previous posts of this series, I detailed a methodology to perform stress tests on a correlation matrix by linearly shrinking a baseline correlation matrix toward an equicorrelation matrix or, more generally, toward the lower and upper bounds of its coefficients. This methodology allows to

Although the relationship is not immediately clear and obvious, the structural engineering has a lot in common with financial assets. In both cases we deal with the objects under stress over their entire lifetimes. There are two possible outcomes: something can break or perform well. The engineers

Sector neutralization is a technique to hedge out sector bets and reduce the impact of sector-specific risks on the portfolio by ranking factors within sectors rather than across sectors. This post uses the debt-to-equity ratio to show why sector neutralization is important and how to perform it in

Principal component analysis (PCA) is used widely in data science. It’s a way to reduce the number of dimensions in a data set. It’s also used in quant finance to find alpha. In a stock portfolio, a dimension might be a column of returns for one of the stocks. Once you get the model built, you

We’ve previously covered Link’s Global Growth Cycle strategy, which uses OECD Composite Leading Indicator (CLI) data to time the market. The strategy has navigated the market gyrations over the last few years well, so naturally it’s gotten the attention of members. Recent strategy results

In 2021, James, I, and a small team decided to set up a crypto trading venture. We faced several problems, but knowing almost nothing about crypto was the most significant. We sensed that the fractured, developing nature of the crypto market would likely be a good place to seek out inefficiencies,

Factor seasonality always seemed to be an idea that was too close to factor timing to help build factor strategies. Surprisingly, the authors find a substantial factor seasonality effect across global markets, suggesting that the assumption is unwarranted. This is the first study I have encountered

Portfolio Selection via Topological Data Analysis – Petr Sokerin, Kristian Kuznetsov, Elizaveta Makhneva, Alexey Zaytsev Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the

In March, we posted a series of three articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets. Each article focused on trading signals based on a specific group of indicators, namely, price-based indicators, macroeconomic

Expanding the Fama-French Factor Model with the Industry Beta Anatoly B. Schmidt (NYU Tandon School of Engineering) August 2023 Recently it was shown that the news-based stock pricing model (NBSPM) outperforms the momentum-enhanced five-factor Fama-French model (FF5M) for a representative list of

We cannot start without a cheap quip: Technical analysis is an astrology for men. Market technicians believe that prices currently contain all information about any asset. It is undoubtedly an oversimplified assumption, as the market is much more complex than that. But suppose you try to use

A big issue for me with this project is: how do we validate this whole approach? We started out with a super simple vol-based timing strategy (long/flat market exposure). I rather glibly state that realized (or perhaps more properly, historical) volatility is a “sensible, logical, statistically

We are often asked about stock market valuation models such as Shiller’s CAPE Ratio and the Buffet Indicator. These models predict long-term returns, usually forecasting the next 10 years. Our recent analysis of one such valuation model, the Aggregate Investor Allocation to Equities, motivated us

Picture this: you're about to board a 10-hour flight. As you board the plane (or maybe some time waiting at the gate), a notification pops up in your pocket. You're busy with other things, so you ignore it and forget about it (in fact, you're used to ignoring notifications because you

One particularly notable indicator change that occurred at the close on Friday is that out NASDAQ/SPX Relative Leadership indicator flipped so that it is now showing the SPX as leading and the NASDAQ as lagging. This can be seen in the chart below. NASDAQ/SPX Relative Strength shows NASDAQ faltering

Buying cheap growth stocks is intuitively appealing to investors Almost 50% of the US stocks are trading below a PEG ratio of 1 currently However, GARP stocks have not generated positive excess returns since 2005 INTRODUCTION In 2019, we published a research note on growth-at-reasonable-price (GARP)

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting – Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang We introduce AutoGluon-TimeSeries – an open-source AutoML library for probabilistic time series forecasting. Focused on

The business cycle is a pattern that captures changes in economic activity over time. The changes in the business cycle occur in a sequential or serial manner, moving through a predictable sequence of phases. These cycles are consistent but vary in both duration and intensity. The phases of the

This is the last of the advanced quant dev series post - next week, we will go back to the basics, and cover the details in how we arrive at the advanced quant backtesting library, which evolved from a rudimentary system consisting of a single signal, single model strategy to a multi signal, multi

In today’s newsletter, we’ll cover the up-market capture ratio, a framework for evaluating investment performance in rising markets. Even though the ratio is used by professional money managers, you can use it to better gauge your own investment performance. Let’s dive in! How to use capture

Nowcasting economic trends can make use of a broad range of machine learning methods. This not only serves the purpose of optimization but also allows replication of past information states of the market and supports realistic backtesting. A practical framework for modern nowcasting is the

In today’s newsletter, you’ll use Polars, a high-speed data-handling tool that’s becoming essential in quantitative finance and algorithmic trading. You’ll see how to compare its performance to pandas for many common data manipulation techniques. By the end of this post, you’ll understand

In his 2012 paper “The Other Side of Value: The Gross Profitability Premium,” Robert Novy-Marx demonstrated that profitability, as measured by gross profits-to-assets, had roughly the same power as book-to-market (value factor) in predicting the cross-section of average returns – profitable

Fractional Brownian motion is defined as a stochastic Gaussian process XtXt​ that starts at zero X0=0X0​=0 has an expectation of zero E[Xt]=0E[Xt​]=0 and has the following covariance1: E[XtXs]=σ212(∣t∣2H+∣s∣2H−∣t−s∣2H)(1)

Sum up: by combining all the vols into one strategy and randomizing key parameters, we can generate useful signals that yield a decent return with some consistency. We’re not meeting all the quant_rv goals yet, but we’re making progress on all the fronts. ~ Links to earlier parts ~ Part 1:

Portfolio Management: A Deep Distributional RL Approach – David Pacheco Aznar This thesis presents the development and implementation of a novel Deep Distributional Reinforcement Learning (DDRL) approach in the field of quantitative finance: the Distributional Soft Actor-Critic (DSAC) with an LSTM

During 2017 I blogged about Statistical Shrinkage. At the end of that post I mentioned the important role signal-to-noise ratio (SNR) plays when it comes to the need for shrinkage. This post shares some recent related empirical results published in the Journal of Machine Learning Research from the

The traditional financial theory attributes security returns to market- or factor-based risk, with no role ascribed to other influences. In this research, the authors argue for including investor demand as an additional variable in explaining returns. Can changes in investor demand generate

We started off with the conceptualisation of trading alpha in different abstract representations, such as mathematical formulas, graphs and visual representations: Alpha-Encoding Data Structures Alpha-Encoding Data Structures HangukQuant · Jun 30 Read full story For machine trading this would

Backtest speed can significantly affect research friction. The ability to form a hypothesis and quickly get an answer from a backtest allows you to investigate more hypotheses. In this article, I explore several factors that affect backtest speed and compare the performance of 3 open-source

The low-beta anomaly for the capital asset pricing model (CAPM)—low-beta stocks outperform high-beta stocks—was first documented more than 50 years ago by Fischer Black, Michael Jensen, and Myron Scholes in their 1972 paper, “The Capital Asset Pricing Model: Some Empirical Tests.” In our