In today’s post we will be using AI to improve a module of the Alternative Data-Driven Investment (ADDI) strategy developed by ETS Asset Management Factory, which is an automatic Long – Short investment strategy that aims to obtain stable performance de-correlated from the market and with a

This past week was the 4th week in a row that the SPX declined. It is quite unusual to see SPX close down for 4 weeks in a row, but still remain above its 40-week moving average. Below is a look at other times since 1975 that this action has occurred. SPX down 4 weeks in a row but above 40-week

The first ETFs emerged in 1993 and closely tracked broad-based indexes for a low fee. Since then, the competitive situation in the ETF industry today has differentiated itself by adding a new breed of ETFs that reflected specialization into popular investment themes. When the evolution of the ETF

Environmental, social, and governance (ESG) investing continues to increase in popularity, with many institutional and individual investors incorporating ESG criteria into their investment decision-making process. Three main themes have driven this massive shift of assets: 1) Many investors are

Buying high-quality stocks and avoiding low-quality ones can improve investment returns. In this post, I use Alphalens and Zipline to analyze the Piotroski F-Score, a composite measure of a firm's financial health and quality. This post is part of the fundamental factors series, which explores

In the first part of my ARMA article series, I covered the background theory of lag operators, the stationarity and invertibility of Autoregressive Moving Average models (ARMA) and the different types of versions you can create from it. Here, we’ll explore theoretically these models using Python.

Following the recent release of the popular large language model ChatGPT, the topic of machine learning and AI seems to have skyrocketed in popularity. The concept of machine learning is, however, a much older one and has been the topic of various research and technology projects over the last

I was working on testing a market timing indicator that I read about it. It was showing some promise and the next step was to compare it to my benchmark. My benchmark is using the 200-day moving average. But an additional rule removes a lot of the whipsaws that can happen. After doing the

Note: This is a rare non-geeky, non-quantitative, stream of thought blog post. Because we’re so deep into this world of Tactical Asset Allocation (TAA), we’re sometimes asked for our thoughts on such-and-such black box TAA strategy. By “black box” we mean a strategy for which the trading

The article explores the limitations of traditional country-level stock market indexes constructed based on issuing firms’ domicile. Additionally, it introduces a new type of national stock market index called the EMindex, which is based on companies’ business activities rather than their

In the last post we did a line-by-line walkthrough of the alpha simulator, using a uniform random variable to represent the signal generating component of the alpha backtest. We also raised a few questions: How can we manage the varying levels of risk profiles of different stocks in our asset

Real government bond yields are indicators of standard market risk premia and implicit subsidies. They can be estimated by subtracting an estimate of inflation expectations from standard yields. And for credible monetary policy regimes, inflation expectations can be estimated based on concurrent

Since the development of the CAPM, which explains about two-thirds of the variation of returns among diversified portfolios, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. Models are not like cameras that provide an exact

Trading technology continues to make trading of stocks easier, cheaper, and faster However, despite this and other financial innovations like ETFs, the US stock market structure hasn’t changed Likely explained by the fact that its core participants are unable to change INTRODUCTION Consider the

As a whole, Tactical Asset Allocation (TAA) did not manage losses during the 2022 bear market as well as it has during previous downturns. Individual strategies varied and some did well, but a primary function of TAA is loss management, and any failure to do so is worth analyzing further. In this

I want to discuss a couple of simple trade-off considerations around quant trading signals that may not be obvious. Here’s the price of some asset: Our main job is to predict how it’s likely to move. To do this, you use information about it that you think is predictive. And at any point in time:

Volatility estimation and forecasting plays a crucial role in many areas of finance. For example, standard risk-based portfolio allocation methods (minimum variance, equal risk contributions, hierarchical risk parity…) critically depend on the ability to build accurate volatility forecasts1.

Value investing originated as an investment strategy in which investors try to beat the stock market by looking for stocks that trade at a price below their intrinsic value or book value. Value investors do not subscribe to the efficient-market hypothesis, which suggests that stock prices always

Pump-and-Dump (P&D) schemes to manipulate the prices of cryptocurrencies are unlike the P&D schemes found in the equity market. They produce very large price distortions on the order of 65%, very large trading volumes of 13.5x the average, and generate very large profits to cryptocurrency

Convertible bonds are typically viewed as debt rather than equity instruments However, these are highly correlated to equities The diversification benefits are limited as these just represent diluted equity proxies INTRODUCTION Let’s say you’re the CEO of a small listed company that isn’t

Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually don’t correlate strongly with market movements,

TL;DR – There really is a low volatility anomaly in the SPY data; low volatility today predicts low volatility tomorrow and risk-adjusted returns are higher investing daily in the lower vol half of predicted market days. Same data, new analysis, better graphs and you’ll see it too. First up, a

The article investigates time-series (TS) momentum strategies and their performance in financial markets based on various speeds or lookback horizons. The study aims to understand the connections between different speeds of TS momentum, unobservable variables like trend, turning points, and noise

Machine Learning (ML) has emerged as a powerful tool in the field of Artificial Intelligence, revolutionising various aspects of our lives. Whether it's recognising human handwriting or enabling self-driving cars, ML has become an integral part of our daily routines. With the exponential growth

There are style factors like value and traditional fixed income factors like term premium The correlations of these factors has been low However, it is not clear which are better suited for a factor exposure analysis INTRODUCTION In our last research article, we compared fixed income factors from

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,