In a previous article I made a detailed comparison of Mathematica and Python and tried to identify areas where the former excels. Despite the many advantages of the Python technology stack, I was able to pinpoint a few areas in which I think Mathematica holds the upper hand. Whether those are

Multi-timeframe (MTF) analysis lets traders build powerful intraday trading strategies. It does this by analyzing asset prices during different timeframes throughout the trading day. The problem is most people get MTF wrong. It requires a vector-based backtest to speed up the operations making it

“Conventional wisdom” can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two examples are that millions of people once believed the conventional wisdom that the Earth is flat, and millions also believed that the Earth is the

Select portions of quant_rv can be combined to craft a new strategy (qrvx) that provides positive returns, negative correlation to SPY and crisis alpha, making it nice for combining with SPY (like a 60/40 combo) to create strong returns with low drawdowns. In my last “Replacing the 40” post, we

After years of waiting, the recent launch of spot Bitcoin ETFs marked a significant milestone in the cryptocurrency market, making Bitcoin even more accessible for investors. Spot ETFs provide a convenient and regulated way to gain exposure to Bitcoin without the need to hold the digital asset

Tail risk ETFs have achieved similar return profiles despite different portfolios TAIL represents a traditional tail risk strategy, but offers limited diversification benefits BTAL is more diversifying, but not more than CTAs INTRODUCTION Although more than 3000 ETFs are trading on U.S. exchanges,

This article continues our recent stat arb series. The previous articles are linked below: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas Ideas for crypto stat arb features Quantifying and combining crypto alphas A simple and effective way to

Portfolio optimization usually requires an estimate of the future returns of the assets in the portfolio. This is hard because we can’t see into the future. Traditional risk parity uses a quadratic optimizer A cutting edge technique called Hierarchical Risk Parity (HRP) uses graph theory and

Regression is one method for combining macro indicators into a single trading signal. Specifically, statistical learning based on regression can optimize model parameters and hyperparameters sequentially and produce signals based on whatever model has predicted returns best up to a point in time.

The academic research, including the 2023 studies “Lottery Preference and Anomalies” and “Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households,” the 2022 study “Lottery Demand and the Asset Growth Anomaly,” and the 2014 study “Do Investors Overpay for

The investment world witnessed a paradigm shift with the introduction of momentum strategies, a concept pioneered by Jagadeesh and Titman in their landmark 1993 study “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”. Their groundbreaking approach, hinged

As an avid user of both Python and Wolfram Language for technical computing, I’m often asked how they compare. Python’s strengths as an open-source language are clear: Ubiquity – With millions of users, Python has become ubiquitous across fields like data science, ML engineering, web

Absolute and relative momentum can be used as simple asset allocation frameworks Both would have generated a higher return than an equal-weighted portfolio across asset classes However, risk-adjusted returns were lower and drawdowns higher INTRODUCTION Investing is often overwhelming given the

The paper aims to provide insights into the dynamics of benchmark selection, the effectiveness of Relative Performance Evaluation ( RPE ) incentivization, and the broader implications for fund performance and market competition. Self-Declared Benchmarks and Fund Manager Intent: “Cheating” or

Modeling features as expected returns can be a useful way to develop trading strategies, but it requires some care. The main advantage is that it directly aligns with the objective of predicting and capitalising on future returns. This can make optimisation and implementation more intuitive. It also

Investing truly is an intense intellectual undertaking. For a Portfolio Manager (PM) to execute an investment, they must first convince themselves, then others, that the rationale behind the investment is sound. The variables they utilize in developing their rationale are of the upmost importance;

Quants, financial analysis, and traders use fundamental data for investing and trading. These data are derived from quarterly and annual statements that companies file with the U.S. Securities Exchange Commission (SEC). These statements are rich with data that can be used to build predictive factor

Expectations of future earnings growth matter a great deal to valuations because investors, in their collective wisdom, assign higher valuations to companies they expect will grow more quickly in the future (growth stocks). In contrast, firms expected to show slower growth (value stocks) are

Like the Federal Reserve, trend following is often said to have a dual mandate. One mandate is to earn a positive return, and the other is to provide some sort of “crisis alpha”, or an offset to drawdowns in traditional, 60/40 type portfolios. There could be tension between these two goals; for

We track 80+ Tactical Asset Allocation (TAA) strategies, most of which were designed from the perspective of a US investor trading US ETFs. Most European investors can’t access US ETFs, instead trading UCITS funds listed on non-US exchanges, often denominated in currencies other than USD. In this

In this blog, I just toy around with a relatively new framework for querying (large) language models: LMQL, a SQL-like for LLMs. It is a first step toward a novel programming paradigm: Language Model Programming (LMP). These ideas are described in the very interesting paper Prompting Is Programming:

One use of the NLP (natural language processing) features of ChatGPT is to search out patterns in the immense amounts of news, data and other sources of information about specific stocks, and then efficiently convert them into summaries valuable for all types of investors. Can this be accomplished

There are multiple ways to measure interest rate sensitivities “High-duration” stocks like tech and biotech were not more sensitive to rising rates The relationship between interest rates and stocks is weak INTRODUCTION In our first article on the duration of U.S. equities (read Duration of U.S.

Every time we trade, we incur a cost. We pay a commission to the exchange or broker, we cross spreads, and we might even have market impact to contend with. A common issue in quant trading is to find an edge, only to discover that if you executed it naively, you’d get killed with costs. In this

Fund managers report their holdings every month. They don’t want to tell investors that they lost money the latest meme stock. So they will sell the meme stocks and buy higher quality assets, like bonds. We might be able to take advantage of this month-end flow effect by buying bonds toward the

Backtesting of macro trading strategies requires good approximate profit-and-loss data for standard derivatives positions, particularly in equity, foreign exchange, and rates markets. Practical calculation methods of generic proxy returns not only deliver valid strategy targets but are also the

In our book The Incredible Shrinking Alpha, Andrew Berkin and I presented the evidence demonstrating that the markets have become more efficient over time, making it more difficult to outperform the market on a risk-adjusted basis. Market efficiency explains the lack of persistent outperformance of

A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations Garth Flannery (BlueCove) and Daniel Bergstresser (Brandeis Intl Business School) December 2023 This paper builds on a framework that uses macroeconomic drivers to explain long-term variation in the correlation between stocks and

As noted in Surz1, the question “Is [a mutual fund’s]2 performance good?” can only be answered relative to something1, typically by comparing that fund to a benchmark like a financial index or to a peer group. Unfortunately, these two methodologies are not without issues. For example, it is

Do you remember this post? https://qoppac.blogspot.com/2022/06/vol-targeting-cagr-race.html Here I introduced a performance metric, the best annualised compounding return at the optimal leverage level for that strategy. This is equivalent to finding the highest geometric return once a strategy is

The correlation of high yield (HY) to investment-grade (IG) bonds has been increasing HY bonds can simply be replicated via a combination of the S&P 500 and IG bonds Replication portfolios offer better Sharpe ratios, which makes a case against using HY bonds in asset allocation INTRODUCTION When

Elliot Rozner published a blog post recently proposing that one could replace the 40% bond portion of a 60/40 portfolio with a Long/Short equity trend following strategy. Here we’ll put that idea (and his suggested approach) into R using quantmod and examine the components, and finally suggest a

Reproducibility has emerged as a major issue in numerous fields of scientific research, ranging from psychology, sociology, economics and finance to biomedicine, scientific computing and physics. Many of these difficulties arise from experimenter bias (also known as “selection bias”):

Interest rates are the driving force behind the economy. They influence everything from the cost of purchasing a home to a company’s decision on capital investments. Quants model how interest rate changes impact portfolios using principal component analysis (PCA). (They also do it for stock

In this article, I’ll take some crypto stat arb features from our recent brainstorming article and show you how you might quantify their strength and decay characteristics and then combine them into a trading signal. This article continues our recent articles on stat arb: A short take on stat arb

The aim of this article is multifold. It aims to answer the research question: does a portfolio consisting of top innovators outperform the S&P 500 index? To address this question, a strategy of investing long in top innovators according to their ranking is developed, and its performance is

In our book, “The Incredible Shrinking Alpha,” Andrew Berkin and I identified four key trends that were increasing the hurdles for active managers in their quest to generate alpha: Academic research has been converting what was once alpha into beta (common factors that could be accessed at much

The 60/40 portfolio (60 percent stocks, 40 percent bonds) has become such a classic, that for many investors, the word “portfolio” means 60/40 by default. Looking at the past few decades, it’s easy to see why this is the case. Stocks, (or at least U.S. stocks), have had outstanding

What are the Research Questions? This paper examines the decisions of sophisticated market participants – experienced institutional portfolio managers (PMs) – and the authors ask the following questions: Is there a significant difference in performance between buying and selling decisions made

Financial advisors primarily use Monte Carlo simulations to forecast returns However, this methodology is flawed as it ignores the valuations of asset classes Using capital market assumptions is likely a better approach INTRODUCTION The Shanghai Composite Index (SSE) was booming in early 2015, and

Hello everyone, Two weeks ago, we promised you a surprise, and now it’s finally time to unveil what we have prepared for you :). Our Quantpedia Awards 2024 aims to be the premier competition for all quantitative trading researchers. If you have an idea in your head about systematic/quantitative

This article continues our recent articles on stat arb: A short take on stat arb trading in the real world A general approach for exploiting stat arb alphas In this article, I’ll brainstorm some ideas for predictive features that you could potentially use in a crypto stat arb model. The ideas draw

The dividend discount model suggests that stock prices are negatively related to expected real interest rates and positively to earnings growth. The economic position of households or consumers influences both. Consumer strength spurs demand and exerts price pressure, thus pushing up real policy

Because of the strong evidence, momentum continues to receive much attention from researchers. Out of the hundreds of exhibits in the factor zoo, one of just five equity factors that met all the criteria (persistent, pervasive, robust, implementable, and intuitive) Andrew Berkin and I established in

Value at risk (VaR) is a tool professional traders use to manage risk. It estimates how much a portfolio might lose, given normal market conditions, over a set time period. There are three ways to compute VaR: the parametric method, the historical method, and the Monte Carlo method. In contrast to

Something I am always thinking about is how the markets are behaving now vs the past few years vs several years ago. My edge on the strategies I trade depends on two main ideas. One, current market behavior is similar to what I tested on which is normally the last 5-10 years. Two, not too many

Last week, I wrote a short article about statistical arbitrage trading in the real world. Statistical arbitrage is a well-understood concept: find pairs or baskets of assets you expect to move together, wait for them to diverge, and bet on them converging again. Simple enough. But making it work,

Portfolio optimization is a balance between maximizing returns and minimizing risk. While it might sound easy, it’s actually very difficult compare investment strategies. First, we have to accurately forecast future returns and risk. Then, we have to use tricky optimization models to build the

Popular benchmarks in academic research studies to evaluate the performance of investment strategies are cap-weighted (market-, or value-weighted), and equal-weighted portfolios. Capitalization-weighted portfolios are used because they are the simplest and cheapest to implement, representing the

Short-only trend following in stocks generated consistent losses across markets However, combining the strategy with an equities portfolio generated diversification benefits Like other hedging strategies it would be difficult to execute this strategy over the long-term INTRODUCTION Trend following