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

This is a test of the “simple” variation of Dr. Keller and Keuning’s strategy from their paper Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA). We’ve covered the “balanced” version of HAA previously. It has become one of the more popular strategies on

Commodity Trading Advisor (CTA) funds are commonly associated with managed futures investing in futures and options, and are a subset of the broader hedge fund universe[1]. Beyond commodities, they have the flexibility to venture into other assets, including interest rates, currencies, fixed income

Trend following is a relatively simple strategy, at least at the concept level: buy when prices go up, and sell when they go down. The main way that trend followers differentiate themselves is the timeframe over which they measure whether the price has gone up or down. For example, one manager might

This post extends the replication from the Adaptive Asset Allocation Replication post by running the analysis on OOS (out-of-sample) data from 2015 through 2023. Thanks to Dale Rosenthal for helpful comments. The paper uses the 5 portfolios below. Each section of this post will give a short

The same multi-vol quant strategy we all love, but now with 2000+ vol signals to choose from. quant_rv is a daily SPY strategy that uses realized volatility measures of SPY to predict days of lower volatility ahead, that in turn predict positive returns. In the net, quant_rv wins by very modest

FX forward-implied carry is a popular ingredient in currency trading strategies because it is related to risk premia and implicit policy subsidies. Its signal value can often be increased by considering inflation differentials, hedging costs, data outliers, and market restrictions. However, even

One of the most common questions I get: What books should I read for quant finance, algorithmic trading, and market data analysis? And one of my favorite hobbies is collecting books on the subject. 46 awesome books for quant finance, algo trading, and market data analysis 46 books for quant finance,

The factor betas of value-focused ETFs range dramatically Non-value-focused funds can have high betas to the value factor However, these often come with large unintended bets INTRODUCTION While some investors are die-hard believers in value investing, others regard this more opportunistically and

The primary motivation behind our study stems from an observation of the Global Tactical Asset Allocation (GTAA) strategies throughout the existing papers – the majority of them require relatively frequent rebalancing from the point of view of the ordinary investor. Portfolio rebalancing is

In textbooks, one often sees pairs trading algorithms start by regressing prices of Asset A on Asset B to calculate a hedge ratio. I’ve rarely seen anyone actually do this in the real world. That’s because it is a very unstable thing – especially for a pair of volatile assets, and especially

As professor John Cochrane observed, the literature on investment factors now fills a veritable “factor zoo,” with hundreds of options. How do investors select from among this huge array of possibilities? In order to minimize the risk that outcomes result from data mining, in our book “Your

Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty Raymond Kan (U. of Toronto) and Nathan Lassance (LFIN/LIDAM) December 2023 Existing portfolio combination rules that optimize the out-of-sample performance under estimation risk are calibrated assuming multivariate normally

An old friend asking for help... how can I resist? Here is the perplexing paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4042239 And here is the (not that senstional) abstract: Bitcoin (BTC) returns exhibit pronounced positive skewness with a third central moment of approximately 150%

Most portfolios are centered around stocks. Stocks are thought of as the primary return driver, while other additions to the portfolio are thought of less as return drivers, and more as diversifiers. The popular 60 / 40 portfolio is a prime example of this. The vast majority of the returns to this

This is a test of Paul Choi’s paper Balance Between Growth and Dividend: Dividend & Growth Allocation (DGA). This strategy would have delivered exceptional performance over the last 50 years, but we would temper future expectations for several reasons we discuss below. Backtested results from