Commodity Futures contracts were established in 1865, but commercially available data starts in 1959, leaving an 80+ year period of unstudied history. In our latest academic paper “Two Centuries of Commodity Futures Premia” Chris Geczy and I use hand-collected futures data to extend the

It can be difficult to disentangle the difference between luck and skill by examining performance on its own. We simulate the returns of investors with different prediction accuracy levels and find that an investor with the skill of a fair coin (i.e. 50%) would likely under-perform a simple

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

Long-only factor portfolios can be used for benchmarking smart beta ETFs Results highlight minor tracking errors Likely explained by relatively homogenous factor definitions by ETF issuers INTRODUCTION Investment professionals are not known for their creativity, but that is perhaps only because

Dear Hedge Fund Enthusiasts, It’s been long since we sent out a newsletter but we would like to report that the Open Source Hedge Fund Project is alive and kicking again! My Msc in Financial Engineering has provided me with the unique opportunity to build an open source python package, like

Salience theory suggests that decision makers exaggerate the probability of extreme events if they are aware of their possibility. This gives rise to subjective probability distributions and undermines conventional rationality. In particular, salience theory explains skewness preference, i.e. the

In this post I discuss a well known stylised fact of the investment industry: "Trend following is a positively skewed strategy". Spoiler alert: yes it is (sort of), but it's much more complicated (and interesting!) than you might think. A quick primer on positive skew So what actually

This post will display some robustness results for KDA asset allocation. Ultimately, the two canary instruments fare much better using the original filter weights in Defensive Asset Allocation than in other variants of the weights for the filter. While this isn’t as worrying (the filter most

My last article used Warren Buffett’s pre-crisis sale of put options to highlight the risk of getting over our financial skis. In both temperament and negotiation, Warren can outlast most bear markets. Many of us cannot. Proponents of rebalancing should acknowledge the real risk that downturns can

This is a test of Ilya Kipnis’ “Defensive Adaptive Asset Allocation” (KDA). KDA is a “Meta” model of sorts, combining successful elements of multiple other tactical asset allocation strategies that we track. Results from 1989 to the present, net of transaction costs, follow. Read more

The last time the SPX closed below its 10-day moving average was January 3rd. That means it has now been 35 straight trading days that SPX has closed above the 10ma. That is a very long streak. Below is a list of all streaks since 1928 of 35 days or longer. (Note: prior to 1957 S&P 90 Index data

Traders and market analysts use volume data, which is the amount of buying and selling of an instrument over a given time period, to gauge the strength of an existing trend or identify a reversal. The back-and-forth movement between buyers and sellers for the best available price allows us to

Low volatility strategies have garnered a fair amount of popularity and a growing body of supporting research. Studies have shown risk reduction levels of 25%, while turnover has varied from 20% to 120%. However, higher turnover produces higher costs of trading, such that the excess return obtained

Trend equity strategies seek to meaningfully participate with equity market growth while side-stepping significant and prolonged drawdowns. These strategies aim to achieve this goal by dynamically adjusting market exposure based upon trend-following signals. A naïve example of such a strategy would

The largest smart beta Low Volatility ETF is technically a Minimum Variance strategy Low Volatility and Minimum Variance have comparable and attractive characteristics However, both currently feature a high sensitivity to interest rates INTRODUCTION The Low Volatility factor was the best performing

One of the first things you quickly come to understand in equity pairs trading is how important it is to spread your risk. The reason is obvious: stocks are subject to a multitude of risk factors – amongst them earning shocks and corporate actions -that can blow up an otherwise profitable pairs

Recently John Cotter and Niall McGeever posted an interesting paper to ssrn.com. They studied the persistence of nine anomalies in the U.K. equity market: · Accruals · Asset growth · Book to market ratio · Profitability · Stock issuance · Return reversal · Momentum · Equity turnover · Size

Everyone in finance remembers 2008–the Global Financial Crisis. Yes, I know, the final downward movement in the stock market was in early 2009. However, many remember 2008 as the year of the crisis. So now we are 10 years removed from the crisis. Why do I mention this? After the crisis, some began

In my last post I discussed SPY/TLT rotation strategies. Today, I will be using the same ideas from the post but on a basket of bond ETFs. The Basket The first difficult decision one must make is what ETFs will be in the basket. What we choose here, can have a big impact on the results. I wanted to

This tutorial is in 2 parts — (you can run the backtester as a separate standalone module) : Learn the Statistical technique of Cointegration. Build a Bitcoin Backtesting engine using Python to analyze the performance of a Cointegration based trading strategy. Just want the code? click here.

Trend following’s simple, systematic, and transparent approach does not make it any less frustrating to allocate to during periods of rapid market reversals. With most trend equity strategies exhibiting whipsaws in 2010, 2011, 2015-2016, and early 2018, it is tempting to ask, “is this something

Do businesses exchange currencies in predictable ways that forex traders can exploit? This post explores an intraday EUR.USD strategy based on the hypothesis that businesses cause currencies to depreciate during local business hours and appreciate during foreign business hours. Business patterns in

In most decisions including investing, there are two ways to be wrong: Doing something that doesn’t work (false positive, type 1 error) Not doing something that would have worked (false negative, type 2 error) Investors and quants in particular worry more about the type 1 error - accepting a fake

Beating benchmarks is challenging for fund managers, even in unique sectors Factor performance in financials, REITs, and MLPs is comparable to the cross-sector factor returns Classic factor investing strategies are likely more attractive than industry expertise INTRODUCTION Stating that active

Academic Quant News is, at heart, an aggregator of academic research articles and journals related to quantitative finance. A question? A suggestion? Drop me an email! Interested in quantitative portfolio allocation? You can find on my GitHub account an open source JavaScript library with algorithms

The business of algorithmic trading strategies creates incentives for model overfitting and backtest embellishment: researchers must pass Sharpe ratio thresholds for their strategies to be considered, while managers lack interest in realistic simulations of ideas. Overfitting leads to bad investment

Six recent asset allocation articles (tactical or otherwise) that you might have missed: 1. Right Now It’s KDA…Asset Allocation (QuantStrat TradeR) Here Ilya shares a TAA strategy that combines elements of two popular strategies that we track: Keller & Keuning’s Defensive Asset Allocation

Markets are, in my view, mostly random. However, they're not completely random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation - transaction costs and overhead

One of the oldest and most persuasive arguments in the stock market is that small stocks outperform large stocks.(1) Warren Buffett, speaking at the 2013 Berkshire Hathaway Annual Meeting, summarized the sentiment when discussing the disadvantages of managing a huge amount of capital: There’s no

Developer: Gerald Appel. Source: Appel, G. (2005). Technical Analysis. NJ: Pearson Education, Inc. Concept: Trend following trading strategy based on the MACD (Moving Average Convergence Divergence) signal line. Research Goal: Performance verification of momentum signals. Specification: Table 1.

Alan Turing, an English mathematician, computer scientist, logician, and cryptanalyst, surmised about machines that, “It would be like a pupil who had learnt much from his master but had added much more by his own work. When this happens I feel that one is obliged to regard the machine as showing

Just a quick warning before I start, this post is going to be math heavy. Those who are not brave enough to traverse these waters, be forewarned! Let's get right to it: To recap, last time I talked about a few basic statistical concepts regarding time series. Stationarity, Memory and

Cryptocurrencies have been a huge distraction when in fact we should be focusing on the currency of the future - “Big Data” Join Industry Leaders For almost a decade, RavenPack Symposiums have consistently provided data-driven finance professionals with riveting forward-looking content, new

Trend following is unique among style premia in that it has historically exhibited a convex payoff profile with positive skew. While the historical premium is anomalous, the convexity makes sense when we use options to replicate trend following strategies. We explore reasons why frequent rebalancing

SUMMARY Smart beta excess returns are different from factor returns The Low Volatility factor shows the highest discrepancy between theoretical and realized returns Investors might be better served by embracing long-short factor products REALITY DYSFUNCTION Steve Jobs’s “reality distortion

The US Federal Reserve’s monetary-policy decisions invariably are closely followed by market participants worldwide since they are of great importance to the development of the capital markets. David O Lucca and Emanuel Moench have examined which patterns occur in the stock market and what these

What are the research questions? The main purpose of this study was to examine the changes in the distribution of the US equity risk premium as the return horizon varies over the short term, medium and long term (see here for a piece that covers those topics). In this recap, we look at ancillary

So somehow you've wandered into this hazy corner of the internet and found my blog. Not sure how...or why you're exactly here but I hope you'll stay. Let me introduce myself, I'm just your run of the mill budding algorithmic trader. I studied Mathematical Economics and Computer

In a previous post: Most popular machine learning R packages, trying to hash out what are the most frequently used machine learning packages, I simply chose few names from my own memory. However, there is a CRAN task views web page which “aims to provide some guidance which packages on CRAN are

The field of deep learning is frequently described as a mix of art and science. One of the most "art-sy" parts of the field, in my experience, is the subject of network topology design - i.e., choosing the right geometry, size, depth, and type of the network. Machine learning practitioners

This post is all about handcrafting; a method for doing portfolio construction which human beings can do without computing power, or at least with a spreadsheet. The method aims to achieve the following goals: Humans can trust it: intuitive and transparent method which produces robust weights Can be

Covered interest parity is an arbitrage condition that equalizes costs of direct USD funding and of synthetic USD funding through FX swaps. Deviations are called dollar cross-currency basis and have become a common occurrence since the great financial crisis. A negative dollar basis means direct

We have all heard the mantra, “You can’t time the market!” But in reality, investors attempt to do just that every day as part of their tactical asset allocation strategies, which are less extreme variants of the classic trend-following “risk-on/risk-off” approach, which many associate

In a previous post, we took a look at the computation of a portfolio’s exposure to its allocations. Then, to show the effects of active management, we compared the return made by two portfolios. But there is so much more to look inside the financial time series. Since we left a couple of

The first risk of investing is the Drawdown Risk - the loss from the peak. The second risk of investing is the Low Return Risk - the under-performance vs. expectations over a stretched period of time. First, a few words about drawdown. Quants measure risk in many ways like Volatility, Skew,

In the afternoon of February 5th, 2018, what looked like a bad day for a group of high flying volatility-based products turned into a devastating decline. Four factors combined to ruin their day: A Flawed Architecture Relying on the Past to Predict the Future Billions Under Management A

What are the Research Questions? The authors investigate the asset pricing implications of corporate manager sentiment, focusing on its predictability for future U.S. stock market returns. Specifically, they ask the following research questions: Does high corporate manager sentiment lead to

In this commentary, we discuss what we mean by the phrase, “no pain, no premium.” We re-frame the discussion of portfolio construction from one about returns to one about risk and argue that without risk, there should be no expectation of return. With a risk-based framework, we argue that

Hot off the press, a new paper by Guido Baltussen, Laurens Swinkels and Pim van Vliet at Dutch quant powerhouse, Robeco, covers global multi-asset factor premiums over an unprecedented sample of 217 years. We thought the topics and findings were important and timely enough to warrant a summary. The

Behavioral-finance models are often written down so that, although each individual trader holds incorrect beliefs, market events nevertheless unfold in such a way that traders can rationalize their own errors. e.g., consider the model in Scheinkman and Xiong (2003). In this model, each individual