The most common question I get is how do you determine that a strategy is no longer working. It is also the question that I don’t have a good answer for. I have written several posts about this: Trading the Equity Curve, How to turn off a strategy using historical volatility, Broken Strategy or

Investing is never easy, but some times are easier than others. Buying US government bonds at 10%+ yields when inflation was steadily decreasing in the 1980s was probably less worrying than buying them today at negative real yields. In today’s world, bonds may have largely lost their

Several years ago, I wrote about some experimentation I’d done with data mining for predictive features from financial data. The article has had several tens of thousands of views and nearly 100 comments. I think the popularity of the article lay in its demonstration of various tools and modeling

Survey data and asset prices can be combined to estimate high-frequency growth expectations. This is a specific form of nowcasting that implicitly captures all types of news on the economy, not just official data releases. Methods for estimation include the Kalman filter, MIDAS regression, and

As most of you know I have a regular(ish) gig talking on the Top Traders Unplugged systematic investor podcast, every month or so with Niels Kaastrup-Larsen and Moritz Seibert. Anyway on the most recent episode we got chatting about whether open or closed equity should matter when trading a

This short article investigates the rebalancing premium that investors may expect from risk parity portfolios¹. It is offered as an appendix to the paper, “Risk Parity: Methods and Measures of Success”. We define rebalancing premium as the difference between the compound return on a portfolio,

Robert Novy-Marx’s 2013 paper “ The Other Side of Value: The Gross Profitability Premium” not only provided investors with new insights into the cross-section of stock returns but also helped further explain some of Warren Buffett’s superior performance. Novy-Marx built upon a 2006 paper,

What happens after stocks suffer large one-day losses? This post finds that the proverbial "dead cat bounce" occurs overnight and is followed by continued losses the next day. Targeting international markets, I explore a trading strategy that aims to profit from the losses that follow a

There are no “right” answers when it comes to financial markets. There are generally trade-offs to all decisions. For example, stocking picking can be incredible and crush every other investment approach; but stock picking can also be horrible. Similiarily, 100% systematic investing,

I have been sharing examples of simple real-time trading research on my Twitter account. I do this kind of thing a lot in the training program of our trading group – and I’m sharing in the hope that it might also help a wider audience. Here’s a piece of analysis I did recently on a really

Decision Trees are one of the best known supervised classification methods. As explained in previous posts, “A decision tree is a way of representing knowledge obtained in the inductive learning process. The space is split using a set of conditions, and the resulting structure is the tree“ A

Below are some more out of sample plots for the Temporal Clustering solutions of the EUR_USD forex pair for the week just gone. The details of how these solutions are derived is explained in my previous post, Temporal Clustering on Real Prices. First is Tuesday's solution where the major (blue

As equity trading moves to less regulated markets and off of exchanges across the world, mutual fund families have increasingly taken advantage of this opportunity to reallocate trades. Fund families are able to offset opposite trades of their affiliated funds within an internal market referred to

I have a couple of equities strategies that I will start trading shortly, and I want to understand the risk from all angles. Towards this end I try to utilize both market signals and exogenous unstructured data to minimize surprise and maximize selection or prediction efficiency. In thinking about

A major problem of indicator-based strategies is that most indicators produce more or less noisy output, resulting in false signals. The faster the indicator reacts on market situations, the noisier is it usually. In the S&C December issue, John Ehlers proposed a de-noising technology based on

A while ago I posted an article titled “INVESTMENT PORTFOLIO OPTIMISATION WITH PYTHON – REVISITED” which dealt with the process of calculating the optimal asset weightings for a portfolio according to the classic Markowitz “mean-variance” approach. With this method we aim to maximise our

In the past have been a HFT market maker for FX and other traditional instruments, however have not investigated exchange-based market making in Crypto. As I have proprietary signals applicable for Crypto, thought it would be worthwhile to investigate the difficulty of market making on crypto

I’m a big fan of Ernie Chan’s quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them.

Given alternative data, machine learning, and AI advances, systematic should beat discretionary investing However, the performance of systematic and discretionary equity market neutral hedge funds has largely been the same since 2009 Both were also correlated to the stock market, offered low

Having now had time to run the code shown in my previous post, Temporal Clustering, part 3, in this post I want to show the results on real prices. Firstly, I have written two functions in Octave to identify market turning points and each function takes as input an n_bar argument which determines

Finance literature is abundant with theories. As academics, we like to think these theories foster behaviors and choices by investors, which in turn translate into asset prices. To test these theories, scholars typically try to infer the validity of these assumptions by examining outcomes. The

In “Stock Market Valuation and the 2020’s in R” I investigated whether the CAPE ratio could forecast the future trajectory of earnings and/or stock returns over the period 1980-2019. From this study, we made a couple of observations: The CAPE ratio cannot be used to forecast future earnings

The time around Thanksgiving has shown some strong tendencies over the years – both bullish and bearish. I have discussed them a number of times over the years. In the updated table below I show SPX performance results based on the day of the week around Thanksgiving. The bottom row is the Monday

There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures markets over plausible lookback windows, under consideration of a leverage constraint, and uses estimated assets under management as a scale factor.

What is survivorship bias, and why should you care about it? This post explains how survivorship bias can trick you into drawing faulty conclusions from your research, and what you need to know to avoid being tricked. What is survivorship bias? Equities datasets are said to have survivorship bias if

Running a marathon is similar to being a value investor, especially in recent years where it seems more like an ultra-marathon. Both activities are painful experiences that require the ability to suffer and persist through physically and emotionally straining times. Moreover, value investors always

If you regularly read this blog then you know I am not one to jump on the “AI Bandwagon”, being quickly weary of anyone flashing the “It’s Artificial Intelligence” joker card. Don’t get me wrong, I understand it is a sexy term I, but to me it always feels a bit like a sales pitch. If the

The S&P index committee recently announced that Tesla, already one of the biggest stocks listed in the country, would be included in the S&P 500. Here’s the press release: Due to TSLA’s size, it was widely expected to have entered the S&P 500 index much earlier – but S&P has

After many years there are many evidences that the low volatility anomaly works in stock markets. We have also mentioned this topic a long time ago to analysis the costs of it. This anomaly says stocks with less price variability deliver higher returns, contrary to everyone’s belief, which expects

Here I share a refreshing idea from the paper “Asymmetric correlations of equity portfolios” which was published in the Journal of financial Economics, a top tier journal in this field. The question is how much the observed conditional correlation on the downside (say) differs from the

There are many ways to find stocks with similar behavior based on how one defines similarity and the data used. In this article we use a 12 period channel where, for each period, we have (current adjusted close price – minimum value)/(maximum value – minimum value). Maximum and minimum values

Shiller’s (1998) original CAPE ratio (the cyclically adjusted price of an equity index/10 year average of real earnings) used to predict long term equity returns, like every online recipe, has been improved over the years by various reviewers. A number of substitutes for real earnings have been

Defined outcome ETFs have quickly gathered almost $5 billion in assets Not unexpected given their much lower drawdowns when the market crashed in March 2020 However, they are complex and expensive products and there are viable alternatives INTRODUCTION If ETFs had an arch enemy, then it probably

Resurrecting the Value Premium David Blitz (Robeco) and Matthias X. Hanauer (Technische Universität München) October 15, 2020 The prolonged poor performance of the value factor has led to doubts about whether the value premium still exists. Some have noted that the observed returns still fall

We decided to allow anyone to take advantage of some tools we constantly use at NAVA Capital. Investors and financial managers often need to perform similar tasks, like analyzing financial time series, comparing two investments, adjusting gross performance by management fees, performance fess and so

Continuing on with the subject matter of my last post, in the code box below there is R code which is a straight forward refactoring of the Octave code contained in the second code box of my last post. This code is my implementation of the cross validation routine described in the paper Cluster

In our last post, we took our analysis of rolling average pairwise correlations on the constituents of the XLI ETF one step further by applying kernel regressions to the data and comparing those results with linear regressions. Using a cross-validation approach to analyze prediction error and

Bloomberg has an article You May Regret Staying Parked in U.S. Stocks which made the case that there’s "widespread agreement" and "the answer isn’t in dispute" that foreign stocks will outperform going forward. Simplified version of my view of that statement.... c'mon

Momentum is the tendency for assets that have performed well (poorly) in the recent past to continue to perform well (poorly) in the future, at least for a short period of time. Initial research on momentum was published by Narasimhan Jegadeesh and Sheridan Titman, authors of the 1993 study

I am happy to announce that I will be hosting a free webinar series on quantitative analysis and algorithmic trading. Dates and times for the first shows can be found over here: Tradesignal Webinar Series Date and Time based patterns will be the topic of the first webinar. It will focus on the

This post contains affiliate links. An affiliate link means CSSA may receive compensation if you make a purchase through the link, without any extra cost to you. CSSA strives to promote only products and services which provide value to my business and those which I believe could help you, the

In the last post I reviewed the Momentum Trading Strategies Course by Quantra (a division of QuantInsti) which I reviewed as part of a recent educational journey to improve my quantitative skill set. The next course that I will be reviewing is Mean-Reversion Strategies in Python which is taught by

I’ve been sitting here for 5 mins trying to come up with a witty intro for this episode about momentum, but I just couldn’t seem to get it going, so…

This post is a BONUS LESSON taken directly from Zero to Robot Master Bootcamp. In this Bootcamp, we teach traders how to research, build and trade a portfolio of 3 strategies including an Intraday FX Strategy, a Risk Premia Strategy and a Volatility Basis Strategy. If you’re interested in adding

Monday saw a massive market rotation. It could be noted by the performance in the IWM vs the QQQ, or in looking at performance among S&P 500 sectors, where Energy beat Technology by 15% on Monday. But to really see how strong the rotation was, you’d need to take a look at individual stock

Do analysts actively exploit anomalies when they recommend stocks? Do analysts’ research efforts contribute to efficiency in the equity markets? Good questions. This research clarifies the relationship between established stock return anomalies and analyst recommendations. Given that anomalies are

Further to my previous post, below is an extended version of the "blurred_maxshift_1d_linear" function. This updated version has two extra outputs: a vector of the cluster centre index ix values and a vector the same length as the input data with the cluster centres to which each datum has

The long-term track record of equity market neutral hedge funds is attractive, but should be viewed with scepticism due to Madoff and survivorship bias Only one index from HFRX seems sound, but his highlights negative alpha since the GFC and positive returns primarily from market beta A factor

After losing 5.6% this in the week ending 10/30/20, the S&P 500 completely reversed the losses this past week with a 7.3% gain. That is a fairly remarkable turnaround. Below is a look at all other times the S&P 500 lost 5% or more one week, and then made up for the losses and more the next

Recent academic papers illustrate how macroeconomic data support predictions of energy market flows and prices. Valid macro indicators include shipping costs, industrial production measures, non-energy industrial commodity prices, transportation data, weather data, financial conditions indices, and