During past months we made a set of articles analyzing the performance of equity factors and selected systematic strategies during coronavirus crisis. These articles were short-ranged with data only from the start of the year 2020, which is enough for the purpose of the quick blog posts, but very

Following on from the prior post, want to discuss the problem of sample independence. Many machine learning models in finance deal with timeseries data, where samples used in training may be close together in time and not be independent of one another. There are very few features in finance that do

There is a large body of academic research demonstrating that the value premium has been persistent over long periods, pervasive across asset classes (stocks, bonds, commodities, and currencies) and also across and within industries, countries, and regions, robust to various fundamental metrics, and

Interactive Brokers has always been a popular brokerage with systematic traders. Initially this could partially be attributed to the fact that IB provided an Application Programming Interface (API) that allowed quants to obtain market data and place trades directly in code. Many competing brokerages

I think one of the most interesting areas in the data analysis field is Natural Language Processing (NLP). These last years this discipline has grown exponentially and now it’s a huge area with a lot of problems we can attempt to solve, like text classification, translations or text generation In

Recently a student of my CBT course asked why he wasn’t seeing the usual output (including dates) when he selected AmiBroker’s “Detailed Log” option and ran a backtest that utilizes a low-level CBT. The answer is that much of the Detailed Log output comes from AmiBroker’s

The coronavirus crisis reordered many things in economics and finance and you can add asset correlations to the list. After markets crashed in March, followed by a strong (so far) rebound, asset classes have continued to move with an unusually deep and broad degree of unison. High, or at least

I have used machine learning in trading strategies over the past 10 years. However my use of ML has often played a relatively small role in the overall design and success of the strategies. I use ML in specific signals or strategy sub-problems where the data / problem setup tends to have a robust

Our first look at calendar influences was in analyzing the best time during the month to execute dual momentum trades. Studies here, here, and here show that stocks perform best early in the month. This is when institutional investors make changes to their portfolios. Prices then are most

In the search for more and better factors, this article examines the cross-sectional relationship between historical skewness (see Jack’s post here) and the returns on a robust set of assets and documents the premium for taking on skewness risk. The authors construct long/short portfolios across

The alpha of global macro funds has been shrinking consistently over time However, correlations to equities & bonds were low on average, offering diversification benefits Capital allocators have been cautious on the strategy in recent years INTRODUCTION He-Man and the Masters of the Universe was

Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits, financial flows and policy decisions, and account for a large part of asset price fluctuations. The main technology

Vitali Apirine, inventor of the OBVM indicator, presented another new tool for the believing technical analyst in the Stocks & Commodities August 2020 issue. His new Compare Price Momentum Oscillator (CPMO) is based on the Price Momentum Oscillator (PMO) by a Carl Swenlin. So we got another

Our last few posts on portfolio construction have simulated various weighting schemes to create a range of possible portfolios. We’ve then chosen portfolios whose average weights yield the type of risk and return we’d like to achieve. However, we’ve noted there is more to portfolio

In this article we begin our discussion of artificial neural networks (ANN). We first motivate the need for a deep learning based approach within quantitative finance. Then we outline one of the most elementary neural networks known as the perceptron. We discuss the architecture of the perceptron

Last week I had the honor of being a guest speaker for the National Association of Active Investment Managers (NAAIM)) webinar series. The topic I discussed was “Quantifiable Edges for Active Investing”. That recording is now available to view on the NAAIM website (email registration required).

Momentum in prices is the tendency of assets that have performed well recently (such as over the prior year) to outperform assets in the same asset class that have performed poorly over the prior year. For a more thorough review of momentum check out this post by Wes Gray. This phenomenon has been

When developing a strategy, exits are often not given a second thought. If you are creating a mean reversion, you may default to using Close greater than the 2-period RSI. If you are trading a trend strategy, you may default to trailing exit using 14-day ATR. You try a bunch of entry filters but

Inside most of the Deep Learning frameworks that are available lies a powerful technique called Automatic Differentiation. If you ever encountered these words but don’t know what they mean or how this procedure works, this post is for you. In a previous post, we saw how to built a deep learning

Technical indicators are the base of algorithmic trading. So wouldn’t it be nice to know tomorrows indicator value in advance? This article is about how to use a simple neural network to do so. Python and Tradesignal will be used to do the programming. A single linear neuron A single neuron /

Some EM asset classes are highly correlated, to the point they can almost be considered interchangeable EM equities and hard-currency government debt are highly correlated to US equities and bonds In crisis times, all EM exposure is sold off and fails to provide meaningful diversification benefits

Can we do impact investing that is both good for us and tastes better? In the past, if an investment had positive non-financial outcomes (positive impact), a return trade-off was expected. Today, some investors find that incorporating aspects such as diversity, stakeholders, and environmental

I just came across an excellent and highly relevant piece of research "A comparison of machine learning model validation schemes for non-stationary time series data" by Matthias Schnaubelt. Features like non-stationarity, concept drift, and structural breaks present serious modelling

The Smart Beta Mirage Shiyang Huang (University of Hong Kong), et al. June 2020 We document sharp performance deterioration of smart beta indexes after the corresponding smart beta ETFs are listed for investments. Adjusted by aggregate market return, the average return of smart beta indexes drops

Momentum in stocks is not only a key strategy in the many portfolios of practitioners, but it is also an attractive research topic for academics. The original idea behind momentum, is that past winner tend to perform well in the near future, and vice versa, past loser tend to underperform (Jegadeesh

In God we trust. All others must bring data. Backtesting is probably the single best method we have to quickly evaluate new trading strategies. However, if used incorrectly it can be our greatest weakness — guiding us on a false path to ruin. For the uninitiated, backtesting is the process where

Earlier in the year we carried out our 2020 QuantStart Content Survey and Advanced Machine Learning & Deep Learning was voted the most popular topic. This article constitutes the first in a series on the topic of modern machine learning via deep learning as applied to systematic trading

Over the last week or so I have been reading about/investigating this post's title matter. Some quotes from various papers' abstracts on the matter are: "We provide empirical evidence that the unique signature of the FX market seasonality is indeed due to the different time zones

Currently, strategies based on premium factors are everywhere: from funds or ETFs built on ratios or statistics perfectly specified, trying to exploit specific factor premia, to boutique instruments more or less opaque that following one or more risk premia. In any case, one of the questions we may

The positive trade-off between risk and expected return is the most fundamental concept in financial economics. Most investors are risk-averse. In order to hold higher-risk securities, they demand higher compensation in the form of higher expected returns. And risk-averse investors are more

This post presents an analysis of the SPY returns process using the QuantConnect research platform. QuantConnect is a strategy development platform that lets you research ideas, import data, create algorithms, and trade in the cloud, all in one place. For this research, I’ve used their online

Based on many years of reviewing investor portfolios, I concluded that most end up closely resembling a 60% Stocks / 40% Bonds Allocation. Yes, many portfolios also have alternatives, nuanced sub-asset classes, individual security selection, and perhaps some tactical components. But when you look at

As a general rule, we recommend you kick your spidey senses into high gear anytime there is a geek bearing formulas (especially if they are trying to sell you something). Simple is always a nice cheap default because complexity often leads to confusion, which leans to a need to have an expert, which

After strong momentum rallies, investors frequently ask if cap-weighted benchmarks are good Momentum bets Factor exposure analysis shows this is not the case Investors should seek smart beta and long-short products if they want Momentum exposure INTRODUCTION Old myths are hard to kill. Good old

Traditionally, correlation matrices have always played a large role in finance. They have been used in tasks ranging from portfolio management to risk management and are calculated based on historical empirical observations. Although they are used so frequently, these correlation matrices often have

There are times when need to label a time series, identifying periods of momentum, trend, mean-reversion, etc. Directionaly labeling timeseries has a wide variety of applications: labels can be used for supervised learning analysis of microstructure around larger price moves conditional analysis

In our last post, we analyzed the performance of our portfolio, built using the historical average method to set return expectations. We calculated return and risk contributions and examined changes in allocation weights due to asset performance. We briefly considered whether such changes warranted

We are going to help make March for the Fallen a virtual event this year (September 26, 2020 at 8am). COVID is bad news, but we can turn lemons into lemonade…and we can still show gratitude for Gold Star Families by breaking into smaller groups and marching outdoors! We’ve already have 20 local

SPX will post a Golden Cross on Thursday afternoon. A Golden Cross occurs when the 50ma crosses over the 200ma. Having the 50ma above the 200ma is commonly considered a bullish market condition – and generally it is. In the 4/2/19 blog post I looked at SPX Golden Crosses dating all the way back to

Is it possible to predict where the Gold price is headed? Yes, let’s use machine learning regression techniques to predict the price of one of the most important precious metal, the Gold. Machine Learning in Trading We will create a machine learning linear regression model that takes information

Get your popcorn ready, the quants are about to do battle… As with all good questions in academic research, there are two sides to the story. We recently published Matthew Bartolini’s blog post explaining the impacts of trading costs on the “Overnight Return Anomaly.” This paper, takes the

Source: Kaufman, P. J. (2020). Trading Systems and Methods (Chapter 5: The Livermore System). New Jersey: John Wiley & Sons, Inc. Concept: Trading strategy based on Jesse Livermore‘s approach to swing trading with DeMark pivots. Research Goal: Performance verification of Pivot Size and

As diversification is the only free lunch in finance, the Hierarchical Equal Risk Contribution Portfolio (HERC) aims at diversifying capital allocation and risk allocation. Briefly, the principle is to retain the correlations that really matter and once the assets are hierarchically clustered, a

For hedging strategies, there is often a trade-off between degree, certainty, and cost. Put options have high certainty and typically offer a high degree of protection, making them costly to hold and roll over the long run. In this note, we briefly explore the application of different tactical

Momentum & Quality are leading the performance scoreboard in 1H 2020 Value & Size generated negative returns, like in recent years Low Volatility failed to preserve capital during the COVID-19 crisis INTRODUCTION We present the performance of five well-known factors on an annual basis for

Enough! Enough with that pointless R versus Python debate. I find it almost as pointless as the Bayesian vs Frequentist “dispute”. I advocate here what I advocated there (“..don’t be a Bayesian, nor be a Frequenist, be opportunist“). Nowadays even marginally tedious computation is being

This is a post about forecasts in trading systems. A forecast is a calibrated expectation for future risk adjusted returns. In more layman like terms, it is a measure of how confident we are about a bullish (positive forecast) or bearish (negative forecast). Perhaps it is easiest to think about

Two of most documented anomalies in the asset pricing literature are the momentum effect and the long-term reversal effect. Momentum is typically defined as the last 12 months of returns excluding the most recent month (i.e., months 2–12) because it tends to show a reversal, which some have

Forecasting Macroeconomic Risk in Real Time: Great and Covid-19 Recessions Roberto A. De Santis (European Central Bank) July 2020 We show that financial variables contribute to the forecast of GDP growth during the Great Recession, providing additional insights on both first and higher moments of

Since the late 80s there has been a tendency for the market to rally on the first day of the month. One theory on why this occurs is that there are often 401k inflows that are put to work on the 1st of the month. I examined this tendency and broke it down by month on the blog in 2013 and 2009. I