Every trader knows that there are market regimes that are favorable to their strategies, and other regimes that are not. Some regimes are obvious, like bull vs bear markets, calm vs choppy markets, etc. These regimes affect many strategies and portfolios (unless they are market-neutral or

Investors are stuck between a rock and a hard place. On one hand, it is painful to buy bonds that deliver paltry yields near all-time lows (Figure 2). On the other hand, many investors’ risk tolerance, compliance guidelines or liabilities preclude them from reducing their fixed income allocations.

In the field of machine learning, when it comes to extracting relationships between variables, we often use Pearson correlation. The problem is that this measure only finds linear relationships, which can lead sometimes to a bad interpretation of the relation between two variables. Nevertheless,

In my previous articles, I introduced how to construct long-short asset pairs according to the concept of cointegration and how to build a sparse mean-reverting multi-asset portfolio. Now that we are able to answer the question “what to trade” with confidence, it is time to get down to the

A firm as an independent entity is engaged in a wide range of activities that affect its value. While the impact of some activities on the firm’s value is immediate and indisputable, there also exists a variety of activities that might impact the firm’s value in the future, while their outcome

As of 2017, and in spite of the documented negative relationship between fund performance and use of ETFs, approximately one-third of US-domiciled, actively managed mutual funds held ETFs at one time or another. Active managers justifiably make use of ETFs to improve their portfolio management

We examine the market consequences of due diligence (DD) reports on Reddit’s Wallstreetbets (WSB) platform. We find average ‘buy’ recommendations result in two-day announcement returns of 1.1%. Further, the returns drift upwards by 2% over the subsequent month and nearly 5% over the subsequent

A quick preview of what I am currently working on: using Matrix Profile to search for time series motifs, using the R tsmp package. The exact motifs I'm looking for are the various "initial balance" set ups of Market Profile charts. To do so, I'm concentrating the investigation

There has been a wave of articles (and press) suggesting that academic research suffers from a replication crisis. A “replication crisis” simply means that other researchers are unable to replicate the results from prior research using similar experimental conditions. Psychology seems to be the

We track more than 60 Tactical Asset Allocation strategies. Members can combine those strategies into what we call “Model Portfolios”. Combining strategies in this way reduces the risk of any single strategy going off the rails and helps to provide smoother, more consistent investment returns.

COVID is killing conference mojo overall, but we were able to host a short and sweet “Democratize Quant” conference this morning. The speakers were terrific and I personally learned a lot from them. This post is a recap of what we heard and some resources we can make available to the public.

The puzzle of Covered Interest Parity (CIP) began in 2008 and has remained as such for many years. There have been multiple attempts to solve the mystery but none of them has reached a complete consensus and the debate is still ongoing. Nevertheless, the discussion has lead to a fair amount of

In recent years the field of empirical finance has faced challenges from papers arguing that there is a replication crisis because the majority of studies cannot be replicated and/or their findings are the result of multiple testing of too many factors. For example, Paul Calluzzo, Fabio Moneta, and

Placing limit orders for trade execution is both quite popular and handy method in (algo)trading. A trader expects that the executed price of his buy/sell trade will ideally match the one requested in his limit order. Unfortunately, depending on a momentary market/asset liquidity, the difference

The 2018 Global Sustainable Investment Review reports over $30 trillion invested with explicit ESG goals as of the beginning of 2018. In the words of the authors: There is a clear tendency for many investors to own ethical companies in a saintly effort to promote good corporate behavior while hoping

A new Fed paper explains how to construct a real-time distress index, using the case of the corporate bond market. The index is based on metrics that describe the functioning of primary and secondary markets and, unlike other distress measures, does not rely on prices and volatility alone. Thus, it

Financial economists have long known that volatility and returns are negatively correlated. Fischer Black documented this in his 1976 paper “Studies of Stock Price Volatility Changes.” This relationship results in the tendency to produce negative equity returns in times of high volatility. In

Predictable Financial Crises Robin Greenwood (Harvard University), et al. March 2021 Using historical data on post-war financial crises around the world, we show that crises are substantially predictable. The combination of rapid credit and asset price growth over the prior three years, whether in

We have seen in previous posts what is machine learning and even how to create our own framework. Combining machine learning and finance always leads to interesting results. Nevertheless, in supervised learning, it is crucial to find a set of appropriate labels to train your model. In today’s

Jack Bogle, the founder of Vanguard, created a simple explanation for predicting future stock returns. The so-called “Occam’s razor” (law of parsimony) approach is an attempt to explain projected returns as simple as possible. Mr. Bogle’s model is pretty simple: Expected returns (nominal,

Rebalancing portfolios is an important event in the life of the portfolio manager, whether we talk about the timing or the degree of the rebalancing, i.e. the portfolio turnover, this is a sensitive operation. As well as the first one is important to avoid bad timing market effects, the second one

They say that small fishes buy and sell driven by unstable waters but only big whales make the waves really huge. Recently, this quite popular phrase, makes sense when it comes to cryptocurrency trading influenced by sudden dives of the Bitcoin price. The strategies of buying and selling executed by

n January 2020, trading in bitcoin exceeded $930 billion and has certainly grown over the past year. Unlike nearly any other asset, bitcoin can be traded 24 hours a day, 7 days a week on trading platforms around the globe. While trading cryptocurrencies has become relatively frequent, the high

With the abundance of raw data and the need for analysis, the concept of unsupervised learning became popular over time. The main goal of unsupervised learning is to discover hidden and exciting patterns in unlabeled data. The most common unsupervised learning algorithm is clustering. Applications

In our last post, we introduced neural networks and formulated some of the questions we want to explore over this series. We explained the underlying architecture, the basics of the algorithm, and showed how a simple neural network could approximate the results and parameters of a linear regression.

Factor investing has been around for some years and has shown to be a valid concept for portfolio strategies. Usually the investor selects a few factors and then goes long the 10% of stocks with the highest factors and goes short (if he wants to trade delta neutral) the 10% of stocks with the lowest

One of the most popular reports in the Portfolio Analysis section of our Quantpedia Pro tool is “Volatility Targeting”. In this article, we will explain some theory behind this portfolio management method. And then, we will go more in-depth, pick several examples and explain some common

It is estimated that about 6000 different technical indicators have been meanwhile published, but few of them are based on volume. In his article in Stocks & Commodities April 2021, Markos Katsanos proposed a new indicator for detecting high-volume breakouts. And he tested it with a trading

Time series modelling is a very powerful tool to forecast future values of time-based data. Time-based data is data observed at different timestamps (time intervals) and is called a time series. These time intervals can be regular or irregular. Based on the pattern, trend, etc. observed in the past

The low-risk effect (aka low volatility) is based on the empirical observation that assets with low risk have high alpha. Specifically in this research, the effect is defined as the risk-adjusted return spread between low-risk and high-risk portfolios and not just low-risk stocks. Since the low-risk

eddit has been at the epicenter of one of the biggest movements in the world of finance, and although it seemed like an unlikely source of such a movement — it’s hardly surprising in hindsight. The trading-focused subreddits of Reddit are the backdrop for a huge amount of discussion about what

A few days ago I was browsing on the elitetrader.com forum site when someone posted this: I am interested to know if anyone change their SMA/EMA/WMA/KAMA/LRMA/etc. when volatility changes? Let say ATR is rising, would you increase/decrease the MA period to make it more/less sensitive? And the bigger

The momentum factor represents one of our core investment beliefs: buy winners. So when research presents itself that may contradict our beliefs it provides the opportunity to dig deeper and think harder about the factors we hold so dearly. Erik Theissen and Can Yilanci begin their paper by warming

My preferred chart style is a candlestick chart but I have never investigated candlestick formations to see if they can help provide an edge in my trading. I recently ran into this blog post, Do Candlesticks Work? A Quantitative Test Of 23 Candlestick Formations, where he did his own investigation.

This is a test of a Risk Premium Value strategy (RPV) that allocates to major US asset classes based on current risk premium valuations relative to historical norms. Readers will note the similarity between RPV and other related strategies, such as CXO Advisory’s SACEVS. Backtested results from

Historically, as Richard Thaler pointed out in his book Misbehaving, financial academics have looked at humans as “Econs.” An Econ, unlike a human, values everything down to a penny before they make a decision, knows all possible alternatives, weighs them accurately, and always optimizes. 1 In

When I was a spotty teenager I was a walking nerd cliche. I liked computers; both for programming and games. I was terrified of girls. I was rubbish at nearly all sports*. And I played D&D (and Tunnels and Trolls, and Runequest). * Nearly all: Not, I'm not talking about the

We start a new series on neural networks and deep learning. Neural networks and their use in finance are not new. But are still only a fraction of the research output. A recent Google scholar search found only 6% of the articles on stock price price forecasting discussed neural networks.1 Artificial

Volatility is notorious for clustering in the short-term, mean-reverting in the medium-term and settling into multi-year macro cycles over the long-term. I have chronicled each of these themes in this space in the past. Apart from volatility, I have also taken great pains to talk about the movements

Practitioners widely use asset pricing models such as CAPM or Fama French models to identify relationships between their portfolios and common factors. Moreover, each asset class has some widely-recognized asset pricing model, from equities through commodities to even cryptocurrencies. However,

This is the fifth post which is concerned with quantifying the dependence between variables. When talking correlations one usually thinks about linear correlation, aka Pearson’s correlation. One serious limitation of linear correlation is that it’s, well.. linear. By construction it’s not

Studies such as the 2019 paper “Value Return Predictability Across Asset Classes and Commonalities in Risk Premia,” have demonstrated that while it is difficult to time investments based on their value spreads 1 which we’ve covered occasionally here and here, value spreads do contain

The Increased Toxicity of the U.S. Treasury Security Market Scott E. Hein (Texas Tech University) January 2, 2021 This short research paper documents the fact that exclusively watching for rising yields on conventional U.S. Treasury securities to reflect increased inflationary fears in the U.S. is

As a continuation to our last post on Time Series Signatures and our running list of posts regarding GANs and synthetic data we now want to present the Signature Conditional Wasserstein GAN, shortened as SigCWGAN, a new GAN architecture presented in [1] that is specifically designed to generate time

We hinted in the past few blogs that we were preparing a small surprise. And now it’s time to unveil what we have been cooking during the previous several months. Quantpedia’s main mission is to help with the discovery of new ideas for systematic trading strategies. Our users can quickly

Ever built an angelic trading strategy that performed heavenly in a backtest, only to find it’s a devil in live trading? Well… there are some very specific “sins” traders make when building trading strategies that destine them (the strategies that is) to a miserable life of soul-sucking

How can we know if moving averages are effective? Can a moving average tell us whether a trend will continue or not? Is the golden cross really useful in predicting trend reversals? What about predicting bear markets with a moving average crossover? First of we start by defining what a moving

The subject of private debt and its associated performance characteristics has not been covered sufficiently in the academic literature. Relatively few research articles have attempted to characterize the returns and risk on the types of private debt strategies available to investors. This is true,

“Buy low, sell high.” One cannot find a more succinct summary of a mean-reversion trading strategy; however, single assets that show stable mean-reversion over a significant period of time such that a mean-reversion trading strategy can readily become profitable are rare to find in markets

Asset pricing models are important because they help us understand which factors explain the variation of returns across diversified portfolios. However, models are not like cameras that provide a perfect picture of the world. If models were perfectly correct, they would be laws, like we have in