Price curves consist of much noise and little signal. For separating the latter from the former, John Ehlers proposed in the Stocks&Commodities May 2021 issue an unusual approach: Treat the price curve like a radio wave. Apply AM and FM demodulating technology for separating trade signals from

We are proponents of focused (i.e., 50 stock) long-only value and momentum factor strategies. 1 There are also plenty of incredibly talented systematic investing shops that build highly diversified factor portfolios with 500+ stocks. We take no stance on the "best" approach because there

If you have some factor that you think predicts future stock returns (or similar) and you are making charts like below, then here are some tips… We’ll go through an example of trying to “time” SPX with the level of VIX. You get daily SPX index prices and daily VIX close data You align them

The grand experiment of combining massive fiscal and monetary stimulus at a time when the economy is already recovering strongly—the Fed’s latest forecast for 2021 GNP growth is 6.5 percent—has led many investors to begin to worry about the risks of rising inflation. And strong growth is

Copula is a great statistical tool to study the relation among multiple random variables: By focusing on the joint cumulative density of quantiles of marginals, we can bypass the idiosyncratic features of marginal distributions and directly look at how they are “related”. Indeed, traders and

Different markets and different timeframes will need different moving average periods. This article will show a way to construct a self optimising moving average, one which automatically adjusts its period to the charted market and timeframe. Reading a simple moving average I would like to start

When looking to generate appreciable returns and increase diversification, it is natural to consider investing in foreign instruments. Currency risk then comes up, since the returns coming from these funds, stocks, bonds… need to be translated into your home currency. The most straightforward

I am always fascinated by patterns as I believe that our world contains some predictable outcomes even though it is extremely difficult to extract signals from noise, but all we can do to face the future is to be prepared, and what is preparing really about? It is anticipating (forecasting) the

It's April, which means the birds are singing, the trees are leafing, and I'm doing my annual review of my investing and trading performance. The format will be familiar from previous years, but I'm going to be using the fact I've upgraded my live trading system to include a lot

Investors are challenged when evaluating investment opportunities with limited track records Factor exposure analysis can be used to create replication portfolios These empower investors to walk backward and forward in time, enhancing the investment decision process INTRODUCTION Investing is all

With a myriad of new crypto-exchanges popping up every quarter, lots of newcomers to this fields can be overwhelmed by their number. Big names can quickly stand out if you filter the list according to daily trading volume or the total number of cryptocurrencies available for trading. Some offer

Last week I wrote about BVOL - bitcoin volatility index launch on Deribit. However this is not the first crypto volatility index. In fact last year T3 Indexes - the folks behind SPIKES volatility index launched both Bitcoin and Etherium volatility indexes, and already executed trades tied to their

Born at Morgan Stanley in the late 1980s, under the wing of Nunzio Tartaglia and his team, who later split up to start several of the world’s best hedge funds, namely PDT Partners and D.E. Shaw (which then lead to Two Sigma). Pairs trading has proven to be a popular and sophisticated trading

This is the third article in a series of three, the first two are available here and here. Those articles focus on examining from a digital signal processing (DSP) perspective 1 various types of digital filters that are designed to model trends in time series, in order to illustrate their properties

How Much Bitcoin Should I Own? A Mathematical Answer Adam Grealish (Betterment) March 9, 2021 It goes without saying that this is a hard question to answer. But we can borrow a page from modern quantitative finance to help us arrive at a potential answer. For years, Wall Street “quants” have

Many beginner traders don’t realize how variable the p&l of a high-performing trading strategy really is. Here’s an example… I simulated ten different 5 year GBM processes with expected annual returns of 20% and annualized volatility of 10%. (If you speak Sharpe Ratios, I’m simulating a

In my previous post, Adding candlesticks to mean reversion setup, we looked at how various candle patterns could help individual trades. Now we will see how those results translate to a portfolio. And why I usually only do portfolio level testing. The Strategy Setup Rules Stock is a member or was a

The correlation between stock and bond returns is an integral component of hedging strategies, risk assessment, and minimization of risk in allocation decisions. In the context of those strategies, the stock-bond correlation is typically estimated using monthly return data over a recent previous

Our last post examined the correspondence between a logistic regression and a simple neural network using a sigmoid activation function. The downside with such models is that they only produce binary outcomes. While we argued (not very forcefully) that if investing is about assessing the probability

It’s no secret that 2021 has started off well for Bitcoin. Having breached a new all time high of $61,788.45 on March 13th it seems that each passing month brings with it a new milestone, new players, and greater acceptance. Recently, significant news has focused on the pace of institutional

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