Quant Mashup Embeddings of Sectors and Industries using Graph Neural Networks [Gautier Marti]You can find the reproducible experiment in this Colab Notebook. In econometrics and financial research, categorical variables, and especially sectors and industries, are usually encoded as dummy variables (also called one-hot encoding in the machine learning community). You can find plenty of such(...) Exploring the rsims package for fast backtesting in R [Robot Wealth]rsims is a new package for fast, realistic (quasi event-driven) backtesting of trading strategies in R. Really?? Does the world really need another backtesting platform…?? It’s hard to argue with that sentiment. Zipline, QuantConnect, Quantstrat, Backtrader, Zorro… there are certainly plenty(...) Community Alpha of QuantConnect - Part 2: Social Trading Factor Strategies [Quantpedia]This blog post is the continuation of series about Quantconnect´s Alpha market strategies. This part is related to the factor strategies notoriously known from the majority of asset classes. Although the results are insightful, they are not straightforward, and further analysis could be made.(...) Research Review | 13 August 2021 | Market and Asset Analytics [Capital Spectator]Decomposing Momentum: Eliminating its Crash Component Pascal Büsing (University of Muenster), et al. July 15, 2021 We propose a purely cross-sectional momentum strategy that avoids crash risk and does not depend on the state of the market. To do so, we simply split up the standard momentum return(...) Relative Sentiment and Market Returns [Alpha Architect]This paper studies the relationship between aggregate investor attention and subsequent market returns over the following week. The authors create two different investor attention indicators—one for aggregate retail attention (ARA) and one for aggregate institutional attention (AIA). ARA is found(...) New Feature: Cluster Analysis [Allocate Smartly]We track a lot of tactical strategies, and it can be difficult to understand how they all fit together in the big picture. The usual correlation matrix (example) is helpful when drilling down on a single strategy, but it’s near impossible to see the forest for the trees among the 1000’s of data(...) Modeling US Stock Market Expected Returns, Part III [Capital Spectator]I recently outlined two models for estimating the US stock market’s return for the decade ahead. Let’s add a third model to the mix with the plan to take the average as a relatively robust forecast. The previous two models (see here and here) used valuation to estimate ex ante performance for(...) Value Investing and the Role of Intangibles [Alpha Architect]Recent research, including the 2020 studies “Explaining the Recent Failure of Value Investing” and “Intangible Capital and the Value Factor: Has Your Value Definition Just Expired?,” have investigated the impact on U.S. value strategies of the increase in the relative importance of(...) Valuing Bitcoin using USD Index [Recession Alert]Of the dozen indicators and metrics we have researched, the fortunes of the US Trade-Weighted U.S Dollar Index (TWDI) has the biggest impact on Bitcoin USD prices. When the TWDI depreciates, this boosts Bitcoin prices strongly. When the TWDI becomes stronger, Bitcoin prices face significant(...) Extended Optimal Arbitrage Strategies [Hudson and Thames]In our previous article, we’ve discussed a couple of trading strategies exploiting arbitrage between similar stocks using stochastic optimal control methods. A major shortcoming of those approaches is that we restricted ourselves to constructing delta-neutral portfolios. Along with this, the ratio(...) Building an Inflation Portfolio Using Stocks [Factor Research]An inflation portfolio can be created by systematically selecting stocks correlated to inflation This would have resulted in a portfolio with strong sector and factor biases However, the correlation to inflation would not have been significantly higher than for stocks overall INTRODUCTION Measuring(...) Should you Trade with the Kelly Criterion? [Raposa Trade]The Kelly Criterion gives an optimal result for betting based on the probability of winning a bet and how much you receive for winning. If you check out Wikipedia or Investopedia, you’ll see formulas like this: f∗=p−1−pb−1f^{*} = p - \frac{1-p}{b-1} f∗=p−b−11−p which gives you(...) Machine learning for portfolio diversification [SR SV]Dimension reduction methods of machine learning are suited for detecting latent factors of a broad set of asset prices. These factors can then be used to improve estimates of the covariance structure of price changes and – by extension – to improve the construction of a well-diversified minimum(...) Paper Review: Algorithmic Financial Trading with Deep Convolutional Neural Networks [Enjine]Of the major machine learning algorithms, the convolutional neural network (CNN) is my favourite. CNNs form some of our company’s most cherished elements that give strength to our investment algorithms. My curiosity was therefore piqued when I came across Sezer and Ozbayoglu’s paper titled(...) The Active vs Passive: Smart Factors, Market Portfolio, or Both? [Alpha Architect]While there may be debates about passive and active investing, and even blogs about the numbers of active funds that were outperformed by the market, history taught us that the outperformance of active or passive investing is cyclical. As a proxy for active investing, the paper examines factor(...) 10 Free Swing Trading Strategies That Work (Backtested Buy And Sell Signals) [Quantified Strategies]The internet is flooded with anecdotal evidence about how to swing trade and how to make money. Unfortunately, almost all articles consist of unproven and untested swing trades. To make money swing trading is difficult, but we believe you face much better odds the more you backtest and generate(...) Distance Approach in Pairs Trading: Part II [Hudson and Thames]We have discussed Basic Distance Approach in the previous blog post. In this post, we’ll look into one of the advanced methods in the Distance Approach and its differences to the Basic Distance Approach. If you haven’t read the previous blog post, we recommend reading it before you read this(...) A quick example on using next day open-to-open returns for Tactical Asset Allocation [QuantStrat TradeR]First off, for the hiring managers out there, after about a one-year contracting role at Bank of America doing some analytical reporting coding for them in Python, I am on the job market. Feel free to find my LinkedIn here. This post will cover how to make tactical asset allocation strategies a bit(...) Building a Long Volatility Strategy without Using Options [Factor Research]Long volatility strategies can be built without using options Portfolios would have primarily consisted of certain currency pairs and treasury bonds They lack explosive returns when volatility spikes, but they also lack the bleed INTRODUCTION Almost all asset classes are implicitly short volatility(...) Factor Investing and International Markets [Alpha Architect]nternational markets have been a fertile testbed for factor research because they offer an opportunity to test old ideas on new data. Much of the previous work studying factor structure and risk premia in international markets uses highly aggregated test assets, such as country portfolios, industry(...) Global Growth Cycle: Identifying Economic Turning Points, a Market Timing Strategy [Grzegorz Link]Fluctuations of economic growth are observed throughout multiple measures of business activity and among countries. Due to their synchronized manner, they are often referred to as business cycles.[1] The problem with this designation is a lack of strict periodicity – as we'll see below, the(...) A Personal Portfolio Allocation Approach [Open Source Quant]My experience in financial markets to date has mostly been related to trading and investment banking. From executing index arbitrage and various other strategies, to product managing a team building execution algorithms for automating strategies which minimize market impact and make relative and(...) Five Small Shards of Insight Hidden in Data [Quantpedia]Around a month ago, we launched a series of short videos called “Quantpedia Explains“, in which we plan to show and explain some of the themes out of quantitative finance that we think are worth mentioning. We have started with a quick intro to individual Quantpedia Pro reports, and now, we have(...) Pricing Deribit Options [Tr8dr]We have been working on some option strategies and wanted to get a sense of how well BTC and ETH options are priced on Deribit, i.e. is there a substantial IV premium over realized volatility or are options fairly priced. At first glance, based on the documentation, it seemed that Deribit options(...) The Benefits of Sin Stocks [Alpha Architect]While environmental, social, and governance (ESG) investing continues to gain in popularity, economic theory suggests the share prices of “sin” businesses (typically those involved in the gambling, tobacco, alcohol, guns, and defense industries) will become depressed if a large enough proportion(...) March for the Fallen 2021: Detailed Logistics Outline and What to Expect [Alpha Architect]March for the Fallen (#MFTF) will happen on September 25, 2021. COVID can't kill the event this year! Action Item: Please let us know your trip details so we can support you as much as possible. Here are the links to prior updates if you'd like to review: Footwear and foot care(...) 7 Things I've learned about trading from the industry's smartest people [Tradologics]The first (annual) Algo Trading Summit was a huge success! Over 2,500 registered for the event, with an average of 500 people watching the live stream at any given moment — and, so far, the video recordings have over 5,000 views. Not too shabby. In this post, I want to share and summarize some of(...) Intro to Partial Sample Regression [Hudson and Thames]Ordinary least squares (OLS) regression is probably the most commonly used statistical method in quantitative finance (and likely in other quantitative fields). It is very fast to compute, and the results are often quite interpretable. Due to its simplicity, it serves as the cornerstone for many(...) Residualization of Risk Factors: Examples and Pitfalls [Portfolio Optimizer]The most common approach to measuring portfolio (risk) factor exposures is linear regression analysis, which describes the relationship between a dependent variable - portfolio returns - and explanatory variables - factors - as linear. One of the outputs of this analysis are the partial regression(...) "Low-effort Trading Strategies" with Cesar Alvarez (@AlvarezQuant) [Better System Trader]Algorithmic trader Cesar Alvarez from Alvarez Quant Trading joins us to discuss low effort trading strategies, including: An explanation of rotational trading and the benefits/challenges of using rotational strategies, Why rotational trading is a fantastic way to diversify time (and also get to(...) Test and Trade RSI Divergence in Python [Raposa Trade]Divergences occur when price and your indicator move in opposite directions. For example, you’re trading with the RSI and it last had a peak at 80, now it peaks at 70. The underlying security you’re trading was at $14 when RSI hit 80, and now hits a new peak at $18. This is a divergence. Traders(...) Digital Asset ETFs: Not Crypto Enough? [Factor Research]Digital asset ETFs have outperformed tech stocks in recent years However, they provide no exposure to cryptocurrencies Their returns are explained by market beta and equity factors INTRODUCTION Cathie Wood, the founder and CEO of Ark Invest, an ETF manager, is the latest entrant to launching a(...) The Role of Book-to-Market in Bond Returns [Alpha Architect]My August 17, 2020, article for Advisor Perspectives, “Factor-Based Investing Beats Active Management for Bonds,” provided the evidence from a series of academic papers on the ability of common factors to explain the variation of returns of bond funds, results which have important implications(...) Accounting data as investment factors [SR SV]Corporate balance sheet data are important building blocks of quantitative-fundamental (“quantamental”) investment factors. However, accounting terms are easily misunderstood and confused with economic concepts. Accounting is as much driven by assessment of risk as it is by economic value. For(...) Growth Trend Timing With US Stocks [Decoding Markets]I’m always on the lookout for interesting ways to time the market. Using a market timing model can help to avoid painful bear markets and indicate when is a good time to buy stocks. Recently, I was looking at some of the strategies on Allocate Smartly and came across one called Growth Trend(...) 4 Simple Strategies to Trade Bollinger Bands [Raposa Trade]Bollinger Bands have been a popular indicator by traders since they were invented in the early 1980’s. They’re calculated in four, easy steps and are intended to provide traders an idea of the price range of a security. We can use these to develop a number of different algorithmic strategies to(...) Beyond linear: the Extended Kalman Filter [Quant Dare]Although linear systems are pretty convenient at many levels, many real world applications cannot rely in this assumption. The Extended Kalman Filter can deal with these nonlinearities in a simple way. Learn how in this post. Introduction In the 1960s, Rufold E. Kalman codeveloped one of the most(...) Stock Market Data And Analysis In Python [Quant Insti]Are you looking to get stock market data and analyse the historical data in Python? You have come to right place. After reading this, you will be able to: Get historical data for stocks Plot the stock market data and analyse the performance Get the fundamental, futures and options data For easy(...) Factor Investing in Sovereign Bond Markets: 221 years of evidence! [Alpha Architect]Despite government bonds being one of the major asset classes invested in global portfolios, 30% of overall market capitalization according to Doeswijk, et al. (2020), little work has been done to investigate whether factors are present in the sovereign bond market. (Here is a deep dive into fixed(...) Statistical Distributions and the Costliness of Hidden Assumptions [Enjine]By the third year of my PhD program, I was impatient. I had endured 8 years of lectures, exams, and keeping close watch over my bank account’s balance. Meanwhile, my colleagues from undergrad had embarked on interesting projects with big potential, and were getting paid well to do so. Their lives(...) Myth Busting: Equities are an Inflation Hedge [Factor Research]Equities generated attractive nominal returns across all inflation regimes However, real returns were zero when inflation was above 10% Energy and materials performed best, consumer-facing sectors worst INTRODUCTION “I came of age and studied economics in the 1970s and I remember what that(...) Create a Personal Portfolio/Wealth Simulation in Python (Part 2) [Python For Finance]Welcome to Part 2 of the series of posts dealing with how to build your own python based personal portfolio /wealth simulation model. At the end of the first post (which can be found here), we got to the point where we had modelled some inflows, some outflows, we had applied an annual salary raise(...) Man vs. Machine: Stock Analysis [Quantpedia]Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021)(...) The Misery Index and Future Equity Returns [Alpha Architect]Prospect theory was developed by Daniel Kahneman and Amos Tversky in 1979. The theory starts with the concept of loss aversion—the observation that people react differently between potential losses and potential gains. Thus, people make decisions based on the potential gain or loss relative to(...) Research Review | 16 July 2021 | Forecasting [Capital Spectator]Forecasting the Long-Term Equity Premium for Asset Allocation Athanasios Sakkas (U. of Nottingham) and Nikolaos Tessaromatis (EDHEC) July 12, 2021 Long-term country equity premium forecasts based on a cross-sectional global factor model (CS-GFM), where factors represent compensation for risks(...) Everything About Faber: A Critical Look at Market Timing [Light Finance]In 2006, Meb Faber wrote a highly influential paper on tactical asset allocation and market timing. The strategy was particularly attractive in part because of its simplicity: Buy when monthly price > 10-month SMA Sell and move to cash when monthly price By applying this simple, mechanical(...) Modeling US Stock Market Expected Returns, Part I [Capital Spectator]In recent posts I reviewed several basic applications for generating fair-value estimates for the 10-year Treasury yield, which can be used as a proxy for projecting return. Let’s expand this effort by forecasting performance for the US equity market over a 10-year window. The goal is developing a(...) The risk of investing: An exploration on SPDR Sector ETFs [Quant Dare]We will examine the relationship between annual returns and largest annual drop. Let’s use some well known Select Sector SPDRs and the SPDR S&P 500 Trust (SPY). Using prices from 1999-01-01 to 2021-06-30 we calculate the annual returns and the biggest drop for each year. For example, if we(...) Volume Positive Negative Indicator for Breakouts [Alvarez Quant Trading]Probably like a lot of you, I am an indicator junkie. Whenever I read about an indicator I have not tested and makes some sense, I got to try it out. Now, most of the time they turn out to not be useful for my strategies. While reading the April 2021 Technical Analysis of Stocks & Commodities, I(...) Metalabeling and the duality between cross-sectional and time-series factors [EP Chan]Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in(...)