Quant Mashup 4 simple ways to label financial data for Machine Learning [Quant Dare]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(...) How to Predict Stock Returns (using a simple model) [Alpha Architect]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,(...) NEW SITE: Portfolio Optimization: Minimize risk with Turnover constraint via Quadratic Programming [Dilequante]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(...) Introduction to Sell-Off Analysis for Crypto-Assets: Triggered by Bitcoin? [Quant at Risk]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(...) How to Measure the Liquidity of Cryptocurrency? [Alpha Architect]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(...) Hierarchical Clustering in Python [Quant Insti]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(...) Activate sigmoid! [OSM]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.(...) Z-Score Factor Portfolio Weighting [Philipp Kahler]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(...) An Introduction to Volatility Targeting [Quantpedia]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(...) Detecting Volume Breakouts [Financial Hacker]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(...) Autoregression: Model, Autocorrelation and Python Implementation [Quant Insti]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(...) Low Volatility Factor Investing: Risk-Based or Behavioral-Based or Both? [Alpha Architect]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(...) NER For Stock Mentions on Reddit (h/t @PyQuantNews)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(...) Does it make sense to change your trading behaviour in different periods of volatility? [Investment Idiocy]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(...) Momentum Factor Investing: What's the Right Risk-Adjustment? [Alpha Architect]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(...) Adding candlesticks to mean reversion setup [Alvarez Quant Trading]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.(...) Testing a Risk Premium Value Strategy [Allocate Smartly]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(...) Does Crowdsourced Investing Work? [Alpha Architect]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(...) Does X work, some brief thoughts and choose your adventure [Investment Idiocy]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(...) Nothing but (neural) net [OSM]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(...) VIX and More: The Evolution of the VIX (1) [VIX and More]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(...) A Robust Approach to Multi-Factor Regression Analysis [Quantpedia]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,(...) Correlation and correlation structure (5) – a new coefficient of correlation [Eran Raviv]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(...) The Forecasting Power of Value, Profitability, and Investment Spreads [Alpha Architect]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(...) Research Review | 26 February 2021 | Inflation [Capital Spectator]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(...) SigCWGAN, a new generation GAN architecture for Time Series Generation [Quant Dare]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(...) Accelerate Design of Multi-Factor Multi-Asset Models with Quantpedia Pro [Quantpedia]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(...) 3 ways traders kill trading strategies w/ Rob Carver of @InvestingIdiocy [Better System Trader]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 useful are Moving Averages - Backtest Results [Milton FMR]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 Risk and Returns to Private Debt Investments [Alpha Architect]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,(...) Sparse Mean-reverting Portfolio Selection [Hudson and Thames]“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(...) The R&D Premium: Is it Risk or Mispricing? [Alpha Architect]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(...) Understanding the disposition effect [SR SV]Investors have a tendency to sell assets that have earned them positive returns and are reluctant to let go of those that have brought them losses. This behavioural bias is called “disposition effect” and is attributed to loss aversion and regret avoidance. It has been widely documented by(...) Finance Database GitHub (h/t @PyQuantNews)As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what type of companies or ETFs are available is incredibly challenging with there being millions of companies amd derivatives available on the market. Sure, the most(...) Identifying Anomalies in Capital Markets: Accrual Anomaly [Milton FMR]Since the financial crisis in 2008 the number of anomaly related academic papers exploded and has grown so quickly that it is impossible to keep up with the full scope of research. To accommodate the need of an overview in this interesting research field we will summarize the most prominent market(...) Copula for Pairs Trading: Strategies Overview [Hudson and Thames]This is the third article of the copula-based statistical arbitrage series. You can read the previous two articles: Copula for Pairs Trading: A Detailed, But Practical Introduction. Copula for Pairs Trading: Sampling and Fitting to Data. Introduction Systematic approaches of pairs trading gained(...) The coastline paradox and the fractal dimension of markets [Philipp Kahler]Coastlines are fractal curves. When you zoom in, you will see similar shaped curves on every scale. The same is true for market data. On a naked chart you can hardly tell if it is a daily or hourly chart. This article will explore this feature of crinkly curves and show how much markets and(...) ESG Factors and Traditional Factors [Alpha Architect]Environmental, Social, and Governance (ESG) investing has become a priority for a lot of investors. We have previously written on ESG being combined with factor investing here and here. However, if one chooses to ignore our previous musings on the subject and only pursue ESG, how would that decision(...) Advanced Pairs Trading Lecture Videos [Hudson and Thames]ArbitrageLab is a python library filled with algorithms from the best academic journals and graduate-level textbooks, which focuses on the branch of statistical arbitrage known as pairs trading. This playlist is a series of lecture videos that explore advanced topics and highlight how your team can(...) Do ETFs Adversely Affect Market Quality? Nope. [Alpha Architect]Editor’s note: Seeing how the results may have shifted since the “ARK phenomenon” would be a great robustness test for this paper. ETFs are growing at a rapid pace and becoming a significant contributor to intraday activity (and we are only making the problem worse!). Naturally, some will(...) Research Review | 12 February 2021 | Equity Factor Risk [Capital Spectator]Why Are High Exposures to Factor Betas Unlikely to Deliver Anticipated Returns? Chris Brightman (Research Affiliates) et al. January 11, 2021 By choosing investment strategies that intentionally create exposure to factor betas, investors may be obtaining uncompensated risks. We show across a wide(...) Three types of systematic strategies that "work" [Robot Wealth]Broadly, there are three types of systematic trading strategy that can “work.” In order of increasing turnover they are: Risk premia harvesting Economically-sensible, statistically-quantifiable slow-converging inefficiencies Trading fast-converging supply/demand imbalances This post provides an(...) Second chances with momentum [Quant Dare]A couple of days ago we were seeing in the news the story about GameStop, and how small investors made some hedge funds abandon their short-selling positions after some big losses. After reading the article I couldn’t resist thinking about short-selling strategies and their performance in the(...) Persistent Moves To New Highs Rarely End Abruptly [Quantifiable Edges]I have not posted many price-action studies to the blog lately, so I thought I would share this one from last night’s subscriber letter. A theme I have seen many times over the years is that persistent uptrends don’t often end abruptly. The study below is an example of this. It considers what(...) Trading with the ISEE Sentiment Index? [Qusma]The ISEE sentiment index is the ratio of opening long call options to opening long short options. The idea is that the greater the ratio of calls, the more bullish the sentiment, and that this is a more reliable indicator (compared to other sentiment indices) because it’s based on actual trades as(...) Will the Real Value Factor Funds Please Stand Up? [Alpha Architect]If you’re a value investor who has determined that you have better things to do with your time, at some point you may have decided to outsource the investment task to a fund manager. And if you read our blog (especially this post) you’re probably looking to oursource to a systematic process(...) Risk-constrained optimization [OSM]Our last post parsed portfolio optimization outputs and examined some of the nuances around the efficient frontier. We noted that when you start building portfolios with a large number of assets, brute force simulation can miss the optimal weighting scheme for a given return or risk profile. While(...) Contagion and self-fulfilling dynamics [SR SV]Contagion and self-fulfilling feedback loops are propagation mechanisms at the heart of systemic financial crises. Contagion refers to the deterioration of fundamentals through the financial network, often through a cascade of insolvencies. A critical factor is the similarity of assets held by(...) Copula for Pairs Trading: Sampling and Fitting to Data [Hudson and Thames]This is the second article of the copula-based statistical arbitrage series. You can read the first article: Copula for Pairs Trading: A Detailed, But Practical Introduction. Overview Whether it is for pairs trading or risk management, two natural questions to ask before putting copula for use are:(...) Improving time series animations in matplotlib (from 2D to 3D) [Quant Dare]Animating time series is a very powerful tool to show evolution over time, but matplotlib default animations are boring and they are not well suited for comparison purposes. Along this blog, animations are widely used: from explaining how neural networks train, to showing synthetic time-series(...)