Quant Mashup Domestic Fixed Income Factor Implementations [Flirting with Models]Prior academic and practitioner research suggests that factor-based fixed income investing can create attractive return profiles and be useful when building fixed income portfolios. Using an investment universe of eight domestic fixed income asset classes, we build dollar-neutral long-short(...) ESG vs Low Carbon Investing [Factor Research]ESG and Low Carbon portfolios feature significant, but different sector & country biases Investors should expect large tracking errors in some ETFs Some products contain stocks that are likely unexpected and undesired INTRODUCTION Investors seeking exposure to global equities with a low carbon(...) Smart Money Indicator Rebuttal [Alpha Architect]In February 2019, Wes asked that I share my research on what I call the “Smart Money Indicator.” I did a guest post on the subject that summarized the results of a paper introducing my research on the topic. The indicator measures the relative sentiment in equities between institutional(...) pandas for Quants: New Video Course from QaR [Quant at Risk]Hi Guys! I’m happy to kick off a new series of free video lectures devoted to Python’s library of pandas. Every week, I will be uploading something between 2 to 4 new videos especially crafted around practicalities of pandas library applied to financial data and their analysis and processing.(...) Global Low Volatility and Momentum Factor Investing Portfolios [Alpha Architect]A Springbok antelope can reach a top speed of 55 miles per hour in the African savanna, whereas the fastest human manages barely half that speed and only for a few meters. In a short race, we are left in the dust. However, we are built for endurance and can run for hours at an almost constant speed,(...) Market will be up 9.7% in 3 months! [Alvarez Quant Trading]When this sell-off indicator triggers, it is correct 100% of the time! On average the market is up only 2.6% in 3 months. OR NOT! After big moves in the market, we often see “research” saying that “when the market has done X it will move Y%.” I had a reader send me such research asking for(...) 15% Off Tickets to The Quant Conference | April 3rd, 2020 in NYC | Promo Code: QUANTOCRACY15Get 15% off tickets to the next Quant Conference with the promo code QUANTOCRACY15. The Quant Conference is a forum that engages the brightest young minds and foremost thought leaders from the industry and academia to dive into the latest innovations in quant finance, foster collaboration and(...) Create your own Deep Learning framework using Numpy [Quant Dare]I have always been curious about how deep learning frameworks are created. I use Keras, TensorFlow, and PyTorch and they all are really good, but sometimes I feel like I am playing with a black box (in some frameworks I feel it more than in others) that hides its secrets. If you feel the same way,(...) Assessing The Damage After Monday’s Sharp Decline In Stocks [Capital Spectator]Well, that was painful. The increasingly hazy risk outlook linked to the coronavirus outbreak inspired a 3.35% haircut in the US stock market (S&P 500). The tumble was certainly a bracing counterpoint to the idea that sunny optimism is the only game in town. But before we let recency bias flip(...) Macroeconomic Risks in Equity Factor Investing: Part 2/2 [Alpha Architect]What are the research questions? Although not a new topic, the first half of the article explored and documented the dependent relationship between factor returns and time-varying macroeconomic environments. In the second half of this paper, the authors provide insightful commentary and a renewed(...) Essential Books on Algorithmic Trading [Quant Insti]When you are completely immersed in wanting to learn something new, you start looking for everything that surrounds the learning process. And with the aspiration to learn Algorithmic Trading, there must be certain questions crowding your mind, like: How do I learn Algorithmic Trading? What are the(...) Ensembles and Rebalancing [Flirting with Models]While rebalancing studies typically focus on the combination of different asset classes, we evaluate a combination of two naïve trend-following strategies. As expected, we find that a rebalanced fixed-mix of the two strategies generates a concave payoff profile. More interestingly, deriving the(...) When The Market Gaps Down Huge During A Long-Term Uptrend [Quantifiable Edges]With corona virus news scaring the market pre-open today, I decided to look back at other time SPY has gapped down more than 2% when it had been in a long-term uptrend. As you might suspect, instances have been fairly rare. Looking ack to SPY inception, there were only 16 other instances. And upping(...) Model Interpretability: The Model Fingerprint Algorithm [Hudson and Thames]“The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as a black box and demand interpretation. Yimou Li, David Turkington, and Alireza Yazdani present a(...) All About Time Series: Analysis and Forecasting [Quant Insti]Since predicting the future stock prices in the stock market is crucial for the investors, Time Series and its related concepts help in organizing the data for accurate prediction. In this article, we are focusing on Time Series, its analysis and forecasting. In this article, we aim to cover the(...) Rebalancing! Really? [OSM]In our last post, we introduced benchmarking as a way to analyze our hero’s investment results apart from comparing it to alternate weightings or Sharpe ratios. In this case, the benchmark was meant to capture the returns available to a global aggregate of investable risk assets. If you could own(...) Detecting market price distortions with neural networks [SR SV]Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at return time series alone and to detect “strict local martingales”, i.e. episodes when the risk-neutral return temporarily follows a random(...) The Massive Performance Divergence Between Large Growth and Small Value Stocks [Alpha Architect]From 2017 through 2019, the Russell 1000 Growth Index returned 20.5 percent per annum, outperforming the Russell 1000 Value Index, which returned 9.7 percent, by 10.8 percentage points a year; and the Russell 2000 Growth Index returned 12.5 percent per year, outperforming the Russell 2000 Value(...) Factor Investing Update: An Analysis of 2019 International Factor Returns [Alpha Architect]Last week I summarized the 2019 factor performance for U.S. stocks. A natural follow-up question was the following–“what about International stocks?” A great question. So below I dig into the 2019 performance for International Factor Portfolios. 1 Let’s dig into the results. Factor Investing(...) Factor Exposure: The Turn of The Screw [Quant Dare]You may have seen in different papers or websites, analysis of how a specific active portfolio is exposed to different financial factors (Value, Growth, Size, Quality, etc). This insight is very interesting in order to know what to expect from a strategy and to explain and understand its behaviour,(...) Diversification with Portable Beta [Flirting with Models]A long/flat tactical equity strategy with a portable beta bond overlay – a tactical 90/60 portfolio – has many moving parts that can make attribution and analysis difficult. By decomposing the strategy into its passive holdings (a 50/50 stock/bond portfolio and U.S. Treasury futures) and active(...) Venture Capital: Worth Venturing Into? [Factor Research]Venture capital returns are likely to be overstated Top-performing VC funds generated attractive returns, but are difficult to access Average venture capital returns can be replicated efficiently with public equities WINNERS & LOSERS The further the global financial crisis retreats into history,(...) Benchmarking the portfolio [OSM]In our last post, we looked at one measure of risk-adjusted returns, the Sharpe ratio, to help our hero decide whether he wanted to alter his portfolio allocations. Then, as opposed to finding the maximum return for our hero’s initial level of risk, we broadened the risk parameters and searched(...) Research Review | 14 February 2020 | Business Cycle Risk [Capital Spectator]A New Index of the Business Cycle William B. Kinlaw (State Street Global Markets), et al. January 2020 The authors introduce a new index of the business cycle that uses the Mahalanobis distance to measure the statistical similarity of current economic conditions to past episodes of recession and(...) Have you tried to calculate derivatives using TensorFlow 2? [Quant Dare]We will learn how to implement a simple function using TensorFlow 2 and how to obtain the derivatives from it. We will implement a Black-Scholes model for pricing a call option and then we are going to obtain the greeks. Matthias Groncki wrote a very interesting post about how to obtain the greeks(...) Simple Vol Estimators [Falkenblog]While short-term asset returns are unpredictable, volatility is highly predictable theoretically and practically. The VIX index is a forward-looking estimate of volatility based on index option prices. Though introduced in 1992 it has been calculated back to 1986, because when released they wanted(...) Factor Investing Update: An Analysis of 2019 U.S. Factor Returns [Alpha Architect]In case you missed it, 2019 was a good year to be an equity investor. Examining market-cap-weighted indices, the U.S. stock market was up ~ 30%, Developed International Markets were up ~ 22%, and Emerging Markets were up ~ 18%. But how did factors do in 2019? Below I update my post from last year,(...) Investing in "Distressed" TAA Strategies [Allocate Smartly]In response to a member question: Have you ever looked at a systematic approach that invested only in tactical asset allocation strategies that were experiencing a significant drawdown? We know that performance chasing is a flawed behavioral bias, so an approach like this could exploit it. We always(...) Payoff Diversification [Flirting with Models]At Newfound, we adopt a holistic view of diversification that encompasses not only what we invest in, but also how and when we make those investment decisions. In this three-dimensional perspective, what is correlation-based, how is payoff-based, and when is opportunity-based. In this piece, we(...) Timing Low Volatility with Factor Valuations [Factor Research]Factors can be valued like stocks or markets The Low Volatility factor in the US had the best subsequent returns when cheapest and worst when most expensive However, the perspective is less clear when analyzing European and Japanese stock markets INTRODUCTION Funds flows are frequently analyzed by(...) Python Regression Analysis: Drivers of German Power Prices [Philipp Kahler]German Power prices can be explained by supply and demand, but also by causal correlations to underlying energy future prices. A properly weighted basket of gas, coal and emissions should therefore be able to resemble the moves of the power price. This article will introduce multivariate regression(...) Deep Learning for Quants: (1) Setting Up Keras and TensorFlow 2.1+ Environment in Python [Quant at Risk]It would be too easy to kick off the series of lectures supplementing my Python for Quants ebooks starting from Machine Learning (ML) as an innovation. ML-based algorithms pay dividends when your problem is fairly well defined and data allow to capture the patterns where they exist. Deep Learning(...) SHARPEn your portfolio [OSM]In our last post, we started building the intuition around constructing a reasonable portfolio to achieve an acceptable return. The hero of our story had built up a small nest egg and then decided to invest it equally across the three major asset classes: stocks, bonds, and real assets. For that we(...) Tracking investor expectations with ETF data [SR SV]Retail investors’ return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate the distribution of such expectations based on actual transactions. A new paper shows how to do this through ETFs that track the(...) What is the Bitcoin's Risk-Free Interest Rate? [Quantpedia]Cryptocurrencies, and most notably Bitcoin, are recognized as decentralized currencies. While some see Bitcoin (BTC) as a payment method of the future, others see it as a speculative asset class. No doubt, many have gained on the skyrocketing prices of BTC, but note that many have lost. Despite the(...) The Case Against REIT's [Alpha Architect]Surveys often reveal investor behavior that is challenging to understand. For example, Preqin’s Alternative Investor Outlook for H2 2019 highlighted the following: 65% of institutional investors believe that real estate is overvalued and a correction likely to occur in 2019, 2020, or beyond.(...) What is the right way to set stop losses? [Investment Idiocy]Stop losses are the most common method used by traders to control risk. However, they're often used inappropriately. In this post I'll quickly bust some of the myths around them, and explain how to use them properly. This is the first of three posts aimed at answering three fundamental(...) Inverse Volatility Sizing Index [Alvarez Quant Trading]In my last post, Inverse Volatility Position Sizing, I tested inverse volatility sizing on a monthly rotation strategy. I saw very little difference in the rest results versus equal position sizing. I was talking to a trading friend about the research and how I was surprised at how there was not any(...) Factor Risk and Return [Falkenblog]Factor returns should reflect risk, in that they have traditionally been interpreted as proxies for some kind of risk not measured by beta. The idea is that perhaps what people really care about is whether there will be another oil shock, and nothing matters as much. Stocks that have a high(...) Visualising ETFs with UMAP [Quant Dare]In previous posts (Visualising Fixed Income ETFs with T-SNE) we have talked about dimensionality reduction algorithms to visualize financial assets and find recognizable patterns. The conclusions were that it didn’t perform well compared to PCA, which is a more classical approach. Can we do any(...) QuantMinds International Conference in Hamburg, Germany, May 11-15 [Quant Minds]The world's leading quant finance conference 450+ experts from banks, buy-side, regulators, Silicon Valley, academia and beyond examine every facet of quant in five amazing days Key themeslatest agenda SPECIALIST FOCUS. SPECIALIST KNOWLEDGE. Maximise your experience with our full-day summit or(...) Book Review: Smart(er) Investing by Elisabetta and Tommi [Alpha Architect]It’s not often I get the opportunity to write a book review for our fellow teammates and the best authors on our website — Elisabetta Basilico and Tommi Johnsen! If you haven’t read Elisabetta and Tommi’s mountain of blog posts on our site you’ve been hiding under a rock somewhere (or(...) How to Learn Advanced Mathematics Without Heading to University - Part 4 [Quant Start]It has been some time since wrote Parts I, II and III of our popular series of articles on How to Learn Advanced Mathematics Without Heading to University. Many of you have contacted us asking for the final Part IV of the series. We have now completed our internal research and can present our view(...) Sneak Peak: Robustness to Noise [Allocate Smartly]This is an early preview of a new analytical tool we’ll be adding to our platform later this month. Learn more about what we do. Broadly speaking, the goal of tactical asset allocation is to take advantage of broad market trends via trend-following and/or momentum. Those trends can be difficult to(...) Can Managed Futures Offset Equity Losses? [Flirting with Models]Managed futures strategies have historically provided meaningful positive returns during left-tail equity events. Yet as a trading strategy, this outcome is by no means guaranteed. While trend following is “mechanically convex,” the diverse nature of managed futures programs may actually prevent(...) Machine learning and macro trading strategies [SR SV]Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously(...) Sentiment and Factor Performance [Factor Research]Stock sentiment can be aggregated from public sources using a big data approach Results indicate that sentiment has some predictability for short-term factor performance Positive sentiment resulted in higher subsequent returns than negative sentiment INTRODUCTION Albert Einstein famously stated that(...) Is AI coming after your job? [Mathematical Investor]It is no secret that artificial intelligence (AI) systems have made enormous strides in recent years, partly due to the adoption of Bayesian (probability-based) machine learning techniques rather than the rule-based techniques used until about 20 years ago. AI systems have advanced in lockstep with(...) Low Volatility-Momentum Factor Investing Portfolios [Alpha Architect]Factor investing is hard and some factors make it harder than others. A value strategy results in a portfolio of stocks that exhibit temporary or structural issues and are usually rated “Sell” by brokers, which makes these emotionally challenging to hold. Small caps are companies that are(...) Generating OHLC bars with Generative Adversarial Networks [Quant Dare]Open-High-Low-Close (OHLC) bars are a type of financial data typically used to represent daily movements in the price of a financial instrument. They give us more information about certain characteristics of the series than line charts, such as intraday volatility or daily momentum. Could Generative(...)