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Quantocracy’s Daily Wrap for 11/07/2024

This is a summary of links featured on Quantocracy on Thursday, 11/07/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 15: Backtest II [OSM]

    On Day 14 we showed how the trading model we built was snooping and provided one way to correct it. Essentially, we ensure the time in which we actually have the target variable data aligns with when the trading signals are produced. We then used the value of the next time step to input into the model to generate a forecast. If the forecast was positive, wed go long the SPY ETF, if negative
  • A time-varying-parameter vector autoregression model with stochastic volatility [Quant Insti]

    The basic Vector Autoregression (VAR) model is heavily used in macro-econometrics for explanatory purposes and forecasting purposes in trading. In recent years, a VAR model with time-varying parameters has been used to understand the interrelationships between macroeconomic variables. Since Primiceri (2005), econometricians have been applying these models using macroeconomic variables such as:
  • Day 14: Snooping [OSM]

    Guess what? The model we built in our last post actually suffers from snooping. We did this deliberately to show how easy it is to get mixed up when translating forecasting models into trading signals. Lets explain. Our momentum model uses a 12-week cumulative return lookback to forecast the next 12-week cumulative return. That may have produced a pretty good explanatory model compared to the

Quantocracy’s Daily Wrap for 11/05/2024

This is a summary of links featured on Quantocracy on Tuesday, 11/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 13: Backtest I [OSM]

    Unlucky 13! Or contrarian indicator? Theres really nothing so heartwarming as magical thinking. Whatever the case, on Day 12 we iterated through the 320 different model and train step iterations to settle on 10 potential candidates. Today, we look at the best performing candidate and discuss the process to see if the forecasts produce a viable trading strategy. As we noted before, we could have
  • Lognormal Distribution: Neither Thin- nor Fat-Tailed [Quant at Risk]

    In probability and statistics, distributions are often classified as either thin-tailed or fat-tailed, a distinction that reflects the likelihood of extreme deviations from the mean. The lognormal distribution, however, defies this binary classification. It possesses characteristics that make it neither fully thin-tailed, as in the case of the Gaussian, nor entirely fat-tailed, like
  • Day 12: Iteration [OSM]

    In Day 11, we presented an initial iteration of train/forecast steps to see if one combination performs better than another. Our metric of choice was root mean-squared error (RMSE)1 which is frequently used to compare model performance in machine learning circles. The advantage of RMSE is that it is in the same units as the forecast variable. The drawback is that it is tough to interpret on its

Quantocracy’s Daily Wrap for 11/03/2024

This is a summary of links featured on Quantocracy on Sunday, 11/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 11: Autocorrelation [OSM]

    On Day 10, we analyzed the performance of the 12-by-12 model by examining the predicted values and residuals. Our initial takeaway suggested the model did seem not overly biased or misspecified in the -10% to 10% region. But when it gets outside that range, watch out! We suspected that there was some autocorrelation in the residuals, which we want to discuss today. There are different statistical
  • Using Trading Volume to Optimize Portfolio Construction and Implementation [Alpha Architect]

    While portfolio optimization typically focuses on risk and return prediction, implementation costs critically matter. Unfortunately, predicting trading costs is challenging because the largest component for a large investor is price impact, which depends on the size of the trade, the amount traded by other traders in that security, and the identity of the trader, thus, impeding a generic solution.
  • Day 10: Residuals [OSM]

    On Day 9 we conducted a walk-forward analysis on the 12-by-12 week lookback-look forward combination. We then presented the canonical the actual vs. predicted value graph with a 45o line overlay to show what a perfect forecast would look like. Heres the graph again. As noted previously, we limited the scale of the axes to make it easier to interpret. This omits some outliers, which well

Quantocracy’s Daily Wrap for 11/01/2024

This is a summary of links featured on Quantocracy on Friday, 11/01/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 9: Forecast [OSM]

    Yesterday we finished up our analysis of the regression models we built using different combinations of lookback and look forward momentum values. Today, we see if we can generate good forecasts using that data. If youre wondering why we still havent tested Fibonacci retracements with Bollinger Band breakouts filtered by Chaikin Volatility, the reason is that were first trying to

Quantocracy’s Daily Wrap for 10/30/2024

This is a summary of links featured on Quantocracy on Wednesday, 10/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding the Invisible Tail of a Power Law [Quant at Risk]

    Understanding the invisible tail of a power law distribution is crucial for accurate extreme value analysis, especially in fields where rare, extreme events have a large impact. In finance, natural disaster modeling, and engineering, rare events, or outliers, are disproportionately impactful. The theory behind the invisible tail demonstrates that traditional methods for estimating risk or
  • Day 8: Baseline effects [OSM]

    Yesterday, we discussed the size effects, their statistical significance (e.g., p-values), and some other summary statistics for the various momentum combinations namely, 3, 6, 9, and 12 week lookback and look forward returns. We found that size effects were small, but a few were significant, and that in the case of the 12-by-12 combination about 75% of the results clustered in the -10% to 10%
  • Day 7: Size effects [OSM]

    Welcome to the last day of the first week of 30 days of backtesting! We hope youre enjoying the ride. If you have any questions or concerns, you can reach us at the contact details listed at the bottom of this post. On Day 6 we defined momentum rather roughly and ran a bunch of tests to identify the linear relationship between different lookback and look forward periods. However, we didnt go

Quantocracy’s Daily Wrap for 10/28/2024

This is a summary of links featured on Quantocracy on Monday, 10/28/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Covariance Matrix Forecasting: Iterated Exponentially Weighted Moving Average Model [Portfolio Optimizer]

    In the previous post of this series on covariance matrix forecasting, I reviewed both the simple and the exponentially weighted moving average covariance matrix forecasting models, which are straightforward extensions of their respective univariate volatility forecasting models to a multivariate setting. With these reference models established, we can now delve into more sophisticated approaches
  • Day 6: Momentum [OSM]

    Yesterday we examined the eponymous Fama-French factors to see if we could find something that will help us develop an investment strategy to backtest. It turned out the best performing factor was the market risk premium, which is essentially the return to the market in excess of the risk-free rate. In other words, the best factor is buy-and-hold! I guess that means weve finished 24 days early.
  • Can Artificial Intelligence outsmart seasoned equity analysts? [Alpha Architect]

    If the task is to identify a firms true profitability, can AI outsmart seasoned analysts? Given the increasingly bloated nature of financial reports, decoding the twists and turns associated with events like obscure one-time gains and out-of-nowhere expenses to extract core earnings has become challenging even for accountants. This research will unravel how AI can successfully attack the

Quantocracy’s Daily Wrap for 10/26/2024

This is a summary of links featured on Quantocracy on Saturday, 10/26/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Build Mean Reversion Strategies in Currencies [Quantpedia]

    Our article explores a simple mean reversion trading strategy applied to FX futures, focusing on identifying undervalued and overvalued currencies to generate returns. Using FX futures rather than spot rates allows for the inclusion of interest rate differentials, simplifying the analysis. The strategy employs two position-sizing methodslinear and exponentialboth rebalanced monthly based on
  • Lognormal Stochastic Volatility Youtube Seminar and Slides [Artur Sepp]

    I would like to share the youtube video of my online seminar at Minnesota Center for Financial and Actuarial Mathematics and presentation slides. I discuss the motivation behind introducing the log-norml stochastic volatility (SV) model in our IJATF paper with Parviz Rakhmonov. I briefly highlight the advantages of this model over exisiting SV models. Then I focus on new features of the model. For
  • New YouTube Series Launched: Building Your AWS Trading Data Pipeline! [Black Arbs]

    I just published Part 1 of my new YouTube series, and I'm excited to share it with you all! After my recent post about automating trading strategies with AWS Cloud, many of you asked for a deeper dive into the technical implementation. Well, here it is! What's in Part 1? In this first video, we're tackling the foundation: configuring your AWS data pipeline and writing the Python
  • Day 5: Trifactor [OSM]

    The day has finally arrived! Time to start backtesting! Weve always wanted to test how Fibonacci retracements with Bollinger Band breakouts filtered by Chaikin Volatility would perform while implementing rolling stop-loss updates based on the ATR scaled by the 7-day minus 5-day implied volatility rank.1 Maybe were getting ahead of ourselves. Expeditions are fun and its always thrilling to
  • Day 4: First analysis [OSM]

    Were four days in and youre probably wondering when are we actually going to start backtesting?! The answer is that while it is natural to want to rush to the fun part the hope and elation of generating outsized returns and Sharpe Ratios greater than 2 the reality is getting the foundation right should serve us well in the future. Or so thats what we always hear from those longer
  • Day 3: Metrics [OSM]

    Yesterday we investigated the effect of using the 200-day simple moving average (200SMA) as a proxy for a rules-based investing method. The idea was to approximate what a reasonably rational actor/agent might do in addition to the buy-and-hold approach. When folks talk about research, backtesting, and forecast comparisons, they usually use a naive model against which one compares performance. In

Quantocracy’s Daily Wrap for 10/23/2024

This is a summary of links featured on Quantocracy on Wednesday, 10/23/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 2: Hello World [OSM]

    On Day 1, we decided on a few benchmarks to use for our backtest. That is, a 60-40 and 50-50 weighting of the SPY and IEF ETFs. What we want to add in now is the Hello World version of trading strategies the 200-DAY MOVING AVERAGE! Why are we adding this to our analysis? As we pointed out yesterday, the typical benchmark against which to compare a trading strategy is buy-and-hold. But, just as

Quantocracy’s Daily Wrap for 10/22/2024

This is a summary of links featured on Quantocracy on Tuesday, 10/22/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 1: Benchmarks [OSM]

    Yesterday we set out our plan to backtest a strategy using the SPY ETF, which tracks the S&P 500. Before we commence, we obviously need to establish a baseline. What metrics will we use to assess the strategy? How will we define success? What benchmarks will we use? Typically, for a single asset strategy the comparison is buy-and-hold performance. That is, if youre using Fibonacci
  • Reinforcement Learning in Finance: Resources and Expert Advice from Paul Bilokon [Quant Insti]

    Reinforcement learning (RL) is one of the most exciting areas of Machine Learning, especially when applied to trading. RL is so appealing because it allows you to optimise strategies and enhance decision-making in ways that traditional methods cant. One of its biggest advantages? You dont have to spend a lot of time manually training the model. Instead, RL learns and makes trading decisions

Quantocracy’s Daily Wrap for 10/20/2024

This is a summary of links featured on Quantocracy on Sunday, 10/20/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Accurately Forecasting Multi-period Stock Market Returns [Six Figure Investing]

    I recently posted a paper, Transforming Stock Market Forecasts with Variable Expected Returns, on the SSRN online repository. This paper resolves an issue that has been bugging me for years. The link is: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=495384 This paper is not about making money, but rather about a fundamental theoretical error regarding stock market forecasts that has
  • Mind the gap [Quantitativo]

    "What we know is a drop; what we don't know is an ocean. Isaac Newton. Many of Isaac Newton's early theories and ideas were met with skepticism or outright failure. Newton spent years working on problems related to motion, optics, and gravity, often facing dead ends and revisions. In fact, throughout most of his career, Newton was very loathe to publish due to his high
  • Artificial Intelligence, Textual Analysis and Hedge Fund Performance [Alpha Architect]

    Artificial Intelligence (AI) offers the intriguing potential to revolutionize investment decision-making by providing important advantages such as: Enhanced Data Analysis: AI can process and analyze vast amounts of data from various sources, including financial news, market trends, and company fundamentals, at a speed and scale far surpassing human capabilities. This enables investors to identify