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Recent Quant Links from Quantocracy as of 12/30/2024

This is a summary of links recently featured on Quantocracy as of Monday, 12/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Top Ten Blog Posts on Quantpedia in 2024 [Quantpedia]

    The year 2024 is nearly behind us, so its an excellent time for a short recapitulation. In the previous 12 months, we have been busy again (as usual) and have published over 70 short analyses of academic papers and our own research articles. The end of the year is a good opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is
  • A “New” Way to Smooth Price [Dekalog Blog]

    Rather than describe it, I'll just paste the "help" description below:- "This function takes an input series and smooths it by projecting a 5 bar rolling linear fit 3 bars into the future and using these 3 bars plus the last 3 bars of the rolling input to fit a FIR filter with a 2.5 bar lag from the last projected point, i.e. a 0.5 bar lead over the last actual rolling point in
  • From Gold to Bitcoin: Exploring the Oldest and Newest Asset Classes [Relative Value Arbitrage]

    Gold, one of the oldest and most enduring asset classes, had an exceptional run in 2024, capturing attention across financial markets. Its role in investment portfolios continues to spark interest, acting as a hedge against uncertainty. On the other end of the spectrum, cryptocurrencies represent the newest frontier in finance. While opinions remain divided, some are enthusiastic supporters, while
  • Linear Congruential Generators in Python [Quant Start]

    Some years ago we wrote a range of articles on random number generation (RNG) using C++. These techniques are primarily used for Monte Carlo simulations that underpin modern derivatives pricing methods. The articles included one that implemented a particular algorithm known as a Linear Congruential Generator (LCG). The LCG is an algorithm for generating random looking numbers, despite being

Recent Quant Links from Quantocracy as of 12/23/2024

This is a summary of links recently featured on Quantocracy as of Monday, 12/23/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Drawdown Implied Correlations (Part 1) [CSS Analytics]

    Diversification is a concept that is critical to most asset managers and traders. The foundation of this body of research is built upon the Pearson correlation coefficient, which is the most popular metric to determine whether adding an asset to a portfolio might enhance diversification. Despite its widespread use, most investment practitioners recognize its limitations. Some of the flawed
  • Intangibles and the Performance of the Value Factor [Alpha Architect]

    Systematic factor-driven value strategies have underperformed broad market indices (such as the S&P 500) over the past 15+ years. That has led many to question whether intangible assets, such as patents and proprietary software, are properly treated. Current accounting standards, which require companies to expenserather than capitalizetheir outlays on activities that create intangible
  • Front Running Commodity Seasonality [Allocate Smartly]

    This is an independent test of a series of interesting studies from Quantpedia (here and here) related to seasonality in commodity ETFs. Weve more than doubled the length of the authors original test using relevant index data (1). Test #1: Front running commodity seasonality In all of our tests, we will be trading the same 4 commodity ETFs chosen by the author: DBA: Agriculture DBB:
  • Front-Running Seasonality in US Stock Sectors [Quantpedia]

    Seasonality plays a significant role in financial markets and has become an essential concept for both practitioners and researchers. This phenomenon is particularly prominent in commodities, where natural cycles like weather or harvest periods directly affect supply and demand, leading to predictable price movements. However, seasonality also plays a role in equity markets, influencing stock

Recent Quant Links from Quantocracy as of 12/17/2024

This is a summary of links recently featured on Quantocracy as of Tuesday, 12/17/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Goldman Sachs’ 3% Annual Return Forecast Based on Bad Data? [Allocate Smartly]

    This paper from Goldman Sachs made big headlines a couple of months back for forecasting an abysmal 3% nominal annual return for US stocks in the coming decade. For anyone who didnt read GSs analysis, the biggest contributor to that poor return was market concentration, or the market cap of the largest stocks relative to the remainder of the stock market. They provided the following
  • The Finance and Economics Problem [Anton Vorobets]

    Getting fundamental assumptions right is essential for successful investment and risk management. The aspects that enable us to build portfolios intelligently and outperform the market are subtle nuances that are not easily accessible to most investors. If you do not believe me, check out this video Note where the legendary Jim Simons explains it. Anton VorobetsOct 22 Imagine you have knowledge of
  • Estimating Long-Term Expected Returns [Alpha Architect]

    This paper examines various frameworks and proxies for forecasting long-term expected returns (E(R)) over periods of 10 to 20 years, focusing on out-of-sample performance and the impact of these forecasts on investment decisions. It compares models based on yield, valuation, and the combination of both to identify the most effective methods for predicting E(R). Time-Varying Drivers of Stock Prices
  • Option Pricing Models and Strategies for Crude Oil Markets [Relative Value Arbitrage]

    Financial models and strategies are usually universal and can be applied across different asset classes. However, in some cases, they must be adapted to the unique characteristics of the underlying asset. In this post, Im going to discuss option pricing models and trading strategies in commodities, specifically in the crude oil market. Volatility Smile in the Commodity Market Paper [1]
  • NLX Finance’s Hybrid Asset Allocation 60/40 [Allocate Smartly]

    This strategy from NLX Finance is an alternative version of a strategy weve covered previously: Dr. Keller & Keunings Hybrid Asset Allocation (HAA). It trades based on all the same rules as the original HAA with one exception: rather than allocating 100% to US stocks when risk on, it holds a 60/40 mix of US stocks and Treasuries. Strategy results from 1970 follow. We perform a much
  • PJ Sutherland – Complementary Dynamics of Mean Reversion and Trend Following [Algorithmic Advantage]

    In the domain of quantitative finance, the juxtaposition of mean reversion and trend-following strategies constitutes a pivotal dialogue in the formulation of robust trading paradigms. Each methodology is underpinned by unique theoretical and empirical foundations, presenting distinct opportunities and inherent vulnerabilities. However, when synthesized within a cohesive portfolio framework, these
  • The Ultimate Strength Index [Financial Hacker]

    The RSI (Relative Strength Index) is a popular indicator used in many trading systems for filters or triggers. In TASC 12/2024 John Ehlers proposed a replacement for this indicator. His USI (Ultimate Strength Index) has the advantage of symmetry the range is -1 to 1 and, especially important, less lag. So it can trigger trades earlier. Like the RSI, it enhances cycles and trends in the

Recent Quant Links from Quantocracy as of 12/12/2024

This is a summary of links recently featured on Quantocracy as of Thursday, 12/12/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 30: Summing up [OSM]

    On Day 29, we conducted our out-of-sample test on the four strategies and found that the adjusted strategy came out on top. We made this conclusion after ranking a cross section of the following metrics: cumulative return, Sharpe Ratio, and max drawdown. If we wanted to commit capital, there would be a lot more work to do. But with the bulk of the backtesting over, its time to sum up what we
  • Can We Use Active Share Measure as a Predictor? [Quantpedia]

    Active Share is a metric introduced to quantify the degree to which a portfolio differs from its benchmark index. It is expressed as a percentage, ranging from 0% (fully overlapping with the benchmark) to 100% (completely different). The concept gained popularity because it was believed that higher Active Share reflects truly active management, which could potentially lead to outperformance. If
  • From the Pits to the Page: A Conversation with Kris Abdelmessih [Robot Wealth]

    It was my absolute pleasure to chat with Kris Abdelmessih about markets and life. Kris was an options market maker who started out in the trading pits of New York and later flipped the script to set up the commodity options business for hedge fund Parallax Advisory. Today, Kris writes the Moontower newsletter on Substack one of the most consistently unique and insightful newsletters youll

Recent Quant Links from Quantocracy as of 12/11/2024

This is a summary of links recently featured on Quantocracy as of Wednesday, 12/11/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fast trend following [Quantitativo]

    I always say that you could publish trading rules in the newspaper and no one would follow them. The key is consistency and discipline. Richard Dennis. Richard Dennis is one of the greatest trend-following traders in history, renowned for transforming a small loan into a fortune in the commodities markets. As a pioneer of systematic trading, Dennis believed that successful trading could be
  • Frog in the Pan Momentum: International Evidence [Alpha Architect]

    This article analyzes various reasons why momentum strategies might work outside US borders. While the US story is firmly rooted in behavioral biases, is the same true on an international scale? That seems logical and likely. In fact, the authors conclude that a slow diffusion of news best explains momentum in the international contextacross all of our tests, we find supportive evidence for
  • When Correlations Break or Hold: Strategies for Effective Hedging and Trading [Relative Value Arbitrage]

    Its well known that there is a negative relationship between an equitys price and its volatility. This can be explained by leverage or, alternatively, by volatility feedback effects. In this post, Ill discuss practical applications to exploit this negative correlation between equity prices and their volatility. A Trading Strategy Based on the Correlation Between the VIX and S&P500

Recent Quant Links from Quantocracy as of 12/06/2024

This is a summary of links recently featured on Quantocracy as of Friday, 12/06/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taking an income from your trading account – probabilistic Kelly with regular withdrawals [Investment Idiocy]

    Programming note: This post has been in draft since … 2016! One question you will see me asked a lot is 'how much money do I need to become a full time trader?'. And I usually have a handwaving answer along the lines of 'Well if you think your strategy will earn you 10% a year, then you probably want to be able to cover 5 years of expenses with no income from your trading
  • Day 29: Out of sample [OSM]

    The moment of truth has arrived! On Day 28, we iterated through all the metrics we had previously used to identify and analyze the robustness of our strategy. We found the new adjusted strategy performed better than the original and adjusted strategies. Such performance was also statistically significant for key scenarios. But on simulation, buy-and-hold beat the new adjusted strategy on average
  • Laying the Groundwork for Ito’s Lemma and Financial Stochastic Models [Quant Insti]

    This is a two-part blog where well explore how Itos Lemma extends traditional calculus to model the randomness in financial markets. Using real-world examples and Python code, well break down concepts like drift, volatility, and geometric Brownian motion, showing how they help us understand and model financial data, and well also have a sneak peek into how to use the same for trading
  • Diversifying Trend Following Strategies Improves Portfolio Efficiency [Alpha Architect]

    Since the turn of the century portfolios have been exposed to four periods of crisis: the bursting of the tech bubble and the events of September 11, 2001, from 2000-2002; the Great Financial Crisis in 2007-2008, the COVID-19 pandemic in 2020, and the period of persistent inflation in 2022 when both stocks and bonds experienced double-digit losses. These experiences increased investor interest in
  • Research Review | 6 December 2024 | Index and Passive Investing [Capital Spectator]

    Limits to Diversification: Passive Investing and Market Risk Lily H. Fang (INSEAD), et al. September 2024 We show that the rise of passive investing leads to higher correlations among stocks and increased market volatility, thereby limiting the benefit of diversification. The extent to which a stock is held by passive funds (index mutual funds and ETFs) positively predicts its beta, correlation,

Recent Quant Links from Quantocracy as of 12/05/2024

This is a summary of links recently featured on Quantocracy as of Thursday, 12/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Naive Backtesting [Anton Vorobets]

    I am occasionally asked about historical backtests proving that CVaR is a better risk measure than variance. I provide such a backtest in Section 2.6 of the Portfolio Construction and Risk Management book1 and explain why it is naive (see the PDF at the bottom of this article). Thanks for reading Quantamental Investing! Subscribe to receive new posts and stay updated. Although many people
  • Trader s Guide to Front-Running Commodity Seasonality [Quantpedia]

    Seasonality is a well-known phenomenon in the commodity markets, with certain sectors exhibiting predictable patterns of performance during specific times of the year. These patterns often attract investors who aim to capitalize on anticipated price movements, creating a self-reinforcing cycle. But what if we could stay one step ahead of the crowd? By front-running these seasonal trendsbuying
  • Day 28: Reveal [OSM]

    On Day 27, we had our strategy enhancement reveal. By modifying the arithmetic behind our error correction, we chiseled another 16% points of outperformance vs. buy-and-hold and the original 12-by-12 strategy. All that remains now is to run the prediction scenario metrics and conduct circular block sampling. Given that weve laid the ground work for these analyses in past posts, we will only

Recent Quant Links from Quantocracy as of 12/03/2024

This is a summary of links recently featured on Quantocracy as of Tuesday, 12/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 27: Enhancement [OSM]

    On Day 26, we extended the comparative error analysis to the original, 12-by-12 strategy and showed how results were similar to the unadjusted strategy relative to the adjusted one. The main observation that emerged was that the adjusted strategy performed better than the others due to identifying most of the big moves when it was correct and not missing the big moves when it was not. This was
  • Hurst Exponent Applications: From Regime Analysis to Arbitrage [Relative Value Arbitrage]

    One of my favourite ways to characterize the market regime is by using the Hurst exponent. However, its applications are not limited to identifying market regimes. There are innovative ways to utilize it. In this post, I will discuss two approaches to applying the Hurst exponent. Using the Hurst Exponent to Time the Market The Hurst exponent can be employed to directly time the market. Reference
  • Day 26: Adjusted vs. Original [OSM]

    The last five days! On Day 25, we compared the peformance of the adjusted vs. unadjusted strategy for different prediction scenarios: true and false positives and negatives. For true positives and false negatives, the adjusted strategy performed better than the unadjusted. For true negatives and false positives, the unadjusted strategy performed better. Today, we run the same comparisons with the
  • Time-Varying Drivers of Stock Prices [Alpha Architect]

    This paper examines the time-varying roles of subjective expectations in driving stock price and return variations. Specifically, it focuses on how subjective cash flow expectations (CF) and discount rate expectations (DR) contribute to stock price fluctuations across different economic conditions, with a special emphasis on periods of financial uncertainty and economic crises. Time-Varying

Recent Quant Links from Quantocracy as of 11/30/2024

This is a summary of links recently featured on Quantocracy as of Saturday, 11/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Modelling UVXY trading strategies with Excel [Robot Wealth]

    UVXY is an ETF that targets 1.5x the daily returns of a 30-day constant-maturity position in VX futures the SPVIXSTR index. Before 2018, it targeted 2x returns but Volmageddon ruined the fun. UVXY has to trade every day: To rebalance its notional exposure back to its target due to: Movements in VX, and Expense fees and trading costs being deducted from AUM. To maintain its target maturity
  • AWS Trading Part 2 – The Strategy [Black Arbs]

    In [part 1] [youtube video link] we covered the data pipeline portion of the AWS trading bot architecture. I demonstrated how to set up your AWS environment, including creating a simple dynamoDB database to hold our price and strategy data. Then we walked through the data pipeline code in detail including how to grab the data and populate our db with it. In this post well cover the strategy
  • Calendar Anomalies, Much Ado About Nothing [Alpha Architect]

    An anomaly is a pattern in stock returns that deviates from what is expected based on established financial theories or models. These patterns can sometimes present opportunities for abnormal returns. However, they are often inconsistent and challenging to exploit. Many anomalies have achieved consensus and, thus, have been incorporated into factor-based models, including size, value, momentum,
  • Day 25: Positives and Negatives [OSM]

    On Day 24, we explained in detail how the error correction term led to somewhat unexpected outperformance relative to the original and unadjusted strategies. The reason? We hypothesized that it was due to the the error term adjusting the prediction in a trending direction when or if the current walk-forward model was mean reverting. We noted that the walk-forward models tended to have negative
  • Triple-70 Breadth Thrust Triggers [Quantifiable Edges]

    The strong breadth readings over the last few days triggered one of my oldest and most favorite studies. It looks at other times that breadth came in strong for 3 days in a row. I have shown this study many times over the years. I often refer to it as a Triple-70 Thrust, because it requires the NYSE Up Issues % to close at 70% or greater for 3 days in a row. Stats are updated. Triple 70 breadth

Quantocracy’s Daily Wrap for 11/25/2024

This is a summary of links featured on Quantocracy on Monday, 11/25/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Risk-Constrained Kelly Criterion: From definition to trading [Quant Insti]

    The Kelly Criterion is good enough for long-term trading where the investor is risk-neutral and can handle big drawdowns. However, we cannot accept long-duration and big drawdowns in real trading. To overcome the big drawdowns caused by the Kelly Criterion, Busseti et al. (2016) offered a risk-constrained Kelly Criterion that incorporates maximizing the long-term log-growth rate together with the
  • Day 24: Lucky Logic [OSM]

    On Day 23 we dove into the deep end to understand why the error correction we used worked as well as it did. We showed how traditional machine learning uses loss functions and then hypothesized how our use helped improve predictions through its effect on the correlation of the signs of the prediction with that of the forward return. We have to admit that our decision to use the error term in the
  • Factors are global, respectable and repeatable [Alpha Architect]

    Do we have a chaotic factor zoo as some critics maintain? Is there a replication crisis in the research on factors? The authors of this research answer in the negative and argue that 82% of factors are replicable, the factor zoo is well-organized, and the factors are legit. Such bold statements given the 35% replication rate reported by other, more pessimistic studies. So, whats the key?