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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?

Quantocracy’s Daily Wrap for 11/24/2024

This is a summary of links featured on Quantocracy on Sunday, 11/24/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Valuation Timing with Excel [Robot Wealth]

    Data analysis plays a central role in making sense of financial markets. But how can you verify the conclusions others draw, or better yet, uncover your own insights? Microsoft Excel remains one of the most powerful and accessible tools for financial data analysis, allowing anyonefrom beginners to seasoned analyststo explore data, test hypotheses, and make informed decisions. Heres an
  • Examining Contango and Backwardation in VIX Futures [Relative Value Arbitrage]

    In this post, I will continue exploring various aspects of the volatility index and the associated volatility futures. Data To conduct this study, data is essential. Below are the data sources: Spot VIX: Yahoo Finance provides data but no longer allows direct downloads. With some programming, a workaround can be found, but the most convenient option is to use Barchart.
  • Improving Low Volatility Strategies [Alpha Architect]

    One of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relationship between risk and return while, empirical studies have found the actual relationship to be basically flat, or even negative. In addition, defensive strategies, at least those based on volatility, have delivered significant Fama-French
  • Takeaways From QuantMinds 2024 In London [Turnleaf Analytics]

Quantocracy’s Daily Wrap for 11/21/2024

This is a summary of links featured on Quantocracy on Thursday, 11/21/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 23: Logic or Luck [OSM]

    On Day 22 we saw a meaningful improvement in our strategy by waiting an additional week to quantify model error and then using that error term to adjust the prediction on the most recently completed week of data. What was even more dramatic was comparing this improved strategy to one that followed the same waiting logic, but did not include the error correct. It turned an underperforming strategy

Quantocracy’s Daily Wrap for 11/20/2024

This is a summary of links featured on Quantocracy on Wednesday, 11/20/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation Performance Lower Bound [Anton Vorobets]

    Asset allocation is commonly split into strategic asset allocation (SAA) and tactical asset allocation (TAA). Strategic usually refers to investment horizons above one year, while tactical usually refers to investments horizons below one year. Almost all institutional investors are required to have a strategic asset allocation, while many decide to have a tactical asset allocation process as well.
  • The Delusion of Market Efficiency [5th Horizon Research]

    Key Point: Markets have potentially become less efficient in recent decades. There are several reasons why this might be the case. Implication: Market inefficiency means more opportunities for outperformance for sufficiently equipped investors. ____________ The question of market efficiency is of great consequence. We introduced the concept in a recent post and suggested that markets have perhaps
  • How to Evaluate the Effectiveness of a Trading Strategy: p-Values and Bootstrapping Methods [Concretum Group]

    One common question we often receive from our readers is: How do you evaluate the effectiveness of a trading strategy? In this post, well explore two fundamental techniques used in quantitative research to assess whether a trading strategy may genuinely offer an advantage or if its performance is likely due to random chance. These techniques are p-values from statistical tests and

Quantocracy’s Daily Wrap for 11/19/2024

This is a summary of links featured on Quantocracy on Tuesday, 11/19/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • CTA index replication and the curse of dimensionality [Investment Idiocy]

    So, first I should apologise for the LONG…. break between blogposts. This started when I decided not to do my usual annual review of performance – it is a lot of work, and I decided that the effort wasn't worth the value I was getting from it (in the interests of transparency, you can still find my regularly updated futures trading performance here). Since then I have been busy with other
  • Day 22: Error Correction [OSM]

    On Day 21, we wrung our hands with frustration over how to proceed. The results of our circular block sampling suggested we shouldnt expect a whole lot of outperformance in our 12-by-12 model out-of-sample. To deal with this our choices were, back to the drawing board or off to the waterboard to start over or to torture the data until it told us what we wanted. However, we found a third way, in