Predicting Stock Market Performance with the Global Anomaly Index

22.August 2023

Today’s article focuses on investigating long-short anomaly portfolio return predictability in international stock markets, which often undergo mispricing due to investors’ sentiment. A paper by Jiang, Fuwei et al. (Apr 2023), suggests using the AAIG (Global Anomaly Index), and it examines the ability of the aggregate anomaly index to predict future returns in 33 stock markets. While previous research finds that a high aggregate anomaly measure predicts a low return in the U.S. market, this study further demonstrates that the global component of AAI (aggregate anomaly indices) is the key that drives international return predictability and reveals that the global anomaly index is a strong and robust predictor of equity risk premiums not just in the U.S. market but also in international markets, both in- and out-of-sample, consistently delivering significant economic values.

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Avoid Equity Bear Markets with a Market Timing Strategy – Revisiting Our Research

18.August 2023

In March, we posted a series of three articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets. In this article, we revisit our research to address the forward-looking bias in our final market timing strategy. Upon careful examination, we identified a bias in our macroeconomic trading signal based on the U.S. S&P Composite dividends. To eliminate the issue, we have replaced the signal from U.S. S&P Composite dividends with Housing Starts Growth sourced from FRED, ensuring the strategy is no longer biased.

The unbiased version of our TrendYCMacro strategy, which uses the HOUSE signal, yields an annual excess return of 6.59%, slightly below the 7.10% of the biased version with the DIVIDEND signal. Interestingly, the unbiased version experiences slightly lower annualized volatility at 11.87% compared to the 11.89% of the biased version. Both versions have suffered the same maximal drawdown of -25.13% and exhibit comparable risk-adjusted returns, with the unbiased version having a Sharpe ratio of 0.56 and the biased version having a Sharpe ratio of 0.60.

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Technical Analysis Report Methodology + Double Bottom Country Trading Strategy

13.August 2023

Some of the more vague terms in Technical Analysis are really hard to quantify as nearly every TA user defines and interprets them differently. We mean mainly TA patterns like supports, resistances, trend lines, double tops, double bottoms, and/or more complex patterns like head-and-shoulders. Now, what we can do with that? We tried to spend some time and fought a little with some of these TA terms, and the following article/study results from our attempts to quantify a tiny subset of the world of Technical Analysis patterns.

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Quantpedia in July 2023

8.August 2023

Hello all,

What have we accomplished in the last month?

– A new Technical Analysis report for Quantpedia Pro subscribers
– 11 new Quantpedia Premium strategies have been added to our database
– 9 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– And finally, 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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Exploring the Factor Zoo with a Machine-Learning Portfolio

3.August 2023

The latest paper by Sak, H. and Chang, M. T., and Huang, T. delves into the world of financial anomalies, exploring the rise and fall of characteristics in what researchers refer to as the “factor zoo.” While significant research effort is devoted to discovering new anomalies, the study highlights the lack of attention given to the evolution of these characteristics over time. By leveraging machine learning (ML) techniques, the paper conducts a comprehensive out-of-sample factor zoo analysis, seeking to uncover the underlying factors driving stock returns. The researchers train ML models on a vast database of firm and trading characteristics, generating a diverse range of linear and non-linear factor structures. The ML portfolio formed based on these findings outperforms entrenched factor models, presenting a novel approach to understanding financial anomalies. Notably, the paper identifies two subsets of dominant characteristics – one related to investor-level arbitrage constraint and the other to firm-level financial constraint – which alternately play a significant role in generating the ML portfolio return.

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How Well Do Factor Investing Funds Replicate Academic Factors?

31.July 2023

Cremers, Liu, B. Riley (Apr 2023) share their view on and try to answer the question: how well do factor investing funds perform? They conclude that, on average, factor-investing funds do not outperform. But using active characteristic share (ACS)—an adaption of Cremers and Petajisto’s (2009) original active share measure—, the authors demonstrate that the factor investing funds that match indexes the most have significantly better performance. An equal-weighted portfolio of factor investing funds in the lowest tercile of ACS outperforms an equal-weighted portfolio of funds in the highest tercile by 3.82% per year (t-stat = 3.89) using the CAPM and by 1.08% per year (t-stat = 2.01) using the CPZ6 model.

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