Five new strategies have been added. Five new related research papers have been included into existing strategy reviews and four short free blog posts has been published during last few weeks. Plus, two trading strategies have been backtested in QuantConnect in the previous two weeks.
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.
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.
In recent years, the Transformer architecture has experienced extensive adoption in the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Google AI Research’s introduction of Bidirectional Encoder Representations from Transformers (BERT) in 2018 set remarkable new standards in NLP. Since then, BERT has paved the way for even more advanced and improved models.
We discussed the BERT model in our previous article. Here we would like to list alternatives for all of the readers that are considering running a project using some large language model (as we do 😀 ), would like to avoid ChatGPT, and would like to see all of the alternatives in one place. So, presented here is a compilation of the most notable alternatives to the widely recognized language model BERT, specifically designed for Natural Language Understanding (NLU) projects.
The emergence of high-frequency trading has led to improvements in numerous algorithmic trading strategies. Consequently, there is a growing demand for quantitative analysis and optimization techniques to develop these strategies. We present a paper by Aydoğan et al. (2022), which discusses the derivation of the optimal prices for HFT to execute the limit buy and sell orders where a stochastic volatility model generates the mid prices of the assets in the market.
Six new strategies have been added. Four new related research papers have been included into existing strategy reviews and one short free blog post has been published during last few weeks. Plus, six trading strategies have been backtested in QuantConnect in the previous two weeks.
Beta-adjusted returns equity factors are considerably more stable, indicating that factor construction methodologies may be improved beyond dollar and size neutrality. Low-beta effect at the level of factors confirms the existence of seasonal and momentum effects in the cross-section of factor returns. Altogether, these insights deepen the understanding of factor behavior and can aid the development of more robust factor-based investment strategies.
Hello all,
What have we accomplished in the last month?
– Extensions of 5 Quantpedia Pro reports
– 12 new Quantpedia Premium strategies have been added to our database
– 12 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 7 new backtests written in QuantConnect code
– And finally, 4 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month
Six new strategies have been added. Five new related research papers have been included into existing strategy reviews and two short free blog posts has been published during last few weeks. Plus, three trading strategies have been backtested in QuantConnect in the previous two weeks.
Even though gold is generally a volatile asset, it is often considered a key diversifier, hedging against inflation or protecting during economic uncertainties. According to the authors (Pim van Vliet and Harald Lohre), in times of extreme macroeconomic events, including war, hyperinflation, or major economic recessions, gold investing is widely regarded as a safe haven. However, using gold as a hedge comes at the cost of lower returns. The authors explored the importance of gold in investment portfolios and its ability to reduce the risk of losses combined with bonds and stocks. Compared to many existing studies, they also consider a longer timeframe and the impact of inflation.
We’ve already analysed tens of thousands of financial research papers and identified more than 700 attractive trading systems together with hundreds of related academic papers.
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