Swing Trading Signals


Since 2013

  • 100% Quantified, data-driven and Backtested
  • We always show our results!
  • Signals every day via our site or email
  • Cancel at any time!

Return Predictability Revisited

Last Updated on 10 February, 2024 by Rejaul Karim

In the intricate tapestry of financial markets, “Return Predictability Revisited,” authored by Ben Jacobsen, Ben R. Marshall, and Nuttawat Visaltanachoti, emerges as a beacon challenging the traditional contours of predictability. Unveiling their findings in the EFA 2007 Ljubljana Meetings Paper and the 21st Australasian Finance and Banking Conference 2008 Paper, the trio navigates through 73 pages of revelations.

Contrary to the norm, they reframe the predictability landscape, exposing the subtle nuances within monthly stock market returns. Their exploration, akin to a refined lens, dissects observation intervals and illuminates the influence of economically significant commodity returns.

As the intervals vary, so does the predictability—short spans mirror near efficiency based on industrial metals, while longer intervals echo the echoes of gradual information diffusion rooted in energy series. Robust against the storms of data mining adjustments and the inclusion of diverse variables, this predictability stands resilient, offering a captivating glimpse into the intricate dance of market forces, challenging established notions of efficiency and risk.

Abstract Of Paper

Monthly stock market returns are predictable when we refine the observation intervals of the variables used to predict these returns. Contrary to other predictability studies we find high out-of-sample adjusted R2s of up to 7% using economically important commodity returns. Shorter intervals reveal predictability consistent with near efficient markets based on price changes in industrial metals. More historical intervals expose predictability consistent with gradual information diffusion based on energy series. This predictability is robust to data mining adjustment, the inclusion of control (including economic) variables, and unrelated to time-varying risk. Inflation explains part of this predictability, but not all.

Original paper – Download PDF

Here you can download the PDF and original paper of Return Predictability Revisited.

(An option to download will come shortly)

Author

Ben Jacobsen
Tilburg University – TIAS School for Business and Society; Massey University

Ben R. Marshall
Massey University – School of Economics and Finance

Nuttawat Visaltanachoti
Massey University – Department of Economics and Finance

Conclusion

In summary, this investigation challenges conventional wisdom on return predictability by delving into refined observation intervals. The findings expose nuanced patterns, showcasing that monthly stock market returns are distinctly predictable when scrutinized with finer granularity.

Notably, the study highlights the impact of economically significant commodity returns, unraveling predictability dynamics across various time frames. Contrary to prevailing notions, the outcomes underscore the relevance of shorter intervals, reflecting market efficiency influenced by price changes in industrial metals. Furthermore, the research sheds light on the persistence of predictability over longer historical intervals, suggesting a nuanced interplay between gradual information diffusion and evolving market dynamics.

These insights withstand rigorous testing, including data mining adjustments, control variable considerations, and remain independent of time-varying risk factors. While inflation contributes to the observed predictability, it doesn’t account for its entirety, emphasizing the multifaceted nature of return predictability in financial markets.

Related Reading:

The Many Colours of CAPE

The 52-Week High, Momentum, and Predicting Mutual Fund Returns

FAQ

Get All Stocks And Equities Research Papers Strategies here

Leave a Reply

{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

Monthly Trading Strategy Club

$42 Per Strategy

>

Login to Your Account



Signup Here
Lost Password