The Quanted Round-up is a curated summary that covers relevant research on key topics in quantitative financial decision-making.

Highlights

This edition looks at the impact of seasonality on various financial markets, including commodities, equities, options, and cryptocurrencies. It highlights how understanding seasonal patterns enhances predictive models and informs better investment strategies. By integrating seasonal insights, the studies offer valuable approaches for optimising asset allocation and refining market predictions across diverse financial landscapes.

Seasonal Volatility in Agricultural Markets: Modelling and Empirical Investigations

Lorenz Schneider & Bertrand Tavin

This paper deals with the issue of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, we employ a maturity-dependent damping term to account for the Samuelson effect. We give the conditions under which the volatility dynamics are well defined and obtain the joint characteristic function of a pair of futures prices. We then derive the state-space representation of our model in order to use the Kalman filter algorithm for estimation and prediction. The empirical analysis is carried out using daily futures data from 2007 to 2019 for corn, cotton, soybeans, sugar and wheat. In-sample, the seasonal models clearly outperform the nested non-seasonal models in all five markets. Out-of- sample, we predict volatility peaks with high accuracy for four of these five commodities.

Momentum, Reversal, and Seasonality in Option Returns

Christopher S. Jones, Mehdi Khorram & Haitao Mo

Option returns display substantial momentum using formation periods ranging from 6 to 36 months long, with long/short portfolios obtaining annualized Sharpe ratios above 1.5. In the short term, option returns exhibit reversal. Options also show marked seasonality at multiples of three and 12 monthly lags. All of these results are highly significant and stable in the cross section and over time. They remain strong after controlling for other characteristics, and momentum and seasonality survive factor risk-adjustment. Momentum is mainly explained by an underreaction to past volatility and other shocks, while seasonality reflects unpriced seasonal variation in stock return volatility.

A New Approach to Understanding Seasonality

Raúl Gómez Sánchez

The seasonality of financial assets is one of the most discussed topics in stock market literature, with numerous studies and gurus in this field, which have led to the generation of great strategies. . In this sense, one of the most famous strategies is perhaps "Sell a may, and go away". But why is it disappearing, why is the seasonality of 20 years not the same in terms of visual form as 40 or 10 years from now? In this article we offer an explanation for this, comparing the seasonality of the S&P500, with one of the great forgotten things in the stock market, the opening gaps. And where we demonstrate how the minimums and maximums of the seasonally adjusted S&P500 can be obtained through the seasonalisation of the gap component of the S&P500, for any time period.

A Seasonality Factor in Asset Allocation

Frank McGroarty, Emmanouil Platanakis, Athanasios Sakkas & Andrew Urquhart

Motivated by the seasonality found in equity returns, we create a Turn-of-the-Month (ToM) allocation strategy in the U.S. equity market and investigate its value in asset allocation. By using a wide variety of portfolio construction techniques in an attempt to address the impact of estimation risk in the input parameters, we show significant out-of- sample benefits from investing in the ToM factor along with a traditional stock-bond portfolio. The out-of-sample benefits remain significant after taking into account transaction costs and by using different rolling estimation windows indicating that a market timing strategy based on the ToM offers substantial benefits to investors when determining the allocation of assets.

Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective

MD Rokibul Hasan

Sales prediction plays a paramount role in the decision- making process for organisations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management.

Seasonality, Trend-following, and Mean reversion in Bitcoin

Matus Padysak & Radovan Vojtko

The cryptocurrency market is not negligible nor minor anymore. With the continuous development of the crypto market, researchers aimed to analyse novel cryptocurrencies thoroughly. An excellent starting point might be in other recognised effects from the developed asset classes. This research examines seasonality effects such as when the major NYSE opened or closed and their intraday, overnight, or daily components. Furthermore, we also examine the distribution of the daily returns and the returns that are significant. The results point to a simple seasonality strategy that is based on holding BTC only for two hours per day. The second aim is to examine trend- following and mean reversion strategies. The data suggests that BTC tends to trend when it is at its maximum and bounce back when at the minimum. These findings support the empirical observations that BTC tends to trend strongly and revert after drawdowns.

Are Return Seasonalities Due to Risk or Mispricing? Evidence from Seasonal Reversals

Matti Keloharju, Juhani T. Linnainmaa & Peter M. Nyberg

Stocks tend to earn high or low returns relative to other stocks every year in the same month (Heston and Sadka 2008). We show these seasonalities are balanced out by seasonal reversals: a stock that has a high expected return relative to other stocks in one month has a low expected return relative to other stocks in the other months. The seasonalities and seasonal reversals add up to zero over the calendar year, which is consistent with seasonalities being driven by temporary mispricing. Seasonal reversals are economically large, statistically highly significant, and they resemble, but are distinct from, long-term reversals.

References

1. Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective. April 2024. Hasan, M.D.R. Journal of Business and Management Studies. Available at AI-Kindi: https://doi.org/10.32996/jbms.2024.6.2.10

2. Are Return Seasonalities Due to Risk or Mispricing? Evidence from Seasonal Reversals. October 2019. Keloharju, M.; Linnainmaa, J.T. and Nyberg, P.M. Journal of Financial Economics (JFE), Forthcoming. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3276334

3. Momentum, Reversal, and Seasonality in Option Returns. November 2020. Jones, C.S.; Khorram, M. and Mo, H. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3705500

4. A New Approach to Understanding Seasonality. June 2024. Gómez, S.R. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4934059

5. Seasonal Volatility in Agricultural Markets: Modelling and Empirical Investigations. May 2021. Schneider, L and Tavin, B. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2620584

6. A Seasonality Factor in Asset Allocation. March 2019. McGroarty, F.; Platanakis, E.; Sakkas, A. and Urquhart, A. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3266285

7. Seasonality, Trend-following, and Mean reversion in Bitcoin. April 2022. Padyšák, M and Vojtko, R. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4081000