Forecasting high-frequency stock returns: a comparison of alternative methods

Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns

“…Fourth, on methodological grounds, we have kept the complexity of our application of HMMs to a minimum by working with two-state models, but concurrent work (also applied to other asset classes; see e.g., Catania et al [54], Koki et al [27], Hotz-Behofsits et al [55]) has shown the advantages of flexible Bayesian MCMC estimation approaches in terms of resulting density forecast accuracy. Akyildirim et al [1] is a recent example of a comparison of range-forecasting techniques to asset (in this case, stock) returns inspired by modern applications of machine learning, which shows that our study might be fruitfully extended to compare the forecasting power of HMM and automatic model selection techniques with machine learning algorithms. Of course, it would be interesting to check whether such an integration may lead to forecast improvements in our application.…”

“…Table 1 delivers only one stark result: in quiet periods (January 2004-December 2007, January 2013-May 2018), the simple AR(1) benchmark is outperformed by the stepwise predictive regressions for most commodities, while in the high-volatility regime (January 2008-December 2012), this is hardly the case. More precisely, for 11 commodities out of 14 (i.e., all excluding of live cattle, silver, and coffee), stepwise regressions forecast better than the AR(1) benchmark in the low-volatility regime; in the case of crude oil future returns, this occurs in both regimes and the RMSFE is between 6 and 7% per month vs. 8.6% in the case of an AR (1). Summing up, in 12 occasions out of 28 (as defined by the combination of the underlying commodity and the volatility regime), the framework yielding superior predictions is different from the AR(1) benchmark.…”

“…In the last few decades, two crucial issues have been identified by the forecasting literature applied to finance (see the recent discussion in Akyildirim et al [1]). First, several predictors may display substantial and statistically significant predictive power, but their predictive content may be unstable over time and, as a result, it is unclear whether they can be exploited reliably (see, e.g., Rapach and Wohar [2]; Ang and Bekaert [3]; Paye and Timmermann [4]).…”

“…However, in the presence of a multiplicity of predictors (including several macro, aggregate, and individual commodity specific factors), this seems important. Additional papers that use flexible methods to incorporate the instability in either the predictors or their coefficients include: Drachal [25], which uses Dynamic Model Averaging and Dynamic Model Selection frameworks to forecast spot oil prices; Luo et al [23], which uses a hidden Markov HAR model to model the volatility of agricultural commodity futures; and Akyildirim et al [1], which compares range stock return forecasts based on machine learning.…”

Forecast combinations for benchmarks of long-term stock returns using machine learning methods

“…However, the seminal work by Fama (1988), Campbell and Shiller (1988), or Stambaugh (1999) suggests the nowadays 'common wisdom' of long term predictability (Lioui & Poncet, 2019). For more recent approaches regarding stock market forecasts, see, for example, Scholz et al (2015), Scholz et al (2016), Lioui and Poncet (2019), or Akyildirim et al (2022) and the discussion therein.…”