nep-for New Economics Papers
on Forecasting
Issue of 2023‒12‒11
three papers chosen by
Rob J Hyndman, Monash University

  1. Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering By George Athanasopoulos; Rob J Hyndman; Raffaele Mattera
  2. Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach By Chen, Ying; Grith, Maria; Lai, Hannah L. H.
  3. Predicting risk/reward ratio in financial markets for asset management using machine learning By Reza Yarbakhsh; Mahdieh Soleymani Baghshah; Hamidreza Karimaghaie

  1. By: George Athanasopoulos; Rob J Hyndman; Raffaele Mattera
    Abstract: This paper discusses the use of forecast reconciliation with stock price time series and the corresponding stock index. The individual stock price series may be grouped using known meta-data or other clustering methods. We propose a novel forecasting framework that combines forecast reconciliation and clustering, to lead to better forecasts of both the index and the individual stock price series. The proposed approach is applied to the Dow Jones Industrial Average Index and its component stocks. The results demonstrate empirically that reconciliation improves forecasts of the stock market index and its constituents.
    Keywords: financial time series, hierarchical forecasting, clustering, unsupervised learning, prediction, machine learning, finance
    JEL: C53 C10
    Date: 2023
  2. By: Chen, Ying; Grith, Maria; Lai, Hannah L. H.
    Abstract: Implied volatility (IV) forecasting is inherently challenging due to its high dimensionality across various moneyness and maturity, and nonlinearity in both spatial and temporal aspects. We utilize implied volatility surfaces (IVS) to represent comprehensive spatial dependence and model the nonlinear temporal dependencies within a series of IVS. Leveraging advanced kernel-based machine learning techniques, we introduce the functional Neural Tangent Kernel (fNTK) estimator within the Nonlinear Functional Autoregression framework, specifically tailored to capture intricate relationships within implied volatilities. We establish the connection between fNTK and kernel regression, emphasizing its role in contemporary nonparametric statistical modeling. Empirically, we analyze S&P 500 Index options from January 2009 to December 2021, encompassing more than 6 million European calls and puts, thereby showcasing the superior forecast accuracy of fNTK.We demonstrate the significant economic value of having an accurate implied volatility forecaster within trading strategies. Notably, short delta-neutral straddle trading, supported by fNTK, achieves a Sharpe ratio ranging from 1.45 to 2.02, resulting in a relative enhancement in trading outcomes ranging from 77% to 583%.
    Keywords: Implied Volatility Surfaces; Neural Networks; Neural Tangent Kernel; Implied Volatility Forecasting; Nonlinear Functional Autoregression; Option Trading Strategies
    JEL: C14 C45 C58 G11 G13 G17
    Date: 2023–10–24
  3. By: Reza Yarbakhsh; Mahdieh Soleymani Baghshah; Hamidreza Karimaghaie
    Abstract: Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions.
    Date: 2023–11

This nep-for issue is ©2023 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.