nep-for New Economics Papers
on Forecasting
Issue of 2023‒02‒13
two papers chosen by
Rob J Hyndman
Monash University

  1. On the Predictability of the DJIA and S&P500 Indices By John B. Guerard; Dimitrios D. Thomakos; Foteini Kyriazi; Konstantinos Mamais
  2. Macro carbon price prediction with support vector regression and Paris accord targets By Jinhui Li

  1. By: John B. Guerard (McKinley Capital Management, LLC); Dimitrios D. Thomakos (Department of Business Administration, National and Kapodistrian University of Athens, Athens, 10559 Greece; International Centre for Economic Analysis, Canada); Foteini Kyriazi (Department of Agribusiness and Supply Chain Management, Agricultural University of Athens); Konstantinos Mamais (Department of Business Administration, National and Kapodistrian University of Athens, Athens, 10559 Greece)
    Abstract: We obtained from Standard and Poor's Corporation, the complete 126-year history of the Dow Jones Industrial Average (DJIA) daily closing prices. We are applying rolling window averaging and adaptive learning methodologies, coupled with robust estimation methods, to examine which are the best forecasting models over a broad range of economic and financial conditions during the life of the index, based on daily and monthly stock index prices and daily, monthly, and semi-annual stock returns. Why is an AR(1) model a reasonable benchmark of stock prices? Why do we have it? What should be our forecasting benchmarks? Let us briefly re-visit the history of stock price research and efficient markets. Do we find forecasting improvements from the Hendry-Castle-Doornik-Clements approach using robust forecasting methodologies and saturation variables in the prices of the index? Given that the DJIA fell over 15% during the first half of 2022, is this one of the worst six-month periods ever? What has happened to the Dow, historically, during such periods in the past with regards to six-month, one-year, and three-year-ahead stock returns? Is capitalism dead or doomed? We report statistically significant forecasting improvement from saturation and robust forecasting techniques during the 1896 -June 2022 period. We report forecasted stock returns for the next 6 months and three years that are bullish. In the King's English, June 30, 2022 was another excellent common stock buying opportunity and capitalism is not dead.
    Keywords: forecasting financial prices, forecasting financial returns, leading economic indicator, return volatility, rolling window averaging
    JEL: C53 C52 C58 G11 G14
    Date: 2023–01
  2. By: Jinhui Li
    Abstract: Carbon neutralization is an urgent task in society because of the global warming threat. And carbon trading is an essential market mechanics to solve carbon reduction targets. Macro carbon price prediction is vital in the useful management and decision-making of the carbon market. We focus on the EU carbon market and we choose oil price, coal price, gas price, and DAX index to be the four market factors in predicting carbon price, and also we select carbon emission targets from Paris Accord as the political factor in the carbon market in terms of the macro view of the carbon price prediction. Thus we use these five factors as inputs to predict the future carbon yearly price in 2030 with the support vector regression models. We use grid search and cross validation to guarantee the prediction performance of our models. We believe this model will have great applications in the macro carbon price prediction.
    Date: 2022–11

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