The Role of Momentum, Sentiment, and Economic Fundamentals in Forecasting Bear Stock Market

Chen, Yi‐Ting; Vincent, Kendro

doi:10.1002/for.2392

Cited by 6 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study concludes that the consumption-wealth ratio can indeed be used for statistically significant forecasts. Chen and Vincent (2016) also use different econometric models applied to full-sample approaches and out-of-sample approaches in order to analyze the informational value of different variables for the development of the Standard and Poor's 500 index (S&P 500) for the period 1964 to 2011. They conclude that the market momentum and the investor sentiment can indeed serve as potential predictors for bear markets.…”

Section: Technological Progress In Stock Market Forecastingmentioning

confidence: 99%

“…In order to successfully design active investment strategies such as market timing, stock picking, or index picking, forecasts of future stock market developments are indispensable. New forecasting methods are constantly being discussed: econometric models (Goyal et al 2021;Chen and Vincent 2016;Welch and Goyal 2008), artificial neural networks (Rajab and Sharma 2019;Atsalakis and Valavanis 2009), artificial intelligence (Mallikarjuna and Rao 2019), capital market simulations with multi-agent models (Yang et al 2020;Krichene and El-Aroui 2018;Arthur et al 1997), modelling based on the expectations of capital market agents (Atmaz et al 2021;Greenwood and Shleifer 2014), and neuro-psycho-economics approaches (Ortiz-Teran et al 2019;Kandasamy et al 2016;Werner et al 2009). However, testing these approaches using ex-post forecasts in an out-ofsample data domain repeatedly leads to apparent forecasting successes that then may not materialize in real ex-ante settings (Kazak and Pohlmeier 2019).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Sticky Stock Market Analysts

Filiz

Judek

Lorenz

et al. 2021

JRFM

View full text Add to dashboard Cite

Technological progress in recent years has made new methods available for making forecasts in a variety of areas. We examine the success of ex-ante stock market forecasts of three major stock market indices, i.e., the German Stock Market Index (DAX), the Dow Jones Industrial Index (DJI), and the Euro Stoxx 50 (SX5E). We test whether the forecasts prove true when they reach their effective dates and are therefore suitable for active investment strategies. We revive the thoughts of the American sociologist William Fielding Ogburn, who argues that forecasters consistently underestimate the variability of the future. In addition, we draw on some contemporary measures of forecast quality (prediction-realization diagram, test of unbiasedness, and Diebold–Mariano test). We reveal that (a) unusual events are underrepresented in the forecasts, (b) the dispersion of the forecasts lags behind that of the actual events, (c) the slope of the regression lines in the prediction-realization diagram is <1, (d) the forecasts are highly biased, and (e) the quality of the forecasts is not significantly better than that of naïve forecasts. The overall behavior of the forecasters can be described as “sticky” because their forecasts adhere too strongly to long-term trends in the indices and are thus characterized by conservatism.

show abstract

Section: Technological Progress In Stock Market Forecastingmentioning

confidence: 99%

mentioning

confidence: 99%

Sticky Stock Market Analysts

Filiz

Judek

Lorenz

et al. 2021

JRFM

View full text Add to dashboard Cite

show abstract

“…In order to successfully design active investment strategies such as market timing, stock picking, or index picking, forecasts of future stock market developments are indispensable. New forecasting methods are constantly being discussed: econometric models (Goyal et al, 2021;Chen & Vincent, 2016;Welch & Goyal, 2008), artificial neural networks (Rajab & Sharma, 2019;Atsalakis & Valavanis, 2009), artificial intelligence (Mallikarjuna & Rao, 2019), capital market simulations with multi-agent models (Yang et al, 2020;Krichene & El-Aroui, 2018;Arthur et al, 1997), modelling based on the expectations of capital market agents (Atmaz et al, 2021;Greenwood & Shleifer, 2014), and neuro-psycho-economics approaches (Ortiz-Teran et al, 2019;Kandasamy et al, 2016;Werner et al, 2009). However, testing these approaches using ex-post forecasts in an out-of-sample data domain repeatedly leads to apparent forecasting successes that then may not materialize in real ex-ante settings (Kazak & Pohlmeier, 2019).…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Decision-Making, Economic Behavior and Predictability in Financial Markets

Judek¹

View full text Add to dashboard Cite

Digital transformation is producing a growing number of technological innovations that have an impact on our daily lives. In a variety of areas, economic agents increasingly have the opportunity to interact with algorithms, as shown, for example, by the offering of robo-advisors, and thus also to influence events on financial markets. This thesis aims to examine the behavior of economic agents when interacting with algorithms and their willingness to use them in order to contribute to a better understanding of Algorithm Aversion. Algorithm Aversion describes the negative attitude towards the use of algorithms that economic agents often exhibit once they realize that algorithms are superior but not error-free. The first part of this thesis consists of five experimental studies in this regard. The first contribution shows that Algorithm Aversion in repeated tasks can be partially reduced by increasing experience over time. The second contribution addresses the scope of a decision and shows that the use of algorithms is often rejected in situations where the consequences of an error are serious, even though their use has a higher chance of success. The third contribution shows that possible user interventions in the prediction generation process reduce Algorithm Aversion more reliably if they are granted on the prediction result (output of the algorithm) instead of on the configuration (input of an algorithm). The fourth contribution examines the impact of proxy decisions on Algorithm Aversion. However, making decisions for third parties does not reduce the extent of Algorithm Aversion. The fifth contribution shows that the decision behavior to use an algorithm varies with the prior adoption rate of other economic agents, and that a prior high adoption rate leads to more frequent use of an algorithm than a prior low adoption rate. Overall, Algorithm Aversion proves to be highly robust and can contribute to suboptimal decisions. Overcoming Algorithm Aversion is essential to exploit the great potential that technological innovation brings to forecasting. The second part of this thesis consists of two more papers that contribute to the literature on the quality of capital market forecasts. While the sixth contribution examines the quality of interest rate forecasts in the Latin American region, the seventh contribution focuses on stock market forecasts for three major indices. Overall, the capital market forecasts examined are in most cases inadequate. While forecasts in the Latin American region largely reflect current rather than future interest rate developments, stock index forecasts show that most stock market analysts underestimate the variability of reality and tend toward conservatism. Therefore, it is crucial to improve forecasting models and to react more flexibly to new developments. vii Zusammenfassung Die digitale Transformation bringt immer mehr technische Innovationen hervor, die Auswirkungen auf unser tägliches Leben haben. In einer Vielzahl von Bereichen haben Wirtschaftsakteure zunehmend die Möglichke...

show abstract

The quality of governance and momentum profits: International evidence

Sherif

Chen

2019

The British Accounting Review

View full text Add to dashboard Cite

The Role of Momentum, Sentiment, and Economic Fundamentals in Forecasting Bear Stock Market

Cited by 6 publications

References 56 publications

Sticky Stock Market Analysts

Sticky Stock Market Analysts

Algorithmic Decision-Making, Economic Behavior and Predictability in Financial Markets

The quality of governance and momentum profits: International evidence

Contact Info

Product

Resources

About