Access the full text.
Sign up today, get DeepDyve free for 14 days.
(2002)
Was there evidence of global on September 11, 2001?
Michael Cliff, Gregory Brown (2001)
Investor Sentiment and Asset ValuationFEN: Behavioral Finance (Topic)
Eric Ghysels, A. Sinko, Rossen Valkanov (2006)
MIDAS Regressions: Further Results and New DirectionsEconometric Reviews, 26
Malcolm Baker (2015)
Global , Local , and Contagious Investor Sentiment 1
R. Sias, L. Starks (1997)
Return autocorrelation and institutional investorsJournal of Financial Economics, 46
N. Barberis, A. Shleifer, R. Vishny (1998)
A model of investor sentimentJournal of Financial Economics, 49
(2023)
Have zero-dated options broken VIX?
M. Baker, J. Wurgler (2006)
Investor sentiment and the cross-section of stock returnsJournal of Finance, 61
Malcolm Baker, Jeffrey Wurgler (2003)
Investor Sentiment and the Cross-Section of Stock ReturnsSPGMI: Compustat Fundamentals (Topic)
Athanasios Tsagkanos (2017)
Stock market development and income inequalityJournal of Economic Studies, 44
R. Nelson, P. Bancel (2011)
Effects of mass consciousness: changes in random data during global events.Explore, 7 6
Bayram Salur (2013)
Investor sentiment in the stock market
(2020)
Stock returns and the mind: an unlikely result that could change our understanding of
Simon So, Violet Lei (2015)
ON THE RELATIONSHIP BETWEEN INVESTOR SENTIMENT, VIX AND TRADING VOLUMERisk Governance and Control: Financial Markets & Institutions, 5
E. May, James Spottiswoode, James Spottiswoode (2011)
The Global Consciousness Project: Identifying the Source of Psi
D. Radin (2006)
Entangled Minds: Extrasensory Experiences in Quantum Reality
Ò. Jordà (2005)
Estimation and Inference of Impulse Responses by Local ProjectionsThe American Economic Review, 95
R. Shiller (2017)
Narrative EconomicsCapital Markets: Asset Pricing & Valuation eJournal
G. Zhou (2018)
Measuring investor sentimentAnnual Review of Financial Economics, 10
J. Long, A. Shleifer, Lawrence Summers, Robert Waldmann (1990)
Noise Trader Risk in Financial MarketsJournal of Political Economy, 98
T. Edwards, H. Preston (2017)
A Practitioner's Guide to Reading VIX?
Martin Zweig (1973)
AN INVESTOR EXPECTATIONS STOCK PRICE PREDICTIVE MODEL USING CLOSED‐END FUND PREMIUMSJournal of Finance, 28
E. Lefèvre (1923)
Reminiscences of a Stock Operator
Ulf Holmberg (2022)
Validating the GCP data hypothesis using internet search data.Explore
R. Nelson, Dean Radin, R. Shoup, P. Bancel (2002)
Correlations of continuous random data with major world eventsFoundations of Physics Letters, 15
A. Lo, A. Mackinlay (1989)
When are Contrarian Profits Due to Stock Market Overreaction?Behavioral & Experimental Finance eJournal
Guofu Zhou (2017)
Measuring Investor SentimentCapital Markets: Asset Pricing & Valuation eJournal
F. Cross (1973)
The Behavior of Stock Prices on Fridays and MondaysFinancial Analysts Journal, 29
S.P. Fraiberger, L. Do, D. Puy, R. Ranciere (2018)
Media Sentiment and International Asset Prices
(2021)
Revisiting stock returns and the mind: digging deeper into the data
G.W. Brown, M.T. Cliff (2005)
Investor sentiment and asset valuationThe Journal of Business, 78
J. Murphy (1986)
Technical Analysis of The Financial Markets
Tarun Chordia, B. Swaminathan (2000)
Trading Volume and Cross‐Autocorrelations in Stock ReturnsJournal of Finance, 55
N. Barberis, A. Shleifer, Robert Vishny (1997)
A Model of Investor SentimentBehavioral & Experimental Finance
J.M. Keynes (1936)
The General Theory of Employment, Interest and Money
A. Pagan, G. Schwert (1989)
Alternative Models for Conditional Stock VolatilityNBER Working Paper Series
P. Bancel (2011)
Reply to May and Spottiswoode’s ‘the global consciousness project: identifying the source of psi’Journal of Scientific Exploration, 25
R. Shiller (2017)
Narrative economicsAmerican Economic Review, 107
L. Rogers, S. Satchell, Youngjun Yoon (1994)
Estimating the volatility of stock prices: a comparison of methods that use high and low pricesApplied Financial Economics, 4
(2002)
Exploring relationships beween random physical events and mass human attention: asking for whom the bell tolls
The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.Design/methodology/approachThis study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.FindingsThe results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.Research limitations/implicationsOne limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.Practical implicationsThe study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.Originality/valueUtilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.
Journal of Economic Studies – Emerald Publishing
Published: Aug 30, 2024
Keywords: Stock market returns; VIX; Global consciousness project
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.