PREDICTIVE POWER OF INFORMATION MARKET PRICES

2012 ◽  
Vol 5 (2) ◽  
pp. 44-74
Author(s):  
Maria Putintseva
Keyword(s):  
Predictive Power ◽  
Prediction Markets ◽  
Behavioral Experiments ◽  
Market Prices ◽  
Underlying Asset ◽  
Information Markets ◽  
Additional Information ◽  
Dow Jones Industrial Average ◽  
Future Events ◽  
Binary Options

Prediction (or information) markets are markets where participants trade contracts whose payoff depends on unknown future events. Studying prediction markets allows to avoid many problems, which arise in some artificially designed behavioral experiments investigating collective decision making or individual's belief formation. This work is aimed, first, to verify whether predictions made by prices of binary options traded in information markets are reliable and whether the prices contain additional information about the future comparing to the information available from the dynamics of underlying asset only. Second, inter- and intraday microstructure of the market of binary options on Dow Jones Industrial Average index is examined and described quantitatively. Third, since some ability to forecast future changes in the underlying asset is detected, a simple trading strategy based on observing the trading process in the prediction market is suggested and its profitability and applicability is evaluated.

Unspanned Global Macro Risks in Bond Returns

2021 ◽  
Author(s):  
Feng Zhao ◽  
Guofu Zhou ◽  
Xiaoneng Zhu
Keyword(s):  
Term Structure ◽  
Predictive Power ◽  
International Markets ◽  
Bond Markets ◽  
Market Prices ◽  
Macroeconomic Variables ◽  
Bond Returns ◽  
Large Panel ◽  
Term Premia ◽  
Macro Finance

We examine the macro-spanning hypothesis for bond returns in international markets. Based on a large panel of real-time macroeconomic variables that are not subject to revisions, we find that global macro factors have predictive power for bond returns unspanned by yield factors. Furthermore, we estimate macro-finance term structure models with the unspanned global macro factors and find that the global macro factors influence the market prices of level and slope risks and induce comovements in forward term premia in global bond markets. This paper was accepted by David Simchi-Levi, finance.

scholarly journals Prediction Markets

2004 ◽  
Vol 18 (2) ◽  
pp. 107-126 ◽  
Author(s):  
Justin Wolfers ◽  
Eric Zitzewitz
Keyword(s):  
Market Design ◽  
Regression Coefficients ◽  
Prediction Markets ◽  
Design Issues ◽  
Future Events ◽  
Uncertain Future ◽  
Insight Into ◽  
Usual Problem

We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.

Using Prediction Market Prices to Differentiate Factors that Influence the Highest and Lowest Priced Tickets in Dynamic Pricing for Major League Baseball

2015 ◽  
Vol 9 (2) ◽  
pp. 43-63
Author(s):  
Rodney Paul ◽  
Andrew Weinbach
Keyword(s):  
Dynamic Pricing ◽  
Major League Baseball ◽  
Prediction Markets ◽  
Market Prices ◽  
Betting Market ◽  
Major League ◽  
Similarities And Differences ◽  
Using Data ◽  
Ticket Prices ◽  
Uncertainty Of Outcome

The use of prediction markets is extended to explain differences in preferences of fans that purchase different price levels of tickets under dynamic pricing for Major League Baseball.  Using data from eleven teams, this research investigates similarities and differences in variables that affect ticket prices for the highest-priced and lowest-priced tickets.  Key contrasts between the groups are found to stem from distinct preferences for uncertainty of outcome, measured by betting market odds, and team quality.  It is also shown that differences between the groups are attributable to sensitivity to factors such as key opponents, weekend games, opening day, and temperature.

scholarly journals HOW PREDICTION MARKETS HELP US UNDERSTAND EVENTS` IMPACT ON THE VOTE IN US PRESIDENTIAL ELECTIONS

2012 ◽  
Vol 5 (3) ◽  
pp. 42-63 ◽  
Author(s):  
Sveinung Arnesen
Keyword(s):  
Business School ◽  
Presidential Elections ◽  
Prediction Markets ◽  
Information Markets ◽  
The Us ◽  
Run Up ◽  
Key Events ◽  
Election Polls ◽  
The Impact ◽  
President Bush

In this paper we argue that pre-election polls and prediction markets reflect two different processes which, by analyzing them together, can help us understand if and how key events which occur during an election campaign influence the final outcome. While polls can be seen as reflecting the voters’ enlightening process towards realizing their vote preferences, prediction markets have this process incorporated into their prediction. We study the movements of weekly poll ratings and IEM market predictions and measure the impact selected events have on these in the run-up to the US 2004 and 2008 presidential elections. We conclude that the Swift Boat ad campaign in 2004 was an enlightening event which moved poll ratings in favor of President Bush, towards the level the IEM market had predicted already before the Swift Boat event. The financial crisis in 2008, on the other hand, was an enlightened event. It came as news to both market traders and poll respondents, sealing the victory for Obama.The paper has previously been presented at the Third International Conference on Prediction and Information Markets, April 3-5 2011, Nottingham Business School.

scholarly journals El modelo de valor razonable: La aproximación de las cifras contables a los valores de mercado (The fair value model: The aproach of the accounting figures to the market values)

2014 ◽  
Vol 8 (3) ◽  
pp. 41
Author(s):  
Eduardo Sosa Mora
Keyword(s):  
Financial Reporting ◽  
Theory Of The Firm ◽  
Financial Statements ◽  
Fair Value ◽  
Reporting Standards ◽  
Market Prices ◽  
Financial Reporting Standards ◽  
Market Values ◽  
Future Events ◽  
Value Model

<p>Desde hace muchos años, en el ámbito académico y en el profesional de la contabilidad, se debate acerca de la importancia de que los estados financieros presenten los activos y pasivos de acuerdo con sus valores de mercado, con el fin de lograr una mejor aproximación a los valores económicos de las empresas. Esto ha propiciado que, en las Normas Internacionales de Información Financiera (NIIF), haya adquirido relevancia el modelo del valor razonable, según el cual los activos y pasivos se miden por sus valores <br />de mercado. La adopción de este modelo significa la instrumentación de la teoría del valor de la empresa y una mayor aproximación de la contabilidad a la teoría de las finanzas, cuyos beneficios deben sopesarse con los riesgos asociados a la obtención de cifras contables a partir de precios de mercado y de supuestos acerca de eventos esperados en el futuro. Este artículo expone los alcances de la adopción de ese modelo en el esfuerzo por lograr que los estados financieros representen fielmente las realidades económicas de las empresas.</p><p> </p><p><strong>Abstract </strong></p><p> </p><p>Since many years ago in the Accounting academic and professional circles there is a debate about the importance that the financial statements represent the assets and liabilities according with their market values, in order to get a better approximation to the economic values of the enterprises. Because of this the fair value model has gained relevance in the International Financial Reporting Standards (IFRS). According with this model, the assets and liabilities are measured by their market values. The adoption of <br />this model means the implementation of the theory of the firm and a greater approximation the Accounting to the Financial Theory, whose benefits must be weighted with the risks of getting accounting figures by using market prices and assumptions about future events. This paper expounds the scopes of adopting this model in the effort to assure that the financial statements represent faithfully the economic realities of the enterprises.</p>

scholarly journals A Model for Traffic Accidents Prediction Based on Driver Personality Traits Assessment

2017 ◽  
Vol 29 (6) ◽  
pp. 631-642 ◽  
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Krsto Lipovac ◽  
Svetlana Čičević ◽  
Boris Antić
Keyword(s):  
Personality Traits ◽  
Structural Equation ◽  
Traffic Accidents ◽  
Predictive Power ◽  
Correlation Coefficients ◽  
Binary Logistic Regression ◽  
Driving Ability ◽  
Hierarchical Regression ◽  
Driver Behaviour ◽  
Additional Information

The model proposed in this paper uses four psychological instruments for assessing driver behaviour and personality traits aiming to find a relationship between the considered constructs and the occurrence of traffic accidents. A Barratt Impulsiveness Scale (BIS-11) was used for the assessment of impulsivity, Aggressive Driving Behaviour Questionnaire (ADBQ) for assessing the aggressiveness while driving, Manchester Driver Attitude Questionnaire (DAQ) and the Questionnaire for self-assessment of driving ability. Besides these instruments, the participants filled out an extensive demographic survey. Within the statistical analysis, in addition to the descriptive indicators, correlation coefficients were calculated and four hierarchical regression analyses were performed to determine the predictive power of personality traits on the occurrence of traffic accidents. Further, to confirm the results and to obtain additional information about the relationship between the considered variables, the structural equation modelling and binary logistic regression have been implemented. A sample of this research covered 305 drivers, of which there were 100 bus drivers and 102 truck drivers, as well as 103 drivers of privately owned vehicles. The results indicate that BIS-11 and ADBQ questionnaires show the best predictive power which means that impulsivity and aggressiveness as personality traits have the greatest influence on the occurrence of traffic accidents. This research could be useful in many fields, such as the design of selection procedures for professional drivers, development of programs for the prevention of traffic accidents and violations of law, rehabilitation of drivers who have been deprived of the driving license, etc.

scholarly journals OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

2021 ◽  
Vol 23 (1) ◽  
pp. 336
Author(s):  
Michele Provenzano ◽  
Raffaele Serra ◽  
Carlo Garofalo ◽  
Ashour Michael ◽  
Giuseppina Crugliano ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Predictive Biomarkers ◽  
Ckd Progression ◽  
Additional Information ◽  
Improve Risk Stratification ◽  
Prognosis And Prediction ◽  
Future Events ◽  
Disease Focus ◽  
Pathophysiologic Mechanisms

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

scholarly journals Community structure follows simple assembly rules in microbial microcosms

2016 ◽  
Author(s):  
Jonathan Friedman ◽  
Logan M. Higgins ◽  
Jeff Gore
Keyword(s):  
Community Structure ◽  
Predictive Power ◽  
Bacterial Species ◽  
Additional Information ◽  
Interacting Species ◽  
Assembly Rule ◽  
Simple Assembly ◽  
Synthetic Microbial Communities ◽  
Tremendous Impact ◽  
Entire Planet

IntroductionMicrobes typically form diverse communities of interacting species, whose activities have tremendous impact on the plants, animals, and humans they associate with1–3, as well as on the biogeochemistry of the entire planet4. The ability to predict the structure of these complex communities is crucial to understanding, managing, and utilizing them5. Here, we propose a simple, qualitative assembly rule that predicts community structure from the outcomes of competitions between small sets of species, and experimentally assess its predictive power using synthetic microbial communities. The rule's accuracy was evaluated by competing combinations of up to eight soil bacterial species, and comparing the experimentally observed outcomes to the predicted ones. Nearly all competitions resulted in a unique, stable community, whose composition was independent of the initial species fractions. Survival in three-species competitions was predicted by the pairwise outcomes with an accuracy of ~90%. Obtaining a similar level of accuracy in competitions between sets of seven or all eight species required incorporating additional information regarding the outcomes of the three-species competitions. Our results demonstrate experimentally the ability of a simple bottom-up approach to predict community structure. Such an approach is key for anticipating the response of communities to changing environments, designing interventions to steer existing communities to more desirable states, and, ultimately, rationally designing communities de novo6,7.

scholarly journals CONDITIONAL PREDICTION MARKETS AS CORPORATE DECISION SUPPORT SYSTEMS – AN EXPERIMENTAL COMPARISON WITH GROUP DELIBERATIONS

2012 ◽  
Vol 1 (3) ◽  
pp. 189-208
Author(s):  
Timm Sprenger ◽  
Paul Bolster ◽  
Anand Venkateswaran
Keyword(s):  
Research Question ◽  
Experimental Comparison ◽  
Prediction Markets ◽  
Experimental Setting ◽  
Primary Research ◽  
Information Markets ◽  
Group Member ◽  
Different Sources ◽  
Conditional Prediction ◽  
Better Than

Predicting the future is an integral part of effective corporate decision making. Most firms face the critical challenge of aggregating information dispersed among its agents. These agents and thus the aggregation process are prone to judgmental biases. The primary research question we address is whether markets correct these biases better than group deliberations. Using an experimental setting, we find that information markets provide more accurate and less volatile forecasts than group deliberations. We also describe different sources of the behavioral biases we observe. For example, while a deliberating group can be led astray by an influential group member, traders tend to overweight personal preferences. Our results indicate that conditional prediction markets provide a more effective medium for aggregating information than group deliberations.

scholarly journals Comparison of carcinogenicity predictions by the Oncologic expert system with NTP 2-year rodent study tumorigenicity results

2018 ◽  
Vol 2 ◽  
pp. 239784731877112
Author(s):  
Carr J Smith ◽  
Thomas A Perfetti
Keyword(s):  
Expert System ◽  
Liver Cancer ◽  
Predictive Power ◽  
Historical Data ◽  
Liver Tumors ◽  
Additional Information ◽  
Ordinal Ranking ◽  
National Toxicology Program ◽  
Toxicological Research ◽  
Actual Formation

The degree of correlation between tumors predicted by OncoLogic™ (Oncologic) and the actual formation of tumors as observed in the National Toxicology Program (NTP) 2-year rodent studies is lower for “justification reports” that incorporate historical data than for “data reports” that do not. The correlation between the ordinal ranking of the observed carcinogenicity of parent NTP chemicals and the predicted “level of carcinogenicity concern” from the justification reports obtained from Oncologic is poor ( r = 0.56). Similarly, the correlation between the ordinal ranking of the carcinogenicity of metabolites from parent NTP chemicals and the predicted “level of carcinogenicity concern” from the justification report obtained from Oncologic is also poor ( r = 0.43). In contrast, the correlation between the ordinal ranking of the observed carcinogenicity of parent NTP chemicals and the predicted level of carcinogenicity concern from the data reports obtained from Oncologic is comparatively better ( r = 0.75). The correlation between the ordinal ranking of the carcinogenicity of metabolites from parent NTP chemicals and the predicted “level of carcinogenicity concern” from the data reports generated from Oncologic is also comparatively good ( r = 0.68). The level of correlation between the ordinal tumorigenicity ranks of parent chemicals and between the ordinal tumorigenicity ranks of chemicals reported to induce liver tumors in the National Center for Toxicological Research liver cancer database was also investigated. There was a higher degree of correlation seen for Oncologic “data reports” as compared with Oncologic “justification reports.” Incorporation of additional information via “justification reports” weakens the predictive power of Oncologic.

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