scholarly journals Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments

2012 ◽  
Vol 4 (4) ◽  
pp. 35-64 ◽  
Author(s):  
Mikhail Anufriev ◽  
Cars Hommes
Keyword(s):  
Asset Pricing ◽  
Simple Model ◽  
Predictive Power ◽  
Experimental Setting ◽  
Price Behavior ◽  
Switching Model ◽  
Evolutionary Selection ◽  
Out Of Sample ◽  
Monotonic Convergence ◽  
Selection Of

In recent “learning to forecast” experiments (Hommes et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations, and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea is evolutionary selection among heterogeneous expectation rules, driven by their relative performance. The out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting and aggregate price behavior. (JEL C53, C91, D83, D84, G12)

scholarly journals German forecasters’ narratives: How informative are German business cycle forecast reports?

2021 ◽  
Author(s):  
Karsten Müller
Keyword(s):  
Business Cycle ◽  
Fixed Effects ◽  
Predictive Power ◽  
Recursive Estimation ◽  
Regression Methods ◽  
Inflation Forecasts ◽  
Fixed Effects Panel Regression ◽  
Weak Evidence ◽  
Out Of Sample ◽  
Different Characteristics

AbstractBased on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

scholarly journals IS MORE MEMORY IN EVOLUTIONARY SELECTION (DE)STABILIZING?

2011 ◽  
Vol 16 (3) ◽  
pp. 335-357 ◽  
Author(s):  
Cars Hommes ◽  
Tatiana Kiseleva ◽  
Yuri Kuznetsov ◽  
Miroslav Verbic
Keyword(s):  
Asset Pricing ◽  
Multinomial Logit Model ◽  
Heterogeneous Beliefs ◽  
Pricing Model ◽  
Key Factors ◽  
Evolutionary Selection ◽  
Asset Pricing Model ◽  
Economic Fundamentals ◽  
Selection Dynamics ◽  
The Stability

We investigate the effects of memory on the stability of evolutionary selection dynamics based on a multinomial logit model in a simple asset pricing model with heterogeneous beliefs. Whether memory is stabilizing or destabilizing depends in general on three key factors: (1) whether or not the weights on past observations are normalized; (2) the ecology or composition of forecasting rules, in particular the average trend extrapolation factor and the spread or diversity in biased forecasts; and (3) whether or not costs for information gathering of economic fundamentals have to be incurred.

Real-Time Profitability of Published Anomalies: An Out-of-Sample Test

2013 ◽  
Vol 03 (03n04) ◽  
pp. 1350016 ◽  
Author(s):  
Jing-Zhi Huang ◽  
Zhijian Huang
Keyword(s):  
Asset Pricing ◽  
Transaction Costs ◽  
Real Time ◽  
Empirical Evidence ◽  
Equity Market ◽  
Data Snooping ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Out Of Sample ◽  
Sample Test

Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published anomalies and non-forward-looking filters and that each year recursively picks the best past-performer among such anomalies over a given training period. We find that this strategy can outperform the equity market even after transaction costs. Overall, our results suggest that published anomalies persist even after controlling for data-snooping bias.

Dynamic Characterization of Brushless DC Motors and Propellers for Flight Applications

2017 ◽  
Vol 05 (03) ◽  
pp. 159-167 ◽  
Author(s):  
Dominic Muzar ◽  
Eric Lanteigne ◽  
Justin McLeod
Keyword(s):  
Simple Model ◽  
Brushless Dc Motor ◽  
Dynamic Characterization ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors ◽  
Simple Motor ◽  
Aerial Vehicle ◽  
Selection Of

Although there exist a number of accurate unmanned aerial vehicle (UAV) thruster models, these models require the precise measurements of several motor and propeller characteristics. This paper presents a simple motor and propeller model that relies solely upon data provided by manufacturers. The model is validated by comparing theoretical motor and propeller behavior to experimental results obtained from thrust tests in a wind tunnel. The objective is to provide an accurate yet simple model to facilitate the selection of appropriate brushless DC motor and propeller combinations for flight applications.

scholarly journals Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Turning Points ◽  
Probit Model ◽  
Economic Conditions ◽  
Var Model ◽  
Leading Indicators ◽  
Out Of Sample ◽  
The Business Cycle

Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

scholarly journals Decision Support System for Share Investment Using The Capital Assetpricing Method (CAPM)

2021 ◽  
Vol 2 (1) ◽  
pp. 18-22
Author(s):  
Tino Feri Efendi ◽  
Andriani Putri Wihartati
Keyword(s):  
Decision Support ◽  
Asset Pricing ◽  
Decision Support System ◽  
Support System ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Asset Pricing Model ◽  
Stock Investment ◽  
Selection Of

Investment is the placement of a number of funds at this time with the hope of obtaining benefits in the future. Stocks are one of the most popular investments. The current millennial generation is interested in investing in stocks because the capital required is not too large. However, in choosing a good stock for investment, the ability to read financial ratios is required. Errors in reading financial ratios will cause stock investment not to go as expected. To help with this, a system capable of supporting decisions is needed. There are several methods that can be used to produce a decision support system. In this study, the authors use the Capital Asset Pricing Model (CAPM) method in designing a decision support system in stock investment selection.The method in this research is through observation, interview, and literature study. The system design is made using Contex Diagram, HIPO, DAD, and database design. The system is made in a program with the PHP programming language. The process of determining the selection of stock investments using the Capital Asset Pricing Model (CAPM) method can simplify the determination process. Then with the method. can make it easier to determine the selection of stock investment.The final result in the stock investment selection process is a report that states investment (Ri> E) or not investment (Ri <E).

Asset Pricing with Liquidity Risk: A Replication and Out-of-Sample Tests with the Recent US and the Japanese Market Data

2019 ◽  
Vol 8 (1-2) ◽  
pp. 73-110 ◽  
Author(s):  
Eiichiro Kazumori ◽  
Fei Fang ◽  
Raj Sharman ◽  
Fumiko Takeda ◽  
Hong Yu
Keyword(s):  
Asset Pricing ◽  
Liquidity Risk ◽  
Japanese Market ◽  
Market Data ◽  
Out Of Sample

The Future is a Moving Target: Predicting Political Instability

2019 ◽  
Vol 50 (4) ◽  
pp. 1405-1417 ◽  
Author(s):  
Drew Bowlsby ◽  
Erica Chenoweth ◽  
Cullen Hendrix ◽  
Jonathan D. Moyer
Keyword(s):  
Political Institutions ◽  
Armed Conflict ◽  
Predictive Power ◽  
Political Instability ◽  
Explanatory Power ◽  
State Level ◽  
Recent Period ◽  
Arab Uprisings ◽  
Out Of Sample ◽  
Over Time

AbstractPrevious research by Goldstone et al. (2010) generated a highly accurate predictive model of state-level political instability. Notably, this model identifies political institutions – and partial democracy with factionalism, specifically – as the most compelling factors explaining when and where instability events are likely to occur. This article reassesses the model’s explanatory power and makes three related points: (1) the model’s predictive power varies substantially over time; (2) its predictive power peaked in the period used for out-of-sample validation (1995–2004) in the original study and (3) the model performs relatively poorly in the more recent period. The authors find that this decline is not simply due to the Arab Uprisings, instability events that occurred in autocracies. Similar issues are found with attempts to predict nonviolent uprisings (Chenoweth and Ulfelder 2017) and armed conflict onset and continuation (Hegre et al. 2013). These results inform two conclusions: (1) the drivers of instability are not constant over time and (2) care must be exercised in interpreting prediction exercises as evidence in favor or dispositive of theoretical mechanisms.

Microstructure in the Machine Age

2020 ◽  
Author(s):  
David Easley ◽  
Marcos López de Prado ◽  
Maureen O’Hara ◽  
Zhibai Zhang
Keyword(s):  
Machine Learning ◽  
Market Efficiency ◽  
Predictive Power ◽  
Explanatory Power ◽  
Futures Contracts ◽  
Machine Age ◽  
Price Process ◽  
Out Of Sample ◽  
Asset Classes ◽  
Out Of Sample Prediction

Abstract Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.

Sign in / Sign up

Export Citation Format

Share Document