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Investor Sentiment and Portfolio Selection

Investor Sentiment and Portfolio Selection
Author: Chengbo Fu
Publisher:
Total Pages: 15
Release: 2017
Genre:
ISBN:

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We provide a theoretical framework to examine how investor sentiment impacts the mean-variance tradeoff. We derive a sentiment-adjusted Markowitz efficient frontier in which investor sentiment alters the first two moments of asset returns, the minimum-variance frontier as well as the Capital Market Line. Our theoretical results are consistent with empirical findings that heightened sentiment-related noise trading activity drives perceived prices away from fundamental and increases market volatility. Rational investor neglecting the effect of investor sentiment may end up selecting a sub-optimal portfolio.


Exploiting Investor Sentiment for Portfolio Optimization

Exploiting Investor Sentiment for Portfolio Optimization
Author: Nicolas Banholzer
Publisher: GRIN Verlag
Total Pages: 118
Release: 2018-09-17
Genre: Mathematics
ISBN: 3668799504

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Master's Thesis from the year 2018 in the subject Mathematics - Statistics, grade: 1.0, University of Augsburg (Wirtschaftswissenschaftliche Fakultät, Lehrstuhl für Statistik), language: English, abstract: In efficient financial markets, there is no room for sentimental investors. Any new information would be immediately absorbed and any mispricing immediately corrected by the forces of rational arbitrageurs doing the maths with the fundamentals. But why should financial markets be different from any other market where humans interact and are subject to psychological biases? There is strong empirical evidence that investor sentiment, broadly defined as "a belief about future cash flows and investment risks that is not justified by the facts at hand", plays an important role in financial markets. It can lead to significant overpricing/underpricing, particularly of assets prone to subjective valuations. With limits/risks to arbitrage in the short term, prices rather correct over the medium to long term as sentimental beliefs mean-revert. Building on the studies by Baker and Wurgler 2006 and Baker, Wurgler, and Y. Yuan 2012, measures of investor sentiment for international markets are constructed. Using the Copula Opinion Pooling approach developed by Attilio Meucci, this thesis shows how to incorporate these sentiment measures into portfolio optimization. Thereby, a sentiment-based trading strategy that exploits the medium-term reversal effect of sentiment is developed and empirically tested. The results are promising as they provide strong evidence that sentiment contains beneficial information that should not be neglected by quantitative portfolio managers.


Investor Sentiment Effect in European Stock Markets

Investor Sentiment Effect in European Stock Markets
Author: Elena Ferrer
Publisher: Ed. Universidad de Cantabria
Total Pages: 86
Release: 2017-04-26
Genre: Business & Economics
ISBN: 8481028010

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La presente obra se adentra en el estudio del potencial efecto del sentimiento del inversor sobre la valoración de activos, su efecto en los pronósticos de beneficios y recomendaciones de los analistas y su impacto sobre los activos derivados. Abarca el efecto del sentimiento del inversor en cuatro de los mercados europeos más importantes, Alemania, España, Francia y Reino Unido, mercados con características diferentes, en cuanto a tamaño, tipología del inversor y funcionamiento, lo que permite extraer importantes conclusiones adicionales.


Inefficient Markets

Inefficient Markets
Author: Andrei Shleifer
Publisher: OUP Oxford
Total Pages: 225
Release: 2000-03-09
Genre: Business & Economics
ISBN: 0191606898

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The efficient markets hypothesis has been the central proposition in finance for nearly thirty years. It states that securities prices in financial markets must equal fundamental values, either because all investors are rational or because arbitrage eliminates pricing anomalies. This book describes an alternative approach to the study of financial markets: behavioral finance. This approach starts with an observation that the assumptions of investor rationality and perfect arbitrage are overwhelmingly contradicted by both psychological and institutional evidence. In actual financial markets, less than fully rational investors trade against arbitrageurs whose resources are limited by risk aversion, short horizons, and agency problems. The book presents and empirically evaluates models of such inefficient markets. Behavioral finance models both explain the available financial data better than does the efficient markets hypothesis and generate new empirical predictions. These models can account for such anomalies as the superior performance of value stocks, the closed end fund puzzle, the high returns on stocks included in market indices, the persistence of stock price bubbles, and even the collapse of several well-known hedge funds in 1998. By summarizing and expanding the research in behavioral finance, the book builds a new theoretical and empirical foundation for the economic analysis of real-world markets.


Sample Selection and the Relation Between Investor Sentiment and Profitable Trading Strategies

Sample Selection and the Relation Between Investor Sentiment and Profitable Trading Strategies
Author: James Bulsiewicz
Publisher:
Total Pages: 78
Release: 2015
Genre:
ISBN:

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Recent evidence suggests that there is strong relation between investor sentiment and cross-sectional anomalies. However, I present evidence of a weak relation between cross-sectional anomalies and investor sentiment. Using a larger collection of cross-sectional anomalies, I find that only a small subsample of these anomalies exhibit a relation with investor sentiment. This result does not appear to be due to certain anomalies being more sensitive to changes in macroeconomic conditions. I then simulate 10,000 long-short portfolios, but find that only 10% of these portfolios have a relation with investor sentiment. Further I show that the predictive power of sentiment diminishes significantly after controlling for the Fama and French (1993) factors. These results suggest that the returns to active trading strategies are generally not due to sentiment-driven mispricing.


Media Sentiment and International Asset Prices

Media Sentiment and International Asset Prices
Author: Samuel P. Fraiberger
Publisher: International Monetary Fund
Total Pages: 33
Release: 2018-12-10
Genre: Business & Economics
ISBN: 1484389212

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We assess the impact of media sentiment on international equity prices using more than 4.5 million Reuters articles published across the globe between 1991 and 2015. News sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news-sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. We also find evidence that news sentiment affects mainly foreign – rather than local – investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment.


Artificial Intelligence in Asset Management

Artificial Intelligence in Asset Management
Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
Total Pages: 95
Release: 2020-08-28
Genre: Business & Economics
ISBN: 195292703X

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Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.