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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.


Can a Corporate Network and News Sentiment Improve Portfolio Optimization Using the Black-Litterman Model?

Can a Corporate Network and News Sentiment Improve Portfolio Optimization Using the Black-Litterman Model?
Author: Germán G. Creamer
Publisher:
Total Pages: 18
Release: 2015
Genre:
ISBN:

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The Black-Litterman (BL) model for portfolio optimization combines investors' expectations with the Markowitz framework. The BL model is designed for investors with private information or knowledge of market behaviour. In this paper, I propose a method where investors' expectations are based on either news sentiment using high-frequency data or on a combination of accounting variables; financial analysts' recommendations, and corporate social network indicators with quarterly data. The results show promise when compared to a market portfolio. I also provide recommendations for trading strategies using the results of this BL model.


Stock Message Boards

Stock Message Boards
Author: Y. Zhang
Publisher: Springer
Total Pages: 309
Release: 2014-12-04
Genre: Business & Economics
ISBN: 1137372591

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Stock Message Boards provides empirical data to reveal how online communication not only impacts stock returns, but also volatility, trading volume, and liquidity, as well as an investing firm's value and reputation.


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.


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.


The Buy-Write Strategy

The Buy-Write Strategy
Author: Oliver Palmer
Publisher:
Total Pages: 170
Release: 2015
Genre: Capitalists and financiers
ISBN:

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Existing research focuses on buy-write strategy performance when index options are used as the underlying asset, finding positive excess risk-adjusted returns which are suggestive of option overpricing. My purpose is to extend this literature by conducting a thorough analysis of strategy performance when individual stock options are used instead of index options. Moreover, I examine whether underlying asset class and investor sentiment has an effect on buy-write performance. Using US data from 2008 - 2015, I sort S&P 500 constituents to form portfolios of large, small, growth and value stocks and test for differences in buy-write performance. The returns of each portfolio are then regressed against 2 separate proxies of investor sentiment and several control variables to test the effects of investor sentiment. Contrary to aforementioned buy-write research, I find no evidence of excess risk-adjusted returns, likely due to the implied vs. realised volatility anomaly which is observed in index options but not stock options. Despite existing evidence that options on small and value stocks are expensive relative to large and growth stocks, I find no evidence that firm characteristic has an effect on buy-write performance. This is potentially explained by the relative illiquidity of small and value options resulting in increased trading costs which are not accounted for in previous studies. Consistent with the literature, my results show that in general, investor sentiment has a positive relationship with buy-write returns, especially for small and value stocks. Additional sub-sample analysis shows that during a market downturn the effect of investor sentiment is much stronger, likely due the limited ability of arbitrageurs to exploit mispriced securities. During times of low market volatility the effect of investor sentiment becomes lagged and much weaker in magnitude.


When to Pick the Losers

When to Pick the Losers
Author: Devraj Basu
Publisher:
Total Pages: 33
Release: 2019
Genre:
ISBN:

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Recent finance research that draws on behavioral psychology suggests that investors systematically make errors in forming expectations about asset returns, and thus that investor sentiment can have predictive power for asset returns. A number of empirical studies using both market and survey data as proxies for investor sentiment have found support for these theories. In this study we investigate whether investor sentiment as measured by a component of the University of Michigan survey can help improve dynamic asset allocation over and above the improvement achieved based on commonly used business cycle indicators. We find that the addition of sentiment variables to business cycle indicators considerably improves the performance of dynamically managed portfolio strategies, both for a standard market-timer as well as for a momentum-type investor. Sentiment-based dynamic trading strategies, even out-of-sample, would not have incurred any significant losses during the October 1987 crash or the collapse of the `dot.com' bubble in late 2000. In contrast, standard business cycle indicators fail to predict these events, so that investors relying on these variables alone would have incurred significant losses. These strategies seem to systematically exploit investor over-reaction and are `active alpha' strategies with low betas and high alphas, in contrast to business cycle based strategies which are effectively `index-trackers' with high betas and considerably lower alphas.


Sentiment Issues in Stock Prices and Mutual Funds

Sentiment Issues in Stock Prices and Mutual Funds
Author: John A. Haslem
Publisher:
Total Pages:
Release: 2019
Genre:
ISBN:

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Baker and Wurgler [2007] take a “top down” approach to behavioral finance and the stock market. Investor sentiment is taken to be exogenous and the focus is on its empirical effects. Sentiment is measurable and its waves have clearly discernible, important, and regular effects on firms and the overall stock market. Stocks that are hardest to arbitrage or value are most affected by sentiment.Other studies discussed relate to aspects of investor sentiment and sentiment indexes in Baker and Wurgler [2007] and/or Baker and Wurgler [2006]. Massa and Yadav [2012] analyze whether mutual funds opportunistically exploit market inability to identify sentiment risk. Gasbarro et al. [2012] determine that when fund investor sentiment is high (low), returns are higher for funds with low (high) sentiment-beta portfolios. Irek and Lehnert [2013] use the sentiment index and find that market risk is not a priced factor of expected fund returns when investor sentiment is positive. Sibley et al. [2013] determine that although sentiment is orthogonal to macroeconomic conditions, sentiment indexes have substantial information related to business cycles. Joseph et al. [2011] find that intensity of searches for ticker symbols serves as a valid proxy for investor sentiment, which is useful for forecasting stock returns and trade volume. Huang et al. [2014] determine that the sentiment index likely understates the predictive power of investor sentiment.