The Experts below are selected from a list of 3777 Experts worldwide ranked by ideXlab platform
Ivan Ho - One of the best experts on this subject based on the ideXlab platform.
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enhancing Portfolio Return based on sentiment of topic
Data and Knowledge Engineering, 2017Co-Authors: Raymond K. Wong, Fang Chen, Ivan HoAbstract:Abstract While time-series analysis is commonly used in financial forecasting, a key source of market-sentiments is often omitted. Financial news is known to be making persuasive impact on the markets. Without considering this additional source of signals, only sub-optimal predictions can be made. This paper proposes a notion of sentiment-of-topic (SoT) to address the problem. It is achieved by considering sentiment-linked topics, which are retrieved from time-series with heterogeneous dimensions (i.e., numbers and texts). Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics associated with different sentiment levels are used to quantify market conditions. In particular, sentiment levels are learned from historical market performances and commentaries instead of using subjective interpretations of human expressions. By capturing the domain knowledge of respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
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BigComp - Enhancing Portfolio Return based on market-sentiment linked topics
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Co-Authors: Fang Chen, Raymond K. Wong, Ivan HoAbstract:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve Portfolio Return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
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Enhancing Portfolio Return based on market-sentiment linked topics
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Co-Authors: Fang Chen, Raymond K. Wong, Ivan HoAbstract:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve Portfolio Return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
Raymond K. Wong - One of the best experts on this subject based on the ideXlab platform.
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enhancing Portfolio Return based on sentiment of topic
Data and Knowledge Engineering, 2017Co-Authors: Raymond K. Wong, Fang Chen, Ivan HoAbstract:Abstract While time-series analysis is commonly used in financial forecasting, a key source of market-sentiments is often omitted. Financial news is known to be making persuasive impact on the markets. Without considering this additional source of signals, only sub-optimal predictions can be made. This paper proposes a notion of sentiment-of-topic (SoT) to address the problem. It is achieved by considering sentiment-linked topics, which are retrieved from time-series with heterogeneous dimensions (i.e., numbers and texts). Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics associated with different sentiment levels are used to quantify market conditions. In particular, sentiment levels are learned from historical market performances and commentaries instead of using subjective interpretations of human expressions. By capturing the domain knowledge of respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
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BigComp - Enhancing Portfolio Return based on market-sentiment linked topics
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Co-Authors: Fang Chen, Raymond K. Wong, Ivan HoAbstract:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve Portfolio Return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
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Enhancing Portfolio Return based on market-sentiment linked topics
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Co-Authors: Fang Chen, Raymond K. Wong, Ivan HoAbstract:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve Portfolio Return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
Fang Chen - One of the best experts on this subject based on the ideXlab platform.
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enhancing Portfolio Return based on sentiment of topic
Data and Knowledge Engineering, 2017Co-Authors: Raymond K. Wong, Fang Chen, Ivan HoAbstract:Abstract While time-series analysis is commonly used in financial forecasting, a key source of market-sentiments is often omitted. Financial news is known to be making persuasive impact on the markets. Without considering this additional source of signals, only sub-optimal predictions can be made. This paper proposes a notion of sentiment-of-topic (SoT) to address the problem. It is achieved by considering sentiment-linked topics, which are retrieved from time-series with heterogeneous dimensions (i.e., numbers and texts). Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics associated with different sentiment levels are used to quantify market conditions. In particular, sentiment levels are learned from historical market performances and commentaries instead of using subjective interpretations of human expressions. By capturing the domain knowledge of respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
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BigComp - Enhancing Portfolio Return based on market-sentiment linked topics
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Co-Authors: Fang Chen, Raymond K. Wong, Ivan HoAbstract:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve Portfolio Return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
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Enhancing Portfolio Return based on market-sentiment linked topics
2016 International Conference on Big Data and Smart Computing (BigComp), 2016Co-Authors: Fang Chen, Raymond K. Wong, Ivan HoAbstract:While time-series analysis techniques are commonly used in financial forecasting, a key source of market volatility is omitted from these models. Financial news is known to be making persuasive impact to the markets. Without considering these additional signals, only sub-optimal predictions can be made. This paper proposes a supervised topic learning approach to improve Portfolio Return. It is achieved by considering market-sentiment linked topics retrieved from financial news. Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics specific to different sentiment levels are identified by our proposed model to quantify market conditions. The topics are learned from historical market performances and commentaries instead of using subjective interpretation of sentiments from human expressions. By capturing the knowledge specific to respective industries and markets, an impressive double-digit improvement in Portfolio Return is obtained as shown in our experiments.
Timothy S Mech - One of the best experts on this subject based on the ideXlab platform.
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Portfolio Return autocorrelation
Journal of Financial Economics, 1993Co-Authors: Timothy S MechAbstract:Abstract This paper investigates whether Portfolio Return autocorrelation can be explained by time-varying expected Returns, nontrading, state limit orders, market maker inventory policy, or transaction costs. Evidence is consistent with the hypothesis that transaction costs cause Portfolio autocorrelation by slowing price adjustment. I develop a transaction-cost model which predicts that prices adjust faster when changes in valuation are large in relation to the bid-ask spread. Cross-sectional tests support this prediction, but time-series tests do not.
Pascal St-amour - One of the best experts on this subject based on the ideXlab platform.
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Recursive Measures of Total Wealth and Portfolio Return
2020Co-Authors: Michel Normandin, Pascal St-amourAbstract:This letter presents and assesses a procedure to generate recursive measures of aggregate total wealth and Portfolio Return. Conceptually, the procedure is more flexible than the classical replacement cost and present value methods. Empirically, the procedure yields recursive measures that appear more realistic than those obtained from the classical methods.
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Recursive Measures of Total Wealth and Portfolio Return
Applied Financial Economics, 2005Co-Authors: Michel Normandin, Pascal St-amourAbstract:This paper presents and assesses a procedure to generate recursive measures of aggregate total wealth and Portfolio Return. The procedure is more flexible and yields more realistic measures, compared to the classical replacement cost and present value methods.