Equity portfolio risk estimation using market information and sentiment

Mitra, Leela; Mitra, Gautam; diBartolomeo, Dan

doi:10.1002/9781118467411.ch13

Cited by 6 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are a number of studies about sentiment-based trading strategies using varied sentiment computation rules [18,26,33]. An earlier study [31] is the most comparable work which used Thomson Reuters news sentiment and the similar sentiment indicators.…”

Section: Resultsmentioning

confidence: 99%

“…In a similar study, Khadjeh Nassirtoussi et al [18] implemented a multi-layer dimension reduction algorithm on news headlines to predict intraday direction of the USD-EUR pair and achieved an accuracy of 83%. Mitra et al [26] incorporated both market information and news sentiment to estimate equity portfolio volatility. In another study, Healy and Lo [14] designed a real-time news analytics framework and used Thomson Reuters News Scope data to manage investment risks and returns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stock portfolio selection using learning-to-rank algorithms with news sentiment

2017

View full text Add to dashboard Cite

In this study, we apply learning-to-rank algorithms to design trading strategies using relative performance of a group of stocks based on investors' sentiment toward these stocks. We show that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors' sentiment. More specifically, we use the sentiment shock and trend indicators introduced in the previous studies, and we design stock selection rules of holding long positions of the top 25% stocks and short positions of the bottom 25% stocks according to rankings produced by learning-to-rank algorithms. We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock selection processes and test long-only and long-short portfolio selection strategies using 10 years of market and news sentiment data. Through backtesting of these strategies from 2006 to 2014, we demonstrate that our portfolio strategies produce risk-adjusted returns superior to the S&P500 index return, the hedge fund industry average performance-HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stock portfolio selection using learning-to-rank algorithms with news sentiment

2017

View full text Add to dashboard Cite

show abstract

“…In both the intra‐week and low‐frequency literature, a number of researchers have investigated the relationship between news and measures of risk (Kothari et al ., ; Mitra et al ., ). Although this subset of the literature is small, news has been related to realised volatility, idiosyncratic volatility, market implied volatility and tail co‐dependence.…”

Section: Empirical Findingsmentioning

confidence: 97%

“…This returned 276 items, to which an additional 10 items (Wüthrich et al ., ; Chan, ; Kothari et al ., ; Mitra et al ., ; Tetlock, ; Leinweber and Sisk, ; Ferguson et al ., ; Yang et al ., ; Sinha, ; Manela and Moreira, ) were added from other sources, such as Google Scholar. To remove irrelevant items and establish a refined corpus subject to individual written review, the 286 initial items were filtered by removing those that:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

News media analytics in finance: a survey

Marty

Vanstone

Hahn³

2019

Accounting & Finance

View full text Add to dashboard Cite

We survey the works applying text analytics to the study of news media in financial markets beyond intraday horizons, and expand into the fundamental economic theory and concepts relevant to the field. We compare and contrast the news sources, textual analysis methods and empirical modelling approaches adopted within the literature. We distil and categorise the key empirical insights, and summarise the bibliographic history of the literature so far. While this rapidly growing field has yielded many exciting discoveries, there are a number of promising avenues for future research which will only benefit from continued advances in computational technology.

show abstract

“…In a similar study, Nassirtoussi et al implemented a multi-layer dimension reduction algorithm on news headlines to predict the intraday direction of the USD-EUR pair and achieved an accuracy of 83% [18]. Mitra incorporated both market information and news sentiment in estimating equity portfolio volatility [19]. In another study, Healy designed a real-time news analytics framework and used Thomson Reuters News Scope data to manage investment risks and returns [20].…”

Section: B Trading Strategies Using News Sentimentmentioning

confidence: 99%

An Extreme Firm-Specific News Sentiment Asymmetry Based Trading Strategy

et al. 2015

View full text Add to dashboard Cite

News sentiment has been empirically observed to have impact on financial market returns. In this study, we investigate firm-specific news from the Thomson Reuters News Analytics data from 2003 to 2014 and propose an optimal trading strategy based on a sentiment shock score and a sentiment trend score which measure extreme positive and negative sentiment levels for individual stocks. The intuition behind this approach is that the impact of events that generate extreme investor sentiment changes tends to have long and lasting effects to market movement and hence provides better prediction to market returns. We document that there exists an optimal signal region for both indicators. And we also show extreme positive sentiment provides better a signal than extreme negative sentiment, which presents an asymmetric market behavior in terms of news sentiment impact. The backtest results show that extreme positive sentiment generates robust and superior trading signals in all market conditions, and its risk-adjusted returns significantly outperform the S&P 500 index over the same time period.

show abstract

Equity portfolio risk estimation using market information and sentiment

Cited by 6 publications

References 10 publications

Stock portfolio selection using learning-to-rank algorithms with news sentiment

Stock portfolio selection using learning-to-rank algorithms with news sentiment

News media analytics in finance: a survey

An Extreme Firm-Specific News Sentiment Asymmetry Based Trading Strategy

Contact Info

Product

Resources

About