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Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization [article]

Felix Ming Fai Wong, Zhenming Liu, Mung Chiang
2014 arXiv   pre-print
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices  ...  Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity.  ...  CONCLUSION In this paper we revisit the problem of mining text data to predict the stock market.  ... 
arXiv:1406.7330v1 fatcat:7hamuknhjbbz5j2nzaynvkluwe

Using Structured Events to Predict Stock Price Movement: An Empirical Investigation

Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan
2014 Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)  
We propose to adapt Open IE technology for event-based stock price movement prediction, extracting structured events from large-scale public news without manual efforts.  ...  However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation information  ...  , the National Natural Science Foundation of China (NSFC) via Grant 61133012 and 61202277, the Singapore Ministry of Education (MOE) AcRF Tier 2 grant T2MOE201301 and SRG ISTD 2012 038 from Singapore University  ... 
doi:10.3115/v1/d14-1148 dblp:conf/emnlp/DingZLD14 fatcat:tgcpdrwnmjct5osl3ow36r6nja

Regularized Latent Semantic Indexing

Quan Wang, Jun Xu, Hang Li, Nick Craswell
2013 ACM Transactions on Information Systems  
It has become a popular tool in many research areas, such as text mining, information retrieval, natural language processing, and other related fields.  ...  This formulation allows the learning process to be decomposed into multiple suboptimization problems which can be optimized in parallel, for example, via MapReduce.  ...  From the viewpoint of matrix factorization, batch RLSI approximates the input term-document matrix D with the product of the term-topic matrix U and the topic-document matrix V, as shown in Figure 2 .  ... 
doi:10.1145/2414782.2414787 fatcat:wqq4oljj5vc3dlqcwz7ydnzs2m

Regularized latent semantic indexing

Quan Wang, Jun Xu, Hang Li, Nick Craswell
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
It has become a popular tool in many research areas, such as text mining, information retrieval, natural language processing, and other related fields.  ...  This formulation allows the learning process to be decomposed into multiple suboptimization problems which can be optimized in parallel, for example, via MapReduce.  ...  From the viewpoint of matrix factorization, batch RLSI approximates the input term-document matrix D with the product of the term-topic matrix U and the topic-document matrix V, as shown in Figure 2 .  ... 
doi:10.1145/2009916.2010008 dblp:conf/sigir/WangXLC11 fatcat:yf7it3hhmrddhjbr5v5xgnqwr4

Credit Rating Change Modeling Using News and Financial Ratios

Hsin-Min Lu, Feng-Tse Tsai, Hsinchun Chen, Mao-Wei Hung, Shu-Hsing Li
2012 ACM Transactions on Management Information Systems  
To leverage the additional information in news full-text for credit rating prediction, we designed and implemented a news full-text analysis system that provides firm-level coverage, topic, and sentiment  ...  Most previous studies on credit rating modeling are based on accounting and market information.  ...  Note that text mining modules are required to extract relevant information from the full-text of news articles [Lau et al. 2011] . Two techniques are often adopted to achieve this goal.  ... 
doi:10.1145/2361256.2361259 fatcat:2tux7af3nbbcrd5tzn7l3e6ude

The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment

David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt
2021 Journal of Statistical Software  
predict other variables.  ...  The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.  ...  The VIX measures the annualized option-implied volatility on the S&P 500 stock market index over the next 30 days.  ... 
doi:10.18637/jss.v099.i02 fatcat:xjebdfojsbg3zbqxgqxb25xn24

Scalable Text Mining with Sparse Generative Models [article]

Antti Puurula
2016 arXiv   pre-print
This thesis proposes a solution to scalable text mining: generative models combined with sparse computation.  ...  The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable.  ...  modeling [Hofmann, 1999 , Blei et al., 2003 instead of general matrix factorization methods, data mining is done with algorithms that operate well on high-dimensional sparse data such as Naive Bayes  ... 
arXiv:1602.02332v1 fatcat:2urzib3btveslj5ggie55irxwq

Representing Big Data as Networks: New Methods and Insights [article]

Jian Xu
2017 arXiv   pre-print
Our world produces massive data every day; they exist in diverse forms, from pairwise data and matrix to time series and trajectories.  ...  Networks also have different forms; from simple networks to higher-order network, each representation has different capabilities in carrying information.  ...  of data mining has two valuable resources. On one side, ubiquitous data exists in different formats, from pairwise data and matrix to time series and trajectories.  ... 
arXiv:1712.09648v1 fatcat:2dlt7jfljnbftk7ag3spk5utw4

Incorporating context within the language modeling approach for ad hoc information retrieval

Leif Azzopardi
2006 SIGIR Forum  
For example, 'the strategic defense initiative' or 'Black Monday the Stock Market Crash'. However, the context of these to the user may be somewhat different.  ...  Given the query text, a prediction can be made of the likelihood that the query text would have been generated by each document model. This prediction is then used to rank the documents.  ...  A3 Discrimination The terms that a user submits as a query will be sufficient in discriminating relevant from non relevant documents A3.1 The user will issue query terms that are highly discriminative  ... 
doi:10.1145/1147197.1147211 fatcat:pt4gihyz4zabdjrux2byg7wnoe

Stock Price Prediction Based on a Sentiment Analysis of Financial News

Stefan Salbrechter, Thomas Dangl
2020
The aim of this thesis is to conduct a sentiment analysis of financial news articles in order to determine whether this news data is suitable for the short-term prediction of stock price movements.  ...  Apart from that, n-grams with lengths from one to three are considered in order to enhance the bag-of-word approach.Subsequently, during the training of the neural networks, it was observed that they tend  ...  We also thank Refinitiv for providing us with a comprehensive collection of financial news data covering the range from January 1996 to January 2020.  ... 
doi:10.34726/hss.2020.80382 fatcat:ppytx66tjbboppcvjxrpsbprqa

Deep Reinforcement Learning [article]

Yuxi Li
2018 arXiv   pre-print
time series to reduce noises, SAEs for generating high-level features for stock price prediction, and LSTM for stock price forecasting by taking the denoised features.  ...  Vezhnevets et al. (2016) validate STRAW on next character prediction in text, 2D maze navigation, and Atari games.  ...  Deep reinforcement learning with a combinatorial action space for predicting popular reddit threads. In EMNLP. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017) . Mask R-CNN. In ICCV.  ... 
arXiv:1810.06339v1 fatcat:kp7atz5pdbeqta352e6b3nmuhy

Unrestricted Bridging Resolution

Yufang Hou, Katja Markert, Michael Strube
2018 Computational Linguistics  
Anaphora plays a major role in discourse comprehension and accounts for the coherence of a text.  ...  It is also a memorable experience for me to receive five or six emails from her within ten minutes and decide to which one to reply first.  ...  They then applied matrix factorization models to operate simultaneously on relations observed in text and in structured databases.  ... 
doi:10.1162/coli_a_00315 fatcat:ezgwhbvbrffi5jgx3edhigvx4a

Essays on the Influence of Textual Sentiment in Real Estate Markets

Jochen Hausler
2019
Using real estate related textual documents as "sentiment provider", capabilities of a machine- and a deep-learning classifier for predicting direct and securitized market returns and liquidity within  ...  This dissertation sheds light on the potential of text-based sentiment indicators within the area of commercial real estate.  ...  For the first time, machines can be trained to assess and extract not only the content, but also opinions from textual documents via what is referred to as opinion mining.  ... 
doi:10.5283/epub.41107 fatcat:qcnjggu5cbbibbtygi5df4bmuu

Web-assisted anaphora resolution

Yifan Li
2010
the text itself.  ...  Various naturallyoccurring definite descriptions found in the WSJ corpus are analyzed from both perspectives of familiarity and uniqueness, and a new classification scheme for definite descriptions is  ...  market direction † . WSJ 231:15 (5.37) makes while heading for the door.  ... 
doi:10.7939/r3mq6z fatcat:lsqcbjjdojbvzoxgdjkhwiye3e

Sentiment Analysis of German Twitter [article]

Wladimir Sidorenko
2019 pre-print
Apart from this, I propose a linear projection algorithm, whose results surpass many existing automatic lexicons.  ...  Afterwords, in the second task, I examine two common approaches to automatic prediction of sentiments, sources, and targets: conditional random fields and recurrent neural networks, obtaining higher scores  ...  the polarity of stock messages, achieving an accuracy of 62% on a corpus of several hundreds stock board messages.  ... 
doi:10.25932/publishup-43742 arXiv:1911.13062v1 fatcat:zggbtdr6lzdf7hh24or46pa6ue
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