Natural language based financial forecasting: a survey

@article{Xing2017NaturalLB,
  title={Natural language based financial forecasting: a survey},
  author={Frank Z. Xing and E. Cambria and Roy E. Welsch},
  journal={Artificial Intelligence Review},
  year={2017},
  volume={50},
  pages={49-73}
}
Natural language processing (NLP), or the pragmatic research perspective of computational linguistics, has become increasingly powerful due to data availability and various techniques developed in the past decade. This increasing capability makes it possible to capture sentiments more accurately and semantics in a more nuanced way. Naturally, many applications are starting to seek improvements by adopting cutting-edge NLP techniques. Financial forecasting is no exception. As a result, articles… Expand
Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process
TLDR
The results indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h, and collective accuracy of textual analytic models is way higher relative to non-textual analytic models. Expand
A review of natural language processing for financial technology
In the past few years, the development of natural language processing has been able to deal with many issues such as emotional analysis, semantic analysis, and so on. This review first introduces theExpand
Analysis of news sentiments using natural language processing and deep learning
This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning (DL), given the current hype on the topic, would be a good tool to do so. DL is builtExpand
NLP Analytics in Finance with DoRe: A French 250M Tokens Corpus of Corporate Annual Reports
TLDR
The construction of the DoRe corpus is related, which is designed to be as modular as possible in order to allow for maximum reuse in different tasks pertaining to Economics, Finance and Regulation, and on the spectrum of possible uses of this new resource for NLP applications. Expand
Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon
TLDR
A novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis that relies on a detailed set of semantic polarity rules that allow understanding the origin of sentiment, in the spirit of the recent trend onpretable AI. Expand
Detection of Financial Opportunities in Micro-Blogging Data With a Stacked Classification System
TLDR
Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that the proposed novel system to detect positive predictions in tweets yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. Expand
The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation
Abstract This is probably the first ever analysis of sell-side daily economic research to use Natural Language Processing, and it shows that the narrative of such reports can be used to predictExpand
Creating A Large-Scale Financial News Corpus for Relation Extraction
TLDR
This paper describes the establishment of a Chinese corpus for researches on financial entity recognition and financial relation extraction based on a large financial news data set, and proposes a mixed pattern with POS tagging and BIES annotation for Generating quadruples from unstructured text. Expand
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
TLDR
Investigating the error patterns of some widely acknowledged sentiment analysis methods in the finance domain finds that those methods belonging to the same clusters are prone to similar error patterns, and there are six types of linguistic features that are pervasive in the common errors. Expand
Deep Learning-Based Sentiment Analysis for Roman Urdu Text
TLDR
Deep Neural Long-short time memory model (LSTM) has extraordinary capability to Capture long-range information and solve gradient attenuation problem, as well as represent future contextual information, semantics of word sequence magnificently. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 164 REFERENCES
Sentiment analysis in financial texts
TLDR
This study addresses key questions related to the explosion of interest in how to extract insight from unstructured data and how to determine if such insight provides any hints concerning the trends of financial markets and proposes a sentiment analysis engine (SAE) which takes advantage of linguistic analyses based on grammars. Expand
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
TLDR
This survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves which will eventually lead NLPResearch to evolve into natural language understanding. Expand
Text mining for market prediction: A systematic review
TLDR
A comparative analysis of the systems based on market prediction based on online-text-mining expands onto the theoretical and technical foundations behind each and should help the research community to structure this emerging field and identify the exact aspects which require further research and are of special significance. Expand
A text-based decision support system for financial sequence prediction
TLDR
A novel text-based decision support system (DSS) that extracts event sequences from shallow text patterns, and predicts the likelihood of the occurrence of events using a classifier-based inference engine, while preserving robustness and without indulging in formalism is proposed. Expand
An Introduction to Concept-Level Sentiment Analysis
The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis, and other onlineExpand
Leveraging temporal properties of news events for stock market prediction
TLDR
This study proposes an approach to market trend prediction based on a recurrent deep neural network to model temporal effects of past events and demonstrates the validity of the proposed approach on the real-world data for ten Nikkei companies. Expand
STOCK MARKET FORECASTING TECHNIQUES : LITERATURE SURVEY
The goal of this paper is to study different techniques to predict stock price movement using the sentiment analysis from social media, data mining. In this paper we will find efficient method whichExpand
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
TLDR
This research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics. Expand
Sentiment Analysis Is a Big Suitcase
TLDR
The authors argue that there are (at least) 15 NLP problems that need to be solved to achieve human-like performance in sentiment analysis, and address the composite nature of the problem via a three-layer structure inspired by the “jumping NLP curves” paradigm. Expand
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
TLDR
SenticNet 4 overcomes limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction. Expand
...
1
2
3
4
5
...