scholarly journals HIDING IN PLAIN SIGHT: SENTIMENT ANALYSIS AND THE EFFICIENT MARKET HYPOTHESIS

2021 ◽  
Author(s):  
DAVID LITWIN
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Academic Research ◽  
Efficient Market Hypothesis ◽  
Press Releases ◽  
Major Research ◽  
Efficient Market ◽  
Machine Learning Model ◽  
Stock Type ◽  
Methodological Component

The stock market is a notoriously complex and unpredictable system, and because of this has always been an alluring subject for academic research seeking to make the unpredictable more predictable. This major research project is no different as it aims to quantify the predictive value of financial sentiment, determine which sentiments are most meaningful, when they are most meaningful, and if meaningful sentiment varies depending on type of stock. To pursue these goals, the project finds its theoretical footing in Eugene Fama’s Efficient Market Hypothesis and Daniel Kahneman’s Prospect Theory. However, the methodological component of this project enters into emerging territory as it employs sentiment analysis and machine learning, which have only recently been made possible by advances in technology and communications practices. Specifically, through the use of the Loughran-McDonald dictionary for financial sentiment, corporate press releases were analyzed and tested using a Random Forest machine learning model. The results from this project show that financial senitiment found in press releases does provide a slight predictive edge, however the sentiments responsible for that edge vary based on type of stock, type of fluctuation being predicted, and timeframe.

scholarly journals HIDING IN PLAIN SIGHT: SENTIMENT ANALYSIS AND THE EFFICIENT MARKET HYPOTHESIS

2021 ◽  
Author(s):  
DAVID LITWIN
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Academic Research ◽  
Efficient Market Hypothesis ◽  
Press Releases ◽  
Major Research ◽  
Efficient Market ◽  
Machine Learning Model ◽  
Stock Type ◽  
Methodological Component

The stock market is a notoriously complex and unpredictable system, and because of this has always been an alluring subject for academic research seeking to make the unpredictable more predictable. This major research project is no different as it aims to quantify the predictive value of financial sentiment, determine which sentiments are most meaningful, when they are most meaningful, and if meaningful sentiment varies depending on type of stock. To pursue these goals, the project finds its theoretical footing in Eugene Fama’s Efficient Market Hypothesis and Daniel Kahneman’s Prospect Theory. However, the methodological component of this project enters into emerging territory as it employs sentiment analysis and machine learning, which have only recently been made possible by advances in technology and communications practices. Specifically, through the use of the Loughran-McDonald dictionary for financial sentiment, corporate press releases were analyzed and tested using a Random Forest machine learning model. The results from this project show that financial senitiment found in press releases does provide a slight predictive edge, however the sentiments responsible for that edge vary based on type of stock, type of fluctuation being predicted, and timeframe.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals Investigating inefficiencies of bookmaker odds in football using machine learning

2020 ◽  
Author(s):  
Benedikt Mangold ◽  
Johannes Stübinger
Keyword(s):  
Machine Learning ◽  
Simulation Study ◽  
Efficient Market Hypothesis ◽  
Learning Models ◽  
Efficient Market ◽  
Simple Machine ◽  
Football Betting ◽  
Available Information ◽  
Over Time ◽  
Machine Learning Models

The efficient-market hypothesis states that it is impossible to beat the market, as the price reflects all available information. Applied to bookmaker odds for football games, there should not be a systematic way of winning money on the long run.However, we show that by using simple machine learning models we can systematically outperform the markets belief manifested through the bookmakers odds. The effect of this inefficiency is diminishing over time, which indicates that the knowledge that has been derived from and the pure amount of the data is also reflected in the odds in recent times.We give some insights how this effect differs across major football leagues in Europe, which algorithms are performing best and statistics on the ROI using machine learning in football betting. Additionally, we share how the simulation study has been designed in more detail.

scholarly journals Oil Market Efficiency under a Machine Learning Perspective

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 157-168 ◽  
Author(s):  
Athanasia Dimitriadou ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou ◽  
Vasilios Plakandaras
Keyword(s):  
Machine Learning ◽  
Selection Process ◽  
Relevant Literature ◽  
Oil Prices ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Support Vector ◽  
Efficient Market ◽  
Explanatory Variables ◽  
Oil Market

Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

scholarly journals CarEnvision: A Data-Driven Machine Learning Framework for Automated Car Value Prediction

2021 ◽  
Author(s):  
TianGe (Terence) Chen ◽  
Angel Chang ◽  
Evan Gunnell ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Retail Price ◽  
Data Driven ◽  
Value Prediction ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Negative Sentiment

When people want to buy or sell a personal car, they struggle to know when the timing is best in order to buy their favorite vehicle for the best price or sell for the most profit. We have come up with a program that can predict each car’s future values based on experts’ opinions and reviews. Our program extracts reviews which undergo sentiment analysis to become our data in the form of positive and negative sentiment. The data is then collected and used to train the Machine Learning model, which will in turn predict the car’s retail price.

scholarly journals SENTIMENT ANALYSIS OF CUSTOMER REVIEWS

2021 ◽  
Vol 4 (1) ◽  
pp. 113-125
Author(s):  
Syed Rashiq Nazar ◽  
◽  
Tapalina Bhattasali
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Binary Classification ◽  
Classification Problem ◽  
Supervised Machine Learning ◽  
Frequency Model ◽  
Customer Reviews ◽  
Machine Learning Model ◽  
Logistic Regression Algorithm

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

scholarly journals A Study on Efficient Market Hypothesis to Predict Exchange Rate Trends Using Sentiment Analysis of Twitter Data

2016 ◽  
Vol 19 (7) ◽  
pp. 1107-1115 ◽  
Author(s):  
Kokoy Siti Komariah ◽  
Carmadi Machbub ◽  
Ary S. Prihatmanto ◽  
Bong-Kee Sin
Keyword(s):  
Exchange Rate ◽  
Sentiment Analysis ◽  
Efficient Market Hypothesis ◽  
Efficient Market ◽  
Twitter Data

scholarly journals Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Abdullah Al-Hashedi ◽  
Belal Al-Fuhaidi ◽  
Abdulqader M. Mohsen ◽  
Yousef Ali ◽  
Hasan Ali Gamal Al-Kaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Word Embedding ◽  
Ensemble Classifiers ◽  
Conspiracy Theories ◽  
Machine Learning Model ◽  
Arabic Sentiment Analysis ◽  
The Impact

Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. With the emergence of COVID-19, most published studies related to COVID-19’s conspiracy theories were surveys on the people's sentiments and opinions and studied the impact of the pandemic on their lives. Just a few studies utilized sentiment analysis of social media using a machine learning approach. These studies focused more on sentiment analysis of Twitter tweets in the English language and did not pay more attention to other languages such as Arabic. This study proposes a machine learning model to analyze the Arabic tweets from Twitter. In this model, we apply Word2Vec for word embedding which formed the main source of features. Two pretrained continuous bag-of-words (CBOW) models are investigated, and Naïve Bayes was used as a baseline classifier. Several single-based and ensemble-based machine learning classifiers have been used with and without SMOTE (synthetic minority oversampling technique). The experimental results show that applying word embedding with an ensemble and SMOTE achieved good improvement on average of F1 score compared to the baseline classifier and other classifiers (single-based and ensemble-based) without SMOTE.

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