scholarly journals A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 250
Author(s):  
Mohammad Kamel Daradkeh
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Data Analytics ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Financial Data ◽  
Trend Prediction ◽  
Hybrid Data ◽  
News Events ◽  
The Impact

Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

scholarly journals Liquidity in global real estate securities markets

2016 ◽  
Vol 34 (4) ◽  
pp. 321-346 ◽  
Author(s):  
Nicole Lux ◽  
Alex Moss
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Market Liquidity ◽  
Company Size ◽  
Small Companies ◽  
Stock Performance ◽  
Content Type ◽  
The Usa ◽  
Market Capitalisation ◽  
The Impact

Purpose – The purpose of this paper is to test the relationship between liquidity in listed real estate markets, company size and geography during different market cycles, specifically pre-crisis (2002-2006) and post-crisis (2010-2014). Further, the study analyses the impact of stock liquidity on stock performance. In a previous study the authors examined the impact of liquidity on the valuation of European real estate shares. The result showed that there is a strong relationship between liquidity, valuation and market capitalisation post the Global Financial Crisis. Design/methodology/approach – The paper studies the linkages between regional market liquidity and company size for 60 listed real estate companies globally and determines the key drivers of company stock market liquidity pre- and post-crisis as well as the impact on stock performance. Analysis of variance is used to test cross-sectional independence in market liquidity combined with the Tukey’s post hoc test. The selected test indicators of liquidity to capture market depth and market tightness are daily stock turnover as percentage of market capitalisation and daily bid-ask spreads. Findings – Findings confirm previous studies that market liquidity factors are correlated globally over time indicating markets interdependence. However, sample groups by company size and geography form independent samples with different sample means, thus specific liquidity levels in each market may be different. First, stock turnover levels have not recovered post-crisis to pre-crisis levels in the majority of markets while spreads have continued moving downward to nearly insignificant levels in line with the rest of the equity market. Second, with regards to stock performance, the European bias previously detected is not apparent in the USA, and there is no evidence of the small cap vs large cap effect of small companies achieving superior returns, although smaller companies have outperformed in Europe and Asia in each of the last three years (2012-2014). Practical implications – The key implication is that although spread levels for smaller companies are higher, implying a slight risk premium when investing in small companies, this did not manifest into consistent superior stock market returns in the periods studied. In a mature market such as the USA or UK, liquidity levels in terms of stock turnover are higher and spreads are lower thus reducing trading costs, making them more attractive for investors. Originality/value – This research brings together previous analysis on stock market liquidity and stock performance on a global market level. It further tests the dependence of market liquidity on two key indicators, namely, geography and company size and analyses market changes with respect to liquidity pre- and post-crisis.

scholarly journals The Impact of the COVID-19 Pandemic on the U.S. Economy: Evidence from the Stock Market

2020 ◽  
Vol 13 (10) ◽  
pp. 233
Author(s):  
Willem Thorbecke
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Stock Returns ◽  
Electrical Equipment ◽  
Production Equipment ◽  
Macroeconomic Factors ◽  
Specific Factors ◽  
The Impact ◽  
Macroeconomic Environment ◽  
The U.S

The coronavirus crisis has damaged the U.S. economy. This paper uses the stock returns of 125 sectors to investigate its impact. It decomposes returns into components driven by sector-specific factors and by macroeconomic factors. Idiosyncratic factors harmed industries such as airlines, aerospace, real estate, tourism, oil, brewers, retail apparel, and funerals. There are thus large swaths of the economy whose recovery depends not on the macroeconomic environment but on controlling the pandemic. Macroeconomic factors generated losses in industries such as production equipment, machinery, and electronic and electrical equipment. Thus, reviving capital goods spending requires not just an end to the pandemic but also a macroeconomic recovery.

scholarly journals Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1881 ◽  
Author(s):  
Xiaorui Shao ◽  
Chang-Soo Kim ◽  
Palash Sontakke
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Sensor Data ◽  
Short Term ◽  
Multi Scale ◽  
Deep Model ◽  
Cost Of Time ◽  
The Impact

Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) algorithm without additional sensor data, and also considers the corresponding statistics. Different from the conventional stacked CNN–LSTM, in the proposed hybrid model, CNN and LSTM extracted features in parallel, which can obtain more robust features with less loss of original information. Chiefly, CNN extracts multi-scale robust features by various filters at three levels and wide convolution technology. LSTM extracts the features which think about the impact of different time-steps. The features extracted by CNN and LSTM are combined with six statistical components as comprehensive features. Therefore, comprehensive features are the fusion of multi-scale, multi-domain (time and statistic domain) and robust due to the utilization of wide convolution technology. We validate the effectiveness of the proposed method on three natural subsets associated with electricity consumption. The comparative study shows the state-of-the-art performance of the proposed hybrid deep model with good robustness for very short-term, short-term, medium-term, and long-term electricity consumption forecasting.

scholarly journals The impact of liquidity on the valuation of European real estate securities

2014 ◽  
Vol 7 (2) ◽  
pp. 139-157 ◽  
Author(s):  
Alex Moss ◽  
Nicole Lux
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Equity Capital ◽  
Market Valuation ◽  
Liquidity Premium ◽  
Content Type ◽  
Stock Market Valuation ◽  
The Uk ◽  
Real Estate Securities ◽  
The Impact

Purpose – The purpose of this paper is to test the hypothesis that the valuations of European real estate securities are, in part, determined by the relative liquidity in the companies’ shares. Design/methodology/approach – Six groups are derived for our sample of European listed real estate companies. They are split between the UK and Europe, and then both sets are categorised by liquidity as large, medium or small. These are then tested for market depth, market tightness and difference in valuations over the cycle 2002-2012. Intuitively, it can be expected that the stock market valuation premium for companies with greater liquidity increases post the global financial crisis. Findings – The key discriminating variable that drives companies’ liquidity and valuations is market capitalisation. For both the UK and Europe, the valuation premium of larger companies vs small companies has increased significantly since 2008 (by 20-40 per cent), which can be attributed to the increased value placed on liquidity post GFC. Research limitations/implications – The sample size is relatively small, and subject to individual company influences on stock market valuation. Practical implications – The key implications from the findings are the cost and quantum of new equity capital available to companies with superior liquidity, and the possibility of exclusion from portfolios for companies with low liquidity. Originality/value – Previous studies have focussed on returns for measuring a liquidity premium. This study focusses on relative valuations and how the liquidity premium changes throughout the cycle.

scholarly journals Stock Price Prediction Using LSTM on Indian Share Market

10.29007/qgcz ◽  
2019 ◽  
Author(s):  
Achyut Ghosh ◽  
Soumik Bose ◽  
Giridhar Maji ◽  
Narayan Debnath ◽  
Soumya Sen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Investor Sentiment ◽  
Spot Price ◽  
Physical Factors ◽  
Stock Price Prediction ◽  
The Impact ◽  
A Company

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

scholarly journals The Evaluation of Financing Efficiency of China’s Stock Market

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Ji-chang Dong ◽  
Lin-lin Zhu ◽  
Bing Wang ◽  
Zhi Dong ◽  
Xiu-ting Li
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Total Factor Productivity ◽  
Stock Exchange ◽  
Listed Companies ◽  
Financial Data ◽  
Factor Productivity ◽  
Equity Financing ◽  
The Impact ◽  
Decision Making Units

Financing is the main way for listed companies to obtain funds in China, and it is the “reservoir” which can guarantee enterprises to operate continuously. Financing efficiency can be used to measure the efficiency in using enterprises’ own funds, and it is one of the main indicators which are concerned by the stakeholders of listed companies. This paper mainly researches on the impact of equity financing on the financing efficiency of listed companies as a whole and selects 300 listed companies in the Shanghai and Shenzhen Stock Exchange as decision-making units. Then this paper analyzes the financial data of sample companies in 2008–2014. Finally, it can be concluded that the financing efficiency of listed companies in China is generally low, and the total factor productivity in the stock market continued to decline between 2003 and 2005 and then rose rapidly.

scholarly journals Ownership Structure and Earnings Management: Empirical Evidence from Vietnam Real Estate Sector

2020 ◽  
Vol 28 (2) ◽  
pp. 37-51
Author(s):  
Anh Huu Nguyen ◽  
Linh Ha Nguyen ◽  
Duong Thuy Doan
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Earnings Management ◽  
Ownership Structure ◽  
Information Quality ◽  
Positive Influence ◽  
Real Estate Market ◽  
State Ownership ◽  
Real Estate Sector ◽  
The Impact

AbstractThe young real estate market in Vietnam, an emerging country in Asia, has been growing remarkably. This is an attractive channel for investors, but it seems to be an unstable market and have high potential source of earnings management while investing in real estate companies listed in Vietnamese stock market. The research has been conducted to investigate the impact of the ownership structure on the earnings management of Vietnamese listed real estate companies. The research methodology includes four statistical approaches OLS, FEM, REM and REM (robust) that are employed to address econometric issues and to improve the accuracy of the regression coefficients. The research sample consists of 180 firm-year observations for 36 real estate companies listed on Vietnamese stock market over a period of five years, i.e. from 2014 to 2018. The results show that, while state ownership showed a positive influence, managerial ownership played negative significant roles in relation to earnings management. This research has implications for designing a better ownership structure in the Vietnamese real estate sector and enhancing information quality to protect investors.

scholarly journals Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiaoyu Wang ◽  
Kai Kang ◽  
Zhihan Zhang ◽  
Demou Cao
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Financial Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
Time Series Prediction ◽  
Machine Learning Algorithms ◽  
Trend Prediction ◽  
Lstm Network

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory.

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