Daily Stock Prices Forecast in the US Stock Market with Deep Learning Method and Least Square Method

2020 ◽  
Vol 37 (2) ◽  
pp. 19-31
Author(s):  
Ik Sun Lee
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Least Square Method ◽  
Least Square ◽  
Learning Method ◽  
The Us

Islamic calendar and stock market behaviour in India

2018 ◽  
Vol 45 (11) ◽  
pp. 1550-1566
Author(s):  
Dharani Munusamy
Keyword(s):  
Stock Market ◽  
Least Square Method ◽  
Least Square ◽  
Stock Market Returns ◽  
Content Type ◽  
Ordinary Least Square ◽  
Average Return ◽  
Year Effect ◽  
The Common ◽  
The Impact

Purpose The purpose of this paper is to examine the behavior of the stock market returns in the different days of the week and different months of the year in accordance with the Islamic calendar. Further, the study estimates the risk-adjusted returns to test the performance of the indices during the Ramadan and non-Ramadan days. Finally, the study investigates the impact of Ramadan on the returns and the volatility of the stock market indices in India. Design/methodology/approach Initially, the study applies the Ordinary Least Square method to test the day-of-the-week and the month-of-the-year effect of the common and Shariah indices. Next, the study employs the risk-adjusted measurement to examine the underperformance and over-performance of the indices for both the periods. Finally, the study estimates the GARCH (1,1) and GJR-GARCH (1,1) models to observe the impact of Ramadan on the returns and the volatility of the Shariah indices in India. Findings The study finds that an average return of the indices during the Ramadan days are higher than non-Ramadan days. Further, the average returns of the Shariah indices are significantly higher on Wednesday than other days of the week. In addition, the highest and significant mean returns and mean risk-adjusted returns of the indices during the Ramadan days are observed. Finally, the study finds an evidence of the Ramadan effect on the returns and volatility of the indices in India. Originality/value The study observes evidence that the Ramadan effect influences the Shariah indices, but not the common indices in the stock market of the non-Muslim countries. It indicates that the Ramadan creates the positive mood and emotions in the investors buying and selling activities. The study suggests that investors can buy the shares before Ramadan period and sell them during the Ramadan days to get an abnormal return in the emerging markets.

scholarly journals Harvesting social media sentiment analysis to enhance stock market prediction using deep learning

2021 ◽  
Vol 7 ◽  
pp. e476
Author(s):  
Pooja Mehta ◽  
Sharnil Pandya ◽  
Ketan Kotecha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Market ◽  
Social Networking ◽  
Stock Prices ◽  
Short Term Memory ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Public Sentiment ◽  
Area Of Interest

Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.

scholarly journals Capital Structure and Stock Prices: Empirical Evidence from Listed Beverage, Food and Tobacco Companies in Sri Lanka

2018 ◽  
Vol 3 (2) ◽  
pp. 83-90
Author(s):  
V.A. Subramaniam ◽  
S. Anandasayanan
Keyword(s):  
Capital Structure ◽  
Stock Prices ◽  
Least Square Method ◽  
Least Square ◽  
Share Price ◽  
Share Prices ◽  
Food And Beverage ◽  
Macroeconomic Indicators ◽  
Level Of Significance ◽  
The Relationship

The share price of a firm is affected by various factors. Determination of share price is not an easy task. The share price movement is based on the firm’s fundamentals, Market efficiency, Macroeconomic Indicators and Perception of the Investors. Several studies have proven that share price of firms are explained by its capital structure. This study tests the relationship between capital structure and share prices of the listed Food and Beverage Tobacco companies in CSE for the period from 2011/2012 to 2016/2017. It analyzes the relationship between share price and capital structure by employing panel least square method approach. According to the results of the study there is a positive relationship between debt to equity and share prices .The results were statistically significant at 1% level of significance. The results indicate adding debt to overall capital positively effects on the share prices. These findings have important implications for managers or people who take decisions regarding capital structure. The changes in capital structure may have a significant impact on stock prices of the companies.

Forecasting

2019 ◽  
pp. 119-135
Author(s):  
Mike Rupert ◽  
Jean C. Essila
Keyword(s):  
Stock Market ◽  
Predictive Model ◽  
Multiple Regression ◽  
Stock Prices ◽  
Stock Price ◽  
Financial Ratios ◽  
Data Set ◽  
Financial Reports ◽  
The Us ◽  
Regression Tests

The chapter covers a study on forecasting stock prices, which can be a challenging task due to the amount of information and variability involved. The test approach, research, and results cover 50 companies on the US stock market over a 6-year period. Company quarterly and annual financial reports, along with daily stock prices, form the data set analyzed. The financial ratios were tested as independent variables against stock price as the dependent variable. Also, ratio type comparisons and timing scenarios for leading or lagging indicators were covered. Correlation and multiple-regression tests were used to eliminate some ratios, and to find a combination of 12 ratios that successfully account for 35% of the variability in stock prices. The results point to leading indicators, statistically significant ratios, and a predictive model for forecasting stock price.

Underreaction to News in the US Stock Market

2016 ◽  
Vol 06 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Nitish Ranjan Sinha
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Institutional Ownership ◽  
Qualitative Information ◽  
Analyst Coverage ◽  
Average Return ◽  
The Us ◽  
Negative Tone

Using a score that quantifies the tone of news articles, I construct a weekly measure of qualitative information that predicts returns over the next 13 weeks. A portfolio long stocks with past positive tone and short stocks with past negative tone has an average return of 16.54 basis points per week (8.60% per year). The findings suggest the market underreacts to the content of news articles. The underreaction is not constrained to small stocks, low analyst-coverage stocks, low institutional ownership, or loser stocks. The findings also suggest the tone of news articles is different from sentiment which is assumed to have no permanent impact on stock prices.

scholarly journals Sparsity Preserving Discriminant Projections with Applications to Face Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Yingchun Ren ◽  
Zhicheng Wang ◽  
Yufei Chen ◽  
Weidong Zhao
Keyword(s):  
Dimensionality Reduction ◽  
Sparse Representation ◽  
Least Square Method ◽  
Least Square ◽  
High Dimensional ◽  
Learning Method ◽  
Intrinsic Structure ◽  
Class Separability ◽  
Generalized Eigenvalue ◽  
Representation Structure

Dimensionality reduction is extremely important for understanding the intrinsic structure hidden in high-dimensional data. In recent years, sparse representation models have been widely used in dimensionality reduction. In this paper, a novel supervised learning method, called Sparsity Preserving Discriminant Projections (SPDP), is proposed. SPDP, which attempts to preserve the sparse representation structure of the data and maximize the between-class separability simultaneously, can be regarded as a combiner of manifold learning and sparse representation. Specifically, SPDP first creates a concatenated dictionary by classwise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least square method. Secondly, a local between-class separability function is defined to characterize the scatter of the samples in the different submanifolds. Then, SPDP integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function. Finally, the proposed method is transformed into a generalized eigenvalue problem. Extensive experimental results on several popular face databases demonstrate the feasibility and effectiveness of the proposed approach.

Broker-dealer leverage volatility and the US stock prices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Khandokar Istiak
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Asset Prices ◽  
Multivariate Garch ◽  
Content Type ◽  
The Us ◽  
Key Driver ◽  
First Moment ◽  
The Impact ◽  
The Relationship

Purpose Broker-dealer leverage volatility increases during booms and crisis periods, but its impact on stock prices is relatively unexplored. This paper aims to investigate whether broker-dealer leverage volatility is a key driver for stock prices. Design/methodology/approach This paper collects the US quarterly data of broker-dealer book leverage and three leading stock market indicators (S&P 500, DJIA and Nasdaq) for the period of 1967–2018. The research uses a multivariate GARCH-in-mean VAR to examine the impact of leverage volatility on each of the stock market indicators. A split-sample analysis (pre-1990 and post-1990) has also been performed to show the robustness of the result. Findings The research finds that broker-dealer leverage volatility does not have any significant impact on stock prices. Originality/value Broker-dealers are important financial intermediaries, and there is a huge literature exploring the relationship between their leverage and asset prices. But, the relationship between broker-dealer leverage volatility and asset prices is not explored yet. This study fills the gap and provides the first evidence that broker-dealer leverage volatility does not play any major role in the theory of stock pricing. The research proposes that the stock holding decisions of the investors should depend only on the first moment of leverage and not on the second moment of leverage. The study concludes that high broker-dealer leverage volatility is not a sinister signal for the US stock market.

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