scholarly journals Trading Volume as a Predictor of Market Movement

2019 ◽  
Vol 8 (2) ◽  
pp. 57-69
Author(s):  
Edson Kambeu
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Regression Model ◽  
Trading Volume ◽  
Logistic Regression Model ◽  
Predictive Analytics ◽  
Stock Exchange ◽  
Stock Market Index ◽  
Trading Volumes ◽  
Popular Language

A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

scholarly journals Modelling of stock market security price Dynamics Using market microstructure Data

2018 ◽  
Vol 22 (5) ◽  
pp. 141-153
Author(s):  
N. A.  Bilev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Stock Market ◽  
Regression Model ◽  
Market Microstructure ◽  
Logistic Regression Model ◽  
Network Models ◽  
Neural Network Models ◽  
Trading System

In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data.

The Timescale Effects of Corporate Governance Measure on Predicting Financial Distress

2008 ◽  
Vol 11 (01) ◽  
pp. 35-46 ◽  
Author(s):  
Hsin-Hung Chen
Keyword(s):  
Corporate Governance ◽  
Logistic Regression ◽  
Regression Model ◽  
Financial Distress ◽  
Logistic Regression Model ◽  
Historical Data ◽  
Stock Exchange ◽  
Taiwan Stock Exchange ◽  
Prediction Horizon ◽  
The Empirical Analysis

This study aims to investigate the timescale effects of the corporate governance measure on predicting financial distress of corporations. A new corporate governance measure is adopted in the logistic regression model. Historical data of the companies listed on the Taiwan Stock Exchange Corporation (TSEC) were used in the empirical analysis. The analysis was based on three different prediction horizons comprising one-, two- and three-year horizons. The results confirmed that the accuracy of the logistic regression model for predicting corporate financial distress can be improved by incorporating the corporate governance measure. Moreover, the improvements of the correct rate for classification by incorporating the corporate governance measure increased as the prediction horizon was raised. The improvements of the correct rate for classification by incorporating the corporate governance measure are 2.9%, 4.4% and 5.8% for "Year 1", "Year 2" and "Year 3" models respectively.

scholarly journals Uji Model Keseimbangan Teori Keagenan: Pengaruh Kebijakan Utang dan Kebijakan Dividen Terhadap Kepemilikan Manajerial [Testing a Model of Balance Agency Theory: The Influence of Policy Liabilities and Dividend Policy towards Managerial Ownership]

2016 ◽  
Vol 11 (1) ◽  
pp. 102 ◽  
Author(s):  
Shiela Liviani ◽  
Putu Anom Mahadwartha ◽  
Liliana Inggrit Wijaya
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Agency Theory ◽  
Logistic Regression Model ◽  
Dividend Policy ◽  
Stock Exchange ◽  
Fixed Effect ◽  
Managerial Ownership ◽  
Trade Off ◽  
Debt Policy

<p>This study aims to analyze the balancing model of agency theory in the effect of debt policy and dividend policy to managerial ownership in manufacture sector companies, which are listed in Indonesia Stock Exchange. Companies in Indonesia tend to have concentrated ownership structure. This condition is contradictory to the previous studies which mostly done in the widely ownership-dispersed countries. Balancing model argues that there is a trade-off between each monitoring and controlling mechanism, i.e. the effect of debt policy to managerial ownership and dividend policy to managerial ownership. This study uses a quantitative approach with fixed effect logistic regression model to analyze the data. Total sample of this study is 1,060 observations of 106 listed Indonesian manufacture firms during the period of 2004-2014. This study found debt policy to be a significant and negative determinant of the level of managerial ownership, as predicted by balancing model of agency theory. This study also found dividend policy has an insignificant positive effect to the level of managerial ownership, which rejected the balancing model of agency theory in the effect of dividend policy to managerial ownership.</p><p>Penelitian ini bertujuan untuk menguji model keseimbangan teori keagenan pada pengaruh kebijakan utang dan kebijakan dividen terhadap kepemilikan manajerial dalam perusahaan sektor manufaktur yang terdaftar di Bursa Efek Indonesia. Struktur kepemilikan dalam perusahaan-perusahaan di Indonesia cenderung terkonsentrasi. Kondisi ini berbeda dengan penelitian terdahulu yang banyak dilakukan di negara yang struktur kepemilikannya cenderung tersebar. Model keseimbangan menyatakan bahwa terdapat <em>trade-off</em> antar suatu mekanisme pengawasan dan pengendalian dengan mekanisme lainnya, yaitu pengaruh kebijakan utang terhadap kepemilikan manajerial dan kebijakan dividen terhadap kepemilikan manajerial. Penelitian ini menggunakan pendekatan kuantitatif dengan <em>fixed effect logistic regression model</em> untuk menganalisis data. Jumlah sampel yang digunakan dalam penelitian ini adalah 1.060 observasi dari 106 perusahaan sektor manufaktur di Indonesia selama periode 2004-2014. Penelitian ini menemukan bahwa kebijakan utang berpengaruh negatif signifikan terhadap tingkat kepemilikan manajerial, seperti yang diprediksi oleh model keseimbangan teori keagenan. Penelitian ini juga menemukan bahwa kebijakan dividen memiliki pengaruh positif tidak signifikan terhadap tingkat kepemilikan manajerial, yang mana menolak model keseimbangan teori keagenan dalam pengaruh kebijakan dividen terhadap kepemilikan manajerial.</p>

scholarly journals Assessing the Impact of S&P SL20 Index Construction on Listed Companies in Colombo Stock Exchange (CSE)

2016 ◽  
Vol 8 (7) ◽  
pp. 159
Author(s):  
Upeksha Perera ◽  
Rohana Dissanayake ◽  
Mangalika Jayasundara
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Market Model ◽  
Stock Market Index ◽  
Index Construction ◽  
Price Series ◽  
Adjusted Model ◽  
The Impact

<p>A stock market index is designed to measure the performance of value of a set of stocks. The set of stock can be entire market of a particular country or a sector. Indices can be used not only to see how the stock market, for instance, has changed over time, but it allows easy comparison between stocks that represent different sectors or even different stocks. An index construction or rebalancing of existing index is a major market event that investor might know before the event take place. The index inclusion reflects a positive situation about the quality, risks and possible future return of the stock. This study examine whether any price and trading volume effects arise from S&amp;P SL 20 index construction. S&amp;P SL 20 index was launched in 26, June 2012, based on 20 blue chip companies in Sri Lanka. The current study employs the standard event study methodology to identify the abnormal returns associated with the launching of the S&amp;P SL 20 index. Three normal return benchmarks, namely the market-adjusted model, mean-adjusted model and the market model have been used for the purpose of finding abnormal returns. Price series and volumes of stocks in S&amp;P SL 20 list (after and before) were considered and those are retrieved from Colombo stock exchange.</p><p>The study finds that the abnormal returns following the launch of the S&amp;P SL 20 index is statistically insignificant.</p>

scholarly journals Logistic Regression Model for Assessing Factors Affecting Timeliness of Financial Statement Submission

2021 ◽  
Vol 31 (12) ◽  
pp. 3000
Author(s):  
Eri Ayu Rumiyani ◽  
Siti Resmi ◽  
Ralina Transistari ◽  
Muhammad Roni Indarto
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Stock Exchange ◽  
Financial Statements ◽  
Financial Statement ◽  
Company Size ◽  
Current Ratio ◽  
Financial Reports ◽  
Factors Affecting

Financial information is helpful if it fulfills fundamental characteristics. One of these characteristics is timeliness. This article aims to examine the elements that affect financial statement timeliness. This research is conducted on mining companies listed on the Indonesian stock exchange from 2016 to 2018. The data analyzed were panel data of 69 observations obtained from 23 companies over three years. The data was processed using Eviews9 and logit regression analysis techniques. The investigation demonstrates that the current ratio has a considerable favorable influence on the timeliness of financial statements for a variety of mining businesses listed on the Indonesian Stock Exchange (IDX) between 2016 and 2018. On the other hand, return on assets, and company size has no significant effect on the timeliness of financial statements. The implication of the results of this study is that better regulations are needed to improve public companies in submitting financial reports on time. Published financial statements are information for making informed decisions by interested users. Keywords : Current Ratio; Return on Asset; Company Size; Financial Statement; Logistic Regression Model.

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