A Review on Applied Data Mining Techniques to Stock Market Prediction

2013 ◽  
pp. 108-126
Author(s):  
Neslihan Fidan ◽  
Beyza Ahlatcioglu Ozkok
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
New Technologies ◽  
Point Of View ◽  
Stock Market Index ◽  
Data Mining Techniques ◽  
Market Index ◽  
Predictive Data Mining ◽  
Using Data ◽  
Analysis Point

A portfolio manager considers forecasting the asset prices and measurement of the market risk of an underlying asset. Financial institutions produce datasets to handle their problems by using data mining tools. Recently new technologies have been developed for tracking, collecting, and processing financial data. From a data analysis point of view, this chapter reviews the published articles based upon predictive data mining applications to stock market index. It is observed that hybrid models that combine data mining techniques or integrate an algorithm to a method work efficiently. Finally, the chapter provides likely directions of future researches.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

Stock Market Prediction Using Data Mining Techniques

2019 ◽  
Author(s):  
Archana Gupta ◽  
Pranay Bhatia ◽  
Kashyap Dave ◽  
Pritesh Jain
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
Stock Market Prediction ◽  
Data Mining Techniques ◽  
Using Data

Regularization of Predictors of GDP and Individual Sectors of the Economy of the Republic of Kazakhstan

2021 ◽  
Vol 1 (59) ◽  
Author(s):  
Amanbay Assylbekov ◽  
Bayan Assylbekova
Keyword(s):  
Stock Market ◽  
Money Supply ◽  
Point Of View ◽  
Civil Aviation ◽  
Stock Market Index ◽  
Financial Measures ◽  
Market Index ◽  
Wide Range ◽  
Macroeconomic Indicators ◽  
The Republic

Today, the point of view is widespread that a developed stock market is a necessary component of sustainable development of the entire economy of a country, region. How true is this approach in the conditions of the frontier  market and the developing economy, to which Kazakhstan belongs? In our study, we tried to quantify this approach. Compression (regularization) of predictors was carried out. The constructed model was tested on the predictors of civil aviation in Kazakhstan. The following results are obtained. The use of regularization with the goal of: data mining, identifying external predictors of development of various sectors of the economy, we consider it necessary and useful. Stock market capitalization is directly related to the money supply and, to a lesser extent, to household deposits. The view is partially confirmed that pumping money into the economy only leads to financial bubbles. The stock market index was sensitive to a wide range of social and macroeconomic indicators: population growth, unemployment, poverty, inflation, investment, devaluation. Our conclusion: for the development of the stock market does not require any specific financial measures, it is necessary to deal with the economy as a whole. Volumes of transactions with corporate securities do not have stable external predictors. The main indicators of civil aviation in the republic have stable external predictors. Passenger turnover, sending passengers directly depend on: the level of unemployment, wages, GDP per capita, various types of services and products, money supply. There are no external predictors for a separate type of aerial work - cargo transportation. Consequently, it is possible to obtain positive results through the reform of this particular segment of services.

Data Mining Prospects in Mobile Social Networks

2013 ◽  
pp. 145-172 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Zhongshi He ◽  
Huazheng Zhu
Keyword(s):  
Data Mining ◽  
Social Networking ◽  
Large Scale ◽  
New Technologies ◽  
Data Mining Techniques ◽  
Large Scale Data ◽  
Social Technologies ◽  
New Ideas ◽  
Using Data ◽  
Fundamental Insight

Unable to accommodating new technologies, including social technology, mobile devices and computing are other potential problems, which are significant challenges to social-networking service. The very broad range of such social-networking challenges and problems are demanding advanced and dynamic tools. Therefore, in this chapter, we introduced and discussed data mining prospects to overcome the traditional social-networking challenges and problems, which led to optimization of MSNs application and performances. The proposed method infers defining and investigating social-networking problems using data mining techniques and algorithms based on the large-scale data. The approach is also exploring the possible potential of users and systems contexts, which leads to mine the personal contexts such as the users’ locations and situations from the mobile logs. In these sections, we discussed and introduced new ideas on social technologies, data mining techniques and algorithm’s prospects, social technology’s key functional and performances, which include social analysis, security and fraud detections by presenting a brief analysis, and modeling based descriptions. The approach also empirically discussed using the real survey data, which the result showed how data mining vitally significant to explore MSNs performance and its crosscutting impacts. Finally, this chapter provides fundamental insight to researchers and practitioners who need to know data mining prospects and techniques to analyze large, complex and frequently changing data. This chapter is also providing a state-of-the-art of data mining techniques and algorithm’s dynamic prospects.

scholarly journals Analysis of investors’ preferences in the Montenegro stock market using data mining techniques

2014 ◽  
Vol 27 (1) ◽  
pp. 463-482 ◽  
Author(s):  
Ljiljana Kašćelan ◽  
Vladimir Kašćelan ◽  
Miomir Jovanović
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
Data Mining Techniques ◽  
Using Data
Sign in / Sign up

Export Citation Format

Share Document