Literature Review of research papers on Stock Market Portfolio
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This literature review summarizes research papers on methods for constructing optimal stock market portfolios. It discusses four papers that describe techniques for minimizing risk and maximizing profits through portfolio optimization. The first paper uses principal component analysis to extract market risk factors from data. The second reviews optimization methods like column generation and decomposition. The third generates scenario trees to model uncertainties in multistage problems. The fourth presents an algorithm for moment matching scenario generation to optimize portfolios based on expected shortfall. The papers outline computational methods for analyzing market data and constructing efficient portfolios to manage risk.
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Approach based on linear regression for
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Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptxIT .docx
Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx
IT Careers
The growing demand
Projected Job Growth (2016-26)
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Median Salary
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Number of Jobs (2016)
Questions?
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image3.png
image4.jpeg
image5.jpeg
image6.jpeg
image7.jpeg
image8.jpeg
Exp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers_Instructions.docx
Grader - Instructions PPT 2022 ProjectExp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers
Project Description:
As the department chair of Information Technology at the college, you often give presentations on IT Careers to potential students. You have been asked to to update the presentation with recent data on the programs you offer at the college that will prepare students for a career in IT.
Steps to Perform:
Step
Instructions
Points Possible
1
Start PowerPoint. Download and open the file named
Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx. Grader has automatically added your last name to the beginning of the filename.
0
2
Insert a Title and Content layout slide after Slide 1. Type
Jobs in IT in the Title Placeholder.
Insert a Trapezoid List SmartArt graphic in the content placeholder. Type the following in the Text pane as first level bullet points. Remove any unneeded bullet points in the Text pane.
Software applications developerInformation security analystComputer systems analystDatabase administratorComputer network architectManagement analyst
20
3
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8
4
On Slide 4, insert a two column, seven row table in the content placeholder.
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Row 1: Col 1:
Profession; Col 2:
Salary Per Year
Row 2: Col 1
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$103,560
Row 3: Col 1:
Information security analyst; Col 2:
$95,510
Row 4: Col 1:
Computer systems analyst; Col 2:
$88,270
Row 5: Col 1:
Database administrator; Col 2:
$87,020
Row 6: Col 1:
Computer network architect; Col 2:
$104,650
Row 7: Col 1:
Management analyst; Col 2:
$82,450
12
5
Apply the Light Style 2 – Accent 4 table style.
4
6
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Applications of Artificial Neural Network in Forecasting of Stock Market Index
Abstract: Prediction in any field is a challenging and unnerving process. Stock market is a promising financial investment that can generate great wealth. However, under the impact of Globalization Stock Market Prediction (SMP) accuracy has become more challenging and rewarding for the researchers and participants in the stock market. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ANN modeling of stock prices of selected stocks under NSE is attempted to predict the next day’s price. The network developed consists of one input layer, hidden layer and output layer with four, nine and one nodes respectively. The input being the closing price of the previous four days and output being the price for the next day. In the first section the adaptability of neural networks in stock market prediction is discussed, in the second section we discuss the traditional methods that were being used earlier for stock market prediction, in the third section we discuss the justification for using neural networks and how it is better over traditional methods, in the fourth section we discuss the basics of neural networks, section five gives an overview of data and methodology being used, in section six we have discussed the various forecasting errors methods to calculate the error, in section seven we have presented our results. The aim of this paper is to provide an overview of the application of artificial neural network in stock market prediction.
Econometrica
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This document discusses portfolio theory and algorithms for solving mean/variance optimization problems with restrictions. It begins by summarizing the fundamentals of portfolio theory, including the mean/variance principle derived from utility theory. It then describes different algorithms - the Markowitz algorithm, generalized Markowitz algorithm, and extended Markowitz algorithm - that can solve portfolio optimization problems subject to various equality and inequality restrictions. Numerical examples are provided to illustrate the algorithms. The goal is to help practitioners implement portfolio strategies based on theoretical concepts.
Dissertation template bcu_format_belinda -sample
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Approach based on linear regression for
The stock exchange is an important apparatus for economic growth as it is an opportunity for investors to acquire equity and, at the same time, provide resources for organizations expansions. On the other hand, a major concern regarding entering this market is related with the dynamic in which deals are made since the pricing of shares happens in a smart and oscillatory way. Due to this context, several researchers are studying techniques in order to predict the stock exchange, maximize profits and reduce risks. Thus, this study proposes a linear regression model for stock exchange prediction which, combined with financial indicators, provides support decision-making by investors.
Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptxIT .docx
Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx
IT Careers
The growing demand
Projected Job Growth (2016-26)
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Median Salary
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook
Number of Jobs (2016)
Questions?
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Exp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers_Instructions.docx
Grader - Instructions PPT 2022 ProjectExp22_PowerPoint_Ch03_CumulativeAssessment_IT_Careers
Project Description:
As the department chair of Information Technology at the college, you often give presentations on IT Careers to potential students. You have been asked to to update the presentation with recent data on the programs you offer at the college that will prepare students for a career in IT.
Steps to Perform:
Step
Instructions
Points Possible
1
Start PowerPoint. Download and open the file named
Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptx. Grader has automatically added your last name to the beginning of the filename.
0
2
Insert a Title and Content layout slide after Slide 1. Type
Jobs in IT in the Title Placeholder.
Insert a Trapezoid List SmartArt graphic in the content placeholder. Type the following in the Text pane as first level bullet points. Remove any unneeded bullet points in the Text pane.
Software applications developerInformation security analystComputer systems analystDatabase administratorComputer network architectManagement analyst
20
3
Apply the Moderate Effect SmartArt style. Change the color to Colorful – Accent Colors.
8
4
On Slide 4, insert a two column, seven row table in the content placeholder.
Type the following information into the table:
Row 1: Col 1:
Profession; Col 2:
Salary Per Year
Row 2: Col 1
Software applications developer; Col 2:
$103,560
Row 3: Col 1:
Information security analyst; Col 2:
$95,510
Row 4: Col 1:
Computer systems analyst; Col 2:
$88,270
Row 5: Col 1:
Database administrator; Col 2:
$87,020
Row 6: Col 1:
Computer network architect; Col 2:
$104,650
Row 7: Col 1:
Management analyst; Col 2:
$82,450
12
5
Apply the Light Style 2 – Accent 4 table style.
4
6
Add a new column to the left of column 1. Merge the cells in the new column. Set the cell width of the merged cell .
Applications of Artificial Neural Network in Forecasting of Stock Market Index
Abstract: Prediction in any field is a challenging and unnerving process. Stock market is a promising financial investment that can generate great wealth. However, under the impact of Globalization Stock Market Prediction (SMP) accuracy has become more challenging and rewarding for the researchers and participants in the stock market. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ANN modeling of stock prices of selected stocks under NSE is attempted to predict the next day’s price. The network developed consists of one input layer, hidden layer and output layer with four, nine and one nodes respectively. The input being the closing price of the previous four days and output being the price for the next day. In the first section the adaptability of neural networks in stock market prediction is discussed, in the second section we discuss the traditional methods that were being used earlier for stock market prediction, in the third section we discuss the justification for using neural networks and how it is better over traditional methods, in the fourth section we discuss the basics of neural networks, section five gives an overview of data and methodology being used, in section six we have discussed the various forecasting errors methods to calculate the error, in section seven we have presented our results. The aim of this paper is to provide an overview of the application of artificial neural network in stock market prediction.
Econometrica
This paper develops a theory of comparative advantage using tools from the mathematics of complementarity. It shows that under certain conditions, comparative advantage can be predicted from differences in factor productivity and endowments across countries and sectors. Specifically, it finds that if factor productivity or endowments are log-supermodular in country and sector characteristics, countries will specialize according to their comparative advantage, with some countries producing relatively more in some sectors. The paper generalizes insights from Ricardian and Heckscher-Ohlin models of trade to settings with multiple countries, goods, and factors of production.
Unifying Technical Analysis and Statistical Arbitrage
Technical Analysis and Statistical Arbitrage employ different tools and speak different languages. But they are trying to solve the same problem: create a good trading strategy. Is it possible to combine them?
Neural trading term paper
This document discusses using artificial neural networks for trading stocks and other financial assets. It explains that neural networks are well-suited for financial markets which are noisy and unpredictable. The document outlines what is required to develop a neural network trading system, including selecting important input variables from fundamental and technical analysis. Fundamental analysis variables discussed include company size, valuation ratios, and attributes identified by Benjamin Graham that may indicate an undervalued stock. Technical analysis techniques covered are charting, indicators, and oscillators. The document aims to discuss how to develop a neural network trading strategy that can operate given real-world constraints.
Modeling market and nonmarket Intangible investments in a macro-econometric f...
Modeling market and nonmarket Intangible investments in a macro-econometric framework. Sociedty for Economic Measurement Annual Conference. Thessaloniki July 2016
Artfcl tchncl an
This document summarizes a research paper that tests the efficient market hypothesis through the use of an artificial technical analyst (ATA) applied to high-frequency stock market data from the Tokyo Stock Exchange. The ATA uses technical analysis and machine learning to identify patterns in historical stock prices and test whether these patterns can be profitably exploited after accounting for transaction costs. The key findings were that the ATA was able to identify past market inefficiencies, as demonstrated by transaction costs needed to break even being below actual market transaction costs, suggesting speculative opportunities exist.
Katin, Igor , Mockus, Jonas , Katina, Joana „Apie eksperimentinį investavimo ...
Katin, Igor , Mockus, Jonas , Katina, Joana „Apie eksperimentinį investavimo ...Lietuvos kompiuterininkų sąjunga
Duomenų tyrybos ir optimizavimo sekcija
XVI kompiuterininkų konferencija
„Kompiuterininkų dienos - 2013“
Šiauliai, 2013.09.21 d.Textual analysis of stock market
This paper examines using different textual representations of financial news articles - bag of words, noun phrases, and named entities - to predict stock prices 20 minutes after an article is released, using support vector machines. The study finds that named entities outperform bag of words, the typical representation used. It introduces the topic of using textual analysis to predict stock prices and reviews prior literature on stock prediction using both fundamental and technical analysis approaches as well as textual data.
vatter_pdm_1.1
This document summarizes a master's thesis on integrating market views into quantitative portfolio allocation. It introduces the concepts of optimal asset allocation, mean-variance optimization, and dimension reduction using linear factor models. It then discusses incorporating investor views through information sets and the efficient market hypothesis. The Black-Litterman model uses a Gaussian market assumption, CAPM reverse optimization, and Bayesian updating to integrate views. An alternative approach uses f-divergences to measure distortions between probability distributions and translate views into information gain. The thesis concludes by proposing directions for future research.
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Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptxIT .docx
Ahmed_Exp22_PPT_Ch03_CumulativeAssessment_IT Careers.pptxIT .docx
Applications of Artificial Neural Network in Forecasting of Stock Market Index
Applications of Artificial Neural Network in Forecasting of Stock Market Index
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Unifying Technical Analysis and Statistical Arbitrage
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Literature Review of research papers on Stock Market Portfolio
- 1. Literature Review: STOCK MARKET PORTFOLIO 1 Literature Review of research papers on Stock Market Portfolio Prakash Ashu, Research Intern National Research Tomsk Polytechnic University Guided by – Prof Mikhail Semenov Department of Higher Mathematics & Mathematical Physics Institute of Physics and Technology National Research Tomsk Polytechnic University, Tomsk, Russia
- 2. STOCK MARKET PORTFOLIO 2 Abstract This paper provides a review of the research papers on Methods of risk measurement and profit generation in Stock Market Portfolio given by Dr. Mikhail Semenov, Associate Professor, Department of High Mathematics and Mathematical Physics, National Research Tomsk Polytechnic University, Tomsk, Russia. I have tried to sum up all the papers of my interest briefly in this literature review. I have developed some interest in Methods section of this project. Keywords: Portfolio, Combinatorial Optimization, Multistage decision problems, Arbitrage pricing, Stochastic Programming.
- 3. STOCK MARKET PORTFOLIO 3 Literature Review of research papers on Stock Market Portfolio Methods This section of papers mainly deals with the techniques of solving the models for a successful business, say by minimizing the risk and maximizing the profits. Papers: Generating market risk scenarios using principal component analysis – Mico Loretan Combinatorial Optimization: Current Successes and directions for the future – Hoffman Generating scenario trees for multistage decision problems – Holland & Wallace + Klaasen* An algorithm for moment matching scenario generation with application to finance portfolio optimization – Ponomareva, Roman & Date Review. The first paper deals with the Principal Component Analysis (PCA) as it is one of the easiest way for extracting market risk factors from observed data. More are the factors, more is the accuracy to minimize the risk. This paper also covers the data analysis of the data observations on spot exchange rates, stock market indexes and interest rates of some countries. The second paper is summed up with the different techniques of optimization using different optimization tools. We have methods like column generation, and different solution strategies like Enumerative approaches, decomposition methods. We also have different hybrid algorithms to solve such problems. Stochastic programming is one important aspect of solving such problems.
- 4. STOCK MARKET PORTFOLIO 4 The third paper on Tree Scenario Generation for multistage decision problems deals with approximating the uncertainties by a limited number of discrete outcomes. Probability distributions are used to generate the samples of data. Arbitrage opportunities are detected via these methods. The fourth paper by Ponomareva, published in 2014 and republished in 2015 is the presentation of an algorithm for moment matching scenario generation and to optimize the financial portfolio. This algorithm is computationally stronger than other optimization techniques. The algorithm generates scenario in an average expected shortfall portfolio optimization model.
- 5. STOCK MARKET PORTFOLIO 5 References Loretan, (1997). Generating market risk scenarios using principal components analysis Hoffman, (2000). Combinatorial Optimization: Current successes and directions for the future Holland, Wallace, (2001). Generating Scenario Trees for Multistage decision problems Klaasen, (2002). Comment on “Generating Scenario Trees for Multistage decision problems Ponomareva, Roman, Date, (2014,15). An algorithm for moment matching scenario generation with application to financial portfolio optimization. Eichhorn, Heitsch, Romisch. Stochastic optimization for electricity portfolios: Scenario Tree Modelling and Risk Management.