This document summarizes a project that used text analysis of SEC filings to predict the relative risk of investing in publicly traded companies. The researchers analyzed sections 7 and 7A of companies' 10-K reports, which describe financial status and risks. They used logistic regression and neural networks to build models predicting investment risk based on textual patterns. Testing on new companies achieved around 90-95% accuracy. The researchers concluded text analysis of 10-K reports provides useful insights into future company performance and stock trends.
Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statementsgerogepatton
The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine
the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The
proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes
them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis.
Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the
10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to
leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at
predicting future returns, the framework uses the alphalens library to perform factor return analysis,
turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that
there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its
future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to
this paper have been open-sourced on Github1.
This document discusses ratio analysis and its importance for evaluating company performance. Ratio analysis involves grouping financial ratios into categories like liquidity and profitability to analyze variables like bankruptcy risk, loan defaults, and stock prices. Ratios allow comparison of a company's performance over time, against industry benchmarks, and between different time periods or industries. Ratio analysis is used by companies, investors, and creditors to evaluate financial position, predict future performance, and identify strengths and weaknesses. The document then provides an overview of the objectives, need, importance, scope, and methodology of ratio analysis as well as a profile of SujalaPipes Private Limited, the company used for this case study.
In this paper, we used financial statements as the main information to calculate the enterprise
value by discounted cash flow model. For the prediction of future cash flows in DCF model, a new method
based on the Markov chain is proposed to get the growth rates of future cash flows, instead of the fixed growth
rate method. The superior performance of it can be illustrated in empirical analysis. And the result shows that
we can improve the accuracy of the enterprise value evaluation with partial information by using the Markov
chain
Bankruptcy Prediction is an art of predicting bankruptcy and various measures of financial
distress of public or private firms. In recent past days we are seeing many cases with distress
and bankrupted. It is a huge area of finance and accounting research. The importance of the
world is due partially to the relevance for creditors and investors in evaluating the likelihood
that a firm may go bankrupt. The quantity of research is additionally a function of the supply of
data: for public firms which went bankrupt or not, numerous accounting ratios which
may indicate danger can be calculated, and various other potential explanatory variables also
are available. Consequently, the world is well-suited for testing of increasingly sophisticated,
data-intensive forecasting approaches.
Manu Nepali presented a project report on equity analysis and portfolio management at PGDM Batch from 2013-2015. The presentation included an overview of Trustline Securities Pvt. Ltd, explanations of equity analysis and the portfolio management process, research objectives to evaluate investment portfolios and analyze scheme performance and charges, a description of the research methodology used, key findings from analyzing client data, and suggestions such as maintaining regular client contact and increasing advertising.
This document is a study submitted by Afzalshah Sayed towards the partial fulfillment of a Master of Business Administration in Finance degree from IES Management College in Mumbai, India. The study examines fundamental analysis and is divided into multiple chapters that will cover qualitative and quantitative factors, financial statements, valuation and more. The student declares the work as original and grants the college rights to publish parts of the study.
Retirement saving with contribution payments and labor income as a benchmark ...Nicha Tatsaneeyapan
This document summarizes a research paper about modeling retirement savings when contributions are made and labor income is used as a benchmark for investments. The key points are:
1) A retirement savings model is presented where a plan sponsor makes contributions to finance an employee's retirement. The goal is to ensure the employee can maintain their consumption after retiring based on their labor income.
2) Dynamic programming is used to derive optimal investment and contribution strategies as functions of the wealth-to-income ratio and wage growth rate.
3) The analysis finds that contribution payments significantly increase risk-taking at low wealth levels. It also finds that considering downside risk can paradoxically increase risky investing at low wealth levels due to increasing relative risk
Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statementsgerogepatton
The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine
the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The
proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes
them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis.
Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the
10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to
leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at
predicting future returns, the framework uses the alphalens library to perform factor return analysis,
turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that
there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its
future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to
this paper have been open-sourced on Github1.
This document discusses ratio analysis and its importance for evaluating company performance. Ratio analysis involves grouping financial ratios into categories like liquidity and profitability to analyze variables like bankruptcy risk, loan defaults, and stock prices. Ratios allow comparison of a company's performance over time, against industry benchmarks, and between different time periods or industries. Ratio analysis is used by companies, investors, and creditors to evaluate financial position, predict future performance, and identify strengths and weaknesses. The document then provides an overview of the objectives, need, importance, scope, and methodology of ratio analysis as well as a profile of SujalaPipes Private Limited, the company used for this case study.
In this paper, we used financial statements as the main information to calculate the enterprise
value by discounted cash flow model. For the prediction of future cash flows in DCF model, a new method
based on the Markov chain is proposed to get the growth rates of future cash flows, instead of the fixed growth
rate method. The superior performance of it can be illustrated in empirical analysis. And the result shows that
we can improve the accuracy of the enterprise value evaluation with partial information by using the Markov
chain
Bankruptcy Prediction is an art of predicting bankruptcy and various measures of financial
distress of public or private firms. In recent past days we are seeing many cases with distress
and bankrupted. It is a huge area of finance and accounting research. The importance of the
world is due partially to the relevance for creditors and investors in evaluating the likelihood
that a firm may go bankrupt. The quantity of research is additionally a function of the supply of
data: for public firms which went bankrupt or not, numerous accounting ratios which
may indicate danger can be calculated, and various other potential explanatory variables also
are available. Consequently, the world is well-suited for testing of increasingly sophisticated,
data-intensive forecasting approaches.
Manu Nepali presented a project report on equity analysis and portfolio management at PGDM Batch from 2013-2015. The presentation included an overview of Trustline Securities Pvt. Ltd, explanations of equity analysis and the portfolio management process, research objectives to evaluate investment portfolios and analyze scheme performance and charges, a description of the research methodology used, key findings from analyzing client data, and suggestions such as maintaining regular client contact and increasing advertising.
This document is a study submitted by Afzalshah Sayed towards the partial fulfillment of a Master of Business Administration in Finance degree from IES Management College in Mumbai, India. The study examines fundamental analysis and is divided into multiple chapters that will cover qualitative and quantitative factors, financial statements, valuation and more. The student declares the work as original and grants the college rights to publish parts of the study.
Retirement saving with contribution payments and labor income as a benchmark ...Nicha Tatsaneeyapan
This document summarizes a research paper about modeling retirement savings when contributions are made and labor income is used as a benchmark for investments. The key points are:
1) A retirement savings model is presented where a plan sponsor makes contributions to finance an employee's retirement. The goal is to ensure the employee can maintain their consumption after retiring based on their labor income.
2) Dynamic programming is used to derive optimal investment and contribution strategies as functions of the wealth-to-income ratio and wage growth rate.
3) The analysis finds that contribution payments significantly increase risk-taking at low wealth levels. It also finds that considering downside risk can paradoxically increase risky investing at low wealth levels due to increasing relative risk
This document provides an overview of the financial services industry in India, including various subsectors like banking, insurance, mutual funds, stock exchanges, and regulatory bodies. It discusses the role of financial intermediaries in channeling funds between investors and businesses. Some key points covered include:
- The financial services industry plays an important role in promoting investment, savings, and long-term economic growth.
- Major segments include banking, insurance, and mutual funds, which together contribute around 6% to India's GDP.
- Financial intermediaries provide services like maturity transformation and risk mitigation by pooling funds from many sources.
- Important financial institutions include commercial banks, investment banks, brokerages, insurance companies, and non-bank
Text Analytics- An application in Indian Stock MarketsSinjana Ghosh
This presentation was created to present the project done as a part of Applied Management Research Project in Vinod Gupta School of Management, IIT Kharagpur
Banking M&A in Asia: Horizontal mergers
Presented/to be presented at
24th Annual Global Finance Conference at the Hofstra University Student Center
SERCONF 2017, August 2-4 at Hotel Mandarin, Singapore
CMES 2017 :- China meeting of the Econometric Society at Wuhan
This document discusses fundamental analysis, which examines economic, industry, and company factors to evaluate a security's intrinsic value and determine if it is under or overpriced. It describes the three phases of fundamental analysis as evaluating the macroeconomic environment, industry prospects, and a company's projected performance. The goals are to predict market movements, identify undervalued securities, and time investments correctly based on a thorough understanding of economic trends, industry drivers, and business fundamentals. Strengths of fundamental analysis include analyzing long-term trends, spotting good value, developing business acumen, understanding key value drivers, and properly categorizing stocks within their industry groups.
This document presents an analysis of factors to use in filtering stocks for long and short positions in a portfolio. For long positions, the factors of alpha, dividend yield, price-to-book ratio, and changes in stock outstanding are analyzed. For short positions, the factors of market value, price-to-book ratio, capital investment, and liquidity are considered. Principal component analysis is used to analyze the factors and scores are calculated to select 50 stocks for long and short positions that are backtested for returns. The results show the filtered portfolio outperformed the total market.
This summary analyzes a document that debates the evolution of the accounting equation. It identifies three key problems with a recently proposed "new form" of the accounting equation. First, including an error term distorts the standard accounting equation, which is an identity. Second, the new equation is not practical for accounting purposes and does not equal assets to capital plus liabilities. Third, empirical evidence does not sufficiently support pegging the rates of change in equity and liabilities to specific values. The paper then proposes using a cross-case analysis approach to individually analyze company data and test the validity of the new accounting equation without data aggregation distortions.
Financial Analysis on Recession Period at M&M TractorsProjects Kart
Financial ANalysis (also stated as financial plan analysis or accounting analysis) refers to an assessment of the viability, stability and profitable of a business, sub-business or project. Visit www.projectskart.com for more information. It is performed by professionals World Health Organization prepare reports exploitation ratios that create use of data taken from monetary statements and different reports. These reports area unit typically given to prime management mutually of their bases in creating business selections.
A study on effect of liquidity management on profitability with select privat...Supriya Mondal
This document provides a literature review on 9 previous research papers related to the relationship between liquidity management and profitability in banks. The papers examined liquidity ratios like CDR, CRDR and IDR and profitability ratios like ROA, ROE and ROI in various public sector, private sector and cooperative banks in India over different time periods. Most of the studies found an inverse or negative relationship between liquidity and profitability, indicating that increased liquidity leads to decreased profits and vice versa. The papers also compared performance between public and private sector banks, with most finding that private banks had better efficiency and profitability.
Liquidity reactions towards dividend announcements and information efficiency...Evans Tee
This document summarizes a study that examines stock returns and information efficiency on the Ghana Stock Exchange in response to dividend announcements. It uses an event study methodology to analyze abnormal stock returns surrounding dividend announcements for 11 major companies listed on the exchange from 2014-2018. The study finds little informational content in the dividend announcements, as Ghanaian investors did not generally view announcements as favorable news. Stock returns did not conclusively react positively to subsequent dividend announcements. The document provides background on theories of dividends and liquidity, prior research on market responses to dividends, and the methodology used in the study.
This document provides a report on a portfolio optimization project. It summarizes the construction, weekly performance, and rebalancing of a portfolio formed using Markowitz's modern portfolio theory. Over the course of a month, the portfolio was initially constructed using 20 stocks and was rebalanced weekly based on updated stock prices. The portfolio achieved a return of 4.58%, outperforming the S&P 500 benchmark. A risk analysis of the portfolio returns was also conducted using measures like the Sharpe ratio, Treynor ratio, and Sortino ratio.
Summer Training Report on Fundamental AnalysisFellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
This document provides an introduction and overview of a project report on the fundamental analysis of the banking industry in India, with special reference to public sector banks. The report analyzes macroeconomic factors, assesses the performance of the banking industry, and uses financial analysis tools to evaluate and select high-performing banking companies over a five-year period from 2009-2013. The analysis focuses on metrics like net interest margin, credit-to-deposit ratio, non-performing asset ratio, earnings per share, and intrinsic value to compare company performance and make investment decisions.
For more course tutorials visit
www.tutorialrank.com
Assignment Content
1. Research how financial markets and institutions influence the US and global economies.
Create an 8- to 12-slide presentation or 350- to 575-word summary to present your research.
This document provides a summary of a presentation on the fundamental analysis of Mahindra & Mahindra. It begins with an introduction to fundamental analysis, explaining that it examines factors like earnings, growth rates, and risks that affect a company's stock value. It then discusses the economic analysis of macroeconomic factors impacting markets. An industry analysis of the automobile sector in India is presented, including growth rates of different vehicle types and market shares of major manufacturers. The document analyzes Mahindra & Mahindra, providing its history, key leaders, financial details like market cap and ratios, and a SWOT analysis.
Stock Return Predictability with Financial Ratios: Evidence from PSX 100 Inde...Wasim Uddin
The objective of the current study is to investigate the stock return’s predictability by using financial ratios and control variable of PSX 100 Index companies during period from 2001-2014.
This document summarizes a research paper that empirically links the value of intellectual capital and intellectual property to firm performance. The researchers conducted a regression analysis using survey data from managers in the pharmaceutical industry. They found that including intellectual property in models linking intellectual capital to firm performance enhances the statistical validity of the models and their relevance for management. Specifically, intellectual property provides a more tangible component of intellectual capital that can be more easily valued. Considering intellectual property alongside human, structural, and relational capital components provides a more complete picture of how intellectual assets impact company performance.
Project report on fundamental analysis of scrips under banking sectoraftabshaikh04
This project report analyzes scrips under the banking sector in India. It provides an overview of SHCIL, including its subsidiaries and services offered. It then discusses the fundamentals of financial analysis, tools used including ratios and technical analysis. The report outlines the problem statement, objectives, methodology used and limitations. It performs analysis of the economy, banking industry and selected public and private sector banks. Key findings are that SBI is fairly valued based on P/E, PNB is undervalued, and HDFC Bank has highest expected future growth. All banks maintained capital requirements and SBI had the highest book value. Recommendations are to buy all banks except SBI and ICICI Bank.
This document discusses factors that influence stock prices of industrial companies listed on the Indonesia Stock Exchange. It presents a literature review on debt ratio, price-earnings ratio, earnings per share, company size, and company value as independent variables that may predict stock price as the dependent variable. The document then describes the research methodology, which uses a quantitative multiple linear regression analysis of secondary data from 114 industrial companies to determine the relationship between the independent and dependent variables. The results of the analysis show that all four independent variables (debt ratio, price-earnings ratio, earnings per share, size) have a significant influence on stock price both simultaneously and partially, with earnings per share having the strongest influence. Conclusions are that companies should manage these
Working capital investment and financing policies of selected pharmaceutical ...Alexander Decker
1) The study examined the relationship between working capital investment and financing policies of 5 listed pharmaceutical companies in Bangladesh over 5 years.
2) It found that the companies had similar working capital investment policies but significant differences in their working capital financing policies.
3) The study also found that companies with more aggressive working capital investment policies tended to have more conservative working capital financing policies and vice versa.
The goal of working capital management is to
ensure that the firm is able to continue its operations and that
it has sufficient cash flow to satisfy both maturing short-term
debt and upcoming operational expenses. The current study
has concentrated on analysing the working capital
management of Larsen & Turbo Company based on their
liquidity, profitability positions and cash flow statements over
a decade. The study is based on secondary data collected
from the financial reports published in the official websites of
the company for a period of thirteen years from 2003-04 to
2015-16. The data have been analyzed using the financial and
statistical tools namely Ratio Analysis, cash flow and
Correlation Analysis. It has been found that the working
capital management of Larsen & Turbo is good and the
company has to improve its turnover ratios in the future.
This research article examines the factors affecting capital structure for companies listed on the BSE30 index in India during pre-recession (2010-2015) and post-recession (2015-2020) periods. Multiple regression analysis was used to analyze the relationship between financial leverage (dependent variable) and 10 independent variables hypothesized to influence capital structure. The study aims to identify the significant determinants of capital structure during each period and compare whether results support pecking order or trade-off theory. Regression results found several factors significantly impacted financial leverage differently in the pre-and post-recession periods.
Accounting Research Center, Booth School of Business, Universi.docxnettletondevon
Accounting Research Center, Booth School of Business, University of Chicago
Comparing the Accuracy and Explainability of Dividend, Free Cash Flow, and Abnormal
Earnings Equity Value Estimates
Author(s): Jennifer Francis, Per Olsson and Dennis R. Oswald
Source: Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 45-70
Published by: Wiley on behalf of Accounting Research Center, Booth School of Business,
University of Chicago
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Journal of Accounting Research
Vol. 38 No. 1 Spring 2000
Printed in US.A.
Comparing the Accuracy and
Explainability of Dividend, Free
Cash Flow, and Abnormal Earnings
Equity Value Estimates
JENNIFER FRANCIS,* PER OLSSON,t
AND DENNIS R. OSWALD:
1. Introduction
This study provides empirical evidence on the reliability of intrinsic
value estimates derived from three theoretically equivalent valuation
models: the discounted dividend (DIV) model, the discounted free cash
flow (FCO) model, and the discounted abnormal earnings (AE) model.
We use Value Line (VL) annual forecasts of the elements in these models
to calculate value estimates for a sample of publicly traded firms fol-
lowed by Value Line during 1989-93.1 We contrast the reliability of value
*Duke University; tUniversity of Wisconsin; London Business School. This research
was supported by the Institute of Professional Accounting and the Graduate School of
Business at the University of Chicago, by the Bank Research Institute, Sweden, and Jan
Wallanders och Tom Hedelius Stiftelse for Samhallsvetenskaplig Forskning, Stockholm,
Sweden. We appreciate the comments and suggestions of workshop participants at the
1998 EAA meetings, Berkeley, Harvard, London Business School, London School of Eco-
nomics, NYU, Ohio State, Portland State, Rochester, Stockholm School of Economics,
Tilburg, and Wisconsin, and from Peter Easton, Frank Gigler, Paul Healy, Thomas Hem-
mer, Joakim Levin, Mark Mitchell, Krishna Palepu, Stephen Penman, Richard Ruback,
Linda Vincent, Terry Warfield, and Jerry Zimmerman.
I We collect third-quarter annual forecast data over a five-year .
This document provides an overview of the financial services industry in India, including various subsectors like banking, insurance, mutual funds, stock exchanges, and regulatory bodies. It discusses the role of financial intermediaries in channeling funds between investors and businesses. Some key points covered include:
- The financial services industry plays an important role in promoting investment, savings, and long-term economic growth.
- Major segments include banking, insurance, and mutual funds, which together contribute around 6% to India's GDP.
- Financial intermediaries provide services like maturity transformation and risk mitigation by pooling funds from many sources.
- Important financial institutions include commercial banks, investment banks, brokerages, insurance companies, and non-bank
Text Analytics- An application in Indian Stock MarketsSinjana Ghosh
This presentation was created to present the project done as a part of Applied Management Research Project in Vinod Gupta School of Management, IIT Kharagpur
Banking M&A in Asia: Horizontal mergers
Presented/to be presented at
24th Annual Global Finance Conference at the Hofstra University Student Center
SERCONF 2017, August 2-4 at Hotel Mandarin, Singapore
CMES 2017 :- China meeting of the Econometric Society at Wuhan
This document discusses fundamental analysis, which examines economic, industry, and company factors to evaluate a security's intrinsic value and determine if it is under or overpriced. It describes the three phases of fundamental analysis as evaluating the macroeconomic environment, industry prospects, and a company's projected performance. The goals are to predict market movements, identify undervalued securities, and time investments correctly based on a thorough understanding of economic trends, industry drivers, and business fundamentals. Strengths of fundamental analysis include analyzing long-term trends, spotting good value, developing business acumen, understanding key value drivers, and properly categorizing stocks within their industry groups.
This document presents an analysis of factors to use in filtering stocks for long and short positions in a portfolio. For long positions, the factors of alpha, dividend yield, price-to-book ratio, and changes in stock outstanding are analyzed. For short positions, the factors of market value, price-to-book ratio, capital investment, and liquidity are considered. Principal component analysis is used to analyze the factors and scores are calculated to select 50 stocks for long and short positions that are backtested for returns. The results show the filtered portfolio outperformed the total market.
This summary analyzes a document that debates the evolution of the accounting equation. It identifies three key problems with a recently proposed "new form" of the accounting equation. First, including an error term distorts the standard accounting equation, which is an identity. Second, the new equation is not practical for accounting purposes and does not equal assets to capital plus liabilities. Third, empirical evidence does not sufficiently support pegging the rates of change in equity and liabilities to specific values. The paper then proposes using a cross-case analysis approach to individually analyze company data and test the validity of the new accounting equation without data aggregation distortions.
Financial Analysis on Recession Period at M&M TractorsProjects Kart
Financial ANalysis (also stated as financial plan analysis or accounting analysis) refers to an assessment of the viability, stability and profitable of a business, sub-business or project. Visit www.projectskart.com for more information. It is performed by professionals World Health Organization prepare reports exploitation ratios that create use of data taken from monetary statements and different reports. These reports area unit typically given to prime management mutually of their bases in creating business selections.
A study on effect of liquidity management on profitability with select privat...Supriya Mondal
This document provides a literature review on 9 previous research papers related to the relationship between liquidity management and profitability in banks. The papers examined liquidity ratios like CDR, CRDR and IDR and profitability ratios like ROA, ROE and ROI in various public sector, private sector and cooperative banks in India over different time periods. Most of the studies found an inverse or negative relationship between liquidity and profitability, indicating that increased liquidity leads to decreased profits and vice versa. The papers also compared performance between public and private sector banks, with most finding that private banks had better efficiency and profitability.
Liquidity reactions towards dividend announcements and information efficiency...Evans Tee
This document summarizes a study that examines stock returns and information efficiency on the Ghana Stock Exchange in response to dividend announcements. It uses an event study methodology to analyze abnormal stock returns surrounding dividend announcements for 11 major companies listed on the exchange from 2014-2018. The study finds little informational content in the dividend announcements, as Ghanaian investors did not generally view announcements as favorable news. Stock returns did not conclusively react positively to subsequent dividend announcements. The document provides background on theories of dividends and liquidity, prior research on market responses to dividends, and the methodology used in the study.
This document provides a report on a portfolio optimization project. It summarizes the construction, weekly performance, and rebalancing of a portfolio formed using Markowitz's modern portfolio theory. Over the course of a month, the portfolio was initially constructed using 20 stocks and was rebalanced weekly based on updated stock prices. The portfolio achieved a return of 4.58%, outperforming the S&P 500 benchmark. A risk analysis of the portfolio returns was also conducted using measures like the Sharpe ratio, Treynor ratio, and Sortino ratio.
Summer Training Report on Fundamental AnalysisFellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
This document provides an introduction and overview of a project report on the fundamental analysis of the banking industry in India, with special reference to public sector banks. The report analyzes macroeconomic factors, assesses the performance of the banking industry, and uses financial analysis tools to evaluate and select high-performing banking companies over a five-year period from 2009-2013. The analysis focuses on metrics like net interest margin, credit-to-deposit ratio, non-performing asset ratio, earnings per share, and intrinsic value to compare company performance and make investment decisions.
For more course tutorials visit
www.tutorialrank.com
Assignment Content
1. Research how financial markets and institutions influence the US and global economies.
Create an 8- to 12-slide presentation or 350- to 575-word summary to present your research.
This document provides a summary of a presentation on the fundamental analysis of Mahindra & Mahindra. It begins with an introduction to fundamental analysis, explaining that it examines factors like earnings, growth rates, and risks that affect a company's stock value. It then discusses the economic analysis of macroeconomic factors impacting markets. An industry analysis of the automobile sector in India is presented, including growth rates of different vehicle types and market shares of major manufacturers. The document analyzes Mahindra & Mahindra, providing its history, key leaders, financial details like market cap and ratios, and a SWOT analysis.
Stock Return Predictability with Financial Ratios: Evidence from PSX 100 Inde...Wasim Uddin
The objective of the current study is to investigate the stock return’s predictability by using financial ratios and control variable of PSX 100 Index companies during period from 2001-2014.
This document summarizes a research paper that empirically links the value of intellectual capital and intellectual property to firm performance. The researchers conducted a regression analysis using survey data from managers in the pharmaceutical industry. They found that including intellectual property in models linking intellectual capital to firm performance enhances the statistical validity of the models and their relevance for management. Specifically, intellectual property provides a more tangible component of intellectual capital that can be more easily valued. Considering intellectual property alongside human, structural, and relational capital components provides a more complete picture of how intellectual assets impact company performance.
Project report on fundamental analysis of scrips under banking sectoraftabshaikh04
This project report analyzes scrips under the banking sector in India. It provides an overview of SHCIL, including its subsidiaries and services offered. It then discusses the fundamentals of financial analysis, tools used including ratios and technical analysis. The report outlines the problem statement, objectives, methodology used and limitations. It performs analysis of the economy, banking industry and selected public and private sector banks. Key findings are that SBI is fairly valued based on P/E, PNB is undervalued, and HDFC Bank has highest expected future growth. All banks maintained capital requirements and SBI had the highest book value. Recommendations are to buy all banks except SBI and ICICI Bank.
This document discusses factors that influence stock prices of industrial companies listed on the Indonesia Stock Exchange. It presents a literature review on debt ratio, price-earnings ratio, earnings per share, company size, and company value as independent variables that may predict stock price as the dependent variable. The document then describes the research methodology, which uses a quantitative multiple linear regression analysis of secondary data from 114 industrial companies to determine the relationship between the independent and dependent variables. The results of the analysis show that all four independent variables (debt ratio, price-earnings ratio, earnings per share, size) have a significant influence on stock price both simultaneously and partially, with earnings per share having the strongest influence. Conclusions are that companies should manage these
Working capital investment and financing policies of selected pharmaceutical ...Alexander Decker
1) The study examined the relationship between working capital investment and financing policies of 5 listed pharmaceutical companies in Bangladesh over 5 years.
2) It found that the companies had similar working capital investment policies but significant differences in their working capital financing policies.
3) The study also found that companies with more aggressive working capital investment policies tended to have more conservative working capital financing policies and vice versa.
The goal of working capital management is to
ensure that the firm is able to continue its operations and that
it has sufficient cash flow to satisfy both maturing short-term
debt and upcoming operational expenses. The current study
has concentrated on analysing the working capital
management of Larsen & Turbo Company based on their
liquidity, profitability positions and cash flow statements over
a decade. The study is based on secondary data collected
from the financial reports published in the official websites of
the company for a period of thirteen years from 2003-04 to
2015-16. The data have been analyzed using the financial and
statistical tools namely Ratio Analysis, cash flow and
Correlation Analysis. It has been found that the working
capital management of Larsen & Turbo is good and the
company has to improve its turnover ratios in the future.
This research article examines the factors affecting capital structure for companies listed on the BSE30 index in India during pre-recession (2010-2015) and post-recession (2015-2020) periods. Multiple regression analysis was used to analyze the relationship between financial leverage (dependent variable) and 10 independent variables hypothesized to influence capital structure. The study aims to identify the significant determinants of capital structure during each period and compare whether results support pecking order or trade-off theory. Regression results found several factors significantly impacted financial leverage differently in the pre-and post-recession periods.
Accounting Research Center, Booth School of Business, Universi.docxnettletondevon
Accounting Research Center, Booth School of Business, University of Chicago
Comparing the Accuracy and Explainability of Dividend, Free Cash Flow, and Abnormal
Earnings Equity Value Estimates
Author(s): Jennifer Francis, Per Olsson and Dennis R. Oswald
Source: Journal of Accounting Research, Vol. 38, No. 1 (Spring, 2000), pp. 45-70
Published by: Wiley on behalf of Accounting Research Center, Booth School of Business,
University of Chicago
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Journal of Accounting Research
Vol. 38 No. 1 Spring 2000
Printed in US.A.
Comparing the Accuracy and
Explainability of Dividend, Free
Cash Flow, and Abnormal Earnings
Equity Value Estimates
JENNIFER FRANCIS,* PER OLSSON,t
AND DENNIS R. OSWALD:
1. Introduction
This study provides empirical evidence on the reliability of intrinsic
value estimates derived from three theoretically equivalent valuation
models: the discounted dividend (DIV) model, the discounted free cash
flow (FCO) model, and the discounted abnormal earnings (AE) model.
We use Value Line (VL) annual forecasts of the elements in these models
to calculate value estimates for a sample of publicly traded firms fol-
lowed by Value Line during 1989-93.1 We contrast the reliability of value
*Duke University; tUniversity of Wisconsin; London Business School. This research
was supported by the Institute of Professional Accounting and the Graduate School of
Business at the University of Chicago, by the Bank Research Institute, Sweden, and Jan
Wallanders och Tom Hedelius Stiftelse for Samhallsvetenskaplig Forskning, Stockholm,
Sweden. We appreciate the comments and suggestions of workshop participants at the
1998 EAA meetings, Berkeley, Harvard, London Business School, London School of Eco-
nomics, NYU, Ohio State, Portland State, Rochester, Stockholm School of Economics,
Tilburg, and Wisconsin, and from Peter Easton, Frank Gigler, Paul Healy, Thomas Hem-
mer, Joakim Levin, Mark Mitchell, Krishna Palepu, Stephen Penman, Richard Ruback,
Linda Vincent, Terry Warfield, and Jerry Zimmerman.
I We collect third-quarter annual forecast data over a five-year .
Finance is the lifeblood and lifeline of any business entity either commercial or non-commercial. The
Survival, Stability and Sustainability of a firm is highly associated with its financial wellness. It can be observed through its ability to pay(re) short-term as well as long term liabilities, meeting the regular financial obligations, to increase the value of firm and ability to generate profit. Financial analysis, evaluation, and assessment help in determines the financial position and financial strength of a firm. Among the plenty of methods and tolls available for financial performance, ratio analysis is more useful and meaningful. These ratios make it possible to analyze the evolution of the financial situation of a firm (trend analysis), cross-sectional analysis and comparative analysis.
STOCK PRICE PREDICTION AND RECOMMENDATION USINGMACHINE LEARNING TECHNIQUES AN...IRJET Journal
This document discusses using machine learning techniques and sentiment analysis of Twitter data to predict stock prices and recommend buying or selling stocks. It evaluates ARIMA, LSTM, and linear regression models for stock price prediction and uses TextBlob to analyze the sentiment of recent tweets about a company and provide recommendations based on the overall sentiment polarity. For Apple stock, ARIMA had the lowest RMSE of 3.54, while LSTM achieved an RMSE of 5.64 after 30 epochs. Sentiment analysis of Apple tweets found an overall positive polarity. The models were also tested on Yes Bank stock.
Module 7 Discussion ForumDiscussion Statement of Cash and Financi.docxhelzerpatrina
Module 7 Discussion Forum
Discussion: Statement of Cash and Financial Analysis:
Discussion: Capital Budgeting and Financial Analysis
Review at least 2 academically reviewed articles on capital budgeting and 2 articles on financial analysis and complete the following:
A. Write an annotated bibliography of each article.
B. Based on the articles you reviewed, discuss what you learned
C. In addition, discuss how a manager would use the concepts in the articles you reviewed in managerial decisions.
Instructions:
1.Completed the above assignment by over 1000 words and references.
2.Read and respond to at least 3 of your classmates' posts. (Below posted my classmate discussions) Read a selection of your colleagues' postings. Respond to at least 3 of your classmates’ posts. (Each response should be 150 words, It should include the stuff like supporting their discussion and
Study Materials Link:
TextBook:https://saylordotorg.github.io/text_managerial-accounting/index.html
Lesson Lecture:
1. https://www.youtube.com/watch?v=TaND4xx28VM
2. https://www.youtube.com/watch?v=kYKqmWJtPKs
3. https://www.youtube.com/watch?v=x6dd0IHuC98
Assigned Reading/Study Materials
Use the following links to study Module 7 topics
Analysis of the Statement of Cash Flows:
https://saylordotorg.github.io/text_managerial-accounting/s16-how-is-the-statement-of-cash-f.html
Analysis of Financial Statements and Nonfinancial Data:
https://saylordotorg.github.io/text_managerial-accounting/s17-how-do-managers-use-financial-.html
3-Clasmate discussion
Discussion1:
PART A
De Motta, A., & Ortega, J. (2018). Incentives, Capital Budgeting, and Organizational Structure. Journal of Economics & Management Strategy, 22(4), 810-831. doi: 10.1111/jems.12033
According to this article, capital budgeting is a crucial element when it comes to shaping and establishing a stable organization structure (De Motta & Ortega, 2018). The article focuses on several vital organizational issues that should be looked at keenly if an organization wants to be successful and competitive in the long run. The authors highlight that for an organization to be successful when making investments, it must do a thorough analysis of a project. As much as diversification and expansion would greatly help the organization achieve its set goals and objective and also improve its profitability, it is paramount to evaluate the investments. Capital investments are known to require substantial capital and resources so that they can successfully be implemented. Additionally, the article highlights that an organization ought to ensure that they have enough capital resources before deciding to invest in many capital projects. Last but not least, the article stresses the importance of organizations comparing the costs incurred when implementing a project and the resulting benefits to know if the project is financially viable.
Hoffmann, S., Krumholz, N., & O’Brien, K. (2018). How Capital Budgeting Helped a Sick City: Thirty Years.
Team Project Deliverable and PresentationYou team works for XY.docxerlindaw
The document outlines the requirements for a team project to identify potential acquisition targets for a company pursuing a strategy of horizontal integration. The team must:
1) Select two potential target companies from the same industry for preliminary analysis. This includes qualitative research on company backgrounds and quantitative financial analysis.
2) Prepare a report of findings recommending one target to pursue for further due diligence. The report must include analysis, interpretations, and a well-supported recommendation.
3) Create a PowerPoint presentation to present the recommendation to the board, dressing in business attire and actively delivering the content.
My name is highlighted in Blue and thatt the portion I am respo.docxgemaherd
My name is highlighted in Blue and that't the portion I am responsible for.
Directions
You team works for XYZ Company, which has a directional strategy focused on expanding the company through horizontal integration. Your team can determine the official name of the company and industry. The company does a great job keeping close watch on its cash position and consistently maintains a positive cash flow; is very solvent; controls its overhead expenses; has solid marketing and sales, production, and human resources performance metrics, and fosters a culture of strategic thinkers. Historically, your company has expanded through a combination of organic (new startups) and inorganic growth and feels it’s time to consider acquisition opportunities.
The Board is looking to engage in a friendly acquisition of a company that will not only increase its market share, but allow it to penetrate new markets and increase the company’s abilities to meet current and future consumer needs and expectations. Since management’s attitude is to pursue a friendly acquisition as opposed to a hostile takeover, your team may consider looking at conglomerates that have experienced significant growth through inorganic growth (acquisitions) and may now be looking to refocus on their core business and are willing to consider divesting some of its businesses that are within your industry. There could be other companies that are under financial duress and receptive to acquisition offers. Your team is a part of the corporate mergers and acquisition (M&A) department and has been assigned the task of identifying two potential acquisition targets. Since your Board is committed to a strategy of horizontally integration, you will be looking for possible acquisitions from within your industry. You will be performing a preliminary analysis of the companies under consideration, and then ultimately recommend one of the companies move forward for a more in-depth valuation by M&A Department.
Notes:
The target acquisitions should be publicly traded and have the same fiscal year end, preferably December 31st. In addition, your team is encouraged to select a proper name for your company and the industry for which it is aligned.
To successfully complete your preliminary analysis of the target acquisitions, your team should follow this high level process flow:
1. Select Comparable Companies that Satisfy Inclusion Criteria
2. Conduct Qualitative Research on the Companies
3. Conduct Quantitative Analyses of the Companies (financial)
4. Prepare Report of Findings with Recommendation
Select Comparable Companies
Describe the methodology used to select the target acquisitions. You may want to consider utilizing the North American Industry Classification System (NAICS) to identify companies within your industry. Of course, there are a variety of Internet sites that can assist you in locating firms within your chosen industry, such as Google Finance and Yahoo Finance.
Conduct Quali.
Financial ratios are created with the use of numerical values taken from financial statements to gain meaningful information about a company. The numbers found on a company’s financial statements – balance sheet, income statement, and cash flow statement – are used to perform quantitative analysis and assess a company’s liquidity, leverage, growth, margins, profitability, rates of return, valuation, and more.
Modes of Expression of Ratios:
Ratios may be expressed in any one or more of the following ways:
(a) Proportion,
(b) Rate or times
(c) Percentage.
Advantages of Ratio Analysis:
The information shown in financial statements does not signify anything individually because the facts shown are inter-related. Hence it is necessary to establish relationships between various items to reveal significant details and throw light on all notable financial and operational aspects. Ratio analysis caters to the needs of various parties interested in financial statements. The basic objective of ratio analysis is to help management in interpretation of financial statements to enable it to perform the managerial functions efficiently.
Limitations of Ratio Analysis:
Ratios are precious tools in the hands of management but the utility lies in the proper utilisation of ratios. Mishandling or misuse of ratios and using them without proper context may lead the management to a wrong direction. The financial analyst should be well versed in computing ratios and proper utilization of ratios. Like all techniques of control, ratio analysis also suffers from several ‘ifs and buts’ and for proper computation and utilization of ratios the analyst should be aware of the limitations of ratio analysis.
Uses and Users of Financial Ratio Analysis
Analysis of financial ratios serves two main purposes:
1. Track company performance
Determining individual financial ratios per period and tracking the change in their values over time is done to spot trends that may be developing in a company. For example, an increasing debt-to-asset ratio may indicate that a company is overburdened with debt and may eventually be facing default risk.
2. Make comparative judgments regarding company performance
Comparing financial ratios with that of major competitors is done to identify whether a company is performing better or worse than the industry average. For example, comparing the return on assets between companies helps an analyst or investor to determine which company is making the most efficient use of its assets.
Users of financial ratios include parties external and internal to the company:
External users: Financial analysts, retail investors, creditors, competitors, tax authorities, regulatory authorities, and industry observers
Internal users: Management team, employees, and owners
IRJET - Stock Recommendation System using Machine Learning ApproacheIRJET Journal
This document proposes a stock recommendation system using machine learning approaches. It uses five machine learning algorithms (linear regression, random forest, ridge regression, stepwise regression, and gradient boosted regression) to predict stock returns based on 20 financial factors. The system selects the top 200 stocks in each sector quarterly based on the model with the lowest mean squared error on past data. It then backtests portfolio strategies using the recommended stocks to demonstrate the system outperforms the S&P 500 index in terms of risk-adjusted returns. The key steps are data preprocessing, model training/selection, stock ranking/selection, and backtesting portfolio strategies.
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
The Supply Chain Index - Improving Strength, Balance and Resiliency - 13 MAY ...Lora Cecere
Supply Chain Metrics That Matter is a series of monthly reports published by Supply Chain Insights LLC. These reports are a deep focus on a specific industry. This was preparatory work to understand the patterns of supply chain ratios for supply chain leaders.
As shown in Figure 1, the Supply Chain Insights team analyzed 15 different industries with deep dives on their progress on the cash-to-cash cycle.
Figure 1. Supply Chain Metrics That Matter Reports Published in 2012-2014
Here we take a next step, and launch the Supply Chain Index. The Supply Chain Index is a mathematical formula that a supply chain leader can use to measure their relative performance to an industry peer group. It was built in cooperation with the Operations Research team at Arizona State University (ASU).
This methodology was designed to measure the balance, strength and resiliency of a company’s supply chain from an objective financial perspective. It is a measurement of supply chain improvement during the period of 2006-2012. In April 2014, we published an in-depth look at the resiliency metric: Supply Chain Metrics That Matter: Improving Supply Chain Resiliency. In this report, adding strength and balance, we examine the calculation of these three values in tandem.
The supply chain is a complex system with increasing complexity. Here we analyze how companies made trade-offs over a period of several years in balancing growth, profitability, cycles, and complexity. Many of the trade-offs were unconscious. As complexity rose, it became more difficult for companies to manage the intersection of growth and inventory turns. For leaders, as you will see in this report, the trade-offs were conscious.
Within the world of Supply Chain Management (SCM), each industry is unique. We believe that it is dangerous to list all industries in a spreadsheet and declare a supply chain leader. Instead, we believe that change needs to be measured over a number of years with a focus on an industry peer group. Here we define, and demonstrate, how the Supply Chain Index can be used to measure supply chain performance. To help the reader, we share insights on three industries—chemical, consumer packaged goods and pharmaceutical—using the methodology.
This document presents research on analyzing auto insurance premium pricing and risk factors using various business intelligence tools. The research aims to examine how factors like car age, duration of previous policies, average customer age, and others affect quoted premium prices and influence risk categories. The research first develops a proposal justifying the use of tools like SPSS, R, Tableau and IBM Cognos to analyze insurance data. It then outlines data cleaning steps to import an insurance database into SPSS. Regression analyses are conducted in R and SPSS to determine relationships between variables. Descriptive analyses in Tableau and IBM Cognos validate regression results by visualizing variable relationships. The research finds factors like lower car age and duration of previous policies correlate with higher
Financial Performance Analysis of Selected Private Sector Banks in IndiaDr. Amarjeet Singh
The performance of the banking system has been
widely recognized as an important element for economic
growth and for enhancing the economic and financial system
buoyancy in facing financial crisis. In fact, such a vital role in
the economy has made banks to be considered as one of the
most strained kinds of businesses in the globe as they are
subject to close scrutiny since banks will otherwise be
counterproductive and severely damage the economy of a
country. Efficient and profitable banks maximize
shareholders’ value and encourage the shareholders to make
additional investments. As a result of which, more
employment opportunities will be created and more goods
and service will be produced and ultimately bring about
economic growth in which private and public sector banking
institutions play equal role. The present study analyses the
financial performance of selected private banks in India with
the help of correlation analysis by considering return on total
assets as the independent variable.
This document is a project report submitted by Mr. Ojas Nitin Narsale, an M.Com student at the Parle Tilak Vidyalaya Association's M.L. Dahanukar College of Commerce in Mumbai, India. The report is on the topic of ratio analysis and was completed in the 2016-2017 academic year under the guidance of Prof. Karim. The report includes an introduction, objectives, methodology, literature review on ratio analysis, calculations of key financial ratios for a company, analysis of the results, and a summary.
This document presents a system for predicting corporate bankruptcy using textual disclosures from SEC filings. It discusses how previous studies have used financial ratios and market data to predict bankruptcy, but that textual disclosures also provide important unstructured qualitative information. The proposed system uses natural language processing and machine learning algorithms to extract features from 10-K and 10-Q filings and predict bankruptcy with high accuracy, even before the final bankruptcy occurs. It aims to improve on previous bankruptcy prediction methods by incorporating both financial and textual data sources.
Capital structure and eps a study on selected financial institutions listed o...Alexander Decker
This study examined the relationship between capital structure and earnings per share (EPS) for 10 financial institutions listed on the Colombo Stock Exchange in Sri Lanka from 2006 to 2010. The capital structure ratios studied were equity ratio, debt ratio, and leverage ratio. Correlation analysis found equity ratio and debt ratio were negatively associated with EPS, while leverage ratio was positively associated. However, the relationships were not statistically significant. Multiple regression analysis also found capital structure ratios explained 22.6% of the variation in EPS, but the individual ratios' impacts were not statistically significant. Therefore, the hypotheses proposing relationships between the ratios and EPS could not be confirmed.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
A Study on Ratio Analysis at Accord Puducherryijtsrd
This document summarizes a study on ratio analysis of ACCORD Puducherry, an organization providing financial assistance to entrepreneurs. The study analyzed ACCORD's financial performance from 2015-2019 using ratio analysis and trend analysis of data collected from financial statements. Ratio analysis showed current ratio, net profit ratio, and other ratios were generally satisfactory. Trend analysis found stock levels fluctuated over the period while cash, receivables, and current assets trended upward. Working capital fluctuated. The study concluded ACCORD's financial position over the period was satisfactory but ratios could be improved further to boost growth. It recommended increasing working capital and profitability ratios to improve performance.
Vencon Research is a trusted global provider of compensation (salary) benchmarking data to the world’s leading management, IT and strategy consulting firms.
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1. Predicting Relative Risk of Financial Investment in
Publicly-Traded Companies
Krishna Vijaywargiy, Kshitij Deshpande, Manish Lingamallu
MSA 8050: UNSTRUCTURED DATA MANAGEMENT FINAL PROJECT REPORT
J. MACK ROBINSON SCHOOL OF BUSINESS, GEORGIA STATE UNIVERSITY
2. Predicting Relative Risk of Financial Investment in
Publicly-Traded Companies
Krishna Vijaywargiy
Master of Science in Analytics
Georgia State University
kvijaywargiy1@student.gsu.edu
Kshitij Deshpande
Master of Science in Analytics
Georgia State University
kdeshpande4@student.gsu.edu
Manish Lingamallu
Master of Science in Analytics
Georgia State University
jlingamallu1@student.gsu.edu
ABSTRACT
We address a text analysis problem: given a set of publicly-traded companies, we try to predict the
relative risk of financial investment by analyzing the text content in the SEC-mandated financial report
published by these companies annually. We focus on the latent information in parts 7 & 7a of this report
which are a detailed description of the company’s financial status in the previous year and use well known
analysis models to predict future trends from this information. By using text instead of numbers, our project
revolves around the modern approaches for financial trend prediction and stretches the accuracy higher.
INTRODUCTION
While a lot of information about the financial health of a publicly-traded company is transmitted through
numbers, insights galore can also be extracted from the textual content in the mandated 10-K reports
submitted yearly to the United States Securities and Exchange Commission. The research for efficiently
using text analysis to improve the accuracy of stock price prediction is still in the early stages but has
certainly achieved a few milestones. This project report forecasts the relative investment risk for a set of
companies by analyzing the text content in parts 7 and 7a of the 10k report. Part 7 gives a description of
“Management’s Discussion and Analysis of Financial Condition and Results of Operations” and part 7a
focuses on “Quantitative and Qualitative Disclosures about Market Risk” which comprise of discussions of
trends, capital resources liquidity, general business review, discontinued operations, interim financial
statements, etc. among other things.
Extracting the latent information about a company’s performance in future and year past from the fore
mentioned subsections of these freely available reports is an important step towards predicting the risk of
investments in stocks. 10-K Reports from 15 companies with positively trending stock prices and 15 with
negatively trending stock prices were evaluated in this project to identify the patterns from text. Before the
data can generate insights, it has to be carefully extracted and cleaned. The process of mining the text
involves extracting only the textual data and ignoring all the tables from the subsections. The text then
undergoes cleaning process which, after removing all special characters, tokenizes the whole document into
3. words and converts all tokens/words to their root words. These processed documents are then fitted with
the models to generate a relative risk prediction model. The two models that are used for prediction are
Logistic Regression model, which takes in a categorical dependent variable to estimate probabilities using
a logistic function for the independent variable(s), and Neural Networks, which is trained on a corpus of
documents and is then improved through feature fusion to generate high ranking summaries. We further
evaluate the results of both these methods to give a comparison of both models.
METHODOLOGY
We started our project by researching the available papers and identifying what parts of the 10-K reports
are most significant to predict financial risk. The paper ‘Predicting Risk from Financial Reports with
Regression’ by Kogan, Levin, Routledge, Sagi and Smith [1] presents an insightful approach of constructing
regression models for volatility of stock returns, which is an empirical measure of financial risk.[ 1] It aims
at providing summarizing statistical facts that are not subject to any kind of human-expertise, knowledge
or regression. This establishes that simplistic representation of text (unigrams and bi-grams) can
substantially improve a strong stock price prediction baseline that does not use text. Volatility is measured
as the standard deviation of a stock’s returns over a finite period of time. Thus, to predict risk, the paper
focuses on using clustering and Support Vector Regression for volatility prediction to identify that text
regression model prediction efficiently correlates true volatility with historical volatility and thus provides
higher accuracy in combination.[1] However, instead of clustering, we followed a pattern using ‘text topics’
and ‘text parser’. We evaluated the 5-year stock price trends for companies along with their 10-K reports
to create a start list for both positive and negative words and added them to the text parsing node.
In their paper “Combining Data and Text Mining techniques for Analyzing Financial Reports” , Antonia
Kloptchenko, Tomas Eklund, Barbro Back provide a different approach to analyze the information form
the Telecommunication companies and state that the textual part contains more precise information in
dealing with company performance than the quantitative data available in the form of financial ratios. They
have used data and text mining methods to study hidden indications about the financial performance of
companies from the qualitative as well as quantitative parts of their financial reports. They gathered
information from all occurring matches in combination with quantitative data clustering making it possible
to conclude that the analysis schema has captured a tendency: the text reports tend to foresee the changes
in financial states of the companies, before those changes influence the financial ratios. The results obtained
after analyzing the overall information has proven that some future changes in the financial performance
can be anticipated by analyzing text from the reports. Thus due to time constraint and limited know how
about the financial ratios and terminology we decided to focus only on the text data contained in the SEC
reports form Item 7 and Item 7A.
4. IMPLEMENTATION
To extract the content from the 10-K reports, we use python to parse the document and acquire the
relevant text. This is a tedious process as all the companies have different formats of the XBRL reports. We
then stored this into pandas data-frame for merging all the companies. The text in the data-frame is then
tokenized and stripped off of markups, punctuation marks and any special characters besides the alphabets.
We used regular expression in python to process this text for analyzing.
After extracting the data in desired format we
created a chain of nodes in enterprise miner to run
our data. The first node in the chain was the data-
source node - “Training Data” that contained a
comprehensive list of 30 companies with over 90
records that were parsed to train the model. An
additional field “Investment” was added to the
spreadsheet and the field was assigned a “0” or a “1”
based on the stock trend of the company. “1” was
assigned to a company which was considered safe to
invest in and “0” for company considered not safe to invest in. The input to the data-source node was the
excel spreadsheet. The output from the data-source included company name, year of filing, and Investment
preferences. If we were to run the experiment again, we would use growth or Profit/Equity ratios in order
to have a numeric value to set as the threshold for our decisions. This data ran through a data-partition node
with the partition set at a 75/25 split between the training and validation data. After the data partition node,
the data was then parsed using the Text Parsing node. We created start list by analyzing textual data from
the good companies and bad companies which we classified on the basis of the stock performance for the
last 5 years.
The output from the Text parsing node flowed to a text
filter node where weights of the terms were assigned based on
Inverse Document Frequency. The weight reflected the
importance of the word in a document.
Fig 2(b): High Frequency Terms
Fig 2(a): Number of Documents by Frequency
5. The output of the text parsing nodes included the terms along with their weights.
The output of the text filter node was passed through the text topic node. The text topic node matched
the terms that were strongly associated and created topics. Topics are collections of terms that describe and
characterize a main theme or idea. For example, the term “profit” would have strong association with terms
like “revenue” and “cash” and have a higher probability to be in a topic. We limited multi-terms topics to
be 5. The results of the text filter node showed significant insights, which included the frequently used
words, which was consistent with our earlier findings.
Fig 3(a): Weights for terms – Result of Text Filter node
Fig 4(a) : Scatter plot of Positive words, Negative terms
and the topics.
Fig 4(b) : Weights of high frequency terms with other attributes like role,
frequency etc.
6. Some of the topics that we got as a result of text topic node included:
From our analysis, we found that documents which included terms of the topic have good investment
preferences, whereas the documents which have terms related to. This method identified a collection of
terms, which in turn helped us determine the performance.
The output included the documents, the topics and the relevance of the document to each of the topics.
This output was given to the Variable selection node. The Variable Selection node helped in reducing
number of input variables to the model by rejecting input variables that were not related to the market. The
results window also displayed a histogram that was called “Variable Importance”.
The histogram shows each variable's
contribution towards the prediction, based on the
R-Square scores. Here, we observed that the topic
“+weak +termination +average +tend” had the
highest importance in deciding the investment
preferences as it had the highest Variable
Importance.
We finally built two models, one using Logistic Regression and the other using neural networks.
We analyzed the effects of both the models based on the results of validation and training data.
Fig 5: Document cutoff value for each topic and the number of documents having satisfied the criteria
Fig 7: Histogram displaying the variable importance for prediction
using R-Square scores.
Fig 8(a) : Mean Predicted against Mean Target for Logistics
Regression
Fig 8(b) : Mean Predicted against Mean Target for Neural
Networks
7. From the results of both the Regression Model and Neural networks, we found a strong relationship
exists between the text of Item 7 and 7A and the company’s performance in the upcoming year. If the
accuracy is compared for the validation data for Logistic and Neural Network model, we could infer it to
be approximately around 90 – 95% respectively. The final model we developed is as seen in Fig 10.
TESTING THE MODEL
To test the model that was built, we decided to pass data and validate its result. The initial task was to
decide whether to go with the Regression Model or the Neural Network model. To resolve this, we used
the Model Comparison node which selects the best performing model based on errors for input models.
The figure below shows the Statistics of both the Regression model and the Neural Network model. The
selected model was a Regression model based on Average Square Error as selection Criterion.
Fig 10: SAS Model Developed
Fig 11(b): Regression Model selected based on Average Square Error as Criterion.
Fig 11(a): The error statistics of Regression Model and Neural Network Model.
8. Finally, we used the Score node with which we used to score new raw data. The input to the score node
was both the output of the model comparison node and a new raw data source. Based on the model selected
from the Model Comparison node, the Score node scored new data. The raw data with no Investment
preference and the final model developed is as seen in the below figure.
The output of the score node included the prediction for the field “Should Invest”, the target variable.
Here, we can see for the company VISA the “Should Invest” field is predicted around 1.059 whereas for
the company Frontier, the “Should Invest” field is predicted around 0.28. This is a clear indication to
showcase that it is worth investing in VISA. To further validate the mode, we decided to check the trends
of the stocks for these companies in Yahoo finance and found the trend lines in the graphs in agreement
with the results of the model.
CONCLUSION
By using the data from the 10k reports from Item 7 and Item 7A we concluded there exists a correlation
between the data from the filings and the trending stock performance of the companies. As mentioned in
one of the research paper we would like to use the quantitative data mentioned in the financial reports to
optimize the relation among the filings and stock performances. Also we would like to add a start list which
is more effective in distinguishing among the positive and negative words in the financial sector which will
definitely enhance the accuracy of the model overall. This methodology would be more meaningful if we
include other sections such as Item 1A which consists of the risk factors prevalent and play an important
role in determining the company performance.
Fig 12(b): Raw data for model testing
Fig 13: The predicted values of the raw data, which is the output of the score
node.
Fig 14(a) : Stock trend of VISA Fig 14(b): Stock trend of Frontier
9. REFERENCES
1. Predicting Risks from financial reports with regression, by Kogan, Levin, Routledge, Sagi and Smith
http://homes.cs.washington.edu/~nasmith/papers/kogan+levin+routledge+sagi+smith.naacl09.pdf
2. Back, B., Toivonen, J., Vanharanta, H., and Visa, A. Comparing numerical data and text information
from annual reports using self-orginizing maps, International Journal of Accounting Information
Systems (2), 2001, pp. 249-269.
3. Kohonen, T. Self-Orginizing Maps, Leipzig, Germany: Springer-Verlag, 1997. Kohut, G., and Segars,
A. The president’s letter to stockholders: An examination of corporate communication strategy, Journal
of Business Communcation (29:1), 1992, pp. 7-21. Lehtinen, J. Financial Ratios in an International
Comparison, Vasa: Acta Wasaensia, 1996