This document summarizes a research paper that uses machine learning and financial ratios to classify stocks traded on the Indian stock market as either "outperformers" or "underperformers" based on their rate of return. The study uses quarterly data from 50 large market capitalization companies over one year. A support vector machine model achieved 80% accuracy in predicting stock performance on a sector-by-sector basis. While promising, the author acknowledges limitations and outlines areas for further improvement, such as incorporating more external factors like macroeconomic data.
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
Stock Market Prediction and Investment Portfolio Selection Using Computationa...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
Stock Market Prediction and Investment Portfolio Selection Using Computationa...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
A LINEAR REGRESSION APPROACH TO PREDICTION OF STOCK MARKET TRADING VOLUME: A ...ijmvsc
Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting behavior of S&P 500 index, we prove that our proposed method has a similar and good performance in comparison to real volumes and the stockholders can invest confidentially based on that.
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one
period and declining in the next. Stock traders make money from buying equity when they are at their
lowest and selling when they are at their highest. The logical question would be: "What Causes Stock
Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply.
In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that
explains all, simply because not all stocks are identical, and one theory that may apply for today, may not
necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the
stock market without extensive prior knowledge or experience in the subject area, highlighting the
advantages and limitations of the different techniques such as regression and classification. We formulate
both short term and long term predictions. Through experimentation we achieve 81% accuracy for future
trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day
change in price using regression techniques. The results obtained in this paper are achieved using only
historic prices and technical indicators. Various methods, tools and evaluation techniques will be
assessed throughout the course of this paper, the result of this contributes as to which techniques will be
selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted
utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide
to researchers wishing to expose themselves to this area.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
A LINEAR REGRESSION APPROACH TO PREDICTION OF STOCK MARKET TRADING VOLUME: A ...ijmvsc
Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting behavior of S&P 500 index, we prove that our proposed method has a similar and good performance in comparison to real volumes and the stockholders can invest confidentially based on that.
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one
period and declining in the next. Stock traders make money from buying equity when they are at their
lowest and selling when they are at their highest. The logical question would be: "What Causes Stock
Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply.
In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that
explains all, simply because not all stocks are identical, and one theory that may apply for today, may not
necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the
stock market without extensive prior knowledge or experience in the subject area, highlighting the
advantages and limitations of the different techniques such as regression and classification. We formulate
both short term and long term predictions. Through experimentation we achieve 81% accuracy for future
trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day
change in price using regression techniques. The results obtained in this paper are achieved using only
historic prices and technical indicators. Various methods, tools and evaluation techniques will be
assessed throughout the course of this paper, the result of this contributes as to which techniques will be
selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted
utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide
to researchers wishing to expose themselves to this area.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
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.
Fundamental analysis of a business involves analyzing its financial statements and health, its management and competitive advantages, and its competitors and markets.
COMPANY ANALYSIS-HINDUSTAN UNILEVER LTDSaiLakshmi115
Introduction to company analysis# About the company in short # vision # mission # Standard of conduct # culture and value # business model of HUL # swot analysis of HUL # management and its structure # corporate culture and governance # Quantitative analysis of the company- HUL: Earnings, Leverages, competitive edge, production efficiency, financial analysis, cash flow, Ratio analysis # conclusion
Determinants of equity share prices of the listed company in dhaka stock exch...MD. Walid Hossain
This is the finance academic project report.This report prepare by MD. WALID HOSSAIN, Patuakhali science and technology University, Faculty of business administration and management. i think that is helpful for business studies students.
Compose a paper using the five sources attached. The paper should .docxdonnajames55
Compose a paper using the five sources attached. The paper should summarize not PLAGARIZE all 5 articles regarding electronic medical records. APA FORMAT AND USE THE SOURCES GIVEN ONLY. MAKE SURE TO USE INTEXT CITATION FOR THEESE SOURCE. PAPER SHOULD BE 6 PAGES LONG.
Financial Ratio Analysis Worksheet
Your Full Name:
Ahmed Alothman
2011
2010
2009
Basic Rules
Liquidity
Current Ratio
1.50
1.6
1.2
Should be >1.00
Quick Ratio
0.86
0.95
0.6
Good to see close to 1
Leverage
Debt to total asset ratio
0.19
0.19
0.26
Good to see less than 1
Debt to Equity ratio
1.003
1.03
1.35
Smaller is better
Activity
Inventory turnover
7.8
8.3
7
Higher turnover will be better --- Smaller inventory level will increase the turnover!
Fixed asset turnover
3.3
3.2
3.2
Higher turnover will be better --- Smaller fixed assets level will increase the turnover (Productivity of the fixed assets)!
Profitability
Gross profit margin
0.3
0.3
0.3
Higher is better (Lower cost of goods sold or Higher sales will increase the margin) --- Strategic directions (Ex. Focusing on sales quantity or Lean operations)
Operating profit margin
0.06
0.06
0.06
Higher is better – Operational efficiency will be indicated. Better cost structure might increase this margin.
Net profit margin
0.04
0.03
0.03
Higher is better. Total profitability (Corporate profitability). Check the interest expense and Discontinued operations.
Return on total Assets (ROA)
0.06
0.06
0.06
Higher is better. Consider EBIT and portion of total assets. The total sales for each $1 of total assets.
Your own financial assessment / Analyses / Suggestions:
Liquidity of Staples:
Liquidity ratios are used to measures the ability of the company to pay off its current liabilities.
Using current ratio it shows that staples can pay off its current liabilities more than 1.50, 1.6, 1.2 times respectively and still remain with enough. The company is stable in paying off its current liabilities
Using quick ratio Staples can pay off its liabilities 86 percent, 95 percent and 60 percent respectively of its current liabilities.
Leverage of Staples:
Leverage measures the risk level. But for staples, the company's assets are far more than its liabilities thus the company can be able to access loan application since its ability to pay is far better and stronger. The company is less risky.
Staples has a Debt to equity ratio of 1 which means that investors and creditors have an equal stake in the company's assets. Lower ratio shoes a more stable business. Creditors always views a higher debt to equity as risky and the investors have not funded the operations as the creditors have. The company should try and look for ways to reduce on the Debt to equity ratio.
Activity of Staples:
This measures efficiency on how Staples can control its stock. Staples has a very good inventory control system. This company can sell off its inventory more than 7 times in a single year.
T.
Security analysis of selected stocks with referance to information technology...Riya Jaju
project report on security analysis of selected stocks of IT sector which has 3 companies -infosys,wipro,TCS .Fundamental analysis and technical analysis is done for the stocks for duration of 6 months jan 2015 to june 2015.
Firm Value is a description of the level of triumph of the company that is related with stock prices.
High stock prices will affect the high firm value as well. This study was conducted to examine the effect of
financial performance on firm value with intellectual capital as an intervening variable in manufacturing
companies listed on the BEI in the 2015-2017 period
Similar to Indian Stock Market Using Machine Learning(Volume1, oct 2017) (20)
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Indian Stock Market Using Machine Learning(Volume1, oct 2017)
1. Volume1, Oct-2017
Prediction of Stock Performance in the Indian Stock Market Using Machine Learning
Santosh Kumar Joshi,
Orange Business Services, Cyber City Gurgaon India
ABSTRACT
I use Machine Learning and various financial ratios as independent variables to investigate indicators that significantly affect the performance of stocks actively traded on the Indian stock
market. The study sample consists of the ratios of 50 large market capitalization companies(Nifty 50) quarterly financial data over one-year period. The study identifies and examines 14
financial indicators that can classify the companies into two categories – “Outperformer” or “Underperformer” – based on their rate of return. The paper asserts that the model developed can
enhance an investor's stock price forecasting ability. Macro-economic variables such as GDP, the unemployment rate, and the inflation rate of the Country , which also can influence the share
price, were not taken into account, however. There is always a risk of Geopolitical development which cause uncertainty globally. The paper discusses the practical implications of using the ML
to predict the probability of good stock performance. The author states that the model can be used by investors, fund managers, and investment companies to enhance their ability to select
out-performing stocks at their own risk.
Keywords: Classification of stock performance, Indian stock market, market rate of return, financial ratios, NIFTY 50
INTRODUCTION1.
It is important for shareholders and potential investors to use relevant financial information to enable them to make good investment decisions in the stock market. Predicting stock
performance is certainly very complicated and difficult. In the history of stock performance literature, no comprehensive, accurate model has been suggested to date for predicting stock
market performance. A stock’s performance can, to some extent, be analyzed based on financial indicators presented in the company’s annual report. The annual report contains a vast
amount of information that can be transformed into various ratios. Previous literature suggests that financial ratios are important tools for assessing future stock performance. Analysts,
investors, and researchers use financial ratios to project future stock price trends. Ratio analysis has emerged, therefore, as one of the key parameters used by fund managers and investors to
determine the intrinsic value of stock shares; thus, financial ratios are used extensively for the valuation of stock. ratios are used extensively in fundamental analysis to predict the future
performance of a company. Various new ratios, such as book value and price/cash earnings per share, have been included in this discipline for share valuation. Financial ratios help to form the
basis of investor stock price expectations and, hence, influence investment decision making. The level of importance given to financial ratios differs from industry to industry and from one
country to another. Thus, selecting appropriate ratios is very crucial in increasing the prediction success rate.
The objective of this paper is to apply statistical methods to survey and analyze financial data in order to develop a simplified model for interpretation. This study aims to develop a model for
classifying stocks into two categories(Underperformer & Outperformer)poor), based on their rate of return. A company’s stock is classified as “good” if its share returns perform above the
market returns provided by the National Stock Exchange composite index of India; i.e., the NIFTY.
In this study, the SVC method has been used to classify selected companies, based on their performance.
REVIEW OF LITERATURE2.
In stock performance literature, little attention has been given in the past to the Indian stock market. In recent years, however, there has been a greater focus on the market because of its
rapid growth and its increasing potential for global investors. A number of research papers predict stock performance as well as pricing of the stock index across the globe. Harvey [1995]
observes that emerging market returns are usually more predictable than developed market returns because emerging market returns are more likely to be influenced by local information
than developed markets.
Fundamental variables such as earnings yield, cash flow yield, book-to-market ratio, and size are demonstrated to have some power in predicting stock returns [Fama and French, 1992].
Studies based on European markets also demonstrate similar findings.
•
Ferson and Harvey [1993] observe that returns are predictable, to an extent, across a number of European markets (e.g., UK, France, and Germany).•
Jung and Boyd [1996], in their study of forecasting UK stock prices, suggest that the predictive strength of their stock performance models is quite significant.•
In the Japanese stock market, studies carried out by Jaffe and Westerfield [1985] and Kato et al. [1990] also demonstrate some evidence of predictability in the behavior of index returns.•
In recent literature, artificial neural networks (ANN) have been successfully used for modeling financial time series [Cheng,1996; Van and Robert, 1997]. In the United States, several studies
have examined the cross-sectional relationship between fundamental variables and stock returns.
RESEARCH OBJECTIVE AND METHODOLOGY3.
The objective of this study is to build a model using financial ratios of the firms for the purpose of predicting out-performing shares in the Indian stock market. This study aims, therefore, to
answer two questions:
(1) Can the yields of stocks be explained with the help of financial ratios?
(2) Can we analyze stock yields using a logistic regression model?
DATA COLLECTION METHODOLOGY
3.1 DATASET
The dataset was obtained from NDTV Profit. I selected stocks from Nifty Index(NIFTY50) . In total I selected 50 stocks. For each stock I obtained the stock price at the end of each quarter from
the first quarter of 2016 until the second quarter of 2017. Along the price, we have also retrieved the following financial indicators about each company in our dataset:
Book value - the net asset value of a company, calculated by total assets minus intangible assets (patents, goodwill) and liabilities.
Market capitalization - the market value of a company's issued share capital; it is equal to the share price times the number of shares outstanding.
Change of stock Net price over the one month period
Percentage change of Net price over the one month period
Dividend yield - indicates how much a company pays out in dividends each year relative to its share price.
Earnings per share - a portion of a company's profit divided by the number of issued shares. Earnings per share serves as an indicator of a company's profitability.
Earnings per share growth – the growth of earnings per share over the trailing one-year period.
Sales revenue turnover -
Net revenue - the proceeds from the sale of an asset, minus commissions, taxes, or other expenses related to the sale.
Net revenue growth – the growth of Net revenue over the trailing one-year period.
Sales growth – sales growth over the trailing one-year period.
Price to earnings ratio – measures company’s current share price relative to its per-share earnings.
Price to earnings ratio, five years average – averaged price to earnings ratio over the period of five years.
Price to book ratio - compares a company's current market price to its book value.
Price to sales ratio – ratio calculated by dividing the company's market cap by the revenue in the most recent year.
Dividend per share - is the total dividends paid out over an entire year divided by the number of ordinary shares issued.
Current ratio - compares a firm's current assets to its current liabilities.
Quick ratio - compares the total amount of cash, marketable securities and accounts receivable to the amount of current liabilities.
Total debt to equity - ratio used to measure a company's financial leverage, calculated by dividing a company's total liabilities by its stockholders' equity.
Analyst ratio – ratio given by human analyst.
Revenue growth adjusted by 5 year compound annual growth ratio
Stock Market Prediction Page 1
2. Revenue growth adjusted by 5 year compound annual growth ratio
Profit margin – a profitability ratio calculated as net income divided by revenue, or net profits divided by sales
Operating margin - ratio used to measure a company's pricing strategy and operating efficiency. It is a measurement of what proportion of a company's revenue is left over after paying for
variable costs of production such as wages, raw materials, etc.
Asset turnover - the ratio of the value of a company’s sales or revenues generated relative to the value of its assets1.
3.2 PREDICTING EQUITY PRICE MOVEMENT METHODOLOGY
I modelled out task of predicting equity price movement as classification task, in which I classify stocks that will have Nifty50 price as the benchmark. I classify Stock price in three months
period as “Outperform” when individual stock price performs better than broader Nift50 index. And “Underperform” when Individual stock underperforms nifty50. Since I collected historical
data retrieved (stock price and nifty index price)from NSE, I created a dataset which had indicator values and price 90 days in future of the recording date . I created a script in Python that was
comparing history price with the price exactly 90 days after first price was measured.
There were many stocks which did not have updated quarterly financial data on NDTV also many stocks have duplicate data. These anomalies would have cause inconsistency in data.
Therefore I dropped all those rows.
Since not all financial indicators were available for all companies in our data set, we assigned value -9999 to not present or not available values.
Analssis of SVM(Support Vector Machine)4.
“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges.
However, it is mostly used in classification problems.
Results5.
Since our dataset contained 50 stocks and we needed to discard some stocks because they would imbalance our dataset, our dataset contained 29 stocks. Each stock data having 4 quarters
financial data.
We Used Linear SVM to test our model . We adopted two approaches to see how the performance differs. The first method was to find outperformer in 29 stocks from all the sectors. Output
can be seen below with accuracy ranging from 45%-65% which was a huge variation in multiple runs.
Stocks to Invest ICICIBANK HDFCBANK IBULHSGFIN YESBANK HDFC
Stocks to Ignore All Others
As these results look highly biased towards one sector which does reflect from the accuracy percentage, I changed the approach . I decided to train and test sector wise i.e to use all energy
sector companies data to test energy sector company performance. This will give our model comparatively relative data to train and test on.
With this approach I found surprisingly good results , Our model gave 80% accuracy while it recommended 12 stocks to looking good to invest and 17 as not so good.
Stocks to Invest
RELIANCE
N/A]
HDFCBANK,HDFC,ICICIBANK,YESBANK, IBULHSGFIN
ITC,HINDUNILVR
HEROMOTOCO
INFRATEL
ULTRACEMCO
VEDL
N/A
N/A
Stocks to Ignore
NTPC
TCS, INFY,WIPRO, HCLTECH, TECHM
KOTAKBANK,AXISBANK, INDUSINDBK
ASIANPAINT
MARUTI, BAJAJ-AUTO
BHARTIARTL
AMBUJACEM, ACC
N/A
ZEEL
DRREDDY
Sectors
ENERGY
IT
FINANCIAL SERVICES
CONSUMER GOODS
AUTOMOBILE
TELECOM
CEMENT & CEMENT PRODUCTS
METALS
MEDIA & ENTERTAINMENT
PHARMA
Future Works6.
Future iterations on this work should first try to improve model generalization error and reduce overfitting. In this quarter we used 5 quarters data to predict latest quarter performance of
stocks. We are very excited to see the outcome of our approach. Future work should include at least 8 quarters data , we should see how our model performs with more data.
At the time of this study overall global market sentiments have been benign. I would like to test the model on volatile times.
I would like to extend the model to predict 1 year stock performance with more variables.
I shall extend the study to involve technical analysis and algo trading in my work.
Additional computing power could be used to work with network-derived data at much more granular periods of time, such as weekly or monthly data, as opposed to the quarterly splits used
in this paper.
Another avenue for further improvement involves the compilation of more external factors in my study. Geopolitical developments, macroeconomic data, sentiment analysis etc. In this paper
we targeted Nifty 50 stocks however there are about 2000 stocks in NSE.
Conclusion7.
In this paper is presented a machine learning aided methodology for equity movement prediction over the long time. With all selected financial indicators, the methodology performs with
accuracy of 80.1%
Stock Market Prediction Page 2
3. accuracy of 80.1%
Some of the features from the larger set were not necessary, since they were not giving any relevant information about company’s valuation, while the others were just duplicating the fact
told by already analyzed financial indicator. For example, it is possible to assume the value of earnings if the value of total stock number and earning per share ratio is available.
It seems that information about growth is not necessary. From this is could be deduced that ratios and information that describes current financial state of the company, without a look at the
past performances is enough for predicting future behavior of the company (with accuracy showed in this work). This principle can be especially useful for investors that want to invest in new
companies. Hypothesis that companies can be valued and their future can be predicted only by looking at present data has to be further tested, however, it proved to be correct in our case for
our dataset. We will leave this hypotheses to be tested by future researchers in more details.
8. Annexure
Later I did a market study (Annexure)of how actually markets have performed relative to my prediction. Below are the graphs with our prediction to invest and ignore.
Energy
Invest RELIANCE
Not Invest NTPC
In above graph we can see out prediction turned right as RELIANCE seems to be outperforming by a huge margin.
IT:
Invest
Not Invest TECHM HCLTECH WIPRO INFY TCS
We recommended to avoid IT stocks for not and result does reflect the similar sentiments. Nifty is outperforming all the IT stocks.
Finance:
Invest ICICIBANK HDFCBANK IBULHSGFIN YESBANK HDFC
Stock Market Prediction Page 3
4. Finance:
Not Invest INDUSINDBK AXISBANK KOTAKBANK
Auto:
Invest HEROMOTO
Not Invest MARUTI BAJAJ-AUTO
Telecom:
Invest INFRATEL
Not Invest BHARTIARTL
Cement:
Invest ULTRACMCO
Not Invest ACC AMBUJACEM
Stock Market Prediction Page 4
6. The results are surprisingly motivating.
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