Fundamental Analysts Research Articles

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

Machining technologies to compute the psychological-financial index

Subject. This article deals with the issues of measuring the emotions of financial decision-makers based on machine learning technologies in finance. Objectives. The article aims to develop a methodology for measuring emotions in the financial sector, algorithms and software based on artificial intelligence for its implementation in finance. Methods. For the study, we used the methods for marking up text data, index method, machine learning methods, computer technologies for natural language processing, methods for assessing emotions with their own rating scale, induction and deduction, and statistical methods for processing observation results. Results. The article reveals the content of methods for comprehensive measurement of the emotions of financial decision-makers. Software tools were created to implement machine learning technologies when computing an emotion measurement index. Conclusions and Relevance. A psycho-financial index based on automatic calculation using new machine learning technologies provides fundamental analysts of financial markets with an analytical tool that brings the forecast result closer to real conditions, when the emerging combinations of traditional and psychological indicators in the markets provide a new interpretation of current events. This improves the quality of analytical work and improves its results. The result can be used in the work of a fundamental analyst of financial markets. The use of new indicators complements and expands the classic analytical tools and improves the quality of market forecasts.

An Empirical Study on Determinants of Price Earnings Ratio: Evidence from Listed Food, Beverage and Tobacco Companies in Colombo Stock Exchange

The Price earnings ratio compares a stock’s price to earnings. By showing the relationship between a company’s stock price and earnings per share (EPS), the Price earnings ratio helps investors to value a stock and gauge market expectations. The ratio is affected by several factors that are responsible for the variations of Price earnings ratio. These variations of Price earnings ratio have significant impact on investor’s perception. This paper attempts to identify the factors and the relationships between the factors and Price earnings ratio of food, beverage, and tobacco companies in Colombo Stock Exchange (CSE). Based on simple sampling, data were taken for this purpose from annual reports of 30 food, beverage, and tobacco companies listed on the CSE for the five-year period from 2015 to 2019. The study focuses on secondary data collected through the published annual reports of the sample. Descriptive statistics, correlation analysis and multiple regression analysis are used to accomplish the objective of this paper. Results revealed that dividend payout ratio and leverage ratio are significant determinants of Price earnings ratio where these both variables have positive influence on Price earnings ratio. Furthermore, return on equity and earnings per share are negative insignificant determinants of Price earnings ratio. Dividend payout ratio and leverage ratio have positive correlation with price earnings ratio. It can be statistically concluded that dividend payout ratio and leverage ratio have positive significant relationship with price earnings ratio. Return on equity and earnings per share have negative correlation with price earnings ratio. Significant values are higher than the test alpha values. Therefore, the researcher can reject the alternative hypothesis. It can be statistically concluded that return on equity and earnings per share have negative insignificant relationship with price earnings ratio. This paper is evidence for fundamental analysts and decision makers to evaluate determinants that explain variations in P/E ratio of food, beverage, and tobacco companies in Sri Lanka.

Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?

We estimate and compare the impact of the coronavirus pandemic (COVID-19) on the performance of Artificial Intelligence (AI) and conventional listed firms using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the news of COVID-19 spread and the pandemic entered the first, second, third, and fourth months (24 February 2020, 23 March 2020, 20 April 2020, and 18 May 2020, respectively). The results show that the negative impact of COVID-19 on the AI stock market was less severe than on the conventional stock market in the first month of the pandemic. The performance of the AI stock market recovered quicker than the conventional stock market when the pandemic went into its third month. The results suggest that the AI stocks were more resilient than conventional stocks when the financial market was exposed to uncertainty caused by the COVID-19 pandemic. The deployment of AI in firms serves as a resilient, crucial driver for sustainable performance in challenging environments. Observing the performance of AI-adopted firms is an interesting direction for technical and fundamental analysts. Investors and portfolio managers should consider an AI market index to minimize risk or invest in stocks of AI-adopted listed firms to maximize excess returns.

THE STUDY OF FUNDAMENTAL & TECHNICAL ANALYSIS

Fundamentalists and chartists have historically always argued about how stocks and markets should be studied. Chartists, also known as technical analysts, estimate market patterns based on historical data, whereas fundamental analysts assess economic, financial, qualitative, and quantitative factors of a security in order to analyze the future of such security they also focus on all the other factors that influence stock prices. The study of Technical analysis begins with charts, but fundamental analysis begins with the financial statements of the organization. In fundamental analysis, you must look at a company's income statement, balance sheet, and cash flow statement to establish its intrinsic worth. Whereas in technical one put more focus on the past trend and the patterns of a particular stock which might help in forecasting the future of that particular stock or security. The main objective of this thesis is to study the important concepts of fundamental analysis and forecast the future trends of certain companies which becomes the part of findings of this study.

Heuristics as a new way of adjusting the end market value

Subject. The study deals with heuristics as measures of the emotional impact of people who judge about the value and the final result of the valuation. I review ranges of the value variance when influenced by irrational factors. From psychological perspectives, some phenomena are explained with a set of heuristics that exist as part of behavioral finance. Objectives. Referring to the completed studies, I implement elements of behavioral finance, such as heuristics into the method for assessing how financial decision-makers and their emotions influence the value. Methods. The article is based on methods of induction and deduction to process survey results. Results. The article reveals the content of key methods for measuring emotions of financial decision-maker, which conclude on the value, being influenced by heuristics. I demonstrate tools for implementing psychological measurement methods as part of valuation. Conclusions and Relevance. Considering heuristics of value decision-makers, the appraiser and the cost analyst approximate the valuation result to the real conditions, when market actors are irrational. Doing so, they contribute to the quality of the result of appraisal. The findings are applicable to the practice of appraisers, cost analysts, fundamental analysts. Heuristics enrich and expands the classical apparatus of valuation and increases its quality.

Is the Configuration of Indian Stock Market Weakly Efficient?

The main aim of this study is to find out the whether the Indian stock market efficiency is in weak form. The aim of this study is to look into the Indian Stock Market’s lack of market performance. From 2000 to 2015, sample is gathered on a daily, weekly, and monthly basis. Unit Root Test, Run Test, and KS Test are used to examine the data. According to the findings, The Runs Test disproves the existence of a random walk and demonstrates that the Indian stock market is not weakly efficient. Through stock valuation strategies, technical and fundamental analysts may generate volatile returns.

Looking into bubbles in financial markets and the emotional side of corporate forecast completion through modeling in the structured query language of financial databases

Subject. The study focuses on a set of financial and economic indicators of corporate performance and metrics of the financial market actors’ sentiment, financial indicators in markets and the way they change over time as markets face dramatic events. The article discusses techniques for applying the information of the financial and economic condition in modeling based on structured word language. Objectives. Analyzing key Russian and foreign sentiment measurement means, I try to create a toolkit, which would be applicable to the valuation and provides a balanced view of the financial and economic condition of companies with reference to the market sentiment. Methods. The article applies methods of induction and deduction, modeling. I demonstrate the relationship of methods and the methodology with new technological means of modern IT systems. Results. Modeling the completion of forecasts, plans and measuring the sentiment, I discovered that key statements and behavioral finance mechanisms manifested in the process. Modeling based on two scenarios and two types of models appeared to be the most illustrative. When forecasts, profit and revenue turn to be higher than expected and when they are lower. Studying bubbles in financial markets, I coined a respective model tested with cases of air lines. The applicable tests, such as the quality of revenue, percentage of deferred income in revenue, sentiment index of the companies show that revenue indicators and sentiment indices are on average poorer in companies with the worst indicators on the sample. Conclusions and Relevance. Given new capabilities of modern information systems, financial analysts get more opportunities for programming, creating user models with needed configurations and extensive database. There we have simplified primary data collection and processing when financial analysts use the data. The findings are applicable to practices of modern appraisers, cost and fundamental analysts. The use of models herein supplements and expands a conventional set of valuation tools and improves the quality of valuation.

A New Investment Strategy with Economic Cycle Alternations Based on Residual Income Valuation Model: Empirical Results from Taiwan Stock Markets

The purpose of this research is an attempt to present a mixed model based on the residual income valuation model, where two new factors, business cycles and financial items, are added. We offer a practical investment opinion by which investors and fundamental analysts can either establish or adjust their portfolios if there is an effort made to reverse the present economic atmosphere due to the ongoing COVID-19 pandemic.We find that the items in financial reports don’t have the same impacts for different business cycles. However, tax has a significantly positive sign when economies are expanding or contracting.

The sentiment analysis method in finance: The psychological-financial index

Subject. The article discusses contemporary means of measuring emotions of those who make financial decisions. Objectives. Analyzing key means of sentiment analysis in Russia and abroad, the study is to create a tool, which would be applicable to valuation and provide the unbiased information about the emotional state of those who locally make financial decisions. I also demonstrate limited capabilities of contemporary information systems in terms of emotion measurement, valuation, and present means to address the imperfection of the existing news tone measurement framework, unveil the content of new emotion measurement techniques, which would be useful to appraisers and cost analysts. Methods. The study is based on the induction and deduction for opinion poll processing, narrative analysis in data environments. I display the nexus with new technological means of modern information systems. Results. The article unveils the substance of key methods for setting the psychological-financial index, modern means of sentiment analysis in the new setting of the digital economy and Big Data. I scrutinize key constituents of the psychological-financial index and its use in the current circumstances of the post-COVID-19 economy. The article shows how psychological measurement methods can be implemented as part of the narrative analysis. Conclusions and Relevance. Financial analysts get new opportunities when using new achievements of behavioral finance and modern psychological studies. As the use of the psychological-financial index shows in analyzing market anomalies, there appear more opportunities for explaining the irrational behavior of market agents in its various segments. New standards are set as they are needed for valuation purposes, when financial analysts use them. Reporting is normalized. I provide an outlook of the analytical apparatus development and new indicators to use valuation results more efficiently. The findings hereof are applicable to the practice of contemporary appraisers, cost and fundamental analysts. It is especially important to use sentiment analysis tools in the digital economy, during the instability and crisis, change in the market paradigm, market shifts, changes in the comparability metrics, distortion of traditional financial and economic indicators, market volatility. The use of the psychological-financial index supplements and expands the scope of classical measurement tools and increase the quality of valuation.

Modern techniques for the comparative method of business valuation

Subject. The article investigates the modern implementation of the comparative approach to appraising the business value via new technologies and software applications. Objectives. Analyzing key issues of the comparative approach theory, I demonstrate capabilities of modern IT systems in terms of valuation. The study presents what may be used to overcome the imperfection of the classical framework for the comparative valuation, unveil the substance of new techniques for applying ordinary methods of the comparative valuation. Methods. The article is based on key classical elements of the comparative approach theory via new software and IT environments. I also present principles of the comparative approach theory, its logical sequence and argumentation. The article shows the nexus with new technological components of modern IT systems. Results. I reveal the substance of key modern methods to implement the comparative approach theory in the time of the digital economy and Big Data. The article scrutinizes the main elements of the comparative approach theory in terms of their contemporary interpretation and use in the post-COVID-19 economy. I showcase methods to use comparative approach methods in modern IT and analytical systems. Conclusions and Relevance. As the use of IT systems shows, raw data are now more easily collected and processed, which is needed for valuation purposes, when they are handled by financial analysts and financial statements are normalized. The findings are applied by today's appraisers, valuation and fundamental analysts. The use of modern valuation methods supplements and amplifies the classical valuation framework and enhances the quality of valuation.

Modeling Traders’ Behavior with Deep Learning and Machine Learning Methods: Evidence from BIST 100 Index

Although the vast majority of fundamental analysts believe that technical analysts’ estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and individual traders are using technical indicators. A correct estimate of the direction of the financial market is a very challenging activity, primarily due to the nonlinear nature of the financial time series. Deep learning and machine learning methods on the other hand have achieved very successful results in many different areas where human beings are challenged. In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. A set of technical indicators has been examined based on their application in technical analysis as input features to predict the oncoming (one-period-ahead) direction of Istanbul Stock Exchange (BIST100) national index. To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. The performance of these models is evaluated on the basis of various performance metrics such as confusion matrix, compound return, and max drawdown.

Fundamental vs market value: A study on of selected pharma stocks

The investment decision to buy or sell a security is always based on the comparison of its intrinsic value with that of its market value. Because Fundamental analysts believe that each share has an intrinsic value and this value is the realization of all the future cash flows in the form of capital appreciation and dividend. This empirical study aims at assessing the fundamental value or intrinsic value of a share using Gordon and Shapiro model (1956) of general dividend discount model (the model assumes a unique dividend growth rate as “g” and Multiplier Approach of valuation using P/E ratio. Both the approaches are based on the principle that the value of any investment is the present value of all its future cash flows. Along with it the study analyses the deviation of the calculated intrinsic value with its market price. It focuses on Indian Pharmaceutical sector taking “A” category shares into consideration as these shares are traded every day. This study checks whether the share is overpriced or under priced by comparing the calculated fundamental value with that of the market value.

Do Daily Short Sales Forecast S&P 500 Sector Returns?

We examine the data for S&P 500 firms for 2009 to 2012 to determine whether daily short selling, aggregated at the industry sector level, forecasts relative returns. Prior research documents short sellers as skilled fundamental analysts and information processors at the individual security level and in aggregate as forecasters of short-term market direction. Intriguingly, we find not only that sector-aggregated daily short sale information does not correctly forecast the short-term relative performance of industry sectors but rather that somecontrarian strategies that are short the least shorted sectors and long the most shorted sectors are profitable on average.

Talking Numbers: Technical versus Fundamental Recommendations

There is an age-old debate between technicians and fundamental analysts, the leading schools of thought in investment management. We answer that debate via a comprehensive analysis of technical and fundamental investment recommendations broadcasted on the TV show “Talking Numbers.” In particular, we study 1,599 dual recommendations, where each recommendation features a fundamental and a technical forecast. The evidence shows that technicians possess superior skills in predicting individual stock returns up to a twelve-month horizon, while fundamental analysts exhibit weaker predictive ability. Beyond that, neither group is able to predict returns on market-wide indexes, equity sectors, bonds, or commodities.

Combining Quantitative and FundamentalAnalysis: A Quant-amental Approach

Over the last 30 years, the complexity of financial markets has expanded because of a growth in the number of markets and listed securities, as well as greater data availability. With an increase in computing power, scalable quantitative models that quickly process signals from multiple sources are becoming increasingly important. The authors argue that in this market environment, a disciplined investment process driven by a rigorously tested quantitative model created to harmoniously support talented fundamental analysts should lead to strong long-term investment performance.

English

Markets play a critical role in economics of the world and the distribution of wealth. Predicting them can help with preventing crashes and avoiding severe losses, or making significant profits. But such prediction is not easy due to the very complex nature of markets and the wide variety of the influence factors involved. Technical analysts or chartists rely on historical chart data to predict patterns based on previous behaviours of graphs. This approach is fairly straightforward and has also been automated to a great extent. There are computer programs or predictor robots that use the technical approach and facilitate buy or sell decisions. However, market behaviour obviously is more than repetition of old patterns and many of the events in the outside world have constant impacts on it. These external pieces of information can vary from political events to economic statistics. Fundamental analysts are those with a knowledge and understanding of the world events on market behaviour. This requires knowledge of politics, micro and macroeconomics to say the least, and hence, there are far fewer of such analysts. However, the very successful analysts like Warren Buffet have repeatedly emphasized on consideration of fundamental data in prediction calculations. Nevertheless, proper fundamental analysis remains to be a challenge and even a bigger challenge when it comes to its automation. There are very few research efforts and approaches which look into possibilities of automation of fundamental analysis. Hence, this work initiated a novel approach on fundamental data manipulation for identification of relationships between market behaviour and external information. This work made an effort to apply the afore in the foreign exchange market by observing the USD/GBP currency pair. In this research, an approach was devised and proposed for integration of fundamental data into automatic prediction. In this approach 3 main sources for fundamental data were identified. From these sources, data was extracted, organized and then fed into a proposed neural network during 6 experiments. The experiments put the possible relationships between the identified fundamental data and the price movements of the chosen currency pair (USD/GBP) to test. The test results indentified the datasets with plausible relationships with the market behaviour. The observed positive output of 3 different sets of data-input proved the proposed methodology to be of considerable value for market prediction. Key words: Foreign exchange market prediction, stock market prediction, neural networks, fundamental analysis, market behaviour.

Impact of Macroeconomic Factors on SENSEX Returns

Movements in SENSEX are the result of a complex interplay of a host of factors. Hence, it is not easy to make a correct assessment of its movement, and the task becomes all the more difficult when SENSEX witnesses a lot of volatility. Macroeconomic factors do have a lot of influence on the SENSEX movements. The objective of this study is to investigate several key macroeconomic factors and their influence on SENSEX variations. For this purpose, key macroeconomic parameters like Balance of Trade, Index of Industrial Production, Money Supply (M3), MIBOR Rates, FOREX Reserves, Wholesale Price Index based inflation and exchange rates between Rupee-Dollar and Rupee-Euro were considered to investigate the key factors amongst them which are influencing most the SENSEX variations. The study uses monthly data for past ten years (i.e. from 2001 to 2010), analyzed using Vector Autoregressive techniques. The findings of the research identified three macroeconomic indicators, viz. MIBOR, FOREX reserves and INR/USD exchange rates, as having a significant impact on the SENSEX returns. Other macroeconomic indicators considered in our study were found to have no significant impact on the SENSEX returns. These findings are expected to help fundamental analysts and other market players to watch out for these variables while making investment decisions.

The Effect of Macroeconomic Factors on Indian Share Prices: A Sectoral Approach

The influence of macroeconomic factors on stock prices has attracted immense attention in recent years. The subject is being considered significant in both academic research and practical investment analysis. It is a common knowledge that fundamental analysts try to identify the trend in several macroeconomic variables and accordingly its effect on stock returns. The success of ‘buy and hold’ strategy adopted by these investors who focuses on ‘intrinsic value’ of assets is assumed to depend largely on future movements in various macroeconomic variables that directly or indirectly affect a firm’s business operations and thereby its stock returns. Considering the two significant sectors of Indian economy viz. manufacturing and financial services sectors, we address the following issues: (A) Does the long-run relationship exists between macroeconomic variables and stock prices in manufacturing and financial services sectors in India? (B) Is there any significant difference between macroeconomic factors affecting stock returns in manufacturing sector and those in financial services sector?

Fundamental vs. Technical Analysis

This article addresses the following key points: Technical analysis is based on the belief that the market is not efficient. Technical analysts use indicators that are independent of the company's financial condition. Fundamental analysts focus on the financial health of companies. Fundamental analysis chooses stocks to buy; technical analysis chooses when to buy for analysts who use both. Proponents of strong form efficient market theory and technical analysts are at opposite ends of the philosophical spectrum.