Individual Stock Prices Research Articles

Topological variability in financial markets

<abstract><p>We investigate market crashes and downturns through the lens of persistent homology and persistence landscape norms. Using individual stock price data from Yahoo! Finance, we find that the variation in the persistence landscape norm as well as other measures of persistence exhibit a marked increase followed by a decline prior to historic incidents. We show that basic descriptions of persistent homology may be useful in addition to more sophisticated tools like the persistence landscape norm.</p></abstract>

Mitigating the Influence of Analysts Who Issue Aggressive Stock Price Targets: The Role of Joint Versus Separate Evaluation*

ABSTRACTInvestors frequently rely on individual analysts' stock price targets. Aggressive price targets often reflect analysts' attempts to strategically influence investors. Therefore, investors' welfare may be compromised if they take aggressive price targets at face value. In this study, we examine conditions under which investors are more likely to infer that analysts who issue aggressive price targets are acting strategically. Investors can evaluate multiple analysts' price targets with or without other related information (e.g., earnings estimates). Investors can also evaluate the information provided by multiple analysts jointly or separately one analyst at a time. Two experiments find that as predicted, when investors evaluate multiple analysts' price targets without earnings estimates, there is no difference in investors' perceptions about whether the aggressive analyst is acting strategically across joint versus separate evaluation. However, also as predicted, when investors evaluate multiple analysts' price targets along with their earnings estimates, investors perceive the aggressive analyst as acting more strategically under joint evaluation than under separate evaluation. Our findings suggest that jointly evaluating multiple analysts' price targets with other related information, such as earnings estimates, can reduce the likelihood that investors would be overly influenced by aggressive analysts.

Empirical validation of ELM trained neural networks for financial modelling

The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for individual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks. Subsequent analysis identified several areas of performance deviations which we highlight as potentially fruitful areas for further research and performance improvement.

함수형 자료 기법을 활용한 로지스틱 회귀 모형과 응용

Stock prices around the world dramatically decreased in 2020 due to COVID-19. Although the general trend seemed to be rebounding in 2021, the degree of recovery might vary by industry. This paper intends to investigate the relationship between the type of the industry companies and the pattern of individual stock indexes for 2021. Specifically, we investigate whether the functional pattern of individual stock indexes can be an important factor that can distinguish the type of the industry company. Traditionally, the stock price data has been analyzed using a time series approach. However, with the recent development of automated devices, the interest on the functional data analysis has been increasing. If individual stock prices are assumed to be random functions defined above continuous time, functional data techniques can be applied. Following this approach, we propose to use a functional logistic regression model that can explain the type of the industry company with the pattern of individual stock prices especially for 2021.

Student‐t stochastic volatility model with composite likelihood EM‐algorithm

A new robust stochastic volatility (SV) model having Student‐ marginals is proposed. Our process is defined through a linear normal regression model driven by a latent gamma process that controls temporal dependence. This gamma process is strategically chosen to enable us to find an explicit expression for the pairwise joint density function of the Student‐t response process. With this at hand, we propose a composite likelihood (CL) based inference for our model, which can be straightforwardly implemented with a low computational cost. This is a remarkable feature of our Student‐t process over existing SV models in the literature that involve computationally heavy algorithms for estimating parameters. Aiming at a precise estimation of the parameters related to the latent process, we propose a CL expectation–maximization algorithm and discuss a bootstrap approach to obtain standard errors. The finite‐sample performance of our CL methods is assessed through Monte Carlo simulations. The methodology is motivated by an empirical application in the financial market. We analyze the relationship, across multiple time periods, between various US sector Exchange‐Traded Funds returns and individual companies' stock price returns based on our novel Student‐t model. This relationship is further utilized in selecting optimal financial portfolios. Generalizations of the Student‐t SV model are also proposed.

Identification of the most influential stocks in financial networks

In the financial market, the stock association network can be used to describe the correlations between stock prices. The rise and fall of stock prices and their mutual influence can be described by a reversible cascading failure process. In the reversible spreading processes, the influence of fluctuation of an individual stock price on companies, industries and even the financial markets can be regarded as the influence of the individual stock in the financial networks. In this paper, based on the stock price association networks of nine sectors in China A-stock market, we studied the identification of the most influential nodes in the reversible spreading processes. It is found that due to the high proportion of core nodes in the stock association networks, the k-coreness centrality can not accurately measure the influence of these nodes. A large number of simulations show that the number of out-leaving edges of neighbor nodes can better evaluate the influence of a node in the spreading processes. Then a node strength centrality and s-coreness centrality based on link importance are developed to measure the spreading influence of nodes. Compared with degree centrality and k-coreness centrality, the node strength and s-coreness based on strength have a better performance in identifying the most influential nodes. The ranking algorithm considering the structures and dynamics of local neighbors can further improve the ranking performance. The proposed framework and ranking method open up a new idea in identifying the most influential stocks in financial networks.

Assessing the diversification risk of a single equity market: evidence from the largest European stock indexes

Abstract Diversification of financial securities is considered a substantial element of portfolio risk. In this context, the construction of an optimal portfolio is an ongoing concern for portfolio managers. This study measures the risk–reward tradeoffs linked to the stock indexes of Germany, Spain, Italy, France, and England. First, the stock indexes are analyzed as individual portfolios and later compared to the hypothetical common equity index. The results show diversification benefits gained from a hypothetical common European stock market. Individual stock prices and trade volumes are collected weekly from January 1, 2008 to December 31, 2018. The results indicate that, on average, the most well-diversified equity indexes are IBEX35, FTSE MIB, and FTSE100. In contrast, DAX, MDAX, and CAC40 on average tend to be less diversified. The diversification risk for DAX, MDAX, and CAC40 decreases from joining a common hypothetical stock market, while for FTSE100, FTSE MIB, and IBEX it increases.

A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions

In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based reasoning, which does not consider how to retrieve similar neighbors from previous patterns in terms of a graphical pattern. In this paper, we show how the proposed method can be used when traders find similar time series patterns among nearest cases. For this, we suggest an interactive prediction system where traders can select similar patterns with individual knowledge among automatically recommended neighbors by case-based reasoning. In this paper, we demonstrate how traders can use their knowledge to select similar patterns using a graphical interface, serving as an exemplar for the target. These concepts are investigated against the backdrop of a practical application involving the prediction of three individual stock prices, i.e., Zoom, Airbnb, and Twitter, as well as the prediction of the Dow Jones Industrial Average (DJIA). The verification of the prediction results is compared with a random walk model based on the RMSE and Hit ratio. The results show that the proposed technique is more effective than the random walk model but it does not statistically surpass the random walk model.

Investors' reactions and firms' actions in the Covid-19 period: The case of Taiwan

This study investigates the stock performance of industries in the panic, rebound, and post-V-shaped periods separated by Covid-19 events in Taiwan, in which industries are classified as the detrimental, impaired, neutral, and beneficial groups. Prices of these four industries slumped about the same in the panic period but subsequently rose differently in the rebound and post-V-shaped periods, implying that investors make investment decisions by perception when facing dread risk but by analytic assessments after dread risk recedes. Regression tests show that prices of individual stocks in the same industry dropped differently in the panic period, reflecting investors' bounded rationality in that they are emotional at the industry level but rational at the firm level. Also, logistic regressions indicate that investors tend to be overoptimistic about the impaired and detrimental industries in the rebound period. More than 10% of firms repurchased their stocks but only a small portion of firms issued equity in the Covid-19 period, revealing that firms would participate in short-run trading but halt long-run investments when facing uncertainty. This study contributes to the literature by showing investors' behavior and firms' actions in different periods of Covid-19.

Power Distance, Political Uncertainty, and Stock Price Crash Risk: International Evidence

This study tests the effect of unbalanced power distance (PD) (i.e., Hofstede’s cultural dimensions PD index) and individual stock price crash risk. We examine the stock price behavior of listed firms in 37 countries from 2004 to 2016 and use multivariate analyses to document that societal PD is important in explaining firms’ propensity to release accounting information. This propensity suggests a psychological tendency regarding timing management, particularly for bad news. As countries with large PD prefer to keep things under control, the result is fewer unexpected stock price crashes during the long windows between election events. However, because large-PD countries focus their markets on maintaining temporary peace before and during periods of political events (i.e., national elections), crash risk increases after the political event window. Consistent with these predictions, we find that in large-PD countries, companies generally have less incentive to hide negative information and thus generate stock price crashes. This situation is substantially changed during the postpolitical windows, when firms and ways of spreading information are more controlled by the government. Our findings suggest that formal mechanisms alone are insufficient to explain the behaviors of corporate disclosure that are entangled with informal instruments.

Statistics and Practice on the Trend’s Reversal and Turning Points of Chinese Stock Indices Based on Gann’s Time Theory and Solar Terms Effect

Despite the future price of individual stocks has long been proved to be unpredictable and irregular according to the EMH, the turning points (or the reversal) of the stock indices trend still remain the rules to follow. Therefore, this study mainly aimed to provide investors with new strategies in buying ETFs of the indices, which not only avoided the instability of individual stocks, but were also able to get a high profit within weeks. Famous theories like Gann theory and the Elliott wave theory suggest that as part of the nature, market regulations and economic activities of human beings shall conform to the laws of nature and the operation of the universe. They further refined only the rules related to specific timepoints and the time cycle rather than the traditional analysis of the complex economic and social factors, which is, to some extent, similar to what the Chinese traditional culture proposes: that every impact on and change in the human society is always attributable to changes in the nature. The study found that the turns of the stock indices trend were inevitable at specific timepoints while the strength and intensity of the turns were uncertain, affected by various factors by then, which meant the market was intertwined with both certainty and uncertainty at the same time. The analysis was based on the data of the Shanghai Index, the Second Board Index and the Shenzhen Index, the three major indices that represent almost all aspects of the Chinese stock market, for the past decades. It could effectively reduce the heteroscedasticity, instability and irregularity of time series models by replacing 250 daily high-frequency data with the extreme points near every twenty-four solar terms per year. The forecasts focusing on the future stock trend of the all-solar-terms group and the eight-solar-terms group were proved accurate. What is more, the indices trend was at a high probability to turn in a range of four days at each solar term. The alert period also provided the readers with a practical example of how it works in the real investment environment.

Research on Emotional Leverage Effect of Capital Flow of Well-known Investment Institutions

China’s capital market started late, compared with developed countries, China’s individual investors account for a larger proportion. Because individual investor does not have the specialized investment knowledge and the mature investment psychology, its investment behavior often will receive the external factor the interference to produce “The herd effect”, therefore, the well-known investor institutions can rely on the reputation effect brought by their own popularity to influence the stock price fluctuation by influencing the investor sentiment, thus exceeding the influence degree of the corresponding buying and selling fund flow. This paper defines the above process as the emotional leverage effect of well-known investment institutions. In this paper, we use principal component analysis to construct the index of investor sentiment, and use correlation test to find that the well-known investment institutions, by virtue of their own reputation, make their capital flow have a significant impact on investor sentiment, there is a significant positive correlation between investor sentiment and the fluctuation of individual stock prices in the industry. Small and medium-sized investors will suffer investment losses because of their blind faith in the trading strategies of well-known institutions. In order to further enhance the protection of investors’ interests, this paper puts forward suggestions to improve the protection mechanism of small and medium-sized investors’ rights and interests from the perspective of information disclosure and policy supervision.

ANALYSIS OF COMPANY PERFORMANCE AS ISSUERS BASED ON THE COMPASS 100 INDEX ON MARKET PRICES

The background of this research is the desire of investors and issuers so that the individual stock price index continues to increase in the Indonesian stock market, in fact, fluctuates where investment stores and previous studies have stated the influence between company performance variables, investor expectations, investment risk on market prices and their impact on the index individual share price The research objective is to give investors confidence to invest in the Indonesian stock market by analyzing the effect of company performance, investor expectations, investment risk on market prices, and their impact on the individual stock price index. The sampling technique used is a nonprobability sampling from the Indonesia Stock Exchange data from September 2015- May 2018 with the Kompas Index 100 as an index of 100 liquid companies which is one of the references for investors to invest, the Analysis Technique uses Structural Equation Modeling (SEM) with the approach WarpPLS The research results concluded Company performance has a direct effect on market prices and has an indirect effect on the individual stock price index through market prices, investor expectations do not have a direct effect on market prices but have an indirect effect on the individual stock price index through market prices, investment risk does not directly affect market prices but indirect effect on the individual stock price index through market prices and market prices have a direct effect on the individual stock price index.

Intervention Analysis and Machine Learning to Evaluate the Impact of COVID-19 on Stock Prices

The purpose of this study is to evaluate the impact of the COVID-19 outbreak on composite and individual stock prices in China, the USA, South Korea, and Indonesia by using an intervention model and comparing the results of its predictions with a machine learning model, i.e. neural network (NN) and deep learning neural network (DLNN). This intervention model can be used not only to find out the magnitude of the effect of COVID-19 on the stock price, but also the period of the effect. The composite stock price data used are KS11, 000001.SS, DJI, and JKSE, while the individual stock price data used are TLKM and EXCL. The data used is the daily stock data. The analysis shows that COVID-19 hurts stock prices both in countries that have passed the peak period and are still in the peak period of COVID-19. The impact is not directly after the first case of COVID-19 in each country. The lowest stock price occurred at the end of March 2020 in each country. Different conditions were shown by individual stock prices in the telecommunications sector that showed a positive trend after the end of April 2020. Generally, for all stock prices, intervention models are better for forecasting in-sample data and explanation impact COVID-19 on stock price, whereas machine learning models are better for forecasting out-of-sample data.

The Influence of Regulation and Financial Performance on The Disclosure of Corporate Social Responsibility and Corporate Reputation Moderated by Ownership Structure

Objective - A study by Ernst and Young (2010) found that 84% of public companies believed that Corporate Social Responsibility (CSR) was an activity that had a positive impact on companies. However, only 11% of those companies disclosed their CSR in their annual reports. This article presents the findings of a study examining the effect of CSR regulation and corporate financial performance, as measured by corporate liquidity, profitability, leverage and firm value, on the disclosure level of the CSR of public companies in Indonesia, and its impact on corporate reputation. Methodology/Technique - Hypothesis tests with multiple regression were used with nonfinancial corporate categories listed on the Indonesian Stock Exchange between 2010 to 2018. Purposive sampling was used, with results from 217 sampled companies and 1953 datasets used in the model. Findings - The study reveals that there is a significant difference in the CSR disclosure of all corporate categories after the regulation of CSR was enacted as a mandatory in 2012. Financial performance measured through Cash Flow from Operations (CFO) and Debt to Equity Ratio (DER) had a positive significant influence on CSR disclosure. However, Return on Assets (ROA) and firm value have no influence. Family ownership as a moderating variable had a negative influence and weakened the association between CFO and CSR disclosure, whereas foreign ownership as a moderating variable had a negative influence and weakened the association between ROA and CSR disclosure. CSR disclosure had a positive influence on corporate reputation from the stakeholders’ perception in terms of awards received by the company and the individual stock price index as an alternative measurement for reputation. Novelty - The impact of CSR disclosure on corporate reputation was analysed based on the alternative measurement of reputation. Ownership structure consisting of family ownership and foreign ownership were taken as the moderating variables in the correlation between financial performance and the disclosure of CSR. Type of Paper: Empirical. JEL Classification: M14, M41. Keywords: Corporate Social Responsibility; Regulation; Financial Performance; Moderation; Ownership Structure Corporate Reputation. Reference to this paper should be made as follows: Wilestari, M.; Syakhroza, A; Djakman, C.D; Diyanty, V. (2021). The Influence of Regulation and Financial Performance on The Disclosure of Corporate Social Responsibility and Corporate Reputation Moderated by Ownership Structure, Accounting and Finance Review, 5(4): 13 – 22. https://doi.org/10.35609/afr.2021.5.4(2)

ELEMENTS INDICATING STOCK PRICE MOVEMENTS: THE CASE OF THE COMPANIES LISTED ON THE V4 STOCK EXCHANGES

Stock markets stand as a financial mechanism that provides liquidity for firms and offers diversification benefits for investors. Stock markets in the Eastern European countries are weakform efficient which exposes them to speculative prices. This study investigates the influence of the macroeconomic and firm-specific factors on stock prices of the listed companies within the Visegrad Stock Markets. The study employs regression analyses based on a Pooled OLS and Fixed Effect models with year dummies and standard errors clustered at the country level, which are robust to autocorrelation and heteroscedasticity. Data collection consists of 55 listed companies based on the weekly stock prices, from January 2013 till December 2018. The results indicate that total equity is the only significant element that influences the individual stock prices of the companies in the four established models. Additionally, increase in supply of shares declines the current stock prices and the other way around. However, the exchange rate and inflation level indicate a negative influence on the stock prices with weaker significance. The findings show that stock markets of the V4 countries are overall inefficient since important indicators, such as economic activity, debt level, cash flow, firm size, oil, and gold prices have limited influence on the stock price movements.

Stock prediction and mutual fund portfolio management using curve fitting techniques.

Investment in the share market helps generate more profit than the other financial instruments but has the threat of market risk that might lead to a high loss. This risk factor refrains many potential investors from investing in the share market directly. Instead, they invest in different mutual funds that are being managed by experienced portfolio managers. To avoid the risk factors and increase the gain, they put the accumulated capital in multiple stocks. They need to perform many calculations and predictions to overcome the uncertainties and unpredictability and need to ensure higher gains to the investors of that mutual fund. In this research work initially, a data mining based approach employs a curve fitting/regression technique to forecast the individual stock price. Based on the above analysis, we propose a framework to diversify the investment of the capital fund. This method employs buy and hold strategy using both statistical features and basic domain knowledge of the share market. The proposed framework distributes the capital first, by distributing sector-wise, and then for each sector, investing company-wise, as a diversified approach among different stocks for higher return but maintaining lower risks. Experimental results show that the proposed framework performs well and generates a good yield compared to some benchmark and ranked mutual funds in the Indian stock market.

The global aspect of investing

Research background: Investing in the capital market is one of the ways for an investor to increase his income. Investing in the capital market is described as very risky, but it provides us with an opportunity to achieve high returns. There is an infinite number of assets on the market that an investor can include in his portfolio, and which assets he chooses to invest in will ultimately affect the return and risk of the created portfolio. Therefore, the investor should think rationally and should try to reduce the risk of his investment. Purpose of the article: This article aims to point out the effect of diversification through the creation of a portfolio of financial assets. Methods: This paper uses modern portfolio theory and focuses on the use of the Markowitz model. The optimal weights of individual assets in the compiled portfolio are calculated using the Lagrange function. We use data on weekly closing prices of individual stocks, commodity and mutual fund. Findings & Value added: The main finding was that the risk associated with the created portfolio is lower than the risk associated with investing in individual assets.

소셜 감성이 개별 기업 주식수익률에 미치는 비대칭적 영향 분석

본 연구는 소셜 감성(social sentimental)을 긍정 및 부정적 의견으로 구분하여 이들 의견이 개별 기업의 주식수익률에 미치는 영향이 비대칭적인지(asymmetric) 분석하였다. 이를 위하여 한국거래소에서 활발하게 거래되고 트위터 의견도 충분한 기아차, 아모레퍼시픽, 포스코, 한국전력 등 4개 기업을 분석대상으로 하였다. 주요 분석 결과는 다음과 같다. 첫째, 긍정적 의견은 개인투자자의 거래 비중이 상대적으로 낮은 아모레퍼시픽의 주식수익률에는 영향을 주지 못한 반면 나머지 3개 기업의 주식수익률에는 유의한 양(+)의 영향을 주었다. 둘째, 부정적 의견은 4기업의 주식수익률에 모두 유의하게 음(-)의 영향을 주는 것으로 나타났다. 특히 부정적 의견이 긍정적 의견보다 주식수익률에 미치는 영향이 더 크게 나타났으며, 이는 투자자들이 손실회피 성향 등으로 수익보다 손실에 더 민감하기 때문으로 보인다. 본 연구는 트위터의 긍정 또는 부정적 의견이 주식수익률에 비대칭적(asymmetric)으로 영향을 미치는 것을 발견하였으며, 이는 트위터의 의견을 투자자 심리(sentiment) 대용변수(proxy)로 활용할 수 있음을 보여준다.

Analysis of Stock Market Returns: Study of Consumer Goods Companies Listed on the Indonesia Stock Exchange

Stocks have the highest risk between other investment instruments, but with the right analysis, the high risk is proportional to the high return that can be obtained. Stock return analysis can be done using a basic approach, namely fundamental analysis and technical analysis. This study aims to examine the effect of the individual stock price index, past stock price, debt to equity ratio (DER), and net profit margin (NPM) in consumer goods industry companies listed on the Indonesia Stock Exchange 2015-2018. This study uses multiple regression analysis models. The population of this study consisted of 60 companies, and the research sample consisted of 27 companies with 108 consumer goods industry companies listed on the Indonesia Stock Exchange 2015-2018 periods. The results showed that individual stock price index, past stock price, DER, and NPM simultaneously have a significant effect on stock return. Partially individual stock price index and past stock prices have a significant effect on stock return. In contrast, DER and NPM have no significant effect on stock return in consumer goods industry companies listed on the Indonesia Stock Exchange in the 2015-2018 periods.