Corporate Diversification and Risk: Portfolio Effects and Resource Redeployability

By analogy with portfolio diversification by stock market investors, managers and researchers have often expected that firms that spread operations across product or geographic markets reduce risk. However, numerous exploratory studies in corporate strategy and in international business have not been able to robustly confirm this expectation. This study develops a formal model to scrutinize implications of corporate diversification for corporate risk. The model incorporates the key distinction of corporate diversification, economies of scope, that qualifies the analogy between corporate and portfolio diversification. The presence of a particular type of economies of scope, resource redeployability, not only inherently increases risk but it can also raise risk over the level in undiversified firms. The model uses determinants of resource redeployability from previous research to derive conditions with which corporate diversification enhances risk. The developed elaborate operationalization of corporate risk should facilitate future research and help corporate managers.

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

Evaluating The Connection of Behavioral Biases and Investment Decisions of Equity Market Investors Using SEM Approach

Objective: The key objective of the paper is to study the magnitude of the disparity in actions between stock holders for short-term and long-term. Methods: Investor traits and how the judgement on investments and behavioral bias are interconnected are contrasted by using a systemic model, as well as to compare relative behavioral bias variations including Framing Bias, Endowment Bias, Representative Bias, Cognitive Dissonance Bias, Self-Control Bias and Overconfidence Bias. Distinguishing evidence of behavioral characteristics that are normally related to investment venture helps to provide assessments and confine trading techniques. Results: Between July 2020 and August 2020, the cognitive effect of investor decision-making is contrasted via test review of 300 substantive responders from deliberate Indian stock market investors. Taking into account the structural equation modelling (SEM), a route study is carried out of the manner in which stock investment and proposed behavioral inclinations are concomitant. Conclusions: Observational outcomes suggest that the systemic path model deliberately correlates with the survey content, demonstrating the influence of behavioral discrimination in decision-making for individual investments. Our results also indicate that short-term and long-term investors’ behavioral patterns vary substantially.

Analysis of Influencing Factors of Investor’s Perceptions on Indian Stock Markets

Investing in the Indian stock market is a great way for groups of investors to pool their funds for a common goal. Stock market schemes in India are managed by companies sponsored by financial institutions, banks, private companies, or international companies. This study was conducted to learn more about people's attitudes toward investing in the Indian stock market. For this study, the researcher spoke with 101 people who have invested in stock markets such as the BSE and NSE. The study is empirical in nature, and the universe for this study is Hyderabad city investors, with convenient sampling as the sampling technique. This study relied on primary data gathered through a structured questionnaire. The findings of the study suggest that stock market investors should pay close attention to other factors such as currency fluctuations, international relationships, tax rates, inflation rates, and political decisions, as these factors have an impact on stock market prices at the BSE and NSE, and that international relationships, tax rates, and political decisions influence investor perceptions.

Decline in Share Prices of Energy and Fuel Companies on the Warsaw Stock Exchange as a Reaction to the COVID-19 Pandemic

Many factors influence the prices of energy commodities and the value of energy and fuel companies. Among them there are the following factors: economic, social, environmental and political, and recently also the COVID-19 pandemic. The aim of the paper is to examine what the probability and intensity of a decrease in the prices of shares of energy and fuel companies listed on the Warsaw Stock Exchange (Poland) was during the first wave of the pandemic in the first quarter of 2020. The study used the survival analysis methods: the Kaplan-Meier estimator, the test of equality of duration curves and the Cox non-proportional hazards model. The analysis showed that the probability and intensity of price decline of energy and fuel companies in the initial period was the same as that of other companies. The differences become apparent only after 50 days from the established maximum of their value. The risk of price declines in energy and fuel companies increased significantly. This situation was related both to a temporary reduction in demand for energy and fuels, pandemic restrictions introduced in individual countries and the behaviour of stock market investors.

Unexpected investment, tunnelling and financial constraints

PurposeBased on a sample of 1,435 Vietnamese listed firms over the period from 2005 to 2017, this study examines the sensitivity of unexpected investment to free cash flow and its mechanism.Design/methodology/approachWe tested three hypotheses using two-step system-GMM to investigate investment–cash flow sensitivity for various firm scenarios while accounting for confounding variables.FindingsFirms with negative free cash flow are more likely to engage in underinvestment; conversely, overinvestment is found primarily in firms with positive free cash flow. In terms of the mechanism, while underinvesting decisions are caused mainly by financial constraints, overinvesting behaviour primarily resulted from agency problems, typically in the form of principal-principal conflicts. Interestingly, under the impact of negative cash flow observations, financial constraints tend to decrease investment–cash flow sensitivity. Conversely, the agency costs hypothesis reveals that agency problems are more likely to increase investment–cash flow sensitivity.Originality/valueThese findings not only contribute to the current corporate literature but also provide some important practical implications for stock market investors, corporate managers, and policy-setting bodies, specifically in the Vietnamese market.

Segmenting Investors on their Biases Manifested in Investment Decision-Making by Individual Investors

Purpose- This study proposes to identify the certain biases affecting investor decision-making and to segment investors accordingly. Design/Methodology- A quantitative research method was applied to measure the existence and impact of the biases on investment decision-making. A survey was administered among the stock market investors in Uttar Pradesh. Factor analysis was used to extract those biases that significantly impact investment decision-making and their mean score to assess the level of agreement that affects their investment decisions. Findings - The finding reveals that eight extracted factors affect the investment decisions and accordingly segment them on the biases they exhibit. The investors tend to fall into Imitator, Stereotypical, Independent Individualist, Risk Intolerant, Efficient Planner, Confident, Passive, and Competent Confirmer. The Imitators, Independent Individualists, and Confident investors show their higher level of agreement that highly affects their equity investment decision-making. Practical Implication- This study provides a base to segment the investors on their biases. In addition, it will help in customizing the investment recommendation based on their biases to improve the investment decisions.

Graph-Based Stock Recommendation by Time-Aware Relational Attention Network

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.

Qualitative Analysis on Investment Decisions of Nepalese Stock Market Investors

The paper attempts to examine the experience of the Nepalese investors at the secondary market. The paper explored the investment decisions process of the Nepalese investors. The paper has adopted the grounded theory to generate the theory from the data collected from the semi-structured interview from the stock market investors having an academic background in management. The findings revealed that the investors are eager to invest in the stock market and go for a better experience from their trading at the NEPSE floor. The study exposed a mixed opinion in context to the understanding of the macroeconomic aspects and their influence on investment decisions. The investors forwarded that there is no relation between their investment decision-making process and the macroeconomic factors, while some of the investors stated that they see a connection of the economy with the stock market directly or indirectly. The study came out that the major concern of the Nepalese investors is a fundamental aspect of the listed companies while selecting for an investment. At the same time, investors stated that they go for technical analysis or follow the market trend for the short-term trading at NEPSE floor. The investors are seen at one point that the unstable political situation and insider trading have been major challenges, in context to the Nepalese stock market. Finally, the excessive flow of information related to the listed companies either with some validity or not, makes an investor’s investment decisions go wrong.