Investors In Financial Market Research Articles

Can ESG Integration Enhance the Stability of Disruptive Technology Stock Investments? Evidence from Copula-Based Approaches

This paper provides an investigation into the dependence structure among different disruptive technology sectors driving the Fourth Industrial Revolution and scrutinizes the impact of ESG integration on shaping investments in different tech stock sectors in the presence of ESG consideration, represented by the ESG stock index, versus without specific ESG consideration, represented by the general stock index. The results show that (i) C-vine outperforms R-vine and D-vine when modeling the dependence structure of tech sectors. Intelligent infrastructure is the most crucial sector, with substantial reliance on smart transportation and advanced manufacturing. (ii) ESG integration reduces dependence, especially tail dependence, between tech sectors and the stock market, which benefits the future security sector the most and future communication the least. (iii) ESG integration mitigates risk spillover between tech sectors and the stock market, particularly benefiting final frontiers and intelligent infrastructure. The decrease in downside spillover is more significant compared to upside scenarios. For downside risk, spillover from tech sectors to stock indices is more reduced than the reverse, while the opposite holds for upside risk. These sectoral findings offer insights for market participants in financial market investments, financial regulators in risk management, and listed companies in ESG disclosure.

Media społecznościowe jako źródło wiedzy wykorzystywanej w inwestycjach na rynku finansowym

Social media are a source of an enormous amount of data that can support investment decisions, with the development of digital technology in the field of data processing making the analysis of the content published on sites such as Twitter, Facebook and YouTube an indispensable part of the investment process for many financial market participants. The aim of this study is to identify the applications of social media in financial market investing, as well as undertaking to determine the position of social media among the available sources for obtaining market information. The empirical data used in the analysis was obtained through a survey carried out using the CAWI method. The results of the survey indicate that social media are an important source of information, especially for respondents with experience in financial market investments, although they are inferior to financial portals in this respect. The varied use of the social media platforms analysed was also recognised. The main advantage of using Twitter was identified as the ability to monitor current trends and follow the profiles of investment experts, for Facebook it was the ability to join investment-themed groups, while YouTube was valued for its access to educational content.

EXCHANGE RATE REGIMES AND ECONOMIC POLICIES: AN INDIAN PERSPECTIVE

This research paper provides a comprehensive analysis of exchange rate regimes (ExRRs) and their implications for economic policies particularly for the Indian economy. The study investigates the relationship between different ExRRs, including fixed, floating, and managed float systems, and their impact on macroeconomic policies which are drivers of economic growth. We have recognised four different regimes for the Indian rupee on the basis of level of flexibility of the exchange rate. In India, before liberalisation, fixed exchange rate system was followed but after economic liberalisation of early 1990s, the Reserve Bank of India (RBI) started intervening in the currency market to regulate the exchange rate. The exchange rate was made comparatively more flexible since the global financial crisis of 2008. After 2013,the exchange rate was efficiently and regularly managed by adopting the currency market intervention methhod. The RBI has acted in an asymmetric manner to reply to the EMP. When there was pressure to appreciate, the RBI acted by buying the foreign exchange reserves whereas rupee depreciation was adopted in case of vice-versa. The present study discusses the broader implications of ExRRs and economic policies for global economic governance, financial market stability, trade and investment flows, policy innovation, and public perception. It provides valuable insights for policymakers, central banks (CBs), financial institutions, and researchers, informing policy formulation processes in the dynamic global economic landscape.

LIFE INSURANCE DEVELOPMENT AND ECONOMIC GROWTH: A CASE STUDY OF NIGERIA (2000-2022)

This research looked at the development of life insurance and economic growth using Nigeria as a case study from 2000 to 2022. Specifically, the authors examined the causal relationship between life insurance development and economic growth, analyzed the effect of life insurance penetration and life insurance density on real GDP growth rate. Data used for the study were collected from sources including the World Development Indicator database, Global Financial Development Indicator database, NIA digest database, and Central Bank of Nigeria statistical bulletin. Granger causality test and Autoregressive Distributed Lag (ARDL) co-integration were used in the investigation approach. The granger causality test result indicated that there is no causal connection between Nigeria's economic growth and the development of life insurance. According to ARDL estimate results, life insurance density and penetration had a negligible positive effect on the real GDP growth rate. Hence, this study established that life insurance development is yet to contribute to economic growth in Nigeria, due mainly to its low level and mode of operations. Therefore, this study recommended among others that life insurance institutions should also increase their scope of operations to be directly involved in business investments other than financial market investment to enhance their level of significance in the growth process of Nigeria.

Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm

In order to cope with the complex risk environment of the current financial market, achieve portfolio optimization and accurate risk prediction, this paper conducts effective research using SVM algorithm. This article uses stock data as a sample to empirically analyze the risk return and risk prediction performance of investment portfolio strategies based on SVM algorithm. Compared with traditional index fund investment strategies, the risk resistance of investment portfolio strategies is significantly improved, and the risk return is also stable at a high level. In addition, with the support of SVM algorithm, the risk prediction error level in the financial market remains within a relatively low range. From the perspective of practical applications, the financial market investment portfolio selection and risk prediction based on SVM algorithm has strong feasibility.

Life insurance with insurance capital funds as a form retirement savings: Determinants for the self-employed

Reduction of the public pension system generosity should encourage all future retirees to accumulate private retirement savings. They have many options in this area, both dedicated pension programs (pension pillars), and a whole range of financial market investment instruments (non-pillar solutions). The main objective of the paper was the empirical identification of factors that determine the level and propensity to save in the form of life insurance with insurance capital funds for retirement purposes by self-employed persons in Poland. The microeconomic perspective of considerations was adopted in the research process. On the basis of a review of economic literature, a set of individual characteristics that affect savings decisions of natural persons was distinguished, then an attempt was made to find out whether the same groups of factors also determined the individual decisions of self-employed persons regarding saving for retirement in the form of insurance capital funds. The following research hypothesis was verified: Ownership of retirement savings in the form of life insurance with insurance capital funds by self-employed natural persons in Poland is determined both by social and economic factors, and the impact of these factors varies. The empirical study showed that the accumulation of retirement savings by self-employed persons in the form of life insurance with insurance capital funds was the least important way to supplement the future pension from the public pillar. Self-employed persons are characterized by a relatively high propensity to save for retirement (higher than the average propensity to save in Poland), prefer non-pillar forms of collecting voluntary savings for retirement, primarily in the form of real estate. The strength of the social and economic factors varies.

The Impact of Stock Selection, Market Timing and Equity Fund Size on Equity Funds Performance during Covid-19

Investors in Indonesia pay attention to the financial market's investment tools, including mutual funds. Some mutual funds even provide a higher return on investment than alternative stock investments, with a lower level of risk be-cause investment managers properly manage them. This research aims to determine how stock mutual fund success is affected by factors such as stock selection, market timing, mutual fund size, and Covid-19, with Covid-19 serving as a moderator. Purposive sampling was utilized to conduct research on Indonesian equity funds. This study's sample consists of equity funds administered by fourteen investment management firms authorized and regulated by the Financial Services Authority (OJK). Panel data regression analysis in Eviews is used to examine the information. It was discovered that neither mutual fund size nor Covid-19 played a statistically important role in determining the success of equity funds. However, equity fund returns are significantly influenced by company picking and market timing. As a supplementary benefit, Covid-19 can significantly reduce the beneficial effects of overly optimistic stock picking on the returns of equity funds. This research also demonstrates that the effects of mutual fund size and market timing on stock fund returns are not reduced by using Covid-19.

Penerapan Model Markowitz dalam Memperoleh Portofolio Optimal di Pasar Saham IDX30

Investment in financial market is inextricably linked to risk. The risk cannot be eliminated, but indeed it can be minimized. By using Markowitz model, investor can gain optimal result while taking on acceptable risk. This study intends to examine the findings of optimal portfolio construction using Markowitz model on IDX 30. The method was quantitative descriptive. The population was all companies listed on IDX 30. Then, there were three companies chosen as the sample. The investigation of the three stocks yielded two efficient portfolios. First were the stocks of PT Bank Negara Indonesia, Tbk. and PT Astra International. The second was stocks of PT Bank Negara Indonesia, Tbk. and PT Adaro Energy Indonesia, Tbk. Each stock provided a return of 1.03 % with the risk level of 7.75%, and 4.4% with the risk level of 8.49%. The conclusion is that by using these two portfolios, investor can get optimal expected returns with the degree of risk comparable to individual equities. Thus, it is suggested for the investor to apply the calculation of Markowitz model on Indonesia Stock Exchange over a longer period than the one used in this study. It is also suggested for them to select the portfolio in accordance to their preferences.

Portfolio Analysis of Different Industries under Constraints

Building an effective portfolio is critical for it can decrease investment risk and the impact of fluctuations in the share price of a single firm. This paper selects five example stocks from diverse industries, using the mean variance model and the Index model to estimate the best combination of these stocks, then adds five significant limits to both models to see how components in the real financial market effect the portfolio. The Minimal Variance Frontier is used to compare the impact of five constraints in this paper, and it is discovered that different models have different effects under the same constraints, constrained portfolios have less volatility than unconstrained portfolios, and not allowing short positions reduces risk while also lowering the overall return rate. The findings in this paper are valuable to the industry's research on the best allocation of financial assets and can benefit financial market investors.

The Impact of Stock Selection, Market Timing and Equity Fund Size on Equity Funds Performance during Covid-19

Investors in Indonesia pay attention to the financial market's investment tools, including mutual funds. Some mutual funds even provide a higher return on investment than alternative stock investments, with a lower level of risk be-cause investment managers properly manage them. This research aims to determine how stock mutual fund success is affected by factors such as stock selection, market timing, mutual fund size, and Covid-19, with Covid-19 serving as a moderator. Purposive sampling was utilized to conduct research on Indonesian equity funds. This study's sample consists of equity funds administered by fourteen investment management firms authorized and regulated by the Financial Services Authority (OJK). Panel data regression analysis in Eviews is used to examine the information. It was discovered that neither mutual fund size nor Covid-19 played a statistically important role in determining the success of equity funds. However, equity fund returns are significantly influenced by company picking and market timing. As a supplementary benefit, Covid-19 can significantly reduce the beneficial effects of overly optimistic stock picking on the returns of equity funds. This research also demonstrates that the effects of mutual fund size and market timing on stock fund returns are not reduced by using Covid-19.

Current status of green finance in Kazakhstan

The article examines the development and formation of green finance in Kazakhstan. It is noted that green finance is now becoming an integral component of achieving global and national sustainable development goals and the formation of a green economy, defining new environmentally sustainable contours and prospects for the development of the international financial system through the active growth of the «green» segment of the global financial market and responsible investment. Financial and economic instruments in the «sphere» of green economy, specifics of formation of ?green? finance in the country, as well as factors impeding their development are analyzed. The article analyzes the current state of «green» finance in Kazakhstan, identifies the problems of «green» finance.

Globalizing financial valuation: International property consultants in São Paulo

AbstractInternational property consultants (IPCs) have become key intermediaries in the globalization of property markets by providing a range of services that generate transparency and comparability in land and property-based investments. While their role in generating standardized information on local markets is well known, what is less is known is how IPCs help turn property into an income-yielding asset in less developed economies. This article investigates the contested diffusion of financialized valuation approaches in São Paulo's local property market. Through a qualitative inquiry into large IPCs and their main clients in the city, we show that IPCs have promoted valuation approaches that are tailored to the needs of financial market investors, thus affecting key investment decisions taken by diverse actors. Though these financialized techniques have at times clashed with more traditional views of property ownership prevalent in the country, we show that most often they co-exist with long-established valuation techniques that reflect the social and economic circumstances of Brazil's economy. The socially contingent nature of property valuation raises theoretical issues concerning the complexity of attributing value to fixed capital, as well as several policy issues.

A Comparative Study on the Application of NPV and IRR in Financial Market Investment Decision

Investment activities in financial markets are the main part of determining whether an enterprise is profitable or not, with large capital investment and long time. The quantitative analysis of the feasibility of investment activities is of great significance in the operation process. The rational use of index analysis can improve the scientificity of project decision-making. The basic indicators of investment decision-making are mainly NPV and IRR, which are representative and scientific. This paper summarizes the NPV and IRR indicators, compares the advantages and disadvantages of their respective indicators, and analyzes the comparison between the independent project and the mutually exclusive project investment in the decision-making process of the financial market.

Portfolio Optimization among Luxury Goods, New Energy, Web 3.0 Industries and Gold

This paper aims to discuss optimal portfolio construction with assets among luxury goods, new energy, web 3.0, and gold industries, with the hope of providing insights into the investigation of the relationships between those industries and their feasibility in the process of portfolio optimization. Based on accessible data from Yahoo Finance, this paper first discusses mean-variance analysis with a focus on the return data of the chosen assets, then utilizes Fama-French three factor model to run a portfolio optimization among those assets with core indexes being the maximization of Sharpe Ratio and the minimization of variance. As a result, two portfolios with different weights assigned to each asset are generated and they could be a good indication of the relationships between the discussed industries and their connections under investment scenario, and therefore may provide valuable information to the related financial market investors.

A survey on machine learning models for financial time series forecasting

Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to facilitate FTS forecasting has been highly pursued for decades. Despite major related challenges, there has been much interest in this topic, and many efforts to forecast financial market pricing and the average movement of various financial assets have been implemented. Researchers have applied different models based on computer science and economics to gain efficient information and earn money through financial market investment decisions. Machine learning (ML) methods are popular and successful algorithms applied in the FTS domain. This paper provides a timely review of ML’s adoption in FTS forecasting. The progress of FTS forecasting models using ML methods is systematically summarized by searching articles published from 2011 to 2021. Focusing on the analysis of ML methods applied to the theoretical basis and empirical application of FTS data forecasting, this paper provides a relevant reference for FTS forecasting and interdisciplinary fusion research against the background of computational intelligence and big data. The literature survey reveals that the most commonly used models for prediction involve long short-term memory (LSTM) and hybrid methods. The main contribution of this paper is not only building a systematic program to compare the merits and demerits of specific FTS forecasting models but also detecting the importance and differences of each model to help researchers and practitioners make good choices. In addition, the limitations to be addressed and future research directions of ML models’ adoption in FTS forecasting are identified.

Stock market prediction and portfolio composition using a hybrid approach combined with self-adaptive evolutionary algorithm

This work presents a new approach to maximize financial market investment returns. It incorporates two Evolutionary Algorithms (EAs) combined with fundamental and technical investment strategies. The first EA (simple) maintains its evolutionary parameters static during evolution. The second (self-adaptive) introduces the variation operators’ parameters’ values in the representation for them to evolve. The EA is responsible for optimizing the weight that financial ratios from the F-Score have on composing static/dynamic portfolios. Furthermore, it is also responsible for defining the importance that selected technical indicators have on revealing the best timing for market positions placement. Results showed that both cases surpassed the S&P500 returns, performing their best results using a self-adaptive EA combined with a static portfolio and a sliding window of 2 years of train/test. The technical case study showed better results in “bear markets” since it predicted some market declines. Its best subtest achieved returns on average 2.2x and in its best 3.5x higher than the benchmark. Its Sharpe Ratio achieved, on average, 4.9x and in its best 9x higher results than the benchmark. The fundamental case study displayed best in the “bull market”, achieving high market prices. Its best subtest achieved returns on average 2.4x and in its best 3.2x higher than the benchmark. Its Sharpe Ratio achieved, on average, 4.4x and in its best 6.5x higher results than the benchmark.

Tail risk, systemic risk and spillover risk of crude oil and precious metals

The relationship between oil prices and metal prices has been extensively investigated. However, the tail risk, systemic risk and spillover risk of oil prices have not been investigated via extreme value theory (EVT). We use this novel approach to determine the tail risk of oil, precious metals, how much risk they pose to the financial system and to what extent a shock in oil prices spill over to other precious metals as well as from the financial system. We use long time series of daily data from 1st January 1987 to 31st December 2021 as long time series is required for the EVT. The data is based on the total return index (RI) of four precious metals including gold, platinum, palladium and silver. Our results show that the tail risk of these metals is lower during the crisis period except the Covid-19 pandemic crisis. Most importantly, gold is a safer asset due to the lowest tail risk among four precious metals, indicating the claim that gold is a precious asset to mitigate the returns during market downturns and acts as a ‘safe haven’. Moreover, we also find that extreme systemic risk (tail-β) for crude oil and selected precious metals reduces during crisis period. This is also recognising the fact that these commodities act as a prospective asset for portfolio diversification to hedge against financial assets' volatility. Finally, the spillover risk among crude oil and selected precious metals varies over time, especially during the crisis period and crude oil is an important stimulator of the spillover risk for precious metals. By using our findings, financial market investors can improve their investment planning to attain the maximum advantage of portfolio diversification. Financial managers can further apply these results in forecasting to estimate future global oil market trends for improving their hedging skills and portfolio performance.

The companies in the European energy sector: Hierarchical Tree Analysis

The energy stock market is characterized by high returns and strong volatility, and little is known about possible interactions between these companies in the literature. We studied the time series of 18 European energy companies’ daily stock prices during 2008–2018, including oil and natural gas prices. To analyze the relationship between the companies’ stocks in the study, the Minimal Spanning Tree Method was used. Results pointed that companies like Engie and OMV, ENI, and Total possibly promote cooperation dynamics, namely clustering. Iberdrola, Total, and OMV companies play an essential role in the network, as an element of linkage and influence that can coordinate the information between the companies’ stocks. Displaying the most relevant links and interactions between them is important for financial market investors and policymakers in terms of competitiveness.

Exploring the Linkages between Financial Literacy, Behavioral Biases, and Stock Market Decisions

The aim of this article is to find out the linkages between financial literacy, behavioral biases, and stock market investment decisions. Further, the primary objective is divided into two parts; first, the relationship between financial literacy and behavioral biases, namely cognitive biases (representative bias, hindsight bias, mental accounting bias, and anchoring bias) and emotional biases (overconfidence, disposition, herding, and familiarity bias) was investigated. Second, the relationship between above stated behavioral biases and stock market investment decisions were explored. To satisfy the objectives of the study, a descriptive cross-sectional research design with judgmental sampling was followed. The data were collected from 477 Indian stock market investors. We found that financial literacy significantly negatively influences all the cognitive biases whereas it does not influence emotional biases. All the cognitive and emotional biases significantly impact the stock market investment decisions. Additionally, it was noticed financial literacy significantly reduces hindsight bias whereas overconfidence bias highly influences the stock market investment decisions.

Housing demolition and financial market investment: evidence from Chinese household data

ABSTRACT This paper estimates the impact of housing demolitions on households’ investment behaviour in financial markets in China using a large microeconomic data set. This study provides new evidence on the effect of demolitions on families’ investment behaviour from a natural experiment created by housing demolition and compensation events. We find that housing demolitions significantly increase households’ participation in risky financial markets. Households’ portfolio choices in responses to demolitions vary substantially across household characteristics. The affected households in urban areas and those with cash compensation are more likely to participate in risky asset markets, while the affected low-income families decrease investment in financial markets. To understand the underlying mechanisms, we propose a framework of how housing demolitions increase households’ financial asset allocation. Our results indicate that changes in precautionary motive for savings and risk preference hold substantial importance.