Hasil untuk "Capital. Capital investments"

A. Nölke, A. Vliegenthart

R. Morck, B. Yeung, Minyuan Zhao

Max Roser

U. Doraszelski, J. Jaumandreu

Majid Aghaei, Amin Razinataj

Given the interconnected nature of financial markets, understanding the relationships among them is essential for investors and traders in selecting optimal portfolios, and for policymakers in adopting appropriate monetary and financial policies. The present study aimed to investigate the interrelationship between risk and return, as well as their spillover effects, between Iran’s stock market and competing markets-namely the foreign exchange, gold, and housing markets-under varying bullish and bearish market conditions. The analysis relied on monthly data from 2011 to 2022, as well as a multivariate GARCH model. The results showed significant spillover effects of returns and volatility from the foreign exchange market to the stock market during both bullish and bearish phases of the foreign exchange market. In addition, return spillovers from the stock market to the foreign exchange market were also observed across both market conditions, underscoring the strong interdependence between these two markets in Iran. However, the study found no evidence of return spillovers from the stock market to the gold market under either market condition. In contrast, return and volatility spillovers from the gold market to the stock market were confirmed in both bullish and bearish phases of the gold market. The results did not confirm the return and volatility spillover effects from the housing market to the stock market under either bearish or bullish conditions in the housing market. However, a return spillover from the housing market to the stock market was observed during bearish conditions in the stock market. This suggests that investors in Iran’s housing and stock markets likely belong to two distinct spectrums.IntroductionThe existence of strong and efficient financial markets, supported by appropriate and active organizations, plays a critical role in promoting investment, economic growth, and development. In recent years, the analysis of inter-market relationships has gained significant attention from capital market practitioners and researchers. Empirically modeling and examining these relationships is essential for investors seeking to implement effective investment and hedging strategies, particularly for portfolio diversification. According to financial theories and prior research, when two markets are weakly correlated, external shocks in one market have less impact on the other. As a result, investors can reduce their risk by diversifying their portfolios across such markets (Kundu & Sarkar, 2016). Asset prices in financial markets are inherently volatile, influenced by relevant market developments as well as sudden, unexpected changes triggered by domestic and global economic, social, and political events (Kang et al., 2011). This volatility often prompts investors to adjust the composition of their asset portfolios (Attarzadeh et al., 2022). This phenomenon is referred to as volatility spillover (Pandey & Vipul, 2018), which can both exacerbate the turmoil in the crisis-stricken market, and transmit volatilities and shocks to other markets (Khalifa et al., 2014). Consequently, the magnitude of price volatility in a given market is influenced not only by its own historical fluctuations but also by the volatility of other markets (Zhang et al., 2008). This issue has gained increasing importance in today’s economies, where advanced communication systems and the interdependence of financial markets have shaped the nature of markets.Understanding the mechanisms of interdependence and volatility and return spillovers among assets is crucial for several reasons, such as assessing market efficiency, optimizing asset portfolios, managing risk, and regulating the market. In other words, accurate identification of the behavior of asset returns and price volatility-along with their interrelationships-is essential for optimal resource allocation, accurate pricing of financial assets, optimal selection of asset portfolios, and better forecasts of future price movements (Hassan & Malik, 2007; Poon & Granger, 2003). Furthermore, as shown by Fabozzi and Francis (1978), the beta coefficient in the Capital Asset Pricing Model (CAPM) can vary across different market conditions, such as during bullish versus bearish markets or periods of high versus low volatility. A review of developments in Iran’s stock, foreign exchange, gold, and housing markets reveals significant and sudden changes in asset prices and heightened volatility, especially in recent years. Due to the economic stagnation and high inflation, many investors in the Iranian economy have turned to the stock, foreign exchange, and gold markets as alternative investment options. Understanding the spillover of volatility and returns across these markets-under varying market conditions-is essential for assessing market efficiency, selecting asset portfolios, and determining asset pricing. Accurately identifying and analyzing the behavior of price volatility and returns enables policymakers to adopt appropriate regulatory policies. This study aimed to examine the interrelationships of risk and return, as well as their spillovers between the stock market and other competing markets (foreign exchange, housing, and gold) under different market conditions, specifically bullish and bearish phases. This focus on varying market conditions ensures the novelty of the present study in terms of its approach.Materials and MethodsThe current study employed a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model to investigate the mutual relationship between the return and risk of Iran’s stock market and its competing markets. The multivariate GARCH model is designed to model the simultaneous volatility of two or more variables. The model examines the relationship between the volatilities of two series of variables, in which the conditional variance is modeled as a function of its own lagged value and the lagged value of its error residuals (Suri, 2013). To analyze the spillover effects of volatility and returns across different markets, the study used the VAR-BEKK-GARCH model. First, the vector autoregression (VAR) method was used to estimate the research model. Then, the system of mean and variance equations was constructed and estimated using the VAR(1)-BEKK(1,1) method. Moreover, the Hannan-Quinn, Schwarz-Bayesian, and Akaike selection criteria were used to select the optimal model intervals. Based on these criteria, the VAR(1)-BEKK(1,1) model was selected to estimate all the models. The equations in the VAR-BEKK-GARCH model were divided into two main categories: the mean equation and the variance equation. The mean equation estimated the spillover effects or the contagion rate of market returns, while the variance equation estimated the spillover or contagion rate of volatility and shocks among the variables.Results and DiscussionAccording to the results, during bullish and bearish conditions in the stock and foreign exchange markets, there is a noticeable spillover effect in both returns and volatility between these two markets. This indicates a strong interdependence between the foreign exchange and stock markets. The findings also showed that under normal market conditions, there is no significant spillover of returns or volatility between these markets. However, during periods of bullish or bearish markets, spillover effects become evident. Regarding the relationship between the stock market and the gold market, the study found no significant spillover of returns from the stock market to the gold market under either bullish or bearish stock market conditions. In contrast, during both bullish and bearish conditions in the gold market, there is a clear spillover effect of returns and volatility from the gold market to the stock market. This finding seems logical, given the high correlation between the gold market and the foreign exchange market in Iran. A positive or negative shock in the foreign exchange market is likely to be transmitted to the gold market, causing return and volatility spillover effects between the gold market and the stock market. In the case of the housing market, the study found no evidence of return or volatility spillovers from the housing market to the stock market, under either bullish or bearish housing market conditions. This could be attributed to the differences in the investor base between the two markets in Iran. Considering the records of high housing returns in Iran, housing market investors tend to be long-term participants who are less responsive to short-term volatility. Furthermore, the lower liquidity of the housing market may contribute to the lack of return spillovers to the stock market, especially during bearish housing market conditions. The results also confirmed a spillover effect of returns from the stock market to the housing market during bearish stock market conditions. This suggests that during downturns in the stock market, investors may shift their capital toward investment in the housing sector, which historically has offered high long-term returns in Iran.ConclusionToday, the significance of the gold, foreign exchange, housing, and stock markets is widely recognized due to their vital role in attracting capital and driving the economic growth and development of countries. Ensuring the proper functioning and coordination of these markets-by strengthening their interconnections and improving their resilience and flexibility-can not only enhance capital attraction and allocation but also serve as a defensive shield against economic shocks. This, in turn, helps mitigate the impact of spillover risks and increases the overall resilience of the economy to shocks. Given the importance for market practitioners and policymakers to understand the relationships among key markets of interest to investors in the Iranian economy, the current study investigated the spillover effects of returns and volatilities between the stock market and its major competing financial markets in Iran-namely the foreign exchange, gold, and housing markets. The analysis focused on both bullish and bearish market conditions, using the VAR-BEKK-GARCH model and monthly data from the period 2011 to 2022. The results indicated a significant interdependence and spillover effect of returns and volatilities between the foreign exchange and stock markets under both bullish and bearish conditions. However, this relationship was not evident under normal market conditions. In the gold market, spillover effects of returns and volatilities toward the stock market were observed only during bullish and bearish phases, but the reverse was not confirmed. As for the housing market, no significant spillover from this market to the stock market was detected, which may be attributed to differences in the investor base across the two markets. Nonetheless, during bearish phases in the stock market, a portion of capital flows into the housing market, reflecting investors’ preference for real estate during stock market downturns. In light of the findings, it is recommended that policymakers work to strengthen economic stability and manage foreign exchange market volatility in order to limit the transmission of shocks across markets. In addition, maintaining investor confidence in the stock market through appropriate incentives is crucial. Special attention should also be given to the correlation between the foreign exchange and gold markets, improvement in the liquidity in the housing market, and development of market forecasting systems.

Murad Bagirzadeh, Olena Churikanova, Marina Celika et al.

The role of foreign direct investment (FDI) in achieving sustainable development has become increasingly significant, especially in transition economies such as Lithuania. Given the dual challenges of economic modernization and environmental sustainability, assessing the impact of FDI is essential. This study aims to evaluate the influence of FDI on economic, social, and environmental indicators of sustainable development in Lithuania over the period 2014–2023, with additional insights from Q1–Q3 of 2024. The research methodology involves a descriptive statistical analysis of dynamic time series, correlation-regression modeling, and international comparisons. Data sources include the official statistical portal of Lithuania, Eurostat, and various policy and investment climate reports. The results reveal that accumulated FDI in Lithuania increased by 170%, and FDI per capita grew by 172.9% over the ten-year period. High positive correlations were identified between FDI and key economic indicators: GDP (r = 0.9782), industrial production (r = 0.9441), and exports (r = 0.9600). Social outcomes also improved markedly, with average monthly earnings increasing by 184.3%, absolute poverty falling from 14.9% to 6.5%, and gross per capita income rising by 188.4%. While environmental outcomes deteriorated moderately, greenhouse gas emissions rose by 63.3% and hazardous waste by 31.5%, the correlations with FDI remained weak to moderate, indicating a relatively limited adverse environmental impact. Overall, the study confirms that FDI has substantially contributed to Lithuania’s economic and social progress, though further policy refinements are needed to align FDI with long-term environmental sustainability goals.

Hadis Javanmard, Ahmad Khodamipour, Omid Pourheidari

The correct understanding of behavioral factors affecting individual investment decisions in the stock market is one of the main goals of this research. This accurate knowledge will increase the efficiency of the market, and the financial resources will be adequately equipped and allocated. Finally, it will save resources in this market. Therefore, the current research seeks to investigate and test the effect of risk aversion based on the past performance of stocks in the financial behavior of investors. In this research, a regression model was used to test the hypotheses. The statistical population of this research is all the firms accepted in the Tehran Stock Exchange over 7 years, from 2016 to 2022. Considering the research period, the total number of data points is 980 years—firm (observation). Also, in this research, the stock price was used to evaluate the variable of past stock performance, which has not been paid attention to in past behavioral financial research due to its importance for investors' decision-making. The analysis of the research hypotheses showed that risk aversion has a positive relationship with investors' decision-making. In addition, the study of research data indicates that the past performance of stocks has a positive moderating role in the relationship between risk aversion and investors' decision-making.

Saizhuo Wang, Hao Kong, Jiadong Guo et al.

The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This gap hinders research progress and limits the practical application of academic innovations. We present QuantBench, an industrial-grade benchmark platform designed to address this critical need. QuantBench offers three key strengths: (1) standardization that aligns with quantitative investment industry practices, (2) flexibility to integrate various AI algorithms, and (3) full-pipeline coverage of the entire quantitative investment process. Our empirical studies using QuantBench reveal some critical research directions, including the need for continual learning to address distribution shifts, improved methods for modeling relational financial data, and more robust approaches to mitigate overfitting in low signal-to-noise environments. By providing a common ground for evaluation and fostering collaboration between researchers and practitioners, QuantBench aims to accelerate progress in AI for quantitative investment, similar to the impact of benchmark platforms in computer vision and natural language processing.

Hui Chen, Giovanni Gambarotta, Simon Scheidegger et al.

We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors' portfolio choices. We use cutting-edge machine learning methods to quantify the optimal investment policies over the life cycle of a fund. Moreover, our model offers regulators a tool for precisely quantifying the trade-offs when setting risk-based capital charges.

Lin Li

Using an intangible intensity factor that is orthogonal to the Fama--French factors, we compare the role of intangible investment in predicting stock returns over the periods 1963--1992 and 1993--2022. For 1963--1992, intangible investment is weak in predicting stock returns, but for 1993--2022, the predictive power of intangible investment becomes very strong. Intangible investment has a significant impact not only on the MTB ratio (Fama--French high minus low [HML] factor) but also on operating profitability (OP) (Fama--French robust minus weak [RMW] factor) when forecasting stock returns from 1993 to 2022. For intangible asset-intensive firms, intangible investment is the main predictor of stock returns, rather than MTB ratio and profitability. Our evidence suggests that intangible investment has become an important factor in explaining stock returns over time, independent of other factors such as profitability and MTB ratio.

Nadya Wells, Vinh-Kim Nguyen, Stephan Harbarth

Abstract The need for novel antibiotics to combat emerging multi-drug resistant bacterial strains is widely acknowledged. The development of new therapeutic agents relies on small and medium-sized biotechnology enterprises (SMEs), representing 75% of the late-stage pipeline. However, most SME sponsors of an antibacterial approved by the FDA since 2010 have gone bankrupt, or exited at a loss, below investment cost. Uncovering financial flows related to the development and commercialisation of a single drug is complex and typically untransparent. There is therefore a lack of empirical research on the financial vulnerabilities of these critical SMEs. The development of plazomicin by Achaogen (2004–2019) entailed financial disclosures as a public company enabling application of financial analysis methods to: determine quantum and timing of public and private investments; quantify development costs; and provide a deeper understanding of the role of capital market dependency in exacerbating pipeline fragility. Achaogen’s widely cited bankruptcy, and plazomicin’s commercialisation failure, created a perception that novel antibiotics have zero market value, causing investors to question the SME developer business model. Our analysis of Achaogen’s inability to fund commercialisation suggests three key implications for the antibiotic investment ecosystem: (1) novel antibiotics with narrow approval for small patient populations affected by severe resistant infections cannot be successfully commercialised in the current US antibiotic market; (2) SMEs need incentive payments structured to enable them to survive the commercialisation cashflow drought, and (3) these changes are necessary to restore industry and financial investor confidence in the antibiotic SME development model. Achaogen’s demise demonstrates that proposals to incentivise innovation, e.g. by providing one-off payments at registration, may be insufficient to ensure access to novel antibiotics developed by SMEs. In plazomicin’s case, moreover, US government biosecurity investments have not resulted in access, as the Indian and Chinese companies which bought post-bankruptcy rights have not widely commercialised the drug. This study is timely as new market-based incentives are currently being proposed by the US, EU, Canada and Japan. In order to make further government funding effective, ensuring access, not only innovation, these must support sustainable financial models for the SMEs critical to novel antibiotic development.

Zahra Nemati, Ali Mohammadi, Ali Bayat et al.

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.

R. Layard

Shuo Han, Yinan Chen, Jiacheng Liu

Market traders often engage in the frequent transaction of volatile assets to optimize their total return. In this study, we introduce a novel investment strategy model, anchored on the 'lazy factor.' Our approach bifurcates into a Price Portfolio Forecasting Model and a Mean-Variance Model with Transaction Costs, utilizing probability weights as the coefficients of laziness factors. The Price Portfolio Forecasting Model, leveraging the EXPMA Mean Method, plots the long-term price trend line and forecasts future price movements, incorporating the tangent slope and rate of change. For short-term investments, we apply the ARIMA Model to predict ensuing prices. The Mean-Variance Model with Transaction Costs employs the Monte Carlo Method to formulate the feasible region. To strike an optimal balance between risk and return, equal probability weights are incorporated as coefficients of the laziness factor. To assess the efficacy of this combined strategy, we executed extensive experiments on a specified dataset. Our findings underscore the model's adaptability and generalizability, indicating its potential to transform investment strategies.

Julian Hölzermann

This paper studies dynamic asset allocation with interest rate risk and several sources of ambiguity. The market consists of a risk-free asset, a zero-coupon bond (both determined by a Vasicek model), and a stock. There is ambiguity about the risk premia, the volatilities, and the correlation. The investor's preferences display both risk aversion and ambiguity aversion. The optimal investment problem admits a closed-form solution. The solution shows that the ambiguity only affects the speculative motives of the investor, representing a hedge against the ambiguity, but not the hedging of interest rate risk. An implementation of the optimal investment strategy shows that ambiguity aversion helps to tame the highly leveraged portfolios neglecting ambiguity and leads to strategies that are more in line with popular investment advice.

Indrani Bose

The well-known Solow growth model is the workhorse model of the theory of economic growth, which studies capital accumulation in a model economy as a function of time with capital stock, labour and technology-based production as the basic ingredients. The capital is assumed to be in the form of manufacturing equipment and materials. Two important parameters of the model are: the saving fraction $s$ of the output of a production function and the technology efficiency parameter $A$, appearing in the production function. The saved fraction of the output is fully invested in the generation of new capital and the rest is consumed. The capital stock also depreciates as a function of time due to the wearing out of old capital and the increase in the size of the labour population. We propose a stochastic Solow growth model assuming the saving fraction to be a sigmoidal function of the per capita capital $k_p$. We derive analytically the steady state probability distribution $P(k_p)$ and demonstrate the existence of a poverty trap, of central concern in development economics. In a parameter regime, $P(k_p)$ is bimodal with the twin peaks corresponding to states of poverty and well-being respectively. The associated potential landscape has two valleys with fluctuation-driven transitions between them. The mean exit times from the valleys are computed and one finds that the escape from a poverty trap is more favourable at higher values of $A$. We identify a critical value of $A_c$ below (above) which the state of poverty (well-being) dominates and propose two early signatures of the regime shift occurring at $A_c$. The economic model, with conceptual foundation in nonlinear dynamics and statistical mechanics, shares universal features with dynamical models from diverse disciplines like ecology and cell biology.

Ang Li, Yubo Wang, Lei Fan et al.

In this paper, we study the strategic investment problem of battery energy storage systems (BESSs) in the wholesale electricity market from the perspective of BESSs owners. Large-scale BESSs planning without considering the possible wholesale market price change may result in possible locational marginal price (LMP) changes. To overcome such limits, we propose a three-phase approach for the BESS investment problem. In Phase-1, we conduct a search for the optimal BESS configurations via a congestion-based heuristics and Bayesian optimization. In Phases 2 and 3, we alternatively dispatch optimization tasks to optimize the wholesale market clearing for LMPs and identify the optimal schedule for BESSs operations. We validate the model using a ten-year simulation on the Electric Reliability Council of Texas (ERCOT) market. Experimental results show that the Bayesian optimization model runs 16 times faster than the grid search model in achieving the same solution quality. Further, the iterative method demonstrates that the model without considering possible LMP changes makes a 21% profit overestimation.

D. A. Borisyuk, O. E. Astafyeva

The increasing volume of construction financing, dictated by the national development goals of the Russian Federation, determines the increased role of rational choice of methods, mechanisms and tools of financial support for construction in order to make managerial decisions on the implementation of projects aimed at achieving the targets within the declared national development benchmarks of the Russian Federation. The aim of the study is to summarise and simplify the current methods, mechanisms and tools of financial support for construction within the framework of project implementation. A review of the current model of financial support for construction in the Russian Federation has been carried out. The main aspects of how to finance construction have been considered. A brief description of the main conditions of the envisaged mechanisms and instruments of financial support for construction has been provided. The analysis confirms the positive trend of the current construction financing model in the project implementation, creating conditions to stimulate the economy by reallocating funds to the real sector and providing a variety of tools in choosing construction financing methods to make rational management decisions on project implementation. The results of this study can be used by public authorities involved in the allocation and use of funds, financial institutions and other stakeholders to prepare management decisions on construction financing in project implementation.

Günter Hofbauer, Andrejs Limanskis

The purpose of this paper is to find out the drivers of and barriers to Foreign Direct Investments (FDI) within the scope of sustainability considerations in order to add new knowledge to the scientific discussion. The sustainability of FDI deserves attention due to the importance of cross-border flows of capital in major fields of the economy and politics. The level of sustainability awareness of investors, however, is not known. Pioneering field research was conducted among 160 German companies in Latvia. Methodologically, a literature review, FDI statistics, primary data from an Internet-based questionnaire, the statistical evaluation of responses with SPSS, and an analysis of correlations measured by Cramér’s V, based on Pearson’s chi-squared statistic, were used. A gap in the scientific literature pertaining to sustainability drivers and outcomes in relation to FDI was addressed. The empirical part disclosed the level of mainstream implementation of sustainability in the operations of German investors in Latvia. The understanding of the sustainability awareness of investors can be used at the level of political decision making in Latvia and other countries in order to promote the sustainability of incoming FDI. A remarkable level of measurable awareness of sustainability issues among German entrepreneurs in Latvia was found. However, there is still room for improvement and further research.

Hyungbin Park, Heejun Yeo

This paper investigates dynamic and static fund separations and their stability for long-term optimal investments under three model classes. An investor maximizes the expected utility with constant relative risk aversion under an incomplete market consisting of a safe asset, several risky assets, and a single state variable. The state variables in two of the model classes follow a 3/2 process and an inverse Bessel process, respectively. The other market model has the partially observed state variable modeled as an Ornstein-Uhlenbeck state process. We show that the dynamic optimal portfolio of this utility maximization consists of m+3 portfolios: the safe asset, the myopic portfolio, the m time-independent portfolios, and the intertemporal portfolio. Over time, the intertemporal portfolio eventually vanishes, leading the dynamic portfolio to converge to m+2 portfolios, referred to as the static portfolio. We also prove that the convergence is stable under model parameter perturbations. In addition, sensitivities of the intertemporal portfolio with respect to small parameters perturbations also vanish in the long run. The convergence rate for the intertemporal portfolio and its sensitivities are computed explicitly for the presented models.