Kensei Sakamoto, Hasan Ugur Koyluoglu, Fuat Alican
et al.
In this paper, we define probabilistic measures for venture portfolio performance based on individual outlier probability for each investment and the dependence across investments. This work is inspired by loan portfolio modeling against default risk used in banking. In mathematical terms, we calculate the probability distribution of the sum of N non-homogeneous Boolean outcomes (investments becoming outliers) that are correlated through common factors such as overall market conditions and sector effects. Specifically, we implemented a latent-factor model in which each investment's success is the exceedance of a Gaussian latent variable composed of idiosyncratic returns and returns from interpretable shared factors (stock markets, industry sector indices, geography and founder type). The formulation follows a simulation approach to preserve heterogeneous deal-level success probabilities and uses empirically estimated correlation matrices. When applied to synthetic portfolios, our model reveals that expected outlier counts alone are insufficient statistics for evaluating venture portfolios. Portfolios with identical expected outcomes can exhibit drastically different levels of reliability and risk when various levels and forms of correlation are embedded. Diversification improves the probability of achieving a minimum number of outliers by reducing exposure to common shocks, but at the cost of lower upside, underscoring a fundamental tradeoff between reliability and magnitude of clustered successes. The framework provides a practical bridge between deal-level outlier probability assessment and objective-aware portfolio construction.
Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets.
Alina Tausch, Magdalena Wischnewski, Mustafa Yalciner
et al.
This online-vignette study investigates the impact of certification and verification as measures for quality assurance of AI on trust and use of a robo-advisor. Confronting 520 participants with an imaginary situation where they were using an online banking service to invest their inherited money, we formed 4 experimental groups. EG1 achieved no further information of their robo-advisor, while EG2 was informed that their robo-advisor was certified by a reliable agency for unbiased processes, and EG3 was presented with a formally verified robo-advisor that was proven to consider their investment preferences. A control group was presented a remote certified human financial advisor. All groups had to decide on how much of their 10,000 euros they would give to their advisor to autonomously invest for them and report on trust and perceived dependability. A second manipulation happened afterwards, confronting participants with either a successful or failed investment. Overall, our results show that the level of quality assurance of the advisor had surprisingly near to no effect of any of our outcome variables, except for people's perception of their own mental model of the advisor. Descriptively, differences between investments show that seem to favor a verified advisor with a median investment of 65,000 euros (vs. 50,000). Success or failure information, though influences only partially by advisor quality, has been perceived as a more important clue for advisor trustworthiness, leading to substantially different trust and dependability ratings. The study shows the importance of thoroughly investigating not only trust, but also trusting behavior with objective measures. It also underlines the need for future research on formal verification, that might be the gold standard in proving AI mathematically, but seems not to take full effect as a cue for trustworthiness for end-users.
Tubagus Aryandi Gunawan, Hongxi Luo, Chris Greig
et al.
The chemicals industry accounts for about 5% of global greenhouse gas emissions today and is among the most difficult industries to abate. We model decarbonization pathways for the most energy-intensive segment of the industry, the production of basic chemicals: olefins, aromatics, methanol, ammonia, and chlor-alkali. Unlike most prior pathways studies, we apply a scenario-analysis approach that recognizes the central role of corporate investment decision making for capital-intensive industries, under highly uncertain long-term future investment environments. We vary the average pace of decarbonization capital allocation allowed under plausible alternative future world contexts and construct least-cost decarbonization timelines by modeling abatement projects individually across more than 2,600 production facilities located in four major producing regions. The timeline for deeply decarbonizing production varies by chemical and region but depends importantly on the investment environment context. In the best-of-all environments, to deeply decarbonize production, annual average capital spending for abatement for the next two to three decades will need to be greater than (and in addition to) historical "business-as-usual" investments, and cumulative investment in abatement projects would exceed $1 trillion. In futures where key drivers constrain investment appetites, timelines for decarbonizing the industry extend well into the second half of the century.
Research on the urban process of capital accumulation has typically examined the state and capital as separate actors. This distinction is problematized by a long-standing, increasingly prominent but largely overlooked attempt by state institutions to drive urban development through venture capital (VC) investments. Conceptualized as urban state venturism in this paper, state-driven VC investments reflect at once a riskier extension of urban entrepreneurialism (through their speculative construction of place) and a transposition of state institutions into firm-level drivers of capitalist urbanization (through their roles as profit-oriented investors). To advance research on the urban process of capital accumulation through examining these imbricated state roles, this paper presents a new research agenda that comprises three dimensions, namely (i) the rationale of urban state venturism, (ii) the distribution of profits and risks, and (iii) the extent to which urban state venturism reflects state institutions’ intrinsic commitment to a ‘developmentalist’ ideology. In turn, the agenda foregrounds the value of assessing ‘new’ state capitalism through urban state venturism.
In the context of the Chinese market, foreign cross-border venture capitalists have devised specific strategies to mitigate the challenges associated with the liabilities of foreignness, such as risks and information asymmetry. They have strategically leveraged social capital to not only decrease investment risk but also to influence their investment preferences and behaviors. To investigate the influence of different types of social capital on the investment decisions of cross-border venture capitalists, hypotheses are proposed and tested using regression analysis. Our research reveals several key findings in this regard. Firstly, cross-border venture capitalists with a robust structural social capital network exhibit a greater propensity to invest in early-stage companies. This suggests that well-established connections and partnerships within the Chinese entrepreneurial ecosystem provide a level of comfort and confidence when investing in ventures at their infancy. Interestingly, relational and cognitive social capital, though undoubtedly valuable, do not significantly impact the decision to make early-stage investments. Furthermore, we have observed that venture capitalists with higher levels of structural and cognitive social capital are more inclined to form syndications. Collaborative partnerships and shared knowledge networks seem to be crucial factors that drive syndication decisions. Lastly, venture capitalists endowed with substantial structural and relational social capital tend to allocate larger investment amounts, signifying the influence of business or personal relationships and network connections on the scale of their investments.
The need to decarbonize the energy system has intensified the focus on district heating networks in urban and suburban areas. Therefore, exploring transformation pathways with reasonable trade-offs between economic viability and environmental goals became necessary. We introduce a network-flow-based model class integrating unit commitment and long-term investment planning for multi-energy systems. While the integration of unit commitment and investment planning has been applied to multi-energy systems, a formal introduction and suitability for the application of long-term portfolio planning of an energy provider on an urban scale has yet to be met. Based on mixed integer linear programming, the model bridges the gap between overly detailed industrial modeling tools not designed for computational efficiency at scale and rather abstract academic models. The formulation is tested on Berlin's district heating network. Hence, the challenge lies in a large number of variables and constraints and the coupling of time steps, for example, through investment decisions. A case study explores different solutions on the Pareto front defined by optimal trade-offs between minimizing costs and CO2 emissions through a lexicographic optimization approach. The resulting solution catalog can provide decision-makers valuable insights into feasible transformation pathways, highlighting distinctions between robust and target-dependent investments.
In our study, we investigate reinsurance issues and optimal investment related to derivatives trading for a mean-variance insurer, employing game theory. Our primary objective is to identify strategies that are time-consistent. In particular, the insurer has the flexibility to purchase insurance in proportion to its needs, explore new business, and engage in capital market investments. This is under the assumption that insurance companies’surplus capital adheres to the classical Cramér-Lundberg model. The capital market is made up of risk-free bonds, equities, and derivatives, with pricing dependent on the underlying stock’s basic price and volatility. To obtain the most profitable expressions and functions for the associated investment strategies and time guarantees, we solve a system of expanded Hamilton–Jacobi–Bellman equations. In addition, we delve into scenarios involving optimal investment and reinsurance issues with no derivatives trading. In the end, we present a few numerical instances to display our findings, demonstrating that the efficient frontier in the case of derivative trading surpasses that in scenarios where derivative trading is absent.
This study analyzes the market value of listed companies in Mainland China across different industries, including capital-intensive, labor-intensive, technology-intensive, and other industries. A generalized neoclassical investment model that considers physical capital, labor, and knowledge capital as input variables is built to theoretically decompose firm value. The empirical results indicate that knowledge capital accounts for an increasing proportion of the market value of companies, rising sharply from 21.5% in 2009 to 37.9% in 2018. In contrast, the share of labor in enterprise market value has been decreasing year by year, dropping from 56.5% in 2009 to 36.4% in 2018. The share of physical capital in enterprise market value remains relatively stable. Based on these findings, the study simulates the optimal investment behaviors and their influence on the firm value of various types of enterprises, providing valuable insights for investment decision-making for managers in different industries.
We investigate the elasticity of portfolio investment of ASEAN and OECD members to geographical distance in a gravity model utilizing a bilateral panel of 86 reporting and 241 counterparty countries/territories for 2007-2017. We find that the elasticity is more negative for ASEAN than OECD members. The difference is larger if we exclude Singapore. This indicates that Singapore's behavior is distinct from other ASEAN members. While Singapore tends to invest in distant OECD countries, other ASEAN members tend to invest in nearby countries. Our study sheds light on the role of a regional financial center in global finance.
This article concerns the optimal choice of flat taxes on labor and capital income, and on consumption, in a tractable economic model in which agents are subject to idiosyncratic investment risk. We identify the tax rates which maximize welfare in stationary equilibrium while preserving tax revenue, finding that an increase in welfare equivalent to a permanent increase in consumption of nearly 7% can be achieved by only taxing capital income and consumption. The Domar-Musgrave effect explains cases where it is optimal to tax capital income. We characterize the dynamic response to the substitution of consumption taxation for labor income taxation.
Olha Rudachenko , Vіktorіya Svitlychna , Svitlana Perminova
et al.
The object of the study is the investment activity of railway transport enterprises. The purpose of the work is to forecast the investment activity of railway transport enterprises by using economic-mathematical methods and models, namely trend models. The information base of the study is the data of the State Statistics Service and the Ministry of Finance. It has been proven that the transport industry is one of the important branches of the national economy, which provides a logistic connection from the transportation of passengers to the transportation of goods. On the basis of economic-mathematical models (one-dimensional trend forecasting methods), a conceptual scheme for building forecast values of investment activities of railway transport enterprises has been suggested, with the help of which it is possible not only to forecast capital investments but also to propose measures for their increase. The results of investment forecasting by funding sources are given (at the expense of state budget funds, local budget funds, own funds of enterprises and organizations, bank loans and other loans, funds of foreign investors, funds of domestic investment companies, funds, etc., public funds for housing construction, other funding sources). Investments are distinguished by types of economic activity: "Transport, warehousing, postal and courier activities". The received forecast values of investment activity have been interpreted. Measures to increase the investment activity of railway transport enterprises are proposed. The results of the study can form the basis of the formation of the state strategy for the regional development of the country and, separately, in the development of plans and recommendations for the development of railway transport enterprises at different levels of management.
Ionut Marius CROITORU, Cosmin Alexandru SPIRIDON, Constantin REBEDEA
et al.
Performance can be seen as a concept that, after six decades of studies and approaches, is still seeking clarification. Over time, the performance has been viewed from several perspectives such as: the equivalent of organizational effectiveness, adaptability, sensitivity, and productivity, up to concepts from the economic-financial sphere such as: increasing added value, profitability, productivity, indebtedness, and solvency. If we look at performance as a term, we notice that it presents a certain imprecision and is rarely defined. In DEX “performance” is represented as “the outcome (especially good) obtained by someone in a sports competition”, “special achievement in a field of activity”, or “the best result obtained by a technical system…” (DEX, 1998). The meaning of word performance comes from the English language from the verb “to perform” which according to the translation made by google translate means to do something regularly, to fulfil an obligation, to execute a contract, to perform in the activity you carry out, to achieve the work. If we look at the word “performance” not from the perspective of the verb but of the subject, this approach leads us to the goal and the way in which an organization achieves its proposed objectives, a fact that can be viewed from the point of view of effectiveness and productivity.
The indicators that are presented can be used to measure the economic-financial performance of the entities from a relative perspective and some of their limits have been identified in absolute size. An important trend that manifests itself in the field of measuring the economic-financial results of companies is represented by the increasing importance of intangible assets and intellectual capital, which must be reflected with the help of specific indicators. With the development of new technologies in the information field, many of the work tools of the industrial era have become outdated. The economic entities can no longer obtain long-term competitive advantages only through the rapid assimilation of new technologies or through very good management of financial resources. The ability of an economic entity to mobilize and exploit its intangible assets has become much more important than investments in fixed assets. The difficulties in the financial evaluation of some elements of the business activity such as the ability to organize production, the knowledge and skills of employees, their motivation, flexibility, customer loyalty, available databases, care for the environment, etc., have not allowed that these assets to be recognized as such in the companies’ balance sheets, although they are essential in the present and future competitive environment for the success of economic entities.
Tamy Ahmed Ali Al-Binali, Ahmet Faruk Aysan, Hasan Dincer
et al.
Determining the right merger strategy for banks is an important step. In this way, risks can be managed more effectively, and long-term financial performance can be achieved. However, there are many different factors that affect this process. It is not optimal for banks to consider all factors due to budget constraints. In this context, it is important to determine the most important ones among these criteria. Accordingly, the purpose of this study is to evaluate alternative merger strategies for banks. For this purpose, 12 different Sustainable Development Goals (SDGs)-based criteria are selected. Multi stepwise weight assessment ratio analysis (M-SWARA) methodology is used to compute the weights of these items. The main contribution of this study is that the implications of the merger process on SDGs can be examined. Furthermore, a new methodology (M-SWARA) is proposed in this study that has an increasing impact on the methodological originality. The findings indicate that increasing profitability has the greatest weight (0.095). Similarly, market share is found as the second most critical factor (0.092) for merger decisions in the banking industry. A profitable bank can attract more investors and with the help of this situation it can be much easier to raise capital and access funding from capital markets. These issues can be used to finance projects that align with SDGs, such as renewable energy, affordable housing, or clean water initiatives. In addition to this situation, profitability can also have a positive impact on innovation and technological advancement. With sufficient resources, a bank can invest in research and development, technological infrastructure, and innovative products and services. Owing to these investments, sustainable development can be promoted.
In addition to being familiar with policies, high investment returns also require extensive knowledge of relevant industry knowledge and news. In addition, it is necessary to leverage relevant theories for investment to make decisions, thereby amplifying investment returns. A effective investment return estimate can feedback the future rate of return of investment behavior. In recent years, deep learning are developing rapidly, and investment return prediction based on deep learning has become an emerging research topic. This paper proposes an embedding-based dual branch approach to predict an investment's return. This approach leverages embedding to encode the investment id into a low-dimensional dense vector, thereby mapping high-dimensional data to a low-dimensional manifold, so that highdimensional features can be represented competitively. In addition, the dual branch model realizes the decoupling of features by separately encoding different information in the two branches. In addition, the swish activation function further improves the model performance. Our approach are validated on the Ubiquant Market Prediction dataset. The results demonstrate the superiority of our approach compared to Xgboost, Lightgbm and Catboost.
This paper studies finite-time optimal consumption-investment problems with power, logarithmic and exponential utilities, in a regime switching market with random coefficients, subject to coupled constraints on the consumption and investment strategies. We provide explicit optimal consumption-investment strategies and optimal values for the problems in terms of the solutions to some diagonally quadratic backward stochastic differential equation (BSDE) systems and linear BSDE systems with unbound coefficients. Some of these BSDEs are new in the literature and solving them is one of the main theoretical contributions of this paper. We accomplish the latter by applying the truncation, approximation technique to get some a priori uniformly lower and upper bounds for their solutions.