Transaction Cost Model Research Articles

Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

A Method for Calculating the Environmental Protection Cost of Cement Plants Taking into Account the Diffusion Characteristics of Air Pollutants

Abstract In recent years, China’s installed capacity of new energy has been continuously improved, and the problem of new energy consumption has become increasingly prominent. The cement load has considerable demand response potential and can effectively help absorb new energy sources, but its high emission characteristics limit the development of the cement industry. In this context, the establishment of a systematic and refined cement load emission cost model will be of great significance to improve the wind power absorption capacity and promote the low-carbon and environmentally friendly operation of the power system. In this paper, the emission characteristics of SO2, NOx, particulate matter and CO2 from the cement load are analyzed. according to the cement production process. Then, the air pollutant emission cost of the cement plant is described from the aspects of environmental protection tax and environmental protection supplementary cost, and the carbon emission cost of the cement plant is calculated by using the laddered carbon transaction cost model, and finally the emission cost model of the cement plant is constructed.

A Transaction Cost Model of Tax-Motivated Income Shifting into Dot-Sized Tax Havens and an Empirical Examination of E-Commerce Effects

ABSTRACT Our paper develops a new transaction cost model of tax-motivated income shifting. We analytically show that income shifted by U.S. multinational parent corporations into their tax haven subsidiaries differs from predictions based only on tax rate differences because of the transaction costs of shifting income into tax haven subsidiaries. Our model predicts that e-commerce facilitates greater income shifting by lowering the transaction costs of income-shifting operations, which theoretically supports our focus on actual income shifting instead of the traditional focus on effective tax rates alone. Empirically, we find that greater numbers of multinational corporations’ dot-sized tax havens significantly increase profits shifted out of their U.S. parent corporations and also show with a separate estimation that this amount is similar to amounts shifted into their tax haven subsidiaries, consistent with income shifting being a zero-sum strategy. This novel approach documents income shifting materially increasing with e-commerce activities that lower transaction costs. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: H26; L81; F38; M48.

Environmental change and rational failure of the firm: application of an integrated model of transaction costs and dynamic capacities

Environmental change and rational failure of the firm: application of an integrated model of transaction costs and dynamic capacities

Impediments to efficient land reallocation in agriculture: Multi‐agent simulation model of transaction costs and farm retirement

AbstractThis study examines the effect of land exchanges among farm households (FHs) on agricultural sustainability and viability using a multi‐agent simulation model. The initial values of the model are fixed so that the simulation results might reproduce the sequence of land use pattern in Japanese agriculture. The novelty of this study is three‐fold. First, it quantitatively assesses the extent to which transaction costs incurred by lessors and lessees impede the sound development of land rental markets. Second, the simulation model considers inter‐regional heterogeneity in land use patterns and the interactive behavior of FHs regarding land exchanges. Third, the model elucidates the mechanisms by which the equilibrium land rent reaches zero and farmland is abandoned when some FHs retire from farming. Farmland abandonment has become a global phenomenon that has profoundly altered the traditional agricultural landscape in various countries. The present study suggests that the development of land rental markets associated with farm retirement alone does not strengthen agricultural production capacity at the national level. Particularly, if FH retirement progresses to the point where the equilibrium land rent drops to zero, farmland abandonment occurs, which threatens agricultural sustainability and viability. This study concludes that preventing farmland abandonment is a policy issue of the highest priority to ensure global food security and the sustainable development of agriculture.

A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response

To enhance the low-carbon level and economic performance of microgrid systems while considering the impact of renewable energy output uncertainty on system operation stability, this paper presents a robust optimization microgrid model based on carbon-trading mechanisms and demand–response mechanisms. Regarding the carbon-trading mechanism, the baseline allocation method is utilized to provide carbon emission quotas to the system at no cost, and a ladder carbon price model is implemented to construct a carbon transaction cost model. Regarding uncertainty set construction, the correlation of distributed generation in time and space is considered, and a new uncertainty set is constructed based on historical data to reduce the conservative type of robust optimization. Based on the column constraint generation algorithm, the model is solved. The findings indicate that upon considering the carbon-trading mechanism, the microgrid tends to increase the output of low-carbon units and renewable energy units, and the carbon emissions of the microgrid can be effectively reduced. However, due to the increase in power purchase from the distribution network and the increase in carbon transaction costs, the operating costs of the microgrid increase. Secondly, through the utilization of demand–response mechanisms, the microgrid can achieve load transfer between peaks and troughs. It is imperative to establish appropriate compensation costs for demand and response that balances both economic efficiency and system stability. At the same time, due to the time-of-use electricity price, the energy storage equipment can also play a load transfer effect and improve the system’s economy. Finally, sensitivity analysis was conducted on the adjustment parameters of distributed power sources and loads that have uncertain values. A comparison was made between the deterministic scheduling model and the two-stage robust optimization model proposed in this study. It was proved that this model has great advantages in coordinating the economy, stability and low carbon level of microgrid operations.

Research on Day-Ahead Optimal Scheduling Considering Carbon Emission Allowance and Carbon Trading

In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access to new energy, considering the limitations of the carbon emissions from different emission subjects plus the construction of a carbon trading model among the emission subjects, a day-ahead optimal scheduling method that takes carbon emission quotas and carbon trading into account is proposed. Firstly, carbon transaction cost models of thermal power and wind power are constructed, respectively, and a carbon emission quota allocation strategy based on the entropy method is proposed to redistribute the weights of baseline emission factors for the regional power grid. Then, considering the additional carbon emissions of conventional thermal power units caused by wind power access, the carbon trading costs of different types of generation units are calculated on the basis of carbon trading price prediction. Thereafter, a day-ahead optimal scheduling model considering carbon emissions trading is constructed with the objective of minimizing the total cost of the system in the scheduling period. The model is solved as an MINLP problem based on MATLAB 2016a software utilizing CPLEX 12.4. Simulation results verify the correctness and effectiveness of the proposed method.

A multi-objective peak regulation transaction optimization and benefits coordination model for multi-sources coupling system considering flexible load response

Based on the intermittent output and inverse peak regulation characteristics of wind power, a multisource peak regulation transaction optimization model that considers the feasibility of combining thermal power, energy storage, and demand response for both power generation and consumption is proposed in this paper. First, a multisource peak regulation transaction cost model is established by considering flexible load participation in peak regulation and price- and incentive-based demand responses. Subsequently, with the objective of minimizing both the peak regulation cost and wind curtailment rate, a reduced half gradient membership function is selected to transform the multiobjective model. Finally, by analyzing the roles of the different subjects in multisource peak regulation transactions, a compensation mechanism based on the Shapley method is designed, and a local power grid in northeast China is chosen as the simulation object. The results show the following: (1) When the thermal power generators (TPGs) transit from regular peak regulation to deep peak regulation, the wind curtailment rate decreases by 5.10%, and the peak regulation cost increases by $ 0.793 × 106. This indicates that the peak regulation cost of the TPGs and the income of the on-grid wind power need to be balanced. (2) When the energy storage and the demand response are combined for peak regulation, both the peak load regulation cost and wind curtailment rate reach the optimal values, decreasing by $ 0.642 × 106 and 5.72%, respectively, showing cooperative optimization. However, the TPGs require a higher regulation cost, whereas the other subjects achieve incremental benefits. (3) The compensation mechanism assists each subject in obtaining incremental benefits depending on the contribution rate. With the strengthening of the peak regulation of the TPGs, more energy storage and demand response output are introduced to meet the urgent peak regulation requirement, which leads to increased regulation revenue and optimal dispatching. In summary, the proposed transaction and compensation mechanism of multisource peak regulation can be used to balance the peak regulation ability and contributions of different subjects and establish the optimal peak regulation scheme.

Aggregating exponential gradient expert advice for online portfolio selection under transaction costs

As an application of machine learning techniques in the field of portfolio management, online portfolio selection (OLPS) aims at optimising the allocation of wealth in an uncertain environment. When making investment decisions, the transaction cost is such an important factor that investor should not ignore. Thus, this paper extends an existing online portfolio selection strategy Continuous Aggregating Exponential Gradient (CAEG) (Yang et al., 2022) in the presence of transaction costs. The proportional transaction costs model is constructed when the transaction costs are incorporated into the decision-making process, and we call this new strategy . Theoretical guarantee proves that the mean of the logarithmic cumulative wealth of has an asymptotic upper bound with that of its benchmark. The numerical examples demonstrate the impact of transaction costs on the proposed strategy on the one hand, and on the other hand, verify that outperforms other related OLPS strategies and is comparable to its benchmark strategy.

Multiagent-based deep reinforcement learning for risk-shifting portfolio management

The growing popularity of quantitative trading in pursuit of a systematic and algorithmic approach to investment has drawn considerable attention among traders and investment firms. Consequently, an effective computational method for evaluating potential risk factors and returns is crucial for the development of algorithmic trading strategies. In traditional finance and financial engineering research, statistical approaches have been widely applied to quantitative analysis. Meanwhile, investor demand for quantitative hedge funds has surged worldwide. In the current study, the multiperiod portfolio selection problem was considered in terms of the realistic transaction cost model, which is a major concern for quantitative hedge fund managers. We developed a dedicated multiagent-based deep reinforcement learning framework with a two-level nested agent structure to determine effective portfolio management methods with different objectives. In addition, we proposed a specially-designed reward function for investment performance evaluation and a novel policy network structure for trading decision-making. To efficiently identify specific asset attributes in a portfolio, each agent is equipped with a refined deep policy network and a special training method that enables the proposed reinforcement learning agent to learn risk transfer behaviors. The results revealed the effectiveness of our proposed framework, which outperformed several established or representative portfolio selection strategies.

The choice of master international franchising – A modified transaction cost model

This study develops and tests a novel transaction cost model of master international franchising. Based on data from international franchise firms headquartered in six countries, we show that master international franchising is the franchisor’s preferred governance mode under the following conditions: large bilateral franchisor’s and franchisees’ transaction-specific investments, high institutional uncertainty and high behavioral uncertainty. Our model extends the literature by presenting a modified transaction cost model of master international franchising that investigates the bonding effect of bilateral transaction-specific investments and environmental uncertainty as determinants of the franchisor’s choice of international governance mode. In addition, by using primary data from international franchise companies, our study contributes to the transaction cost literature in international business and international franchising that is mainly based on secondary data.

Research on transformation mode of scientific and technological achievements with different transaction costs: taking the electric power industry as an example

Technological innovation and achievement transformation are an important starting point for constructing an industrial innovation ecosystem and coping with hindered external demand. This article starts with the ecological chain of technological achievement transformation, constructs a transaction cost and benefit model of three transformation modes of direct transformation, intermediary participation, and co-construction cooperation, and systematically analyses its transaction costs, benefits, application and the value space of the transaction subject of each mode. With the participation of intermediaries and investors, the "government, industry, university, research, and financial agency" achievement transformation service platform not only reduces transaction costs but also greatly increases returns. Using the advantages of big data and blockchain can obtain more added value than other models, bringing more opportunities for both supply and demand.

The Diversification Benefits of Cryptocurrency Asset Categories and Estimation Risk: Pre and Post COVID-19

We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-COVID-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.

Liquidity Stress Testing in Asset Management - Part 2. Modeling the Asset Liquidity Risk

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.

Reimagining Section 94.1: The Offshore Investment Fund Property Rule

In this article, the author contemplates how section 94.1 of the Income Tax Act (Canada), the offshore investment fund property (OIFP) rule, might be reimagined considering its inefficacy in the face of unique non-resident investment funds. By tracing the anti-avoidance origins of the rule, the author shows that the source of this inadequacy is an expectation overload rather than legislative failure. While the OIFP rule was introduced as a stop-gap designed only to tackle a certain kind of basic intermediation strategy, it is now tasked with the much grander objective of creating a level playing field with respect to the tax treatment of domestic and offshore investment funds. The author shows how this mismatch of aspiration and reality has created the opportunity for savvy investors to manipulate the "motive" test within the rule, and avoid its application, simply by proving themselves to have a sufficiently credible and pervasive alternative reason for investing in an offshore fund (for example, the unique expertise of its manager). The author postulates that this flaw is likely to become more frequently tested as funds specializing in novel asset classes (such as cryptocurrency funds) proliferate and grow. The article closes with the proposition that if the test at the centre of the rule is recast as a purely objective analysis, and certain other changes are made, section 94.1 can be transformed to apply a transaction-cost model that will deter undesirable intermediation attempts and ensure that Canada's revenue authorities get a cut of any profits from superreturn investment opportunities. The author draws on key elements of the ultimately abandoned foreign investment entity rules and certain anti-avoidance provisions from elsewhere in the Act to provide a conceptual foundation for the proposed amendments. He also seeks to demonstrate how revising the OIFP rule in this way will achieve a greater degree of capital export neutrality—a policy thread that runs through the Canadian approach to international taxation—by creating a more (though not wholly) level tax playing field, but without the crippling administrative burden and incomplete tax coverage that may accompany a more capital export neutrality-driven approach.

Risk arbitrage and hedging to acceptability under transaction costs

The classical discrete-time model of proportional transaction costs relies on the assumption that a feasible portfolio process has solvent increments at each step. We extend this setting in two directions, allowing convex transaction costs and assuming that increments of the portfolio process belong to the sum of a solvency set and a family of multivariate acceptable positions, e.g. with respect to a dynamic risk measure. We describe the sets of superhedging prices, formulate several no (risk) arbitrage conditions and explore connections between them. In the special case when multivariate positions are converted into a single fixed asset, our framework turns into the no-good-deals setting. However, in general, the possibilities of assessing the risk with respect to any asset or a basket of assets lead to a decrease of superhedging prices and the no-arbitrage conditions become stronger. The mathematical techniques rely on results for unbounded and possibly non-closed random sets in Euclidean space.

Asset pricing implications of money: New evidence

We provide new evidence on the role of real money balances in terms of explaining equity risk premia by using a rich cross-section of average stock returns (associated with 11 major CAPM anomalies). By estimating Euler equations associated with a cash-in-advance (CIA) model, we find that such model produces substantially smaller pricing errors than the baseline consumption model, while still generating lower estimates of the risk aversion coefficient. The estimates of the parameter governing the share of cash goods are highly significant and plausible in economic terms. A transaction-costs model and a money-in-the-utility model perform considerably worse than the CIA model, both in terms of statistical fit and in terms of the plausibility of the structural parameter estimates. Moreover, a linear version of the CIA model also largely underperforms the corresponding non-linear model.

Error Analysis of Meshfree Approximation in Nonlinear Black-Scholes Model

The transaction cost model of Guy Barles and Halil Mete Soner is incorporated into the standard Black Scholes Equation. The resulting model is solved by a numerical method, called, the meshfree approximation using radial basis function. The errors produced by the scheme are discussed and presented in diagrams and tables.

SHARING ECONOMY IMPACT ON OWNERSHIP TRANSFORMATION AND REGIONAL PERFORMANCE

The phenomenon of sharing economy as a model of productivity growth and transaction costs’ decline considers distributed use of underutilized assets containing excess capacity which is available for other economic agents and demanded by them. Several researchers and mass media foster fears that sharing propagation will facilitate inequality growth and unfair property distribution thus causing property transformations in different territories. Those transformations will obviously follow different patterns and result into a specific structure of a region’s economy resulting from structural shifts between the private and public sectors. Suh an ownership transformation will presumably have specific impact on regional performance, measured traditionally by gross regional product. Yet, GRP is not flexible enough to catch low-scale shifts of regional property complex employment practices as sharing. That is why an alternative approach to regional performance assessment is suggested and implemented. The private sector’s capital expenditure in fixed assets is proved to be subject to change under the novel phenomena like sharing economy which is potentially changing patterns of capital consumption. This would lead to gross regional product changes in short-term and long-term perspective.

Multivariate risk measures in the non-convex setting

Abstract The family of admissible positions in a transaction costs model is a random closed set, which is convex in case of proportional transaction costs. However, the convexity fails, e.g., in case of fixed transaction costs or when only a finite number of transfers are possible. The paper presents an approach to measure risks of such positions based on the idea of considering all selections of the portfolio and checking if one of them is acceptable. Properties and basic examples of risk measures of non-convex portfolios are presented.