Real Market Data Research Articles

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

Whether SORF Is a More Effective Substitute for LIBOR in the Market?

This paper examines the transition from LIBOR to SOFR and its impact on financial markets. LIBOR, once a key benchmark rate, has faced credibility issues, leading to the proposal of new benchmark rates like SOFR. The study investigates whether SOFR is a suitable LIBOR replacement and analyzes its effects on financial markets. Using statistical analysis, including covered interest rate parity theory and hypothesis testing, the study compares the accuracy of SOFR and LIBOR in determining the risk-free rate. The findings support the hypothesis that SOFR calculations more accurately reflect real market data compared to LIBOR. However, the study is limited in its focus on the US dollar market, which may limit its generalizability. Future research could explore the transition to alternative benchmark rates in other currencies and regions. Additionally, incorporating real-time data could provide more current insights into SOFR's effectiveness as a LIBOR replacement. Overall, the transition from LIBOR to SOFR represents a significant change in financial markets. Further research is needed to fully understand its implications. Addressing these limitations and exploring future research directions can enhance our understanding of the transition's impact on the global financial system.

Rare earth recycling and remanufacturing: Impacts on oligopoly markets and industry development from a closed-loop supply chain perspective

The recycling of rare earth elements (REEs) yielded a dual benefit by alleviating supply constraints and contributing to environmental preservation. Independent recyclers (IRs), upon extracting REEs from waste materials, not only supplied the recovered REEs to the REE conglomerates but also engaged in competitive relationships by remanufacturing REE products. This study established a closed-loop supply framework, allowing the IR flexibly to choose between remanufacturing or direct sales strategies. Using the Cournot model under inequation constraints and real market data, to explores the impact of REE acquisition prices and recycling technologies on the closed-loop supply chain (CLSC) and society. The results indicated that: (1) The recycling mode and technology do not affect market share. Compared to the IR, REE conglomerate exhibit significant monopolistic advantages, occupying at least 85.1% of the market share, which can increase to a maximum of 98% as acquisition prices rise. (2) The acquisition price of recycled REE has an “inverse U-shaped” relationship with the profits of REE conglomerate and a positive relationship with the profits of IR. (3) The recovery rate is negatively related to the profits of REE conglomerate and positively related to the profits of IR. Increasing the REE recovery rate will enhance the profitability of the IR. (4) In terms of social effects, higher acquisition price of recycled REE continuously reduce consumer surplus and social welfare. Although the REE recovery rate does not affect consumer surplus, it has a significant positive impact on social welfare.

Techno-Economic Assessment of Industrial Vanadium Flow Batteries Based on Experimental Data

This work presents a techno-economic model based on experimental and market data to provide forecasts of the profitability of vanadium flow batteries (VFBs), which are emerging as a promising technology for specific stationary energy services [1‒2]. Each component affecting the capital and operative costs was analyzed and the impact of side phenomena on capacity losses was considered. Relevant economic parameters were taken from real market data: discount rate was calculated based on the Capital Asset Pricing Model (CAPM) to account for real market conditions; the electricity price follows an arbitrage strategy to profit of the daily price fluctuations taken from the historical data of the Italian energy market [3‒4]. Technical parameters are taken from large-area multi-cell stack, rather than from small single cell experiments, thus allow to characterize the behavior of real industrial reactors [5‒6]. The economic performance indicators obtained are the capital cost, the operative cost, the Levelized Cost of Storage (LCOS) and the Net Present Value (NPV). The resulting values are: a discount rate of 9.12% and electricity prices of 200 € MWh–1 in selling and of 100 € MWh–1 in buying. Capital costs and LCOS were calculated for different system power and energy (E/P) ratings. At E/P = 2 h, the values of capital costs and LCOS were in the range of 800 – 900 € kWh–1 and 0.50 – 0.55 € kWh–1, respectively, whereas at E/P = 10 h they reduced to 350 – 380 € kWh–1 and 0.29 – 0.32 € kWh–1, respectively. In addition, a NPV analysis was carried out in order to evaluate whether these capital costs could provide a profit. The analysis suggests that only at capital cost < 105 € kWh–1 the break-even point (NPV = 0) was reached. The result is that the assumed techno-economic scenario VFBs were not profitable for every considered E/P. A perspective analysis was developed to reveal when VFBs can become profitable. Different screenings were made on both technical and economic parameters. At a technical level, a power density of 0.25 W cm–2, a RTE = 85%, a SOC = 90%, and a reduced number of regenerating processes in the lifespan (Nreg = 4) were considered. Regarding the economic parameters, a discount rate of 7 % was assumed, an electricity selling price of 250 € MWh–1 and a purchasing price of 75 € MWh–1 were used, compatible with a more pronounced Duck Curve of the daily electricity price, induced by the expansion of renewable energy sources expected in the near future. Under this scenario, the system costs decreased considerably: at E/P = 2 h, the Capital Costs and LCOS ranged as 530 – 570 € kWh–1 and 0.25 – 0.27 € kWh–1, respectively, whereas at E/P = 10 h, the Capital Costs and LCOS ranges as 260 – 270 € kWh–1 and around 0.17 € kWh–1, respectively. The latter figures made VFBs profitable for E/P in the range of 4 – 10 hours, as shown in the figure below.

Price transparency in international retailing on digital platforms

PurposeIn the digital era, price transparency—the practice of disclosing cost breakdowns in product manufacturing—has become present on digital platforms. Although its benefits are well-documented and consumers should theoretically desire costless and relevant information for informed decision-making, this paper proposes that consumers may resist overly transparent pricing, particularly when it pertains to premium-priced (vs regular-priced) products from countries with high equity.Design/methodology/approachOur research comprises three experimental studies utilizing both student and representative online Prolific samples, covering various products and countries with different equity levels. Initially, a pilot study identifies an interpersonal should-want conflict induced by price transparency when purchasing premium-priced products, leading to information avoidance. Subsequent studies further explore this phenomenon by examining the moderating role of country equity and the mediating role of price unfairness perceptions.FindingsPrice transparency can backfire when purchasing premium-priced products due to the want-should conflict among consumers—the desire to receive disclosure of cost breakdowns versus the inclination not to view it. This conflict results in increased resistance to receiving transparent price information and decreased brand attitudes and purchase intentions, especially for products originating from high-equity countries. Heightened perceptions of price unfairness explain these dynamics.Research limitations/implicationsThe study primarily relies on experimental designs with limited sample sizes. To enhance the generalizability of the findings, incorporating large-scale real market data across diverse domains and countries would be beneficial.Originality/valueGrounded in the should-want conflict and information avoidance theories, this paper uniquely explores the adverse effects of price transparency on digital platforms. We extend this by demonstrating that this conflict is influenced by country equity, where the perceived value added by the association of a product with a given country name affects whether consumers experience the conflict. Our investigation of perceived price unfairness further deepens our understanding of the nuanced effects of price transparency.

Analytically pricing European options in dynamic markets: Incorporating liquidity variations and economic cycles

This paper discusses the European option pricing problem in the context of asset prices being influenced by liquidity risks and economic cycles. We employ regime switching for asset volatility, and liquidity risks are captured by market-wide liquidity. We obtain an analytical formulation of European option prices, allowing for fast model calibration using real-market data. By estimating model parameters using real option data, we show that pricing errors can be significantly reduced using our model that considers stochastic liquidity, indicating that our model has real-world applications. Our results can help investors and regulators better understand the market and provide a potentially effective risk management tool.

Does Managerial Overconfidence Change with Market Conditions? Risk Management for Financial Institutions

Overconfidence (hubris or overestimation of one’s ability to perform) has been viewed in the finance literature as a character trait that is stable over time, e.g., assuming that if a manager is overconfident, he/she is overconfident all the time. In this paper, we aim to show that managerial overconfidence can be state-contingent, i.e., the level of managerial overconfidence could be influenced by an external economic shock such as the global financial crisis in 2008. A novelty of this paper is to provide evidence for and application of the concept of state-based managerial overconfidence, which is new in the finance literature. Two empirical studies were reported. In the first study (Study 1), we analyzed real market data by linear regression. We found that managerial overconfidence could vary according to changes in the state of the macroeconomy or tightening of corporate governance policies. In the second study (Study 2), we conducted a lab experiment simulating how external manipulations could alter participants’ confidence level. Both our empirical studies provide strong evidence of state-contingent overconfidence by Student’s t-test and contribute to the current finance literature, which assumes overconfidence as a personality trait. Our findings have important practical implications for the credit market. According to the state-contingent overconfidence hypothesis, creditors might reduce the loan amount or the loan duration (or other loan contract terms) too excessively by more than the efficient level during an economic downturn if the offsetting effect of state-contingent overconfidence is ignored.

Mechanisms of investors’ bounded rationality and market herding effect by the stochastic Ising financial model

The widespread herding effect prevalent in China’s stock market is caused by investors’ bounded rationality behavior. However, the underlying mechanisms are not fully understood. This article aims to use the stochastic Ising financial model, which explains this problem from the perspective of Econophysics. The probability density function and power-law distribution of return data simulated by the stochastic Ising financial model show sharp peaks and fat tails, similar to the stylized facts of real Chinese stock market data. Further, we use this financial model to simulate herding behavior in the market. The results show that when the degree of investor irrationality (represented by β) increases, the Herd Effect Index (HERDI) also rises. When β exceeds a critical point, the HERDI index fluctuates more violently. Through empirical analysis, we find that there is an interactive relationship between investors’ bounded rational behavior and market herding effects, and this model can effectively explain these two phenomena.

Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. First, we consider the conventional GARCH-based volatility models. Next, we extend the corresponding GARCH-based forecasting and calculate a specific probability associated with the predicted volatility levels. As the probability evaluation is based on a stochastic model, we develop an advanced data-driven estimation of this probability. The novel statistical estimation we propose uses real market data. The obtained analytical results for the statistical probability of the levels are also discussed in the framework of the integrated volatility concept. The possible application of the established probability estimation approach to the volatility clustering problem is also mentioned. Our paper includes a concrete implementation of the proposed volatility prediction tool and considers a novel trading and volatility estimation module for crypto markets recently developed by the 1ex Trading Board group in collaboration with GoldenGate Venture. We also briefly discuss the possible application of a model combined with the data-driven volatility prediction methodology to financial risk management.

Optimal trading with regime switching: Numerical and analytic techniques applied to valuing storage in an electricity balancing market

Accurately valuing storage in the electricity market recognizes its role in enhancing grid flexibility, integrating renewable energy, managing peak loads, providing ancillary services and improving market efficiency. In this paper we outline an optimal trading problem for an Energy Storage Device trading on the electricity balancing (or regulating) market. To capture the features of the balancing (or regulating) market price we combine stochastic differential equations with Markov regime switching to create a novel model, and outline how this can be calibrated to real market data available from NordPool. By modelling a battery that can be filled or emptied instantaneously, this simplifying assumption allows us to generate numerical and quasi analytic solutions.We implement a case study to investigate the behaviour of the optimal strategy, how it is affected by price and underlying model parameters. Using numerical (finite-difference) techniques to solve the dynamic programming problem, we can estimate the value of operating an Energy Storage Device in the market given fixed costs to charge or discharge. Finally we use properties of the numerical solution to propose a simple quasi-analytic approximation to the problem. We find that analytic techniques can be used to give a benchmark value for the storage price when price variations during the day are relatively small.

Model predictive control for multi-period portfolio optimization: a trading-oriented learning approach

Portfolio optimization aims at finding the optimal allocation of a given wealth among different assets to maximize an investor's utility function. A major critical issue concerns the prediction of future asset returns to forecast market evolution, due to the non-stationarity and volatility of asset prices. In this work, a learning-based Model Predictive Control (MPC) strategy for multi-period portfolio optimization is proposed, where the return prediction model is estimated via a novel trading-oriented learning paradigm. According to such a perspective, the model parameters are not the ones minimizing the prediction error but those that directly maximize the investor's utility. An extensive experimental study carried out on real-market data shows the potential improvements introduced by the proposed methodology compared to benchmark financial strategies.

Machine learning Ethereum cryptocurrency prediction and knowledge-based investment strategies

This work proposes a novel methodology to help in decision making in the cryptocurrency market. Two investment strategies have been designed for Ethereum (ETH), based on predictions of the price and trend of this cryptocurrency using real data. The two Ethereum cryptocurrency prediction systems rely solely on past values of other contextual stock indices, market indicators and online trends, and ignore any technical indicators of price evolution. Real data from cryptocurrency market has been collected and processed with different feature selection methods. Applying a regression approach with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, prediction models for the ETH price for 1, 7 and 15 days are obtained and compared. Also, support vector machine (SVM) is applied to predict the ETH price trend by applying a classification approach. In both approaches, sentiment analysis has been included to check its effect on the prediction results. The reliability of these prediction models in the current market has been evaluated by designing two original knowledge-based investment strategies. They are tested over two different time periods with real cryptocurrency market data. The results show that it is possible to generate up to 5.16 profit factor with few operations using these models. Furthermore, adding sentiment analysis has shown to have little influence. In this way, we contribute to the advancement of our knowledge of this volatile and still young cryptocurrency market, and specifically of the evolution of Ethereum and the factors that can influence its behavior.

Deep learning for enhanced index tracking

We develop a novel deep learning method for the enhanced index tracking problem, which aims to outperform an index while effectively controlling the tracking error. We generate a dynamic trading policy from a neural network that accepts a set of features as inputs. We design four blocks in the neural network architecture to handle different types of features, including regimes of the index and stocks, their short-term characteristics, and the current allocation. Outputs from the blocks are integrated into the final output that changes the portfolio allocation. We test our model on several indexes in empirical studies based on real market data. Out-of-sample results reveal the importance of different features and demonstrate the ability of our method in obtaining excess returns while effectively controlling the tracking error, downside risk, and transaction costs.

A gradient-based calibration method for the Heston model

The Heston model is a well-known two-dimensional financial model. Because the Heston model contains implicit parameters that cannot be determined directly from real market data, calibrating the parameters to real market data is challenging. In addition, some of the parameters in the model are non-linear, which makes it difficult to find the global minimum of the optimization problem within the calibration. In this paper, we present a first step towards a novel space mapping approach for parameter calibration of the Heston model. Since the space mapping approach requires an optimization algorithm, we focus on deriving a gradient descent algorithm. To this end, we determine the formal adjoint of the Heston PDE, which is then used to update the Heston parameters. Since the methods are similar, we consider a variation of constant and time-dependent parameter sets. Numerical results show that our calibration of the Heston PDE works well for the various challenges in the calibration process and meets the requirements for later incorporation into the space mapping approach. Since the model and the algorithm are well known, this work is formulated as a proof of concept.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

Study of Influential Factors on Housing Prices by Using MLR

With the emergence of the 2019 pandemic, the global economy experienced a downturn, but the housing market quickly rebounded post-pandemic, heightening the anxiety surrounding home buying. Previous research predominantly employed regression models to forecast housing prices, but these studies largely focused on specific factors. Hence, this article aims to offer a more comprehensive viewpoint by exploring the impact of multiple independent variables on housing prices, particularly emphasizing the effects of the age of buildings, the surrounding environment, and architectural factors. The methodology used in this study is the Multiple Linear Regression (MLR) model. It analyzes the real estate market data in Taipei, meticulously constructing and validating multiple models. The findings reveal that the age of buildings and the distance to the nearest subway station negatively influence housing prices, whereas the number of convenience stores positively impacts them. Among these factors, the quantity of convenience stores exerts the most significant effect on housing prices. Overall, this article provides a novel perspective and tools for understanding and predicting housing prices, assisting real estate developers and buyers in making more informed decisions in the complex real estate market. This study highlights the multidimensional nature of real estate value and contributes to the sustainable development of the real estate market.

Navigating the Stock Market: Modeling Wealth Exchange and Network Interaction with Loss Aversion, Disposition Effect and Anchoring and Adjustment Bias

This study aims to integrate meta-analysis into agent-based models to provide foundational insights into biased agent interactions. It delves into how various behavioral biases, such as anchoring, disposition effect, loss aversion, and others, influence market dynamics and investor decisions. By utilizing agent-based models, it offers simulations of market scenarios and investor behaviors, highlighting the impact of individual decisions on market dynamics. The innovative approach of this study lies in its integration of behavioral finance theories with real market data, offering a nuanced analysis of market behaviors. This work contributes a new perspective to behavioral finance and encourages the use of agent-based models to deepen the understanding of market dynamics and investor behaviors, potentially aiding in financial market analysis and policy formulation. This study aims to provide a foundational infrastructure for research that wishes to integrate meta-analysis into agent-based models and enable the examination of biased agent behaviors. The findings of this study have managed to model the interactions of loss aversion, disposition effect, and anchoring and adjustment bias in the closest manner to the real world by considering agents' socio-demographic and psychological factors. The results are highly conducive to more accurately modeling human behaviors in portfolio optimizations and expanding the applications of Generalized Artificial Intelligence in financial markets.

Predicting New York Housing Prices: A Machine Learning Approach Incorporating School, Living facilities and Real Estate Market Factors

This study aims to predict housing prices in New York by utilizing machine learning methods that incorporate factors such as schools, living facilities, and the real estate market. This paper collected extensive data, including metrics on school quality, assessments of living facility convenience, and real estate market data. Employing a regression-based machine learning algorithm, the study incorporated these factors into a predictive model. Through training and testing the model, this study discovered that school quantity and the convenience of living facilities significantly impact housing prices. The predictive model demonstrated good accuracy and predictive capability on the test set, validating the effectiveness of this approach. The findings of this study provide valuable insights for real estate market participants, policymakers, and investors to better understand and forecast housing price trends in New York.

How far can reformulation participate in improving the nutritional quality of diets at population level? A modelling study using real food market data in France

BackgroundFood reformulation is promoted as a tool to improve the nutritional quality of population diets. However, the potential impact of industry-wide reformulation on dietary intake has been investigated minimally.ObjectivesThe aim...

A data-driven approach for optimal operational and financial commodity hedging

Commodity price risk management has been subject to various modeling and optimization approaches. Recently, data-driven policies focusing on the decision rather than prediction quality have been developed to overcome price model misspecification. Yet, in the context of data-driven commodity purchasing, the existing literature either considers anticipatory inventory management or forward contracting where the decision frequency corresponds to the maturity of the traded contracts. We prove the optimality of a novel procurement policy combining operational and financial instruments with decision granularities independent of the derivative’s maturity. A mixed-integer programming model is developed to train policy parameters efficiently. We study the implications of policy complexity for learning-stability and out-of-sample generalization. Finally, we backtest the data-driven policy on real market data of four major commodities (i.e., copper, nickel, corn, and soybean) over ten years and show that the average savings potential of a combined financial and operational procurement policy compared to single-instrument strategies is up to 6.38% for corn where warehousing can efficiently mitigate price seasonality. The approach hedges corn and soybean commodities more efficiently through inventories while copper and nickel can be hedged efficiently by leveraging available financial instruments. Best model results are identified for a decision granularity with fewer parameters as high-frequent decisions deteriorate learning stability and model generalization.