Actual Market Data Research Articles

Trend following deep Q‐Learning strategy for stock trading

AbstractComputers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q‐Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q‐Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting‐based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.

Only one room left! How scarcity cues affect booking intentions on hospitality platforms

Abstract Communication of scarcity has emerged as a fundamental marketing principle in electronic commerce and for hospitality platforms in particular. We investigate the effect of scarcity cues on consumer behavior. First, we explore actual market data from Airbnb and Booking.com, and find that scarcity cues are used differently by hotel versus peer-based hospitality platforms. Second, we conduct an online experiment on consumer perceptions of scarcity. Specifically, we find support for two distinct effect paths, along which scarcity perceptions lead to increased booking intentions through urgency (the get-it-before-it’s-gone effect) and value (the must-be-good effect). We discuss practical implications of our results as well as the potential for the misuse of scarcity claims for unfair competition.

Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – Probabilities vs discrete choices

Health economists often use discrete choice experiments (DCEs) to predict behavior, as actual market data is often unavailable. Manski (1990) argues that due to the incompleteness of the hypothetical scenarios used in DCEs, substantial uncertainty surrounds stated choice. Uncertainty can be decomposed into “resolvable” and “unresolvable”; the former is expected to become resolved in actual choice, as individuals collect further information. To enable its identification, Manski suggests eliciting subjective choice probabilities (ECPs) rather than discrete choices. We introduce the ECP approach in health economics and explore its convergent validity. The context is future physicians’ stated choices of job in rural general practice in Denmark. Our results are mixed, but show remarkable similarities in forecasting abilities, despite the ECP models being less econometrically demanding and relying on different preference distributional assumptions.

Fluctuation behavior analysis of stochastic exclusion financial dynamics with random jump

A novel financial price model combining stochastic exclusion system and compound Poisson process with lattice fractal Sierpinski carpet percolation jumps is established to reflect fluctuation behaviors of energy markets. The exclusion system (one of statistical physics systems) is applied in implementing interaction behaviors of traders, and the percolation theory is used to implement gusty jumps of price volatilities. Further, the actual market data and the simulation data are combined to analyze statistical properties and complex characteristics by applying autocorrelation analysis, detrended fluctuation analysis method and fractional fuzzy entropy. Moreover, a new synchronization measurement method named the cross CID FuzzyEn, which is composed of cross-fuzzy entropy and complexity-invariant distance, is firstly proposed in this work. Empirical results show that the analog returns and the actual returns have parallel characteristics and strong synchronicity, which displays that the proposed model is rational and can effectively reflect the internal mechanism of real energy markets to some extent.

IGDT-Based Complementarity Approach for Dealing With Strategic Decision Making of Price-Maker VPP Considering Demand Flexibility

In this article, we outline a novel bilevel decision-making framework for a price-maker virtual power plant (VPP) to participate in both day-ahead and balancing oligopoly markets considering multiple forward contracts. In principle, the VPP operator with having the possession of financial transmission rights can manage its financial risk through trading electricity among various markets such as centralized pool and contract markets aimed at maximizing its own profit and minimizing the associated risk. Besides, the VPP operator will be able to optimize its procurement expenditures by incentivizing flexible demands proportion to different electricity tariffs. In the proposed bilevel model, the VPP aggregator strives to maximize its own profit at the upper level while an independent system operator seeks to clear both markets at the lower levels with an eye to maximize social welfare. Each lower level is then replaced by its complementarity slackness conditions and, consequently, is recast as a mathematical program with equilibrium constraints that can be solved using off-the-shelf software packages. Furthermore, the uncertainty pertaining to renewables has been envisaged through information gap decision theory resulting in robustness/opportunity function to deal with self-scheduling of VPP. This article ends up with different illustrative case studies through performing after-the-fact actual market data to verify the applicability of the model.

Wind balancing costs in a power system with high wind penetration – Evidence from Portugal

Abstract. The growth of intermittent renewable power generation has been drawing attention to the design of balancing markets. Portugal is an interesting case study because wind generation already accounts for a high fraction of demand (23% in 2012–2016), but still there are no economic incentives for efficient wind forecasting (wind balancing costs are passed to end consumers). We analyze the evolution of the balancing market from 2012 to 2016. Using actual market data, we find wind balancing costs around 2 euros per MWh of generated energy. One main reason for these low costs is the existence of a robust transmission grid, which allows for the compensation of positive with negative wind imbalances across the system. Nevertheless, the results suggest that final consumers could save several million euros per year if wind generators were made responsible for the economic cost of their imbalances, in line with other European markets.

Competitiveness of open-cycle gas turbine and its potential in the future Korean electricity market with high renewable energy mix

In this paper, we analyzed the competitiveness of an open-cycle gas turbine (OCGT) in the Korean electricity market and found reasons why OCGT has not been constructed since 2001, when the market opened. Through the analysis, we found that OCGT was weak in the Korean electricity market due to 3 factors: high load factor, high price of liquefied natural gas, and existing inefficient power plants. Using the conformity theory of the optimum in resource planning and the equilibrium in market dynamics, we verified the reasons by implementing resource planning using the Wien Automatic System Planning (WASP)-Ⅳ package with actual market data for the 16 years from 2001 to 2016. In addition, considering the new energy policy of Korea, shifting main sources of electricity generation from nuclear and coal to clean renewable energies and natural gas, we analyzed the competitiveness of OCGT in the future Korean electricity market. We identified the factors unfavorable for OCGT in the current market and suggested what should be changed to cope with high renewable energy penetration.

온라인 소비자 리뷰의 문맥정보 마이닝 : 정보 이득과 제품 판매량의 상관관계

Product sales forecasting is one of the factors that gives significant impact on the company's sales and production plans. In order to increase the accuracy of sales forecast, companies are trying to reflect the opinions of consumers. In this study, we reconfirm the work of Hwang(2017) which analyzed the consumer review by applying the information gain to predict the product sales. This study is meaningful in that it applies the research model to the automobile market, which is one of the traditional durable goods market. Compared to the smartphone market, which has a short product life cycle, the automobile market has well-defined consumers and products are specifically targeted to each consumer group. In this paper, we verify the applicability and predictive power of proposed methodology of Hwang(2017) using actual market data, and show the relationship between information gain from online consumer review and product sales.

Wind Balancing Costs in a Power System with High Wind Penetration – Evidence from Portugal

The growth of intermittent renewable power generation has been drawing attention to the design of balancing markets. Portugal is an interesting case study because wind generation already accounts for a high fraction of demand (23% in 2012-2016), but still there are no economic incentives for efficient wind forecasting (wind balancing costs are passed to end consumers). We analyze the evolution of the balancing market from 2012 to 2016. Using actual market data, we find wind balancing costs around 2 euros per MWh of generated energy. One main reason for these low costs is the existence of a robust transmission grid, which allows for the compensation of positive with negative wind imbalances across the system. Nevertheless, the results suggest that final consumers could save several million euros per year if wind generators were made responsible for the economic cost of their imbalances, in line with other European markets.

An Efficient Portfolio with Several Objectives and Varying Parameters

We investigate a portfolio selection model with several objective functions, whose coefficients are uncertain and vary between some bounds. A preferable efficient portfolio of the model is obtained, which provides the range within which the portfolio return and the moments would vary. An optimal portfolio for the forecasted returns of stocks is found with actual market data from the Bombay Stock Exchange, India.

How Platforms Build Trust

Two-sided platforms have gained increasing importance as a business model and research attention over the last couple of years. Examples in the consumer-to-consumer domain include platforms for accommodation sharing, shared mobility services, car sharing, resale, crowd-work, and many more. As these consumer-to-consumer business models rely on the realization of transactions among users, a central aspect to virtually all platforms is the maintenance of trust. While recent research has considered the effects of trust-building mechanisms on diverse platforms specifically, the overall platform landscape has received much less attention. Based on actual market data, this paper provides a cross-platform overview on the mechanisms that platform operators employ to build and maintain trust and examines how these mechanisms are actually being utilized by the platforms’ users.

Cross-Border Exchange and Sharing of Generation Reserve Capacity

This paper develops a stylized model of cross-border balancing. We distinguish three degrees of cooperation: autarky, reserves exchange and reserves sharing. The model shows that TSO cooperation reduces costs. The gains of cooperation increase with cost asymmetry and decrease with correlation of real-time imbalances. Based on actual market data of reserves procurement of positive and negative automatic frequency restoration reserves in Belgium, France, Germany, the Netherlands, Portugal and Spain, we estimate the procurement cost decrease of exchange to be €165 million per year without transmission constraints and €135 million per year with transmission constraints. The cost decrease of sharing is estimated to be €500 million per year. The model also shows that voluntary cross-border cooperation could be hard to achieve, as TSOs do not necessarily have correct incentives.

Mobility from Renewable Electricity: Infrastructure Comparison for Battery and Hydrogen Fuel Cell Vehicles

This work presents a detailed breakdown of the energy conversion chains from intermittent electricity to a vehicle, considering battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs). The traditional well-to-wheel analysis is adapted to a grid to mobility approach by introducing the intermediate steps of useful electricity, energy carrier and on-board storage. Specific attention is given to an effective coupling with renewable electricity sources and associated storage needs. Actual market data show that, compared to FCEVs, BEVs and their infrastructure are twice as efficient in the conversion of renewable electricity to a mobility service. A much larger difference between BEVs and FCEVs is usually reported in the literature. Focusing on recharging events, this work additionally shows that the infrastructure efficiencies of both electric vehicle (EV) types are very close, with 57% from grid to on-board storage for hydrogen refilling stations and 66% for fast chargers coupled with battery storage. The transfer from the energy carrier at the station to on-board storage in the vehicle accounts for 9% and 12% of the total energy losses of these two modes, respectively. Slow charging modes can achieve a charging infrastructure efficiency of 78% with residential energy storage systems coupled with AC chargers.

Evaluating the durability of brand alliances using Bayesian methods

This study examines the duration of brand alliances (or co-brands), a brand strategy in which two brands are offered as one joint product. Previous research has suggested these alliances are short-lived, but little empirical evidence exists to explore what may drive the longevity of such alliances. The study uses actual market data for 524 brand alliances in 83 product categories of consumer packaged goods during a 13-year period. Controlling for market share, several factors that might influence brand alliance duration are examined: type of alliance, ownership by the same parent company, and the number of relationships. Using a Bayesian hazard model to estimate duration, the results show a brand alliance lasts longer if it is an ingredient brand alliance (rather than licensed) and both brands are owned by the same parent company. Additionally, having more partnerships helps a brand alliance last longer, but too many alliances have a negative effect on staying in the marketplace. The findings suggest that brand managers looking to enter into a brand alliance can anticipate how long the product might last based on these partnership factors.

A different approach to the European option pricing model with new fractional operator

In this work, we have derived an approximate solution of the fractional Black-Scholes models using an iterative method. The fractional differentiation operator used in this paper is the well-known conformable derivative. Firstly, we redefine the fractional Black-Scholes equation, conformable fractional Adomian decomposition method (CFADM) and conformable fractional modified homotopy perturbation method (CFMHPM). Then, we have solved the fractional Black-Scholes (FBS) and generalized fractional Black-Scholes (GFBS) equations by using the proposed methods, which can analytically solve the fractional partial differential equations (FPDE). In order to show the efficiencies of these methods, we have compared the numerical and exact solutions of these two option pricing problems by using in pricing the actual market data. Also, we have found out that the proposed models are very efficient and powerful techniques in finding approximate solutions of the fractional Black-Scholes models which are considered in conformable sense.

The influence of shelf life on food waste: A model-based approach by empirical market evidence

Several million tons of food are wasted annually and solutions to completely face the problem are not still available. It is well recognized that shelf life prolongation could play a key role for reducing food waste but the real relation between shelf life and food waste is not well assessed for lack of actual market data. Therefore, the aim of this work was to identify the relationship between the shelf life and an important food waste component, i.e. the product returned from the market, by using a proper statistical approach, adapted to data kindly offered by a domestic industry. The mathematical model highlighted an inverse function between shelf life and product returned only when the shelf life is between 30 and 50 days. This means that for specific products with shelf life in this range (30–50 days), a proper prolongation could significantly reduce the amount of products returned from the market, if unsold within their commercial life.

Estimation and prediction under local volatility jump–diffusion model

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump–diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump–diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump–diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

Valuation of Carbon Emission Allowances and Related Derivatives Under a Closed Trading Phase

This paper provides a complete-market valuation framework for emission allowances and related derivatives. In particular we present a structural model by assuming an emission rate with time-homogeneous parameters, where closed-form expressions are derived for allowances, allowance futures, and option prices. In addition, we also discuss the completeness of the allowance market and the source of incompleteness as supported by empirical evidence. While identifying limitations of the models, we present in detail their applications under information incompleteness, with a focus on deriving implied emission values from actual market data. Finally, the model is implemented using real world allowance data, based on which a comparative analysis is undertaken.

Price premiums for ecolabelled seafood: MSC certification in Germany

Whether ecolabelled seafood actually provides incentives to improve the management of fisheries remains a controversial issue. A number of stated preference studies indicate a substantial willingness to pay for ecolabelled seafood. Early evidence from actual market data supports the existence of a premium, while more recent papers provide a more nuanced picture. In this paper, a hedonic price model for whitefish species on the German market is estimated that includes information on Marine Stewardship Council (MSC) labelling, the leading seafood ecolabel in Germany. The model also allows the potential premium to vary by species. Results indicate that MSC premiums in Germany vary substantially between species, from a hefty 30.6 per cent for the high-end species cod, to a 4 per cent premium for Alaska pollock, and no premiums for saithe.

Differential Impact of a New RMB Exchange Rate Index on the U.S. and Chinese Financial Sectors

This paper analyzes the impact of launching a new RMB exchange rate index on the financial industry sector for both U.S. and Chinese stock markets by applying the methodology of event study. The abnormal returns of selected listed companies were calculated based on the actual market data for 261 trading days. The results show that introducing the new RMB exchange rate index affects the financial industry sector of the Chinese stock market for a greater number of days than the impact on the financial industry sector of the U.S. stock market.