Market Efficiency Research Articles

Do News Reports and Google Searches Impact Bitcoin Prices? An Analysis of Granger Causality

Objective: to analyze the Granger causality relationship between the number of Bitcoin news articles, the number of Google searches related to the term "Bitcoin," and the price of the cryptocurrency. The study aims to understand how these variables influence each other using an econometric time series model. Theoretical framework: the paper is based on studies on financial market efficiency, cryptocurrencies, and Granger causality theory. It reviews research that explores the impact of media information and online searches on the price of financial assets, such as Bitcoin, while also discussing Bitcoin’s role as both a speculative asset and a transactional currency. Method: the construction of a time series with weekly data, from 2015 to 2018, covering Bitcoin price, the number of news articles, and Google searches. The Vector Autoregressive (VAR) model was used to conduct the Granger causality test and verify if there are causal relationships between these variables. Results and Discussion: the findings indicate a triple causality relationship among the studied variables. Bitcoin's price influences both the number of news articles and Google searches, and in turn, the articles and searches also influence Bitcoin's price. This suggests that increased interest and media coverage of Bitcoin drive its demand and price. Research implications: the research suggests that Bitcoin's price behavior is influenced not only by traditional economic factors but also by media information and the volume of online searches. This highlights the importance of informational factors in the cryptocurrency market and the influence of digital media on asset pricing. Originality/value: the study proposes integrating data from news articles and Google searches with Bitcoin price behavior, applying Granger causality analysis. Its contribution lies in demonstrating the importance of media in the dissemination and pricing of cryptocurrencies, providing a new perspective for the literature on digital assets and speculative behavior.

Is the Chinese stock market efficient? Evidence from a combined liquidity trading strategy

PurposeThis study examines the stock market efficiency in China to offer trading strategy guidance to investors and efficiency evaluation insight to policymakers.Design/methodology/approachThis study examines the stock market efficiency in China with a new combined liquidity trading strategy by blending technical analysis into a liquidity buy-and-hold strategy.FindingsOur results show that the combined strategy generates significant excess returns in the whole sample period, suggesting that the Chinese stock market is not consistent with the weak form efficient hypothesis. In addition, the combined strategy yields more significant risk-adjusted excess returns after the 2004 split-share reform, indicating the stock market efficiency in China does not exhibit a distinct upgrade after the reform. Our further test results reinforce the main conclusions after taking transaction costs, market states, short-selling reform and other issues into consideration.Originality/valueOur study contributes to the literature in two ways: First, we shed light on the mixed documented results about the market efficiency form in China. Second, we contribute to the mixed relation between the 2004 split-share reform and market efficiency in China.

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

Analisis Return on Investment (ROI) Digital pada UMKM: Sebuah Pendekatan Metodologis

As productive economic enterprises owned by individuals or individual business entities, MSMEs are faced with the reality where digital technology is developing increasingly rapidly, which forces them to immediately carry out digital transformation to achieve business growth and sustainability. Investments in digital technology, from digital marketing to the use of e-commerce platforms, have become important to maintain the competitiveness of MSMEs in an increasingly competitive market. In carrying out digital transformation, MSMEs need to measure and assess the performance of digital investments by comparing the profits obtained from the investment against the costs incurred in the process of digitizing their business. This is the main challenge faced by MSMEs in measuring the effectiveness of their digital investments. This is where the concept of Return on Investment (ROI) becomes very relevant. In this research, it was found that calculating digital ROI requires careful planning and continuous evaluation so that MSMEs can make the right decisions regarding the technology they invest in, both short-term and long-term calculations. Increasing ROI requires a strategic approach that involves reducing costs, increasing revenue, monitoring performance, and utilizing technology and automation that is appropriate to the business being run, through a combination of digitalization strategies, product innovation, good customer service, operational efficiency, and creative marketing. By optimizing every element in investment, it is ensured that every rupiah invested provides maximum returns. By implementing these strategies, MSMEs can increase their competitiveness in the market, strengthen relationships with customers, to increase the competitiveness and sustainability of their business.

Credit Impact on Profitability and Marketing Efficiency of Sawn Wood Marketers

Aim of study: Credit impact on profitability and marketing efficiency of sawn wood marketers was examined. Area of study: Ndokwa West Local Government Area, Delta State. Material and methods: 120 respondents were obtained using multistage sampling technique and interviewed with structured questionnaires. Data were analyzed using descriptive statistics and profitability model. Main results: Respondents were male (70.0%) with 50 years average age. Sizes of sawn wood sold varied (4"x12", 1"x12", 2"x3", 2"x2"). The 2"x4" (16.8%) and 2"x2" (14.8%) sawn wood were most sold. Purchase price (₦1306.5) and selling price (₦1503.3k) of 4"x12" was higher than other sawn wood sizes. Total cost, revenue, net profit, benefit cost ratio and market efficiency of sawn wood business were ₦456604.20, ₦845843.30, ₦389239.20, 2.17 and 85.7% respectively. Credit had significant improvement on profitability (69.6%) with z-value of 0.00000127 (P

Creditors' treatment under the new Italian “Concordato Preventivo” and directive (EU) 2019/1023: A comparison with chapter 11

AbstractThis article compares the treatment of creditors in Italy under the “new” judicial composition with creditors (CWC) (“concordato preventivo”) procedure (as recently amended by the new Italian Business Crisis and Insolvency Code of 2022) with Chapter 11 of the US Bankruptcy Code, focusing on two main issues. The first issue analyses the impact of the Directive (EU) 2019/1023, which aims to remove barriers to fundamental freedoms and promote market efficiency by introducing, among others, preventive restructuring frameworks (PRFs). The Directive's guidelines on the treatment of creditors, including the requirement to allocate restructuring proceeds on the basis of the “relative priority rule (RPR)”, are examined. The second issue discusses Italy's new Business Crisis and Insolvency Code of 2022, which introduces several innovations to judicial CWC proceedings. These include facilitations for CWC on a going concern basis, a mandatory division of creditors into classes, a new voting and cramdown system, and significant changes to the distribution of restructuring proceeds, also to comply with Directive (EU) 2019/1023.

Reputation, commitment, and financial market regulation

This paper explores the interplay between reputation mechanisms, pre-commitment strategies, and financial market regulation. Through a theoretical model, we analyze how repeated interactions between market participants, combined with regulatory oversight, can lead to improved compliance and market efficiency. The study demonstrates that reputation mechanisms serve as powerful self-regulatory tools, as negative reputational feedback exerts a stronger influence on market behavior than positive feedback, particularly when the cost of non-compliance is high. Additionally, we investigate the role of pre-commitment in promoting market cooperation and separating equilibria, where high-quality suppliers distinguish themselves from lower-quality competitors. However, in the absence of stringent regulatory enforcement, pre-commitments may be exploited by low-quality suppliers, leading to market inefficiencies. Our findings suggest that a reputation-based, tiered regulatory framework can optimize resource allocation and enhance the effectiveness of financial market regulation. The paper concludes with policy recommendations for integrating these mechanisms into current regulatory practices and outlines areas for future research.

How Does The Transition From Conventional Marketing To Online Marketing Align With Economic Theories On Market Efficiency And Resource Allocation?"

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Pembuatan Barcode Kemasan Produk Pada CV. Kuliner Makmur Sejahtera Kota Padang

Rendang, a traditional Minangkabau dish from West Sumatra, Indonesia, has gained international recognition and has inspired many entrepreneurs to promote this cuisine globally. One such entrepreneur is CV. Kuliner Makmur Sejahtera (KMS), which produces rendang under the brand Katuju. To enhance operational efficiency and support global market expansion, KMS requires an improved inventory management system. The primary challenge is the difficulty in monitoring inventory and tracking products without a barcode system. This community service program aims to address this issue by implementing barcode technology on Katuju's product packaging. The activities include designing, creating, and printing barcodes, as well as training KMS staff on how to use them. The process starts with observation and interviews to identify needs, followed by barcode design and production using an online barcode generator, and testing the barcode functionality with a scanning system. The results indicate that the community service team successfully trained KMS in barcode creation and printing, and integrated barcodes into product packaging. Evaluation through questionnaires showed participant satisfaction with the program and readiness to apply the barcode system to their products. This program is expected to improve inventory management, data accuracy, and support CV. KMS's global market expansion by providing tools necessary for efficient product tracking and valuable consumer data collection.

Can long-term institutional owners improve market efficiency in parsing complex legal disputes?

Long-horizon institutional investors can help mitigate information asymmetries around securities class action (SCA) lawsuits. We find that the machine readability of SCA complaint filings can predict the outcome and duration of class actions. Long-term institutional investor ownership leads to a more positive post-SCA announcement price reaction and increases the volatility ratio of prices as a measure of price informativeness. Furthermore, there is a significant interaction effect between long-term institutional ownership and SCA complexity on price informativeness consistent with a superior information processing ability about complex corporate events affecting portfolio firms.

Multifractal Analysis of the Impact of Fuel Cell Introduction in the Korean Electricity Market

This study employs multifractal detrended fluctuation analysis to investigate the impact of fuel cell introduction in the Korean electricity market via the lens of multifractal scaling behavior. Using multifractal analysis, the research delineates discrepancies between peak and off-peak hours, accounting for the daily cyclicity of the electricity market, and proposes a crossover point detection method based on the Chow test. Furthermore, the impacts of fuel cell introduction are evidenced through various methods that encompass multifractal spectra and market efficiency. The findings initially indicate a higher degree of multifractality during off-peak hours relative to peak hours. In particular, the crossover points emerged solely during off-peak hours, unveiling short- and long-term dynamics predicated on a near-annual cycle. Additionally, the average Hurst exponent for the short-term was 0.542, while the average for the long-term was 0.098, representing a notable discrepancy. The introduction of fuel cells attenuated the heterogeneity in the scaling behavior, which is potentially attributable to the decreased volatility in both the supply and demand spectra. Remarkably, after the introduction of fuel cells, there was a discernible decrease in the influence of long-range correlation within multifractality, and the market exhibited an increased propensity toward random-walk behavior. This phenomenon was also detected in the market deficiency measure, from an average of 0.536, prior to the introduction, to an average of 0.267, following the introduction, signifying an improvement in market efficiency. This implies that the introduction of fuel cells into the market engendered increased supply stability and a consistent increase in demand, mitigating volatility on both the supply and demand sides, thus increasing market efficiency.

Review of AI-Driven Solutions in Business Value and Operational Efficiency

Introduction. Artificial intelligence (AI) refers to a wide spectrum of breakthroughs that offer multiple advantages to companies in terms of increased sales. Considering the abundance of data and the significant increase in computational resources, organisations have rapidly turned to artificial intelligence (AI) to create financial benefits. Nevertheless, businesses continue to discover it is challenging to implement and employ AI in their everyday activities. Therefore, a comprehensive understanding is required due to the absence of an integrated comprehension of how artificial intelligence creates business value and what kind of corporate worth is anticipated. Aim and tasks. The study aims to review value-generating methods and explain how enterprises might use AI technology in their business activities. To accomplish its main aims, this study offers a thorough literature review. The working hypothesis claims that the use of AI can increase business value. Results. This study examines the research capabilities of AI, its use in the corporate environment, and its initial and secondary impacts. The impact of AI includes process efficiency, generating insights hidden in huge amounts of data, and transforming business processes in terms of procedural actions, operational efficiency, financial efficiency, market efficiency, and sustainability in terms of company profile. In addition to the favourable impacts, several recent cases have shown that unwanted and undesired consequences may develop in the absence of effective management procedures. These effects hurt the reputation of the businesses concerned and, in certain cases, resulted in huge fines and financial losses. Such findings increase the responsibility of AI enterprises to incorporate solutions that reduce the bias in data and algorithms at every stage of implementation. Conclusions. The role of artificial intelligence in the corporate environment in value creation and operational efficiency is extending. AI technologies can be used by companies to increase automation of corporate processes without direct interaction with customers, including applications that mean the use of AI in customer-facing services and products. Learning about the means by which AI might be employed will assist businesses in generating rational choices regarding the strength of implementing technologies in the supply chain. Assessing the possible implications of AI acceptance of artificial intelligence may enable firms to plan more successfully on a technology’s launch.

Machine Learning Driven Options Pricing Model for Changing Market Conditions

This paper presents the design and implementation of an Adaptive Options Pricing Model utilizing deep learning techniques to enhance the accuracy of options pricing in volatile financial markets. Traditional models such as the Black-Scholes and Binomial frameworks have served as foundational tools in options pricing; however, they are constrained by assumptions of constant volatility and market efficiency, which often fail to reflect real-world dynamics. To address these limitations, the proposed model incorporates historical pricing data alongside real-time market sentiment derived from news articles and social media. By leveraging advanced deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, the model dynamically adapts to changing market conditions, capturing complex, nonlinear relationships in the data. The performance of the model is evaluated against traditional pricing methods, demonstrating significant improvements in pricing accuracy and responsiveness to market fluctuations. This research contributes valuable insights for traders and risk managers seeking to optimize their strategies in the derivatives market.

Predictive Patterns and Market Efficiency: A Deep Learning Approach to Financial Time Series Forecasting

This study explores market efficiency and behavior by integrating key theories such as the Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational Efficiency and Random Walk theory. Using LSTM enhanced by optimizers like Stochastic Gradient Descent (SGD), Adam, AdaGrad, and RMSprop, we analyze market inefficiencies in the Standard and Poor’s (SPX) index over a 22-year period. Our results reveal “pockets in time” that challenge EMH predictions, particularly with the AdaGrad optimizer at a size of the hidden layer (HS) of 64. Beyond forecasting, we apply the Dominguez–Lobato (DL) and General Spectral (GS) tests as part of the Martingale Difference Hypothesis to assess statistical inefficiencies and deviations from the Random Walk model. By emphasizing “informational efficiency”, we examine how quickly new information is reflected in stock prices. We argue that market inefficiencies are transient phenomena influenced by structural shifts and information flow, challenging the notion that forecasting alone can refute EMH. Additionally, we compare LSTM with ARIMA with Exponential Smoothing, and LightGBM to highlight the strengths and limitations of these models in financial forecasting. The LSTM model excels at capturing temporal dependencies, while LightGBM demonstrates its effectiveness in detecting non-linear relationships. Our comprehensive approach offers a nuanced understanding of market dynamics and inefficiencies.

Efisiensi Pemasaran Karet Di Kabupaten Ogan Ilir Sumatera Selatan

The rubber commodity in Ogan Ilir Regency plays an important role in increasing family income, but currently, the income of rubber farmers is still relatively low because farmers are still traditional in their marketing, causing the prices received by farmers to be low. The objectives of this study are to 1) Identify the functions of rubber marketing institutions in Ogan Ilir Regency, 2) Analyze the efficiency of rubber marketing in Ogan Ilir Regency.. Sampling was carried out randomly. 75 samples were used in this research, with a population of rubber farmers, namely 236 farmers in Tanjung Batu District and 254 in Payaraman District. Locations were chosen purposively based on the same considerations: the subdistricts and villages that produce the largest rubber production. Data collection uses cross-section data. Rubber marketing efficiency is analyzed using marketing margin, farmer's share, profit to cost ratio and the Data Envelopment Analysis (DEA) model. The research results show that rubber farmers in Ogan Ilir Regency use three marketing channel patterns. Based on the analysis of rubber marketing efficiency, it is recommended that rubber farmers in Ogan Ilir Regency use marketing channel 1, namely farmers-UPPB-factories and marketing channel 3, namely farmers-wholesalers-factories.

Sistem Pemasaran Beras Bujang Marantau Di Kecamatan Sungai Tarab, Kabupaten Tanah Datar, Provinsi Sumatera Barat

The “Bujang Marantau” rice variety is local rice from Tanah Datar Regency which was registered as a national superior rice variety by BPTP West Sumatra in 2015 and grows well in Tanah Datar Regency. This increase in “Bujang Marantau” rice production must be balanced with a good marketing system so that farmers and institutions marketing can receive more favorable prices. The study is aimed to analyze the channels, functions and operational efficiency of rice marketing (marketing margin, farmer’s share, and profit to cost ratio) in Sungai Tarab District. This research involved 45 farmers as samples obtained through a simple random sampling method. Marketing institution respondents were obtained using the snowball method based on the flow of information from farmers and 15 marketing institution respondents were obtained. Qualitative data is used to analyze marketing channels and institutions. Quantitative data is used to analyze marketing margin, farmer’s share, profit to cost ratio and marketing efficiency index. The results showed that there are 3 marketing channels and 3 types of marketing institutions involved. The results of the marketing operational efficiency analysis show that the relatively efficient marketing channel is channel III because it has the lowest marketing margin value, the highest farmer’s share, and the shortest marketing channel compared to other channels. Marketing institutions should further optimize the marketing functions of each institution, such as the transportation function, so that transportation costs can be lower and minimize marketing costs more efficiently.

Increased Production, Waterglass Roll Saving Dyeing Efficiency, Branding Strategy, and Digital Marketing in Muara Bungo Mulia Batik

Batik is a cultural heritage of the Indonesian nation that has been recognized by the world through recognition by UNESCO. The development of Bungo Batik began during the national event, Indonesia Fashion Craft in Makassar in 2002. The demand for Bungo Batik increases every year because it is supported by the policies of the Bungo Regency Regional Government. But on the one hand, the obstacle is precisely the craftsmen themselves who are not able to produce on a large scale. The partner in this activity is Batik Mulia which was established in 2021. The limited production capacity of Bungo batik is because the equipment used is still very simple, such as the use of waterglass in the manual batik dyeing process so that it is wasteful and immeasurable. In addition, partners also need training in management, entrepreneurship, branding strategies and the use of digital marketing. The method of implementing the activity begins with an agreement between the service team and partners to solve problems. The solution offered by the team to partners is the procurement of appropriate technological tools in the form of Roll Saving Water Glass machines and several trainings needed by partners. The success of this activity can be seen by several positive impacts from the savings or efficiency of the use of water glass in batik dyeing, increasing batik production, and improving partner skills in online promotion, creating marketing content and using digital marketing.

Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate

In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.

Testing of Semi Strong form of Market Efficiency of Indian Capital Market with Reference to Annual Dividend Announcement

The investment decision is influenced by many factors, of which one such factor is the return. The shareholders may get the return in the form of dividends which affects the share prices. The behavior of stock prices is unpredictable as price movement for different activities will move in different ways. The stock price influence activities can be divided into economic and corporate activities. The impact of economic activities will be more or less the same on all the stock prices, while the impact of corporate action varies from one stock to the other. Dividend payment is one of the essential corporate actions that will impact the behavior of stock prices. This research highlights the impact of dividend payment one the behavior of stock prices and their abnormal returns.

Determinan Yang Memengaruhi Pemilihan Saluran Pemasaran Kopi Robusta Di Kabupaten Malang

The demand for coffee in Malang Regency by determining the quantity of commodities is very dependent on the marketing subsystem. Robusta coffee marketing channels in Malang Regency include marketing institutions, namely small traders, sub-district collectors and large traders. Coffee marketing is dominated by small traders. This research aims to analyze the determinants that influence the choice of marketing channels for Robusta coffee in Malang Regency. The location of this research was carried out in two villages, namely Srimulyo Village and Sukodono Village, Kec. Dampit, Kab. Poor. The respondents in this research were Robusta coffee farmers in two villages in a total of 60 respondents. Marketing agency respondents were then determined using snowball sampling techniques. Snowball sampling method by collecting data based on information from previous respondents based on existing marketing patterns in Malang Regency. This research uses multinomial logit analysis by determining efficient marketing channels as a reference variable. Based on the results of marketing channel analysis, it was found that there are 5 marketing channel patterns. Marketing channel 1 consists of 27 farmers and the largest channel choice is 45%, while the results of the marketing efficiency analysis show that channel 5, namely direct farmers to wholesalers, is the most efficient. Marketing channel 5 consists of 11 farmers with a percentage of 18.3%. The results of the research show that 3 variables influence the choice of marketing channels, namely production volume, farming experience, and education level. Suggestions from research for extension workers and local governments need to assist farmers regarding financing and coffee market information so that marketing channel choices are more varied.