Analysis Of Market Data Research Articles

Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong

Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sentiments better, which brings more prediction performance improvements than the other three dictionaries.

Exponential Smoothing Methods for Detection of the Movement of Stock Prices

Business Intelligence is a set of processes, architecture and technologies that convert raw data into meaningful information. BI has a direct impact on an organization’s strategic statistical and operational business decisions. In BI one of the most interesting areas is time series data analysis to predict are stock prices. Prediction and analysis of stock market data has got an important role in today’s economy. The aim of this paper is to predict the daily previous closing stock prices of the major tech giants of NSE (i.e. HCLTECH and TCS), using information from the historical data with the help Exponential Smoothing Methods. The historical stock prices of the stated companies for three years will be used for the training and testing of the methods. It is found that Holt-Winter’s method of exponential smoothing the given the best results out of the other exponential smoothing methods.

Analisis Bisnis Dan Marketing Dalam Islam

This research was conducted to obtain information about business and marketing in Islam which is used by entrepreneurs in market competition and to increase income. The research conducted is a qualitative research type with descriptive form and the subjects used as sources in the research are entrepreneurs and businessmen consisting of several informants who meet interview techniques that can INTERVIEW and DOCUMENTATION and data analysis using SWOT analysis. Marketing data analysis is a technique of retrieving information available in the market to form the basis for creating a marketing strategy for a business. This analysis is an important part of every business, because it can show the performance of your business in the market so far. All this information is obtained from all marketing channels and then consolidated to produce one marketing view. Predicting the future can be done through marketing data analysis such as finding out what trends will prevail, which markets will grow fast and making assumptions about these findings. This makes it easier for your company to determine the right marketing plan for the future. Customer loyalty and ROI that can affect your current market as well as the future of your company. keyword: Bisnis dan Marketing

From technology to market: a bibliometric and integrative review on autonomous vehicles

The present study aims to understand the direction that research on autonomous vehicles (AVs) has taken in the academy, in order to bring to surface and detail gaps on the disruptive aspect of this innovative sector. A bibliometric revision was used to map the research present in the Clarivate Analytics Web of Science database, followed by a systematic integrative review to structure the evolution of the research and present the main aspects. Five categories that show the evolution of the research on AV technology were retrieved, such as a new focus on the users of technology. We also identified gaps in each category on how this technology should reach the market. Additionally, it was also possible to draw a global view of the technology development landscape by the analysis of both academic and market data, according to the KPMG report, and also to identify the recent market trend of mobility as a service (MaaS).

Fractal Markets, Frontiers, and Factors

In this work we develop an alternative view to the modern finance theory that essentially suggests equilibria in efficient markets by taking a risk-based view of asset returns in stock markets. Based on a mathematical analysis of stock market data using multi-scale approaches, we will alternatively describe markets and factors as trend-based fractal processes and analyze well-known factor premiums, which leads to a return-based view of markets and a model of investors reacting to market environments. We conclude that markets could be viewed alternatively as fractal, non-stationary and, at most, asymptotically efficient.

Analysis of stock market data by using Dynamic Fourier and Wavelets techniques

This work deals with the analysis of daily and minute sampled financial stock market data. We propose a Dynamic Fourier Transform (DFT) and a Wavelet Transform to estimate the power spectrum of returns. In order to estimate the power spectrum, we used the tapering process with the DFT technique and the scaling function with the wavelets methodology to avoid the spectral leakage or discontinuity in the sequence. Our result suggest that the power spectrum are effective in characterizing the minute and daily based data corresponding to different frequencies. This type of modeling techniques help to characterize some key variables of stationary time series that are very useful for making informed decisions in the stock market such as assessing financial risk in the market.

The SER Spread Under the ECB Quantitative Easing

This paper discusses the effects of the ECB's asset purchase programmes (APPs) on the SER spread, while the main focus is given to detail intraday analysis of implementation of the Public Sector Purchase Programme (PSPP). The SER spread is perceived as an important indicator of trust in the Eurozone and its elevated level normally signals distortion and mistrust among commercial banks with a power to spill over into the whole financial sector. Recent development on markets and especially within monetary policy in the Eurozone could have impaired the ability of the SER spread to act as a proxy for global systemic risk. The SER spread in this study was constructed and calculated using relevant European financial data and the consequent analysis was made on intraday and high-frequency (HF) 2015-2017 data. The ECB's APP, mainly PSPP, together with other instruments of monetary policy have impact on both legs of the SER spread and this paper tries to identify and quantify the degree of this effect by detailed HF market data analysis. HF intraday approach analysis is also being implemented in order to identify which of the SER spread was decisive in determining the SER spread change in the first three years of the PSPP implementation. Whether it was the sovereign bond-based leg directly affected by the ECB's PSPP purchases or the interbank lending / STIR-based leg.

Optimized Feature Extraction Based Artificial Intelligence Technique for Empirical Analysis of Stock Market Data

Stock market forecasting is considered as a challenging task in financial time series forecasting. The core idea of a successful stock market forecast is to get the best results with the least amount of input data required and the least complex stock market model. So in this paper, to achieve these goals, this paper proposes a new algorithm Cuckoo Neural for Financial Market (CN_FM), which integrates Cuckoo Search (CS) with a Feed forward back propagation Neural Network (FFBPNN) and build a stock price forecasting proficient system. This research follows some basic steps to design a model for sentiment analysis from Stock market dataset and firstly data is pre-processed to remove the unwanted text. In pre-processing steps the techniques such as normalization, punctuation, stop word removal and tokenization are applied. The extracted features are optimized using CS and in turns used to train the FFBPNN. The results obtained from experiments in the stock market show that the proposed method can reduce the dimensions and produce accurate results for emotion-based text categorization with an accuracy of 99.44%.

Different Policy Measures and Practices between Swedish Counties Influence Market Dynamics: Part 2-Biosimilar and Originator Etanercept in the Outpatient Setting.

BackgroundDiverging approaches towards market entry and uptake of biosimilars, even within a country, leads to regional variation in biosimilar use. This is the case in Sweden, where the 21 county councils control the healthcare budget and offer regional guidance.ObjectivesThis study aimed to analyse the market dynamics of originator and biosimilar etanercept (outpatient setting) in the different counties of Sweden, and examine the influence of local policy measures and practices, in addition to national policy.MethodsThis study was performed in three steps: (1) a structured review of the literature on (biosimilar) policies in Sweden; (2) analysis of market data on the counties’ originator and biosimilar etanercept uptake (quarter two 2012 to quarter four 2017) provided by IQVIA™; and (3) discussion of findings in face-to-face semi-structured interviews with the national pricing and reimbursement agency, key experts in the county councils of Skåne, Västra Götaland and Stockholm, and an industry representative.ResultsNotwithstanding the existence of a national managed entry agreement for etanercept, wide variations in biosimilar market shares between counties were observed (40–82% in 2017). Over time, early and late adopters of biosimilar etanercept can be distinguished. In quarter four 2017, biosimilar market shares of all counties slightly decreased in accordance with the lower priced originator product from 1 October 2017. As prescriptions for treatment with etanercept are often provided for a year, two approaches are possible to switch patients: active pullback of prescriptions resulting in additional workload, or wait until the patient’s next visit. Qualitative analysis indicated that the choice to use the biosimilar or the originator product depends on differences in rebated prices of the biosimilar and originator product, the presence of key opinion leaders, local guidelines, and financial streams and local gainsharing arrangements. Our estimates of current rebated prices and costs after gainsharing for the county councils and Government reveal only limited price differences between products.ConclusionsRegional variations in use of biosimilar etanercept can be seen although prices are coordinated nationally. This suggests that counties react differently to price differences and highlights the role of local policy and attitudes of stakeholders towards biosimilars and switching. It seems that some counties are hesitant to switch patients, as it is associated with an increased administrative workload that might not be compensated for by savings associated with a lower priced product.

Identification of advanced data analysis in marketing: a systematic literature review

Identification of advanced data analysis in marketing: a systematic literature review

Research on Task Pricing of Self-service Platform of Product-Service System

The realization of mobile Internet self-service platform provides product maintenance in product-service system of enterprises with more diversified and efficient market research methods. This paper combines service pricing theory, cost-oriented pricing theory and competition-oriented pricing theory, through market data analysis, discusses the influencing factors of the task pricing of product maintenance in product-service system, and establishes a pricing model to analyze the influencing factors of pricing.

DETERMINANTS OF THE DEVELOPMENT OF THE CORPORATE BOND MARKET IN LATVIA

The development of the corporate bond market in Latvia while being quick and robust in the period 2012-2017 (the average growth rate 131%, median 44%), has faced major challenges in 2018 (Nasdaq Baltic, 2018). The latter has demonstrated the vulnerability of this alternative to banking financing method in Latvia. In the environment, where the ongoing changes in the banking financing as initiated by the Basel III and Capital Markets Union initiatives, besides to the decision on creation of pan-Baltic capital market with the support of the European Commission and the European Bank for Reconstruction and Development, are bringing more emphasis on the increasing role of the corporate bond market in Latvia; the need to understand its development and factors affecting this development is essential. The goal of this article is to explore the corporate bond market development frameworks as elaborated by the academic research and recognise corporate bond market development determinants, identify the determinants of the corporate bond market in Latvia by running the statistical analysis. Tasks of the research include examining types of corporate bond development frameworks with the focus on revealing the determinants of the corporate bond market development as recognised by the academics, performing the econometric analysis of the determinants selected and building an econometric model of the determinants of the development of the corporate bond market in Latvia as well as drawing corresponding conclusions. In order to accomplish the tasks of the research the following research methods were used: analysis of the previously performed research, analysis of the legislative framework; quantitative research methods: statistical data analysis of macroeconomic data from Bank for International Settlement, The World Bank Database, Bloomberg and Reuters databases; financial market indicator and data analysis from Nasdaq Baltic, Bank for International Settlement, Treasury of the Republic of Latvia, Bloomberg and Reuters databases; correlation analysis, regression analysis. In the result of the analysis, the determinants of the corporate bond market development in Latvia were analysed, where 27 factors as detected by the theoretical analysis to be influencing the corporate bond market development in a country were applied to Latvia. The regression analysis has demonstrated the influence of Gross Domestic Product (GDP) per capita, amount of domestic savings, real GDP growth, amount of government bonds as the share of GDP and regulatory quality on the amount of the corporate bonds outstanding. DOI: http://dx.doi.org/10.5755/j01.eis.0.12.21875

Detecting the Fluctuations in Large Samples Using Wavelet Transform

structure break is a famous features in stock market data that gain consideration from many kind of researchers. Generally, it occurs because of unexpected variations in the strategy of the government. Recently, wavelet method (WT) is more popular in the stock market data analysis since it has significant benefits than the other traditional methods. In this research paper, the discrete wavelet transform (DWT) based on Daubechies model will be used to capture the structure break in Amman stocks market /Jordan (ASE) using dataset from 2010 until 2018.

Who should own a renewable technology? Ownership theory and an application

We investigate the market implications of ownership of a new low-cost production technology. We relate our theoretical findings to measure the impact of renewable energy penetration into electricity markets and examine how the ownership of renewable capacity changes market outcomes (prices, outputs, emissions). As current public policies influence renewable energy ownership, this research provides useful insights for policy makers. We show how and why ownership of renewable capacity matters when there is market power in energy market. We apply our findings to the wholesale electricity market in Ontario, Canada, to analyze the impact of different ownership structures for wind capacity expansions. Using both simulation analysis and empirical analysis of market data, we show that the price-reducing effects of wind expansion are smaller when a larger strategic firm owns new wind capacity. Lastly, we show that the effect of wind ownership on emissions depends on both the amount of generation displaced by wind output and the emissions rate of displaced generation.

PMS70 - BIOSIMILAR AND ORIGINATOR ETANERCEPT: INFLUENCE OF LOCAL POLICY MEASURES AND PRACTICES ON MARKET DYNAMICS BETWEEN SWEDISH COUNTIES

This study aims to analyse market dynamics of originator and biosimilar etanercept in the different counties of Sweden, and examines the influence of local policy measures and practices, in addition to national policy. This study was performed in three steps: (1) a structured review of the literature on (biosimilar) policies in Sweden, (2) analysis of market data on the counties’ originator and biosimilar etanercept uptake (Q2 2012 to Q4 2017) provided by IQVIA, and (3) discussion of findings with a national health authority, key experts in Skåne, Västra Götaland, and Stockholm, and an industry representative. Notwithstanding the existence of a national managed entry agreement for etanercept, wide variations in biosimilar market shares between counties were observed (40% to 82% in 2017). Over time, early and late adopters of biosimilar etanercept can be distinguished. As prescriptions are often provided for a year, two possible approaches to switching patients were observed in counties: active pullback of prescriptions resulting in additional workload, or wait until the patient’s next visit. It can be noticed that switching is generally more driven by hospitals or individual physicians than by the county councils. Except for price differences between originator and biosimilar, the following factors that influence market dynamics differ between counties: presence of KOLs, local guidelines, and gainsharing arrangements. Regional variations in use of biosimilar etanercept can be seen although prices are coordinated nationally. The decision to switch or wait for lower prices is only affected by the county’s policy and attitudes of stakeholders towards biosimilars and switching. It seems that, for etanercept, counties are hesitant to switch patients, as it is associated with an increased administrative workload. Furthermore, a substantial price difference and guarantees that this will not change for a longer period are lacking in the etanercept market.

Design of Data Mining of WeChat Public Platform Based on Python

The 21st century is the artificial intelligence age; the new media in this condition quickly appeared people's eyes and become the information dissemination and the exchange important platform. In the 2011, people went into the "WeChat age", such as how to obtain the data in the WeChat public platform for interesting data has become the focus of the current research. This article is mainly aimed at the WeChat of the public platform Crawler search technology. First of all, the paper introduces the importance of web crawler in the age of data explosion, and then in the second part of the paper summarizes the domestic and foreign micro-trust Platform Network Crawler technology research, and then a brief overview of the network crawler related theory, working principle and Python in the field of the development of web crawler advantages, Finally, a Python-based WeChat public platform reptile system was designed to successfully obtain data. The crawled data can be used for market research and data analysis as raw data for machine learning and data mining.

Exposure of Brazilian soil and groundwater to pollution by coccidiostats and antimicrobial agents used as growth promoters

The World Health Organization has identified antimicrobial resistance as one of the most important threats to global health. Brazil is one of the world's leading meat producers and the Brazilian use of veterinary antimicrobials as therapeutic agents and prophylactic or growth promoters in animal production remains problematic. Many antimicrobials are not completely metabolized and their excretion represents a significant source of environmental exposure. The aim of this work was to estimate the exposure risk of soil and groundwater to pollution by growth promoters (GPs) and anticoccidial additives (AAs) in Brazil by using a method based on a geographical information system (GIS). The principle adopted is that the greater the amount of animals, the greater the quantity of antimicrobials present, and the greater the soil vulnerability to pollution. Our research showed that GPs and AAs are extensively used in the Brazilian animal production system. An analysis of market data showed that zinc bacitracin, monensin, salinomycin, colistin and tylosin are representative GPs and AAs. This study presents a qualitative approach for risk assessment based on worst-case scenarios. First, the probable environmental concentration was estimated using a correlation between the number of heads of the herds of poultry and cattle, and the amounts of drug released. The leaching risk potential was characterized for each compound, as proposed in ISO 15175. The potential of soil pollution was evaluated for each antimicrobial as a function of its binding and dissipation rates. These rates were calculated using georeferenced data of organic carbon, average temperature, water balance and hydro-geological parameters. The consequences were modeled based on Brazilian soil usage. Finally, the risk was calculated by combining the different maps generated using spatial multi-criteria decision analysis. Higher risk was found for the midwest, southeast and south regions of Brazil. Groundwater was found to be more vulnerable than soil.

Estimation for a Second-Order Jump Diffusion Model from Discrete Observations: Application to Stock Market Returns

This paper proposes a second-order jump diffusion model to study the jump dynamics of stock market returns via adding a jump term to traditional diffusion model. We develop an appropriate maximum likelihood approach to estimate model parameters. A simulation study is conducted to evaluate the performance of the estimation method in finite samples. Furthermore, we consider a likelihood ratio test to identify the statistically significant presence of jump factor. The empirical analysis of stock market data from North America, Asia, and Europe is provided for illustration.

Appliance labeling and consumer heterogeneity: A discrete choice experiment in India

Appliance labels are a widely used policy instrument to increase the adoption of energy-efficient technologies. Most countries use categorical labels to facilitate comparison of appliances based on energy consumption. Studies have found that consumers respond positively to the labels, but the differentiated response to the categories on labels is not well understood in the literature. This study presents the results of a discrete choice experiment corroborated by an analysis of secondary market data to demonstrate that consumers differentiate between label categories; with a greater value being placed on appliances with higher levels of energy efficiency performance. We examine two appliances - air conditioners and refrigerators and observe that there are some differences in consumer response - likely due to the different usage patterns of the two appliances. Consumer responses were observed in hypothetical choice situations constructed ensuring statistical and design efficiency. In a mixed logit model specification with correlated random parameters, the magnitude and the distribution of consumer preference for the highest category of energy efficiency are estimated. The results are validated by estimating the incremental price of higher category of energy efficiency using market data for comparison. The sources of consumer heterogeneity for the highest category of energy efficiency are identified after controlling for other consumer variables. The findings presented in this paper are useful for improving the effectiveness of existing energy efficiency programs and designing new programs to accelerate adoption of energy efficient technologies.

Study on sterol fraction of commercial brands of milk, yoghurt and butter sold in Iranian market and chemometric data analysis

A gas chromatographic procedure for analysis of sterols was developed to detect possible addition of vegetable oils to milk, yoghurt and butter samples. The method involved fat extraction, saponification, hexane extraction, silylation of sterols and then analysis by gas chromatography. The sterol content of 102 samples of milk, yoghurt and butter was determined. Phytosterol content higher than 5% of total sterol was observed in four samples of milk and yoghurt. Principal component analysis showed that there was a direct relationship between the level of phytosterols and fat percentage in yoghurt samples. The described method is accurate and reliable enough to be employed regularly in food quality laboratories.