Search results for: market crash prediction

Authors: Serpil Türkyılmaz, Mesut Balıbey

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In this study, long memory properties in volatility of Turkey Stock Market are being examined through the FIGARCH, FIEGARCH and FIAPARCH models under different distribution assumptions as normal and skewed student-t distributions. Furthermore, structural changes in volatility of Turkey Stock Market are investigated. The results display long memory property and the presence of asymmetric effects of shocks in volatility of Turkey Stock Market.

Keywords: FIAPARCH model, FIEGARCH model, FIGARCH model, structural break

Procedia PDF Downloads 266

Authors: Kamel Malik Bensafta, Gervasio Semedo

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The purpose of this paper is to investigate the relationship between oil prices and socks markets. The empirical analysis in this paper is conducted within the context of Multivariate GARCH models, using a transform version of the so-called BEKK parameterization. We show that mean and uncertainty of US market are transmitted to oil market and European market. We also identify an important transmission from WTI prices to Brent Prices.

Keywords: oil volatility, stock markets, MGARCH, transmission, structural break

Procedia PDF Downloads 493

Authors: L. Maiza, A. Cantagrel, M. Forestier, G. Laucoin, T. Regali

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Evaluation of High Frequency Trading (HFT) impact on financial markets is very important for traders who use market analysis to detect winning transaction opportunity. Analysis of HFT data on tobacco commodity market is discussed here and interesting linear relationship has been shown between trading frequency and difference between averaged trading prices above and below considered trading frequency. This may open new perspectives on markets data understanding and could provide possible interpretation of Adam Smith invisible hand.

Keywords: financial market, high frequency trading, analysis, impacts, Adam Smith invisible hand

Procedia PDF Downloads 331

Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

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In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, cross-correlation, avalanches, prediction method

Procedia PDF Downloads 351

Authors: Mario Zgela

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Application of Benford’s Law is very rarely covered in the field of stock market analysis, especially in percentage change of stock market indices. Deutscher Aktien IndeX (DAX) is very important stock market index of Frankfurt Deutsche Börse which serves as underlying basis for large number of financial instruments. It is calculated for selected 30 German blue chips stocks. In this paper, Benford's Law first digit test is applied on 10 year DAX daily percentage changes in order to check compliance. Deviations of 10 year DAX percentage changes set as well as distortions of certain subsets from Benford's Law distribution are detected. It is possible that deviations are the outcome of speculations; and psychological influence should not be eliminated.

Keywords: Benford's Law, DAX, index percentage changes, stock market

Procedia PDF Downloads 268

Authors: Yalçın Bozkurt

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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breeds

Keywords: artificial neural networks, bodyweight, cattle, digital body measurements

Procedia PDF Downloads 342

Authors: Weera Weerasophon

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The purpose of this research was to study Tourists’s satisfaction strategies case of Tourists who attended and shopped in Chatuchak weekend market (Bangkok) in order to improve service operation of Chatuchak weekend market to serve tourists’ need to impress them. The researcher used the marketing mix as a main factor that affect to tourist satisfaction. This research was emphasized as quantitative research as 400 of questionnaires were used for collecting the data from international tourists around Chatuchak weekend market that questionnaires divided in to 3 parts as a personal information part, satisfaction of marketing/services and facilities and suggestion part. After collecting all the data that would be processed in statistic program of SPSS to use for analyze the data later on. The result is described that most of international tourists satisfied Chatuchak weekend market in the level of 4 as more satisfaction for example friendly staff, Chatuchak information, price of product, facilities and service by the way, the environment of Chatuchak weekend market is the most satisfaction level.

Keywords: Chatuchak, satisfaction, Thailand tourism, marketing mix, tourists

Procedia PDF Downloads 337

Authors: A. Novikova, L. Rocchi, G. Startiene

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Market and non-market outputs are produced jointly in agriculture. Their supply depends on the intensity and type of production. The role of agriculture as an economic activity and its effects are important for the Lithuanian case study, as agricultural land covers more than a half of country. Positive and negative externalities, created in agriculture are not considered in the market. Therefore, specific techniques such as stated preferences methods, in particular choice experiments (CE) are used for evaluation of non-market outputs in agriculture. The main aim of this paper is to present construction of the research path for evaluation of non-market farming system outputs in Lithuania. The conventional and organic farming, covering crops (including both cereal and industrial crops) and livestock (including dairy and cattle) production has been selected. The CE method and nested logit (NL) model were selected as appropriate for evaluation of non-market outputs of different farming systems in Lithuania. A pilot survey was implemented between October–November 2018, in order to test and improve the CE questionnaire. The results of the survey showed that the questionnaire is accepted and well understood by the respondents. The econometric modelling showed that the selected NL model could be used for the main survey. The understanding of the differences between organic and conventional farming by residents was identified. It was revealed that they are more willing to choose organic farming in comparison to conventional farming.

Keywords: choice experiments, farming system, Lithuania market outputs, non-market outputs

Procedia PDF Downloads 104

Authors: Nguyen Ngoc Que Tran

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Purpose: The purpose of this paper is to investigate the relationship between internal market orientation, external market orientation, employee commitment and job satisfaction. Design/methodology/approach: This study collected data through a survey and utilized simple linear regression and multiple regression analysis to determine if there was any support for the research hypotheses as presented in the previous chapter. Findings: Using data from 256 employees of four leading joint stock banks in Vietnam, the empirical results indicates that employee commitment is positively related with external market orientation, job satisfaction is positively related to employee commitment, and employee commitment and job satisfaction are positively related to external market orientation. However, job satisfaction has no significant positive effect on external market orientation. Theoretical contribution: The primary contribution to marketing theory arising from this study is the integration of job satisfaction, employee commitment, and external market orientation in a single research model. Practical implications: The major contribution to practice is an external market oriented bank has to respond rapidly to the future needs and preferences of its customers. This could result in high levels of commitment to the service process and in doing so provide Vietnamese joint-stock commercial banks with a competitive advantage. The finding is important for the banking service sector in general and the Vietnamese banking industry in particular.

Keywords: employee commitment, job satisfaction and external market orientation, vietnam, bank

Procedia PDF Downloads 383

Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Riktesh Srivastava

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Online Shopping have established an astonishing evolution in the last few years. And it is now apparent that B2C architecture is becoming progressively imperative channel for even traditional brick and mortar type traders as well. In this completion knowing customers and predicting behavior are extremely important. More important, when any customer logs onto the B2C architecture, the traces of their buying patterns can be stored and used for future predictions. Such a prediction is called Customer Lifetime Value (CLV). Earlier, we used Net Present Value to do so, however, it ignores two important aspects of B2C architecture, “market risks” and “big amount of customer data”. Now, we use RFM- Recency, Frequency and Monetary Value to estimate the CLV, and as the term exemplifies, market risks, is well sheltered. Big Data Analysis is also roofed in RFM, which gives real exploration of the Big Data and lead to a better estimation for future cash flow from customers. In the present paper, 6 factors (collected from varied sources) are used to determine as to what attracts the customers to the B2C architecture. For these 6 factors, RFM is computed for 3 years (2013, 2014 and 2015) respectively. CLV and Revenue are the two parameters defined using RFM analysis, which gives the clear picture of the future predictions.

Keywords: CLV, RFM, revenue, recency, frequency, monetary value

Procedia PDF Downloads 192

Authors: Bin Li, Mei Liu

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Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours.

Keywords: bus transit, arrival time prediction, link-based, path-based

Procedia PDF Downloads 335

Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

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In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: churn prediction, data mining, decision-theoretic rough set, feature selection

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

Procedia PDF Downloads 46

Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

Procedia PDF Downloads 280

Authors: Pallavi Mane, Nikita Mankar, Shraddha Mazire, Rasika Pashankar

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Content sharing sites are very useful in sharing information and images. However, with the increasing demand of content sharing sites privacy and security concern have also increased. There is need to develop a tool for controlling user access to their shared content. Therefore, we are developing an Adaptive Privacy Policy Prediction (A3P) system which is helpful for users to create privacy settings for their images. We propose the two-level framework which assigns the best available privacy policy for the users images according to users available histories on the site.

Keywords: online information services, prediction, security and protection, web based services

Procedia PDF Downloads 332

Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 132

Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

Procedia PDF Downloads 274

Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

Procedia PDF Downloads 68

Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 106

Authors: Belayneh Yohannes

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In Ethiopia, onion production is increasing from time to time mainly due to its high profitability per unit area. Onion has a significant contribution to generating cash income for farmers in the Raya Azebo district. Therefore, enhancing onion producers’ access to the market and improving market linkage is an essential issue. Hence, this study aimed to analyze structure-conduct-performance of onion market and identifying factors affecting the market supply of onion producers. Data were collected from both primary and secondary sources. Primary data were collected from 150 farm households and 20 traders. Four onion marketing channels were identified in the study area. The highest total gross margin is 27.6 in channel IV. The highest gross marketing margin of producers of the onion market is 88% in channel II. The result from the analysis of market concentration indicated that the onion market is characterized by a strong oligopolistic market structure, with the buyers’ concentration ratio of 88.7 in Maichew town and 82.7 in Mekelle town. Lack of capital, licensing problems, and seasonal supply was identified as the major entry barrier to onion marketing. Market conduct shows that the price of onion is set by traders while producers are price takers. Multiple linear regression model results indicated that family size in adult equivalent, irrigated land size, access to information, frequency of extension contact, and ownership of transport significantly determined the quantity of onion supplied to the market. It is recommended that strengthening and diversifying extension services in information, marketing, post-harvest handling, irrigation application, and water harvest technology is highly important.

Keywords: oligopoly, onion, market chain, multiple linear regression

Procedia PDF Downloads 87

Authors: Hananto Prakoso, Jean-Pierre Orfeuil

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The relationship between city size, urban transport efficiency (speed), employment proximity (distance) and accessibility of labour market is rarely examined especially in developing countries. This paper reveals the relationship using 2 points of views (active population and company). Then the analysis is divided according to 3 transport modes (car, public transport and motorcycle) and takes into account the vehicle ownership rate. We employ data across 111 districts in 4 big cities of Indonesia. In our result, speed indicator contributed positively to accessibility of labour market while distance elasticity is negative. In absolute value, elasticity of speed indicator is higher than that of distance.

Keywords: labour market, travel time, travel cost threshold, transportation

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Shogo Kato, Daisuke Okamoto, Satoko Tsuru, Yoshinori Iizuka, Ryoko Shimono

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Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, though a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital.

Keywords: trouble prevention, knowledge structure, structured knowledge, reusable knowledge

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Authors: Gerald Abdesaken, Thomas O. Miller

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This paper analyzes the performance of gold as a safe-haven investment. Assuming high market volatility as an impetus to seek a safe haven in gold, the return of gold relative to the stock market, as measured by the S&P 500, is tracked. Using the Chicago Board Options Exchange (CBOE) volatility index (VIX) as a measure of stock market volatility, various criteria are established for when an investor would seek a safe haven to avoid high levels of risk. The results show that in a vast majority of cases, the S&P 500 outperforms gold during these periods of high volatility and suggests investors who seek safe haven are underperforming the market.

Keywords: gold, portfolio management, safe haven, VIX

Procedia PDF Downloads 130

Authors: Aliyatul Widyan, Denie Heriyadi, An An Nurmeidiansyah

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Garut Sheep is a commodity of sheep originally from West Java Indonesia, specifically it has combination rumpung ears less than 4 cm or ngadaun hiris (4-8cm) with ngabuntut bagong, or ngabuntut beurit. West Java culture diversity one of those is the Garut Ram’s Art and Fighting Contest. Garut Ram’s Art and Fighting Contest is an activity of competitive fighting between sheep which comes from Garut. The method used is a survey method in which watching and directly interviewing the farmers who competed in the event. This activity had some aspects of animal welfare in the context of the assessment of the fighting sheep, which are health 10%, performance and body conformation called adeg-adeg 25%, courage 10%, technical field 30% called with teknik pamidangan, technical crash 25%, the health assessment is the assessment conducted during registration by showing a letter issued by related agency declaring that the sheep is eligible to compete in the event, and then when the fighting time the health also will be assessed. Adeg-adeg assessed an aspect of conformity assessment of body posture Garut ram from the physical performance is assessed on the body posture, horn, and the face. Technical of pamidangan assessed by the harmony of music and the movement of sheep to carry out the attack. Courage is assessed based on a mental condition and stamina when the fighting time, in addition to the assessments the activity has some other the component of culture and arts, such as, the audience called bobotoh, the clothes worn called pangsi, tarumpah or sandals, belts, and totopong, hats called laken, instructor of the match, and nayaga or group of people who play traditional Sundanese music to accompany this activity. Art aspect of animal welfare of this activity included the percentage of stroke technique is only around 25%, it makes the beauty of this art is not only measured by the Technical crash but also health, courage, and technique in the field has the highest mark in the assessment with 75 %, the event is certainly very different from sports such as boxing, taekwondo, karate or other martial sports which 100% only based on stroke or crash technique. Local culture value of Garut Ram’s Art and Fighting Contest results in the art of the local animal welfare.

Keywords: Garut sheep, Indonesia, the art of Garut Ram’s Art and Fighting Contest , animal welfare

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

Procedia PDF Downloads 421

Authors: Haruna Maama

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Environmental, social and governance (ESG) reporting is gaining considerable attention despite being voluntary. Meanwhile, it consumes resources to provide ESG reporting, raising a question of its value relevance. The study examined the impact of ESG reporting on the market value of listed firms in SSA. The annual and integrated reports of 276 listed sub-Sahara Africa (SSA) firms. The integrated reporting scores of the firm were analysed using a content analysis method. A multiple regression estimation technique using a GMM approach was employed for the analysis. The results revealed that ESG has a positive relationship with firms’ market value, suggesting that investors are interested in the ESG information disclosure of firms in SSA. This suggests that extensive ESG disclosures are attempts by firms to obtain the approval of powerful social, political and environmental stakeholders, especially institutional investors. Furthermore, the market value analysis evidence is consistent with signalling theory, which postulates that firms provide integrated reports as a signal to influence the behaviour of stakeholders. This finding reflects the value placed on investors' social, environmental and governance disclosures, which affirms the views that conventional investors would care about the social, environmental and governance issues of their potential or existing investee firms. Overall, the evidence is consistent with the prediction of signalling theory. In the context of this theory, integrated reporting is seen as part of firms' overall competitive strategy to influence investors' behaviour. The findings of this study make unique contributions to knowledge and practice in corporate reporting.

Keywords: environmental accounting, ESG accounting, signalling theory, sustainability reporting, sub-saharan Africa

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

Procedia PDF Downloads 98