Stock Prediction Model Using TensorFlow

In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend available in market prediction technologies is that the use of machine learning that makes predictions on the basis of values of current stock exchange indices by training on their previous values. Machine learning itself employs completely different models to create prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Considering the factors are open, close, low, high and volume. Keywords: Stock Prediction, Machine Learning, Data Visualization, Yahoo Finance Dataset

Productivity Analysis of Crude Palm Oil (CPO) in PT. Ramajaya Pramukti Using Value Stream Mapping Approach

This study aims to analyze the productivity of CPO processing using Value Stream Mapping (VSM) approach. A case study conducted in PT. Ramajaya Pramukti, Indonesia. The research method used the VSM and Process Activity Mapping (PAM) to determine wastes in the process flow of CPO production. The data was collected in 1 month. The preliminary result of CPO productivity process was average of 73.67%. Based the Future Value Stream Mapping (FVSM) the CPO processing time can be efficient from 1.981 seconds/kg to 1.963 seconds/kg. The productivity processing for value added 1.525 seconds/kg, which non value added of 0.012 and the non necessary value added of 0.383 seconds/kg. The quality of raw materials was the biggest waste contributor. It was caused a longer processing time due to poor quality of raw materials.

Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies

Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities.

Een mensgericht, gezondheidsgegevens-gedreven ecosysteem

A human-centered, health data-driven ecosystem Value-based, connected and integrated healthcare are gaining momentum in the healthcare landscape. Industry 4.0 is transforming healthcare into a data-driven sector. Data and innovation are the foundations of future value-driven healthcare ecosystems. But how will the human aspect remain to play a lasting role? Healthcare continuums are being rolled out as healthcare goes beyond traditional diagnosis and treatment towards prevention and early detection. Health institutions are facing a new generation of ‘health-conscious’ consumers and ‘technology-minded and -adapted’ healthcare professionals. In order to accelerate innovation within healthcare institutions, it must be powered by the personal health-data of an individual, independent of location, life phase and health status. This data will be continuously generated by the daily use of different technologies. Based upon these concepts and shifts, this paper describes a human-centered, health data-driven ecosystem that is built upon the interaction and balance of human actors in every life phase, different environments and societal changes and with different technologies. International and national guidelines and regulations, and ethical norms and values need to be taken into account. Implementation of this health data model will create future value at any time, place and location within this ecosystem. This ecosystem will create value through the realized integrated care by connecting diverse human actors, different environments and various technologies, while still maintaining the empathic relations between the caretaker and the patient/client.

Process mining at Lufthansa CityLine: The path to process excellence

Process mining is a big data technology, which focuses on the discovery, monitoring, and improvement of business processes, based on real data from information systems. This teaching case describes the objectives of a German airline as it introduces process mining and discusses current and future value potentials of this technology. The case is particularly useful for executive MBA courses on Strategy (the value of IT investments) or master’s-level courses on Business Process Management. This case has three main learning objectives. First, students will evaluate the capabilities of different (technological) approaches to reaching the airline’s business goals and will make a justified decision on the feasibility of implementing process mining. Second, students will analyze the airline’s approach to implementing process mining and the challenges along the way. They will derive lessons learned and discuss approaches to solving challenges. Third, students will evaluate the value potentials of process mining. This will enable the students to make well-informed decisions on technology investments and to discover how these decisions can contribute to business goals. The case is designed to be taught in two formats. In a 90-min lecture, students need to prepare short assignments for classroom discussions. In a 180-min lecture, the assignments are included as group work during the lecture, but they require the students to read the case before class. Teaching Notes, including videos and additional study material to support group work, are available to eligible lecturers upon request.

Modeling the Future Value Distribution of a Life Insurance Portfolio

This paper addresses the problem of approximating the future value distribution of a large and heterogeneous life insurance portfolio which would play a relevant role, for instance, for solvency capital requirement valuations. Based on a metamodel, we first select a subset of representative policies in the portfolio. Then, by using Monte Carlo simulations, we obtain a rough estimate of the policies’ values at the chosen future date and finally we approximate the distribution of a single policy and of the entire portfolio by means of two different approaches, the ordinary least-squares method and a regression method based on the class of generalized beta distribution of the second kind. Extensive numerical experiments are provided to assess the performance of the proposed models.

JUSTIFICATION FOR CHOOSING THE DISCOUNT RATE IN INVESTMENT ANALYSIS

Статья посвящена вопросам выбора ставки дисконта для приведения будущей стоимости денежных потоков к настоящей стоимости с помощью коэффициента дисконтирования. Целью исследования является анализ и обоснование выбора численного значения ставки дисконта в зависимости от источников финансирования инвестиционного проекта. Авторами приведены практические рекомендации по выбору метода оценки экономической эффективности привлечения инвестиций как для экономически обособленного инвестиционного проекта, так и для инвестиционного проекта, интегрированного в действующее предприятие. Научная новизна полученных результатов заключается в разработке методического подхода к выбору численного значения ставки дисконта для приведения будущей стоимости денежных потоков к настоящей стоимости с помощью коэффициента дисконтирования и к выбору приоритетного метода оценки экономической эффективности для экономически обособленных и интегрированных в действующее предприятие инвестиционных проектов. The article is devoted to the issues of choosing the discount rate for bringing the future value of cash flows to the present value using the discount coefficient. The purpose of the study is to analyze and justify the choice of the numerical value of the discount rate depending on the sources of financing of the investment project. The authors provide practical recommendations on the choice of a method for assessing the economic efficiency of attracting investment both for an economically isolated investment project and for an investment project integrated into an operating enterprise. The scientific novelty of the obtained results consists in the development of a methodological approach to the choice of the numerical value of the discount rate for bringing the future value of cash flows to the present value using the discount coefficient and the choice of a priority method for assessing economic efficiency for both economically isolated and integrated investment projects in an operating enterprise.

New Approaches to Documenting Performance in the Museum: Value, History, and Strategy

This chapter shows how over the past two decades the relationship between the museum and performance has undergone a radical shift with the acquisition of performance-based artworks into the collection, shifting the role of the museum from that of a repository to that of a vital participant in the activation of the work. This chapter reflects on the new value this turn affords to documentation, and on how it is being used to support the effective activation of performance-based artworks in the museum. It reflects particularly on Tate’s development of documentation practices that address these new institutional needs and on how these navigate both immediate and potential future value.

A host–guest semibiological photosynthesis system coupling artificial and natural enzymes for solar alcohol splitting

Development of a versatile, sustainable and efficient photosynthesis system that integrates intricate catalytic networks and energy modules at the same location is of considerable future value to energy transformation. In the present study, we develop a coenzyme-mediated supramolecular host-guest semibiological system that combines artificial and enzymatic catalysis for photocatalytic hydrogen evolution from alcohol dehydrogenation. This approach involves modification of the microenvironment of a dithiolene-embedded metal-organic cage to trap an organic dye and NADH molecule simultaneously, serving as a hydrogenase analogue to induce effective proton reduction inside the artificial host. This abiotic photocatalytic system is further embedded into the pocket of the alcohol dehydrogenase to couple enzymatic alcohol dehydrogenation. This host-guest approach allows in situ regeneration of NAD+/NADH couple to transfer protons and electrons between the two catalytic cycles, thereby paving a unique avenue for a synergic combination of abiotic and biotic synthetic sequences for photocatalytic fuel and chemical transformation.