For a while, Zoom was the most important company in America. Three years ago, the pandemic had forced offices to come up with extended remote-working arrangements, and Zoom became the indispensable videoconferencing platform of choice for millions of stuck-at-home Americans. This humble enterprise app was suddenly there for everything: work, school, social gatherings, activism, dating, telehealth, government hearings, funerals, sex parties, and pretty much anything else that made up life when everyone was locked indoors. Already a profitable company by the end of 2019, Zoom became a stock trader's dream after it landed on the NASDAQ in early 2020, growing its customer base by 470 percent, quadrupling its revenue (without paying any income tax, according to one report), and expanding its workforce throughout the year. Since then, as vaccination and reduced transmission allowed American enterprise to adjust back to normalish routines, Zoom has struggled to maintain its pandemic-era success.

Managing discount promotional events ("markdown") is a significant part of running an e-commerce business, and inefficiencies here can significantly hamper a retailer's profitability. Traditional approaches for tackling this problem rely heavily on price elasticity modelling. However, the partial information nature of price elasticity modelling, together with the non-negotiable responsibility for protecting profitability, mean that machine learning practitioners must often go through great lengths to define strategies for measuring offline model quality. In the face of this, many retailers fall back on rule-based methods, thus forgoing significant gains in profitability that can be captured by machine learning. In this paper, we introduce two novel end-to-end markdown management systems for optimising markdown at different stages of a retailer's journey. The first system, "Ithax", enacts a rational supply-side pricing strategy without demand estimation, and can be usefully deployed as a "cold start" solution to collect markdown data while maintaining revenue control. The second system, "Promotheus", presents a full framework for markdown optimization with price elasticity. We describe in detail the specific modelling and validation procedures that, within our experience, have been crucial to building a system that performs robustly in the real world. Both markdown systems achieve superior profitability compared to decisions made by our experienced operations teams in a controlled online test, with improvements of 86% (Promotheus) and 79% (Ithax) relative to manual strategies. These systems have been deployed to manage markdown at ASOS.com, and both systems can be fruitfully deployed for price optimization across a wide variety of retail e-commerce settings.

Stock market prediction and analysis are some of the most difficult jobs to complete. There are numerous causes for this, including market volatility and a variety of other dependent and independent variables that influence the value of a certain stock in the market. These variables make it extremely difficult for any stock market expert to anticipate the rise and fall of the market with great precision. However, with the introduction of Machine Learning and its strong algorithms, the most recent market research and Stock Market Prediction advancements have begun to include such approaches in analyzing stock market data. In summary, Machine Learning Algorithms are widely utilized by many organizations in Stock market prediction.

Artificial Intelligence is a recent notion that we've all heard about and may even be familiar with. Because you choose to read this post, you will undoubtedly gain from the trading features of AI that we have discussed ahead. It is essential to understand how they have aided profitable trading in today's world. The study and engineering of developing intelligent robots in their most basic form are known as artificial intelligence (AI). It takes into consideration intelligent computer programs that can calculate, reason, learn from experience, adapt to new conditions, and handle complicated issues, to name a few examples.

You can read about the potential of data science everywhere. Data is a source of concern for everyone. Businesses are interested in learning how data may help them cut costs and boost their profits. Data science has piqued the interest of the healthcare business, which wants to know how it may help them forecast illnesses and deliver better treatment to its patients. Data science is being utilized in this way to give an in-depth understanding of the stock market and financial statistics.

School of the Built Environment, Oxford Brookes University, Oxford, OX3 0BP, UK. Abstract: This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models. LSDL Large-Scale Deep Learning LSTM Long Short-Term Memory LWDNN List-Wise Deep Neural Network MACN Multi-Agent Collaborated Network MB-LSTM Multivariate Bidirectional LSTM MDNN Multilayer Deep Neural Network MFNN Multi-Filters Neural Network MLP Multiple Layer Perceptron MLP Multi-Layer Perceptron NNRE Neural Network Regression Ensemble O-LSRM Optimal Long Short-Term Memory PCA Principal Component Analysis pSVM Proportion Support Vector Machines RBFNN Radial Basis Function Neural Network RBM Restricted Boltzmann Machine REP Reduced Error Pruning RF Random Forest RFR Random Forest Regression RNN Recurrent Neural Network SAE Stacked Autoencoders SLR Stepwise Linear Regressions SN-CFM Similarity, Neighborhood-Based Collaborative Filtering Model STI Stock Technical Indicators SVM Support Vector Machine SVR Support Vector Regression SVRE Support Vector Regression Ensemble, TDFA Time-Driven Feature-Aware TS-GRU Two-Stream GRU WA Wavelet Analysis WT Wavelet Transforms 1. Introduction Application of data science in different disciplines is exponentially increasing. Because data science has had tremendous progresses in analysis and use of data. Like other disciplines, economics has benefited from the advancements of data science. Advancements of data science in economics have been progressive and have recorded promising results in the literature.

FinTech industry is known to use artificial intelligence for a wide range of purposes. Digital enterprises use it for efficient chatbot response systems. Some businesses offer AI as an assistant for asset management and market analysis. The use cases of AI are widespread among the industry, and we can safely assume that technology will be further used. According to Mordor Intelligence, the AI market in fintech is projected to grow beyond $7 billion from only $1.2 bln in 2017.

The prediction of stock prices is an important task in economics, investment and financial decision-making. It has for several decades, spurred the interest of many researchers to design stock price predictive models. In this paper, the symbiotic organisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks (FFNN). The training process is used to build a better stock price predictive model. The Straits Times Index, Nikkei 225, NASDAQ Composite, S&P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model. Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolution Deviation are used to compare the results of the implemented model. The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models. The new model is a promising predictive technique for solving high dimensional nonlinear time series data that are difficult to capture by traditional models.

Machine Learning (ML) is closely related to computational statistics which focuses on prediction-making through the use of computers. ML is a modern approach to an old problem: predictive inference. It makes an inference from "feature" space to "outcome/target" space. In order to work properly, an ML algorithm has to discover and model hidden relationships between the feature space and the outcome space and create links between the two. Doing so requires overcoming barriers such as feature noise (randomness of features due to unexplained mechanisms). In this article we argue that "Emergence" is also a barrier for predictive inference.