Historically, the economic recession often came abruptly and disastrously. For instance, during the 2008 financial crisis, the SP 500 fell 46 percent from October 2007 to March 2009. If we could detect the signals of the crisis earlier, we could have taken preventive measures. Therefore, driven by such motivation, we use advanced machine learning techniques, including Random Forest and Extreme Gradient Boosting, to predict any potential market crashes mainly in the US market. Also, we would like to compare the performance of these methods and examine which model is better for forecasting US stock market crashes. We apply our models on the daily financial market data, which tend to be more responsive with higher reporting frequencies. We consider 75 explanatory variables, including general US stock market indexes, SP 500 sector indexes, as well as market indicators that can be used for the purpose of crisis prediction. Finally, we conclude, with selected classification metrics, that the Extreme Gradient Boosting method performs the best in predicting US stock market crisis events.

This study investigates the impact of biased language, specifically 'Words that Wound,' on sentiment analysis in a dataset of 45,379 South Korean daily economic news articles. Using Word2Vec, cosine similarity, and an expanded lexicon, we analyzed the influence of these words on news titles' sentiment scores. Our findings reveal that incorporating biased language significantly amplifies sentiment scores' intensity, particularly negativity. The research examines the effect of heightened negativity in news titles on the KOSPI200 index using linear regression and sentiment analysis. Results indicate that the augmented sentiment lexicon (Sent1000), which includes the top 1,000 negative words with high cosine similarity to 'Crisis,' more effectively captures the impact of news sentiment on the stock market index than the original KNU sentiment lexicon (Sent0). The ARDL model and Impulse Response Function (IRF) analyses disclose that Sent1000 has a stronger and more persistent impact on KOSPI200 compared to Sent0. These findings emphasize the importance of understanding language's role in shaping market dynamics and investor sentiment, particularly the impact of negatively biased language on stock market indices. The study highlights the need for considering context and linguistic nuances when analyzing news content and its potential effects on public opinion and market dynamics.

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on financial movement of Crude Oil price and three U.S. stock indexes: DJI, S&P 500 and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the Stationary Wavelet Transform (SWT) and Bidirectional Long Short-Term Memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

"It should be better known as artificially inflated," says Fabrice Brossart, CRO of AIG, upon discussing the role that Artificial Intelligence (AI) plays within the insurance giant. A buzzword of many years, Brossart believes that there is not enough penetration into the various sectors of AI. Chris Gledhill, a former Lloyds Group developer and current CEO of Secco, is of much the same opinion when it comes to AI. "That's the problem with emerging tech," says Gledhill, "For whatever reason we don't think about the unknowns-unknowns, chaos-style disruptions that may arise from the technology." For Gledhill, that technology, the one that he is most excited about, is neural networks, a sub-sector of the AI label. Artificial Neural Network (ANN) mirrors the concept of biological neural networks within the human brain.

We live in a data-driven world. For instance, if you activate location services on Google Maps and, a year later, go to your timeline, it can tell you where you were on the same day the year prior. If you turn on Facebook Activation services, it suggests friends you should request when you go somewhere. Successful enterprises are extracting information and intelligence from all the data being collected to identify their target customers and sell products and services to them. In the current disruptive market environment, data is driving change to business models.