Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market situations. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand technique for processing very large data. We test our algorithms on the 30 Dow Jones stocks that have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble strategy is shown to outperform the three individual algorithms and two baselines in terms of the risk-adjusted return measured by the Sharpe ratio. This work is fully open-sourced at \href{https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020}{GitHub}.

As some financial leaders fret publicly about the stock market falling to earth, Andrew Ross Sorkin's new book recounts the greatest crash of them all. As stocks plummeted on the morning of October 24th, 1929, a large crowd gathered on Wall Street outside of the New York Stock Exchange. Pat Bologna, a local shoeshiner whose life savings were invested in the market, dodged into a packed brokerage nearby. "Everybody is shouting," he later recalled. "They're all trying to reach the glass booth where the clerks are. Everybody wants to sell out. The boy at the quotation board is running scared. He can't keep up with the speed of the way stocks are dropping. The guy who runs it is Irish. I can't hear what he's saying. But a guy near me shouts, 'the sonofabitch has sold me out!' " The stock-market crash of 1929 occupies a dark but indelible place in the national imagination, and for good reason.

We introduce DataTales, a novel benchmark designed to assess the proficiency of language models in data narration, a task crucial for transforming complex tabular data into accessible narratives. Existing benchmarks often fall short in capturing the requisite analytical complexity for practical applications. DataTales addresses this gap by offering 4.9k financial reports paired with corresponding market data, showcasing the demand for models to create clear narratives and analyze large datasets while understanding specialized terminology in the field. Our findings highlights the significant challenge that language models face in achieving the necessary precision and analytical depth for proficient data narration, suggesting promising avenues for future model development and evaluation methodologies.

Apple Watch is one of the best-designed smartwatches out there and it provides you with a lot more functionality than just tracking your activities and showing time. There are a lot of hidden Apple watch tips and tricks that you may not know about, so here’s a guide on how to enable them. Ease of use is often the focus for many of Apple’s consumer products and Apple Watch is not that different. The Apple Watch packs a lot of functions and you can actually use it for things other than checking the time or tracking your workouts. For most of the part, you may be familiar with many basic functions on the watch but there are some hidden features and recent improvements that enable you to get more out of your watch.  Here are some Apple Watch Uses, tips and tricks that you should try: Continue Reading . . . . . . . . . . . . . . . . . . . . . . . . Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK), Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK), Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK), Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK)

This is the first in a series of articles about Information Theory and its relationship to data driven enterprises and strategy. While there will be some equations in each section, they can largely be ignored for those less interested in the details and more in the implications. The early and middle parts of the 20th century saw an explosion in telecommunication technologies and capabilities. In doing so these researchers accidentally built one of the most powerful set of tools applied math has given us for approaching and solving a myriad of otherwise'not so mathy' problems. Over the course of the first few article we'll introduce 5 tools -- Entropy, Mutual Information, Huffman Codes, Kolmogorov Complexity and Fisher Information -- for assessing these and other challenges in both personal and professional life. If you talk to a quantitative professional and ask them how long they've been at it the usual, diplomatic answer involves their number of years since finishing undergrad.

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.

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.

Jon Ball, the chief investment officer of the Intrinsic Asset Fund, expect the Dow Jones Industrial Average to reach 30,000 in 18 months. As the bull market passed its ninth birthday in March, it became the longest and greatest in terms of percentage gains for the Dow Jones Industrial Average since World War II, according the Leuthold Group. Since business cycles end and bull markets turn into bears, where do we stand now? Is there more room to grow? "I think the Dow is going to 30,000 in the next 18 months," said Jon Ball, chief investment officer of the Intrinsic Asset Fund, a West Palm Beach, Fla., hedge fund.