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AI-Driven Market Analysis: Revolutionizing Financial InsightsIntroduction Market analysis has long been the cornerstone of financial decision-making, offering insights into market trends, asset valuation, and investment opportunities. Traditionally, this analysis has relied on a combination of statistical methods, fundamental analysis, and expert judgment to interpret market dynamics and forecast future movements. However, the finance industry is currently undergoing a seismic shift with the introduction and integration of Artificial Intelligence (AI). AI, with its unparalleled ability to process and analyze vast quantities of data at unprecedented speeds, is revolutionizing market analysis. Unlike traditional methods, which often struggle with the sheer volume and complexity of modern financial data, AI algorithms can quickly sift through global market data, news, and financial reports, identifying patterns and correlations that might escape human analysts. This capability is not just about handling data efficiently; it's about uncovering deeper market insights and offering more nuanced, informed perspectives on market movements. The growing role of AI in financial market analysis is multifaceted. It encompasses predictive analytics, which forecasts market trends and asset price movements; risk assessment, which evaluates potential risks and market volatility; and sentiment analysis, which gauges market sentiment by analyzing news, social media, and financial reports. These AI-driven approaches are transforming how investors, traders, and financial institutions make decisions, offering a more data-driven, precise, and comprehensive view of the markets. As we delve deeper into the world of AI-driven market analysis, it's crucial to understand both its potential and its limitations. While AI provides powerful tools for market analysis, it also introduces new challenges and considerations, particularly around data quality, algorithmic bias, and ethical implications. In this article, we'll explore how AI is changing the landscape of market analysis, examining its applications, benefits, and future prospects in the ever-evolving world of finance. The Evolution of Market Analysis A Brief History of Market Analysis in Finance Market analysis in finance has a storied history, evolving through various stages as it adapted to changing markets and technological advancements. Initially, market analysis was predominantly fundamental, focusing on the intrinsic value of assets based on economic indicators, financial statements, and industry trends. Technical analysis, which emerged later, shifted the focus to statistical trends in market prices and volumes, seeking to predict future movements based on historical patterns. Over the decades, these approaches were refined, incorporating increasingly sophisticated statistical models. However, they remained limited by the human capacity to process information. Analysts were constrained by the volume of data they could analyze and the speed at which they could process it. This often led to a reactive approach to market changes, rather than a predictive one. Transition from Traditional Methods to AI Integration The advent of computer technology brought the first major shift in market analysis. Computers enabled quicker processing of data and complex mathematical modeling, allowing for more sophisticated analyses that could keep pace with the growing volume and velocity of financial market data. The introduction of quantitative analysis in the latter part of the 20th century marked a significant step in this evolution, as it used complex mathematical and statistical techniques to identify market opportunities. The real transformation, however, began with the integration of AI and machine learning into market analysis. AI's ability to learn from data, identify patterns, and make predictions, has taken market analysis to an entirely new level. AI algorithms can analyze vast datasets — including historical price data, financial news, social media sentiment, and economic indicators — much faster and more accurately than any human analyst could. This integration of AI into market analysis has led to the development of predictive models that can forecast market trends and anomalies with a higher degree of accuracy. AI-driven tools are now capable of real-time analysis, providing instantaneous insights that help traders and investors make more informed decisions. Furthermore, AI's ability to continually learn and adapt to new data sets it apart from static traditional models, allowing for a more dynamic and responsive approach to market analysis. The transition from traditional methods to AI integration represents a paradigm shift in market analysis. This evolution is not just about adopting new tools but signifies a fundamental change in how financial markets are understood and navigated. As we continue to advance in the realm of AI, the potential for even more sophisticated and insightful market analysis grows, promising to reshape the landscape of finance in ways we are only beginning to comprehend. Fundamentals of AI in Market Analysis The integration of Artificial Intelligence (AI) and machine learning into market analysis marks a significant advancement in the way financial data is interpreted and utilized. Understanding the fundamentals of these technologies is essential to appreciate their impact on market analysis. Explanation of AI and Machine Learning AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. In the context of market analysis, AI enables the automation of complex tasks, including data processing, pattern recognition, and predictive analytics. Machine learning, a subset of AI, involves the development of algorithms that can learn and improve from experience without being explicitly programmed. In market analysis, machine learning algorithms analyze historical data to identify patterns and predict future market behavior. The more data these algorithms are exposed to, the more accurate their predictions become. Types of AI Models Used in Market Analysis 1. Neural Networks: Inspired by the human brain's structure, neural networks consist of layers of interconnected nodes that process data in a manner similar to human neurons. In market analysis, neural networks are used for their ability to detect complex patterns and relationships within large datasets. They are particularly effective in predicting price movements and identifying trading opportunities based on historical market data. 2. Regression Models: These models are fundamental in statistical analysis and are used to understand relationships between variables. In finance, regression models help in forecasting asset prices and understanding the impact of various factors (like interest rates, GDP growth, etc.) on market trends. 3. Time Series Analysis Models: Time series models are crucial in financial market analysis, as they are specifically designed to analyze and forecast data points collected over time. These models help in understanding and predicting trends, cyclicality, and seasonal variations in market data. 4. Natural Language Processing (NLP): NLP is used to analyze textual data, such as financial news, earnings reports, and social media posts, to gauge market sentiment. By processing and interpreting the nuances of human language, NLP models can provide insights into how public sentiment is likely to impact market movements. 5. Decision Trees and Random Forests: These models are used for classification and regression tasks. In market analysis, they can help in categorizing stocks into different classes based on their characteristics or in predicting the likelihood of certain market events. 6. Reinforcement Learning: This type of machine learning involves algorithms learning optimal actions through trial and error. In trading, reinforcement learning can be used to develop strategies that adapt to changing market conditions to maximize returns. Each of these AI models brings a unique set of capabilities to market analysis. Their ability to handle large volumes of data, recognize complex patterns, and make informed predictions is transforming the field of financial analysis, allowing for more nuanced and sophisticated market insights. As AI technology continues to evolve, its applications in market analysis are poised to become even more integral to financial decision-making. Key Applications of AI in Market Analysis The incorporation of Artificial Intelligence (AI) in market analysis has opened up new frontiers in understanding and predicting market behavior. AI's ability to process vast datasets and uncover intricate patterns provides invaluable insights for investors, traders, and financial analysts. Here are some key applications of AI in market analysis: 1. Predictive Analytics for Market Trends One of the most significant contributions of AI in market analysis is predictive analytics. AI algorithms, particularly those based on machine learning, are adept at analyzing historical data to forecast future market trends. These algorithms can identify subtle patterns and correlations that might be invisible to the human eye, enabling predictions about price movements, market volatility, and potential trading opportunities. As these models are exposed to more data over time, their accuracy in forecasting trends continues to improve. 2. Real-time Data Processing and Interpretation The financial markets generate vast amounts of data every second. AI excels in processing this data in real-time, providing instantaneous insights that are critical in a fast-paced trading environment. This capability allows for the monitoring of live market conditions, immediate identification of market shifts, and quick response to unforeseen events. Real-time analysis ensures that trading strategies can be adjusted promptly to capitalize on market opportunities or mitigate risks. 3. Automated Technical Analysis Technical analysis involves the study of historical market data, primarily price and volume, to forecast future market behavior. AI-driven automated technical analysis takes this to a new level by using algorithms to scan and interpret market data at scale. These algorithms can automatically identify technical indicators, chart patterns, and other key metrics used in technical analysis. This automation not only speeds up the analysis process but also eliminates human bias and error, leading to more objective and reliable insights. 4. Sentiment Analysis from News and Social Media Market sentiment, the overall attitude of investors towards a particular market or security, can significantly influence market movements. AI, particularly through Natural Language Processing (NLP), plays a crucial role in analyzing sentiment. It processes vast amounts of unstructured data from news articles, financial reports, social media posts, and other textual sources to gauge public sentiment towards the market or specific investments. By analyzing this data, AI can provide insights into how collective sentiment is likely to impact market trends and investment decisions. These applications highlight the transformative role of AI in market analysis. By leveraging AI for predictive analytics, real-time data processing, automated technical analysis, and sentiment analysis, market participants can gain a more comprehensive, accurate, and nuanced understanding of market dynamics. This advanced level of analysis is not only enhancing traditional market analysis methods but is also shaping new strategies and approaches in the financial sector. Case Studies: Success Stories of AI-Driven Market Analysis The integration of Artificial Intelligence (AI) in market analysis has not only been a topic of academic interest but has also seen practical applications with significant impacts on market decisions. Several real-world case studies illustrate how AI-driven analysis has transformed trading strategies and financial insights. Here are a couple of notable examples: Case Study 1: AI in Predicting Stock Market Trends One of the most prominent examples is the use of AI by a leading investment firm to predict stock market trends. The firm developed a machine learning model that analyzed decades of market data, including stock prices, trading volumes, and economic indicators. This model was designed to identify patterns that precede significant market movements. In one instance, the AI system predicted a substantial market correction based on unusual trading patterns it detected, which were subtle enough to be overlooked by traditional analysis methods. The firm acted on this insight, adjusting its portfolio to mitigate risk. When the market did correct as predicted, the firm was able to avoid significant losses, outperforming the market and its competitors. Case Study 2: Enhancing Hedge Fund Strategies with AI Another case involves a hedge fund that integrated AI into its trading strategies. The fund employed deep learning algorithms to analyze not just market data but also alternative data sources such as satellite images, social media sentiment, and supply chain information. This comprehensive analysis allowed the fund to identify unique investment opportunities and trends before they became apparent to the market at large. For example, by analyzing satellite images of retail parking lots, the AI could predict quarterly sales trends for certain companies before their earnings reports were released. Combining these insights with traditional financial analysis, the fund made informed decisions that led to substantial returns, demonstrating the power of AI in enhancing traditional investment strategies. Impact of AI on Specific Market Decisions These case studies illustrate the profound impact AI can have on market decisions. AI-driven market analysis allows for more accurate predictions, better risk management, and the identification of unique investment opportunities. It enables market participants to make more informed, data-driven decisions, often leading to better financial outcomes. Moreover, the use of AI in these examples highlights a shift towards a more proactive approach in market analysis. Rather than reacting to market events, AI allows analysts and investors to anticipate changes and act preemptively. This shift is not just about leveraging new technologies but represents a broader change in the philosophy of market analysis and investment strategy. In summary, these real-world applications of AI in market analysis showcase its potential to transform financial strategies and decision-making processes. As AI technology continues to evolve and become more sophisticated, its role in market analysis is set to become even more integral and impactful. Future of AI in Market Analysis The landscape of market analysis is rapidly evolving, with Artificial Intelligence (AI) at the forefront of this transformation. The future of AI in market analysis is not just about incremental improvements but also about paradigm shifts in how financial data is processed, interpreted, and utilized for decision-making. Here are some emerging trends and potential shifts that could redefine the role of AI in market analysis: Emerging Trends and Technologies 1. Advanced Predictive Analytics: The future will likely see more sophisticated predictive models using AI. These models will not only forecast market trends but also provide probabilistic scenarios, offering a range of possible outcomes with associated probabilities. 2. Explainable AI (XAI): As AI models become more complex, there will be a greater need for transparency and interpretability. XAI aims to make AI decision-making processes understandable to humans, which is crucial for trust and compliance in financial markets. 3. Integration of Alternative Data: AI's ability to process and analyze non-traditional data sources, such as satellite imagery, IoT sensor data, and social media content, will become more prevalent. This will provide deeper, more diverse insights into market dynamics. 4. Real-time Risk Management: AI will enable more dynamic risk assessment models that update in real-time, considering the latest market data and trends. This will allow for more agile and responsive risk management strategies. 5. Automated Compliance and Regulation Monitoring: AI systems will increasingly monitor and ensure compliance with changing regulatory requirements, reducing the risk of human error and the burden of manual oversight. 6. Quantum Computing in Market Analysis: The potential integration of quantum computing could exponentially increase the speed and capacity of market data analysis, allowing for even more complex and comprehensive market models. Potential Shifts in Market Analysis Strategies 1. From Reactive to Proactive Analysis: AI enables a shift from reacting to market events to proactively predicting and preparing for them. This will lead to more forward-thinking investment strategies. 2. Personalization of Investment Strategies: AI can tailor investment advice and strategies to individual investors' profiles, risk appetites, and goals, leading to more personalized financial planning and portfolio management. 3. Democratization of Market Analysis: Advanced AI tools could become more accessible to a broader range of investors and firms, leveling the playing field between large institutions and smaller players. 4. Increased Emphasis on Data Strategy: As AI becomes more central to market analysis, there will be an increased focus on data strategy - how to source, manage, and leverage data effectively. 5. Redefining Skill Sets in Finance: The rising importance of AI will change the skill sets valued in finance professionals. There will be a greater emphasis on data science skills alongside traditional financial analysis expertise. In conclusion, the future of AI in market analysis is not just promising but revolutionary. It is poised to redefine traditional practices, introduce new capabilities, and create opportunities for innovation in the financial sector. As these technologies advance, they will continue to shape the strategies and decisions of market participants, marking a new era in financial market analysis.
AMEX:SPYEducation
by JS_TechTrading
Dividend Growth InvestingDividend Growth Investing - Building Wealth One Payout at a Time Introduction In a world of volatile markets and uncertain returns, dividend growth investing has emerged as a popular strategy for investors seeking steady income and long-term wealth accumulation. This approach focuses on investing in companies with a history of consistent dividend payments and a commitment to increasing those payouts over time. In this blog post, we will delve into the art of dividend growth investing and how it can be a powerful tool for building wealth, one payout at a time. Understanding Dividend Growth Investing Dividend growth investing involves selecting and holding shares of companies that not only pay dividends but also have a track record of regularly increasing those dividend payments. These companies typically exhibit financial stability, strong cash flows, and a commitment to rewarding shareholders with a share of their profits. The Principles of Dividend Growth Investing Dividend Yield: Dividend yield measures the annual dividend payment as a percentage of the stock's current price. Dividend growth investors often seek companies with reasonable dividend yields, balancing income with growth potential. Dividend Growth Rate: The dividend growth rate measures the annual percentage increase in a company's dividend payments. Investors look for companies with a history of steadily growing dividends, signaling financial health and shareholder-friendly management. Long-Term Horizon: Dividend growth investing is a long-term strategy. Investors aim to benefit from the compounding effect of increasing dividends over time. Benefits of Dividend Growth Investing Steady Income Stream: Dividend growth investing provides a reliable income stream for investors, which can be especially beneficial during market downturns. Inflation Hedge: As companies increase their dividends over time, investors can potentially beat inflation and preserve the purchasing power of their income. Potential for Capital Appreciation: Companies that consistently grow their dividends often attract investors, leading to potential capital appreciation in the stock price. Key Strategies for Dividend Growth Investing Research and Analysis: Conduct thorough research on companies' dividend histories, financials, and future growth prospects. Look for companies with sustainable dividend growth potential. Diversification: Diversify your dividend growth portfolio across different sectors and industries to reduce risks associated with individual company performance. Reinvestment: Consider reinvesting dividends back into the same dividend growth stocks or other investments to maximize the compounding effect. Dividend Aristocrats: Explore companies that are part of the "Dividend Aristocrats" or similar lists, which consist of companies with a history of consistently increasing dividends for many years. Conclusion Dividend growth investing is a disciplined approach that rewards patient investors with a growing income stream and potential capital appreciation. By selecting companies with a commitment to increasing dividends over time and holding them for the long haul, investors can build wealth, one payout at a time. Embrace the principles of dividend growth investing, do your due diligence, and let the power of compounding dividends work its magic on your investment journey. With the right mix of dividend growth stocks, you can create a robust and resilient portfolio that supports your financial goals for years to come. Here's to the journey of building wealth through the steady flow of dividends, and may your investment endeavors be filled with prosperity and success!
NASDAQ:TSLAEducation
by MindOverMarket
From Zero to Hero: The Art of Finding Winning Crypto Projects!!!Hello there, fellow traders👨‍💻! As a trader, I know that choosing the right crypto project to invest in can feel like navigating a sea of uncertainty. But fear not mateys😎! Today, we will set sail on a journey to discover the best crypto projects.😉 I will examine critical factors to help identify the most promising crypto projects💡. But I won't be venturing blindly into the unknown. Oh no, I have a trusty checklist for each crypto project to guide us on our quest. I give a score from 1 to 10 for each factor. With this checklist in hand✅, we will be able to evaluate each crypto project based on essential factors(But I must say that the ✨ starred factors ✨ are more important in our checklist). So let's dive into the factors. Founders ✨: The founders' vision, expertise, reputation, leadership, and decision-making abilities are essential to a crypto project's success and sustainability. Project's Goal ✨: The project goal is a critical component of a crypto project that defines its purpose, attracts investors, guides development, and measures success. Source Code ✨: The importance of source code in a crypto project lies in its ability to determine its functionality, security, and transparency. Access to source code enables security experts and auditors to review the project's security measures, identify weaknesses, and recommend improvements. Open-source projects promote transparency and accountability, building trust among stakeholders. Also, new commits submitted to the project can be analyzed through the project's repository. Token Inflation Rate ✨: The importance of a crypto project's token inflation rate lies in its impact on the token's value, liquidity, and long-term sustainability. A high inflation rate can decrease the token's value and liquidity, while a low inflation rate can promote token scarcity and sustainability. White Paper Analysis ✨: The importance of a whitepaper in a crypto project lies in its ability to communicate the project's vision, value proposition, and technical specifications to investors. It is a marketing tool, technical specification document, project blueprint, and credibility establishment tool. Community ✨: This is a significant factor when analyzing a crypto project. Community in a crypto project provides the ability to support the project's growth, adoption, and sustainability. A strong community can promote adoption and awareness, provide feedback and insights, offer support and resources, and promote the project's values and mission. Tokenomics : Can determine the token's value, utility, and sustainability. Tokenomics can help balance token supply, demand, and circulation, design token utilities that incentivize user participation, and regulate token supply to promote. Developers : They play a crucial role in a crypto project, as they are responsible for designing, building, and maintaining the project's software and infrastructure. The importance of developers in a crypto project lies in their ability to ensure the project's functionality, security, and scalability. Developers are responsible for designing, building, and maintaining the project's software and infrastructure, promoting innovation and creativity, and promoting the project's vision and values. Venture Capital (VC) Investors : The importance of VC investors in a crypto project lies in their ability to provide the project with funding, expertise, and connections to help it grow and succeed. VC investors can help the project overcome challenges, expand its reach, and promote its legitimacy and credibility. Competitors : Comparing a crypto project to its competitors is essential to understand its strengths and weaknesses, assess its potential for growth and profitability, identify any potential risks, and evaluate the project's unique features. These factors are critical for making a well-informed investment decision in crypto. 👆According to the factors mentioned, getting lost in this sea is challenging.👆 With this map or lantern, you will find your way to the safe shore and the treasure.💎 Warren Buffett once said, "Risk comes from not knowing what you're doing." In today's ever-changing financial markets, staying informed and making well-informed investment decisions is more critical than ever. So hoist the anchor and embark on this exciting adventure together.✌🏻 With this checklist and knowledge, you'll be able to navigate the treacherous waters of the crypto market and find the projects that will lead you to the ultimate booty - success! 🙏🏻😍 Share your ideas with me💡, and if you have any questions❓, you can ask in the comments.💬 Learn and always stay updated📚. Don't forget to invest what you can afford to lose.💸 Discretion is the greater part of valor.🤗
Education
by pejman_zwin
10
Shopping stocks for my Old Lady- Dogs of the dow pre recession? When in doubt, choose positive carry. Dividend paying companies that are lagging. Good and bad. Good because they are priced with less optimism. Bad because the price my correctly reelect to opportunity going going forward. Either way, I like looking at dogs of the dow and dogs of the sp500 to do research. Ive found many gems that way, even if I choose not to use the strategy. We dont control outcomes. We only control our habits and decisions. The exercise effort is the gold. Dividends pay me while I wait. Selling premium pays me while I wait. Earning interest pays me while I wait. Ideas can take longer than you think to work. May as well get some cashflow and reduce your cost basis over time. P.S. Dont tell my girlfriend I made a video mentioning her. kthankxbye
TVC:VIXEducation
by optionfarmers
7
Don’t bite the bait. Time is money. This is a case of comparative advantage. Which means the less time it takes the coin to produce oil or energy, maybe even transactions. All will go down and then pop back up, much like a sinking ship. Within the next 5 hours, an entry point will be present. It’s time to glean for Q2, because after this the prices will hit an all time high and then go back down until July. A lot will assume now is too soon, but yesterdays price isn’t todays price. However, the cup and handle has presented its self. Now the next sign of equilibrium the price will make will be like a Nike check. Or a Wolfe wave. The eagle has left the nest. 2. API CRUDE OIL U.S.: anything under 5 is a sink. Forecast is in the -1.0 range. TOTAL VEHICLE CAR SALES: Elon and Twitter Don’t hire the bait just yet.
BITSTAMP:BTCUSDEducation
by Traliciaa
2
How to Invest in the S&P 500 [FOR DUMMIES]In the investment world everybody expects you to know exactly how to buy into an Index Fund, which makes it very hard to find a good detailed non-outdated resource to learn from. While it’s easy to do once your set up, learning how to from nothing was difficult (at least for me). Before you even think about investing into the S&P 500 you need to know WHY. Because if you don't know WHY your investing into this you will panic sell when its the best time to be buying. Now while this part can be answered by a YouTube video I put some of the main reasons below. - The s&p 500 is a diverse Index Fund. (The term index fund means a portfolio set up for you to invest in.) - The s&p 500 holds the top 500 USA companies. (The diversity in big companies makes it a safe investment in the long term.) - The s&p 500, over a 15-year period, beat nearly 90% of actively managed investment funds. (Meaning us noobies can beat the pros!) - The S&P 500 has always recovered, there are lost decades which the market has stayed down for 10 years but in those 10 years you could be buying every single month! (Dollar Cost Averaging) - With the power of compounding your money will grow exponentially. Now what is Dollar Cost Averaging..? Dollar Cost Averaging is buying roughly equal amounts of an asset per month. Doesn't have to be equal but nothing to different, for example you don't want to buy $500 worth's one month and $1000 worth's another (only spend what you know you can be consistent with in the future). Dollar-cost averaging is a great investing strategy because, in the long term, it can protect the investor (you) from market volatility (up and down movement) and reduce the amount you'll spend buying shares. So, over time, you will end up investing in more assets for less. Now what is compounding..? Compounding is re-investing both your capital gains and dividends in order to get a higher payout the next time around again and again and again.. till your rich. Although with compounding comes a catch; if you panic sell before your desired target you've fell into your own trap, because compounding depends on time, and you just smashed the watch. Plus, you should never panic sell when the market crashes; be happy you’re getting everything on a sale! Now we have reviewed why you should invest into the S&P 500, what dollar cost averaging is, what compounding is, and why panic selling is stupid. But how do you buy it?!? I started by trying a brokerage called Vanguard. (a brokerage company is pretty much a middleman that connects buyers and sellers). I wanted to use Vanguard because I knew that I wanted low purchase fees; low purchase fees are good because in the long term it impacts how much you’re actually investing (less fees = more invested long term). Now let me tell you this, vanguard SUCKS, their customer service is terrible, the website is terrible, and they wouldn't even let me open an account for god’s sake because "their website was down". The only thing good about them is their index funds and low fees. What took me a while to learn was that I can purchase the SAME index funds but with a different broker. Now I do recommend you get an account with Charles Schwab they have real branches you can go to and ask questions in (not just a phone number like Vanguard) plus if you do want to call their wait time isn't over an hour like Vanguard, and their website is user friendly. How to make an account with Charles Schwab..? Search up "Charles Schwab", click on their website, Open an Account, and decide what type of brokerage account you want (if your just one person pick individual), then continue with the steps. If you’re below the age of 18 search up "create a custodial account Charles Schwab" and start from there, you will need your parents SSN, and other info. Now that you have a basic account set up your ready to invest; but wait there's more. You currently have a brokerage account which means your eligible to invest however much you want per year, although once you pull the money out you will be taxed on it based off your tax bracket. Along with your brokerage account you should set up a Roth IRA account. A Roth IRA account is a retirement account in short, your allowed to invest up to $6000 per year into it and once your 50 you can pull it out TAX FREE. (if you pull it out any sooner it will act as a brokerage account and tax you, so don't do that). Making a Roth IRA account requires paperwork which you fill in and then go to one of the many "Charles Schwab Branches" to turn in. You can ask customer support to send you the paperwork to your email which you must print out. This account pretty much assures you will be a millionaire at retirement. Ok I have both accounts.. now how to buy? Click on "trade", make sure you’re on the "Stocks & ETFs" Tab, click the "symbol search bar", and type "VOO" (Vanguard S&P 500 ETF). Now decide on how many shares you want (you can check the price here on trading view). It will have an option to turn on auto-reinvest dividends make sure to click that, & make sure you select "Market Order" so you get filled in immediately then click "order". Always invest the maximum of 6K into your Roth IRA and invest as much as you can into your brokerage account. Every 3 months re-invest your capital gains on both accounts. You can see how much your projected to earn in the future. Search up "compounding calculator" put in how much you’re going to be investing per month, how long, and at a 10% average rate of return. I hope this helps, comment and like. :)
AMEX:VOOEducation
by pastworks
3
How to adjust your charts for dividend paymentsBond funds like the SPDR Portfolio Mortgage Backed Bond ETF (SPMB) often look like money-losers when you view their returns on a non-adjusted basis. In this case, the price is down about -0.74% over the life of the fund. The picture looks very different when you adjust for dividends. For SPMB, the return changes to +46.09% over the life of the fund: That's obviously a very different chart than the non-adjusted chart. Dividend adjustment can also make a large difference for high-yield dividend stocks. For instance, IBM is down over the last ten years on a non-adjusted basis, but on an adjusted basis it has gone sideways. IBM, non-adjusted: IBM, adjusted: The commonly accepted adjustment methodology is that the most recent closing price will be the same on an adjusted and non-adjusted chart, but historical closing prices will be different. On an adjusted chart, the stock price on a historical date will be shown as the current closing price minus all dividends paid since then. Dividend subtractions typically are made on a percentage rather than dollar basis to prevent historical prices from showing as negative values. To actually perform the calculation is a little technical, but that's the overall idea. To apply dividend adjustment to a TradingView chart is super easy. In the lower right-hand corner of your chart, you will see the letters "adj". Click to toggle between adjusted and non-adjusted price data. When the text is blue, you are viewing the adjusted chart. When the text is black, adjustment is turned off. Right next to the letters "adj" is a "%" symbol. Toggling this on and off will switch the axis of the chart between dollars and percent change over the period visible on the chart. This is useful for comparing adjusted and non-adjusted returns. One implication of using adjusted charts is that the support levels and moving averages will be in different places. For instance, on a non-adjusted basis, VALE is currently below its 200-week moving average. On an adjusted basis, it is well above the average. VALE, non-adjusted: Vale, adjusted: In short, on an adjusted basis a stock may not be as cheap as it looks on a non-adjusted basis. Many quant traders and hedge funds will be using adjusted moving averages rather than non-adjusted ones.
Editors' picks
AMEX:SPMBEducation
by ChristopherCarrollSmith
28
Eco/monetary news n°32> Dividends and buybacks are coming back in the United States and Europe *********************** Europe allowed companies to resume dividends and buybacks in December 2020 but they set limits and the ECB "ECB calls on banks to refrain from or limit dividends until 30 September 2021". They might have eased it but I cannot find a source, I just know that Banque of France president was agreeing a few weeks ago with people calling for the end of restrictions. otherwise it will be by September. The United States, this week, allowed buyback programs to resume. The Nasdaq website has a 1 line article about it but only about banks. Seriously it's hard to find sources about the subject. Some would call me a conspiracy theorist. I get called so when I have ample proof so here... In any case companies are apparently sitting on a pile of cash and the stock market has rallied without buybacks, the biggest price support of the last 10 years, and companies have put their "buybacking" to the side for over a whole year. When that money pours in... It's going to create an upwards storm. Haha and to think 85% of FXCM clients are still short, and are actually adding now that the price is going higher! I do not think they know the IQS of their clients, but could we ask them their socio-economic background? Journalists, sociology and economics college professors, boomer state worker leeching taxpayer money, burger flippers and unemployed welfare recipients? > Record everything in stock markets including retail fomo & fx traders short selling *********************** While FX brokers continue to publish their short term traders positions which are around 85% short, the S&P 500 breaks record after record. Yesterday the S&P 500 was at its highest level in history, again. According to TD Ameritrade saw the biggest influx in investors ever, it started recording it in 2010. According to Bloomberg the indice had a record run, the longest streak since 1997, and retail investors poured in. "Whether they'll stick around when volatility inevitably resurfaces remains to be seen". I already know the answer to that question. The New York FED "Expectations of higher stock prices" number is at its average of 40% close to where it has almost always been for the last 7 years. Investors are worried about a covid wave and I think that's all. They can always panic sell on the way, but if something real happens, by the time it reaches the donkeys brains I'd have sold long ago. > Western politicians are refusing to return their covid emergency powers *********************** Several politicians have declared they had no plans, or saw no reason, to relinquish their emergency covid powers. The same, and others, have also ignored CDC guidelines as well as the WHO recommendations about re-opening and granting freedoms. Noooo how could be possibly have seen this coming? There was absolutely no way to predict this would happen! It's only going to get worse. Look at Ivory Coast investors ~20 years ago. I think they invested in the sugar industry a lot. I think investors in the UK take into account this kind of risk, but US, French, and probably Germans too have an ideology and separate politics from economics. And they're always all in complete denial. Well, just wait and see. Better keep an eye open for certain keywords "outlaw buybacks", "capital controls" and so on. You want to get out BEFORE. > Turkey central bank governor found to have copy pasted central bank report in his thesis *********************** I am crying. The previous governor got replaced after hiking interest rates from 17 to 19% in the pyramid scheme country, something Erdogan dislikes because it gets in the way of him stealing the people's money to pretend there is growth. So the new guy took office Şahap Kavcıoğlu, and a few weeks later he ends up under investigation because they found this irl griefer shamelessly copy pasted, in his thesis in 2003, entire blocks from a central bank report of 2001. You know when I copy paste bloomberg first I only take a sentence, then whether I copy paste the whole thing or write it with my own words I say "according to Bloomberg" or something. This guy takes entire sections with no source whatsoever and just claims it as his own "oh boy that was some hard work". It's not the first time! A previous governor, in 2019, faced similar accusations. Can you guess what he hadn't done his own research on and simply copy pasted? The part about INFLATION TARGETING. I am actually crying. > ECB aims for 2 percent inflation target and focusses on changing climate change *********************** When asked if they wanted to allow it to go higher because it was below target for a while they mumbled something incoherent. Are they following the FED & Yellen guidelines? No one knows. They are in a sea of lies they don't even know themselves anymore. All I can say is we will see. The USA still want to fight a stock market crash because Hitler and Germany wants to fight inflation because Hitler and the ECB continues to pull away from the post-war Bundesbank dogma. There are hints they are open to "easy money" and no surprise here German officials think so. Hey, let me quote from Bloomberg: "Officials from Germany in particular easy money that undermined the postwar inflation-fighting on which the ECB was modeled as a condition for the country’s participation in the euro."" After Brexit and before Frexit (cucks) Germanyxit? Diverging visions, goals, and interests. I think the Brussels unelected globalist ideologists want to fire the money bazookas with the USA, but the Germans still don't want that. And since the EU is (now) centered around France-Germany plus Germany is a huge net contributor. Without the UK, Germany is responsible for HALF of the net contributions to Europe, with the UK 41%. By 2011 France (nb 2) "only" contributed for 17% (104 billion), lol 104 billion, they did more than half that in 2020 alone. The covid EU budget was one of the greatest "redistribution of wealth" in history, directly from german and french workers to EU unemployed. By 2011 Italy contributed a significant 12% but I doubt this is the case anymore. By allowing their country to get destroyed one may say they contributed 100%. The UK cut their losses just in time, while France & Germany will be the suckers for the rest of eternity. Meanwhile Romania and Slovenia have better or close to as good standards of living. Hey, and these astonishing net contributions are GIFTS. Does not take into account the hundreds of billions in "loans" the 2 pigeons have made to countries like Greece that will NEVER pay back. I'm dying 🤣 www.aalep.eu > Yay crypto adoption: China shuts down yet more digital currency competition *********************** Man I told these Bitcoin donkeys countries would never adopt their risible ponzis and would just make their own digital currencies. What do they have in the head seriously? Paté? In a statement on July 6, the PBoC announced it ordered Beijing Qudao Cultural Development, a firm providing crypto trading software, to shut down its operations. Ah but this is anecdotal, and surely these guys were doing something fishy right? Doesn't prove anything. Yup. They were doing something fishy. Here is the reason given as to why they got shut down *clears throat* "suspicions of being involved in crypto-related trading" (he proudly said). There is no interpretation possible, they could not be more clear. I wonder how crypto gamblers will weasel themselves out of that one.
Education
by MrRenev
Apple Market Cycle TOP!!My point of view for what it's worth.. -------------------------------------------------- TA= on the H1, H4, D at lower BB just holding on. MACD= BEAR, worse on the weekly, daily. Stoch= BEAR COSSED on the daily, about to on the Weekly. BIG GAP AT 90$ needs to be filled. test lower weekly BB. Market Cycle top in distribution phase. plateau Logistic curve top. do your on FA if you really don't understand. I'll give you : 7nm(A13) EOL CHIP SHORTAGE!!! market saturation dividend increases .. just need the silver sparrow( unknown fundamentals) to really kick it off, but it will not take much now. Adam
NASDAQ:AAPLEducation
by UnknownUnicorn3811160