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U.S. hikes royalty fees for drilling on federal lands for first time in a century
Israel says iran has launched drones, u.s. says operation 'likely to unfold' over hours, most ‘buy now, pay later’ users have come across issues, survey shows, j&j cough syrup recall classified as most serious in south africa, fundstrat's tom lee sees s&p 500 potentially ending the year at 5,700 points, coinbase appeals key point in sec lawsuit, trending analysis.
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5 Best Free Stock Analysis Tools for Investors
A tremendous amount of free stock research and analysis tools are available online.
Best Free Stock Analysis and Research
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It's best to do your homework before you pull the trigger on a stock option.
The U.S. stock market is off to a great start in 2024. Through Feb. 23, both the S&P 500 and the Nasdaq-100 are up more than 7% year to date (YTD). The Dow Jones Industrial Average is up nicely as well, though not as dramatically. That well-known index has gained just over 3% YTD.
You may be thinking about taking money off the sidelines and investing in this red-hot market. If you have a long-term time horizon, that's probably a good idea. The equities markets go up and down, but historically, good quality stocks tend to appreciate over time.
Before you pull the trigger on that stock you've got your eye on, you need to do a little homework first. It's smart to review past performance, become familiar with company fundamentals , learn about management and get an independent, third-party opinion before you place a buy order.
The problem is, high-quality stock analysis tools and professional research can be very expensive. Where can a retail investor go for high-level stock research and helpful analysis tools without breaking the bank?
Thankfully, there are plenty of investing resources that any investor can access for free. Here are some of the best places to conduct your equity research at no cost to you:
- Your online broker.
- TradingView.
- Morningstar at the public library.
- S&P Global Market Intelligence.
Your Online Broker
Most brokerage platforms provide their clients with reasonable access to their proprietary equity research as well as valuable analysis tools such as charting and backtesting utilities. Generally, the larger and more established the broker, the better the research. You can expect excellent quality research, tools and advice to be included on platforms like E*Trade, which is owned by Morgan Stanley (ticker: MS ), TD Ameritrade, owned by Charles Schwab Corp. ( SCHW ) and Merrill Edge, owned by Bank of America Corp. ( BAC ), and the online brokerage of Charles Schwab Corp. ( SCHW ), which includes features from the recently acquired TD Ameritrade. Popular online and app-based brokers like Interactive Brokers Group Inc. ( IBKR ) and Robinhood Markets Inc. ( HOOD ) get high marks in this department as well.
Broker-provided research reports are usually only one or two pages long and are well worth reading. You'll also find easy-to-access tools like stock screeners and news feeds as well.
Just log on to your account and look for a tab or button that says "research." Most systems are designed to be highly intuitive and user-friendly. If you run into difficulty accessing their tools or research, just contact customer service via chat, email or phone.
Did you know that a public company's financial filings and virtually all other government-mandated reports are public information and can be accessed by anyone, at any time free of charge? It's true. And it makes the Securities and Exchange Commission (SEC) an excellent free resources for stock research.
The SEC doesn't publish research reports or give opinions on stocks, but through its Electronic Data Gathering, Analysis and Retrieval System, called EDGAR for short, it does catalog and provide free access to every document a public company files with that regulatory agency.
Want to know a stock's revenue, profit, expenses or cash flow for the latest quarter or for the last few decades? Are you curious about the CEO's compensation? It's all on EDGAR for you to download and view, and it's very easy to find. Just navigate over to the SEC's website and click on the "filings" tab. From there, page down to the EDGAR - Search and Access . There you can navigate to a search that works for you, and an incredible wealth of information is available with a few clicks of your mouse.
TradingView
There is a category of investment analysis websites broadly called charting platforms. That name, however, is insufficient in light of all they offer and all the free investing tools they provide. With more than 50 million users, TradingView is among the most popular charting and investing platforms on the internet.
TradingView is a comprehensive combination of charting, analysis and research tools. All members have access to breaking news, historical investment data and the site's social network. It offers premium subscriptions for a reasonable fee, but investors can conduct a surprising amount of in-depth research with basic access which is available for free.
You can view customizable charts with multiple layers of pricing, volume and trend data , you can screen stocks by market capitalization, stock market sector and other criteria, and you can develop your own strategies and ideas and test them in various hypothetical situations. Beyond the research capabilities, TradingView offers an opportunity to communicate and learn from other investors on the platform.
It will ask you to open an account by registering with an email and password, but access isn't temporary or on a trial basis. You can use the many free tools for as long as you want and upgrade to premium only when you're ready.
Morningstar at the Public Library
A standard library card can be your key to an incredible amount of free market research. Many public libraries around the country maintain a premium subscription to Morningstar as well as other professional investment research outlets. Like almost all public library resources, Morningstar equity research is free to registered members.
Check with your local or state library system to see if it has Morningstar. While you're at it, look for other subscription-based research companies like CFRA or Zacks. Chances are they will have one or more. Once you've confirmed it has what you're looking for, dust off your old library card – or apply for a new one for free – and start browsing the catalog of investment resources.
In most cases, you won't even have to visit a physical library branch. Modern libraries offer members access to Morningstar online at no additional cost. You can download detailed financial and analyst reports, use the available portfolio and stock screening tools, and benefit from the educational resources Morningstar produces, all for free.
S&P Global Market Intelligence
S&P Global is one of the most respected research firms in the world. Institutional investors pay tens of thousands of dollars a year for premium access to its S&P Capital IQ research and trading platform. Those exorbitant price levels are out of reach for most retail investors but, thankfully, S&P offers many market insights and research online for free.
The Research & Insights page of S&P's website is loaded with articles, posts, notes and news that can be an invaluable resource to the small investor. Some information requires you have an account, but you can create one for free to access those reports. The site is updated several times a week so the data and information is always current.
You'll find sophisticated but accessible market intelligence on sectors from tech to health care and everything in between. The page has a search bar in the upper right corner so you can quickly find exactly what you're looking for.
Click on the "subscribe now" button and you'll be directed to the S&P newsletter subscription hub. There you can sign up for any of dozens of professional quality investment industry newsletters on a wide variety of subjects. Free research will be delivered to your email inbox on a monthly basis at no cost and with no obligation.
Best Long-Term Stocks to Buy Now
Glenn Fydenkevez Feb. 12, 2024
Tags: investing , stock market , money , financial literacy , Morgan Stanley , TD Ameritrade , Charles Schwab , Bank of America , Interactive Brokers Group
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Stock Market News More News
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Academics used AI and machine learning to test 29,000 financial ratios. Here's the metric they found was the best predictor of stock-market returns.
- Alejandro Lopez-Lira, along with colleagues, used machine learning to test 200 investing theories.
- They also set the algorithm up to seek out ratios with better performance returns.
- The algorithm identified a specific ratio involving sales from acquisitions and rental expenses.
Determining the best predictors of stock-market returns is like seeking the Holy Grail for investors. The problem is that the number of deciding factors can seem limitless, while the human power to test them all is not.
It may take an academic two years to test and publish findings for a financial theory, said Alejandro Lopez-Lira, an assistant professor of finance at the University of Florida who has been studying the capabilities of machine learning and artificial intelligence to see how useful technology can be in the stock-picking process.
In a previous study, he fed ChatGPT news headlines to determine if it could discern between buying or shorting a stock based on whether it perceived the information to be positive or negative for a company. The exercise produced a 512% return .
In this round, Lopez-Lira, along with Andrew Y. Chen, a principal economist at the Fed, and Tom Zimmermann, a professor for data analytics in economics and finance at the University of Cologne, tried to test the viability of 200 previously published academic investing theories using a machine learning algorithm to backtest for performance. They also allowed the algorithm to come up with its own ratios in an attempt to determine whether there are better indicators of stock-market performance.
Studies on financial theories are key in developing investment strategies and have helped establish ways to gain a competitive advantage in the market. But Michael Robbins, the author of "Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing," says what works in theory may not always work in practice, and these results should be taken with a grain of salt. Investors and traders need to consider things such as market frictions that can delay the execution of a trade, like the ability of a broker to execute transactions. In the real world, fees and taxes could impact your gains, depending on how much you buy and sell.
Using the standard statistical programming language R and a hand-coded algorithm, they began to backtest peer-reviewed theories within their original sample period and outside it for an overall test period from 1985 to 2022. They then compared the results to alternative outcomes that the algorithm determined had similar or better returns within the same periods to see whether age-old academic investing guidelines are optimal or if there are better predictors of stock-market returns.
The study used 242 accounting variables from companies. Examples of variables included sales, market value, and cost of goods. The data points were then scaled using ratios to correlate a company's sales revenue to other variables, such as its size. The combination of different ratios led to roughly 29,000 possibilities, Lopez-Lira noted. An average of 3,300 companies were fed into the algorithm. Companies were rebalanced annually during the first quarter of every year after the annual earnings releases for the previous year were posted.
The minimum performance requirement for gains that meant the academic and algorithmic predictors were economically significant was 15 basis points monthly or 5% yearly.
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In one example, a 1993 study called "Common risk factors in the returns on stocks and bonds," originally tested between 1963 and 1990, concluded that the book-to-market variable is a predictor of high returns. It has been widely used as part of investing strategies for decades, Lopez-Lira said. However, the algorithm determined that 171 different accounting and financial ratios performed just as well within the sample period, and some even better after 1993.
Best-performing metric
In an additional step, Lopez-Lira fed the algorithm stocks from the New York Stock Exchange, the American Stock Exchange, and the Nasdaq Stock Market to find a ratio that showed the highest return. The result was initially counterintuitive: the buy signal was for publicly traded companies that experienced a drop in sales from other companies acquired during the previous year. The algorithm chose to divide sales from acquisitions by the publicly traded company's previous year's rental expenses to compare it to a fixed expense and determine company size relative to sales.
The backtest was run from 1985 to 2022, and the portfolio rebalanced companies in May of each year to account for annual earnings releases. On average, 62% of the names remained in the portfolio in the following year. The average monthly returns gained from buying companies based on this outcome was 0.38% for the years preceding 2012 and 1.03% after 2012. The book-to-market variable theory returned 0.62% per month before 2012 and less than 0.1% after 2012, which suggests its performance lagged the algorithm's metric in more recent years, according to results provided by Lopez-Lira.
The algorithm's final metric was:
"Inverse of Change in[Acquisitions sales contribution]/lag[Rental expense]"
Lopez-Lira noted that the algorithm they hard coded doesn't really know why a certain ratio performs well. It only detects that this is a very good predictive variable both in the sample period and outside it. And while the output isn't likely due to chance, there's no guarantee that this ratio will continue to perform well, he added.
Lopez-Lira referred to ChatGPT to ask why this ratio may have performed well. Its response was in line with his conclusion: the algorithm is picking up on negative human sentiment toward a temporary setback, which happens when companies have unexpected negative outcomes from acquiring a new company. The result is a sell-off that makes the stock undervalued or cheaply priced.
ChatGPT also noted that it could indicate inefficiencies in integrating acquisitions, overpaying for acquisitions, or a strategic misalignment.
Robbins added that there are two parts to this. First, he agrees that it's likely because investor sentiment is fickle and often short-sighted. However, management teams who make decisions about an acquisition often do their research and are playing the long game. Over time, their business plan could be proven correct.
In another part of ChatGPT's response, it acknowledged a similar conclusion:
"The market might not fully appreciate the future potential of companies with a low metric, especially if these companies are in the process of optimizing their acquisition strategies or renegotiating their rental agreements. The market might be overly focused on past performance and not on the potential for turnaround."
Robbins said that the equation suggests that the signal is stronger if the rental expense is small. These companies are more likely to show up on either the list to short or long the stock. This might exclude many types of companies, including those that have multiple brick-and-mortar branches, locations in big cities where rent is expensive, as well as real estate and industrial companies.
But all this is guessing. Robbins noted that traditionally, academics begin with a theory for why certain company fundamentals could be good predictors of stock market returns before testing them. Data mining for results and then coming up with a theory for the best-performing ratios is more challenging because there could be numerous reasons a researcher could come up with. More importantly, it might not mean anything. So you could rack your brain trying to find the theory behind it and there may be no theory, he said.
Robbins added that it's easy to find successful ratios by data mining thousands of possibilities because it discovers new information that's not widely used, but once it goes mainstream, it loses its advantage. Whether these findings can be put to work depends, says Robbins. It's definitely not a strategy an amateur trader would want to try. But proprietary trading shops or quantitative hedge funds that have pods each trading on a different strategy can.
"But you have to manage them properly," Robbins said. "It's a management exercise, not a money-making exercise. You have all these different strategies. If one of them is losing money, stop it right away. If somebody's making money, you let them keep making money. And if you keep doing that systematically with a lot of strategies over time, you'll make money."
ChatGPT's response to the use of the metric to trade:
"A long-short portfolio sorted inversely on this metric aims to exploit these potential mispricings. By going long on companies with a low metric (betting on their undervaluation and potential for turnaround) and shorting companies with a high metric (betting against their possible overvaluation and unsustainable growth rates), investors can potentially capture high expected returns. This strategy hinges on the belief that the market is not efficiently pricing the future potential of companies based on their current acquisition efficiency relative to rental expenses. Over time, as the market corrects these mispricings, the portfolio is expected to generate significant returns."
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A disappointing trump media stock (djt) analysis.
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A reminder that risk accompanies return
The March and April SEC filings provide the information needed to analyze the Trump Media merger. Unfortunately for the new shareholders coming from Digital Acquisition, the result is disappointing.
What happened? Four negative developments
First , after an enthusiastic 2021-22 market environment, conditions turned negative in 2023. The delay decreased interest, so the merger actions slowed, and the Digital Acquisition stock stagnated.
Digital Acquisition stock chart through Trump Media merger
Second , Digital Acquisition expenses remained high, made worse by a misleading SEC filing that ended up costing an $18M settlement. That amount, along with the expenses, ate into the money that would transfer over to Trump Media in the case of a merger.
Third , Trump Media was running negative earnings, with moderate revenues and large expenses. As a result, they turned to private loans to keep the firm afloat, meaning the company's shareholder (equity) balance was getting more negative.
Fourth , there began a questionable strategy used by both Digital Acquisition and Trump Media management as they sought money. The idea was to raise cash by offering a conversion into new, bargain-priced shares of stock when/if the merger was completed. The result was the lenders received stock for repayment, not cash. Trump Media used convertible promissory notes, and Digital Acquisition used other convertible items.
Best High-Yield Savings Accounts Of 2024
Best 5% interest savings accounts of 2024, what is wrong with that.
That strategy undermines the value of all shareholders that bought the Digital Acquisition shares at full market value. Look at it this way:
Digital Acquisition was shrinking the per share assets behind the IPO shares. Management has a duty to protect shareholders, not to take advantage of them.
Trump Media took the strategy a step further. The merger was not complete, so they were offering conversion into something they did not own.
So, how big a deal are those convertible borrowings?
Very. It is visible in the March 25 8-K filing, on page C-7, under "Note 5 — Loss Per Share." Their table shows all the new shares coming together because of the merger:
All the sources of Trump Media shares
Now to the book value
Shareholders undoubtedly remember the Digital Acquisition IPO at $10 per share. And we know that money was in trust, earning interest. Perhaps forgotten is that there was a "founder" position that equaled a quarter of the offering at a near-zero cost. Therefore, spreading that $10 over all shares drops the per share book value to $8.
However, as discussed above, there have been sizable Digital Acquisition expenses, so the book value in the merger was $7.60.
Now to the bad news. Coming into the merger, Trump Media had negative book value. Worse, the number of original Trump Media shares was three times the number that came from Digital Acquisition. That pushed the per share book value down to $1.88. Include the other shares in the tables above, and the book value drops further.
The bottom line: Do not expect magic from the cash infusion
Trump Media has been struggling with low revenues compared to expenses. Moreover, the common stock is approaching the 200M shares shown in the tables above (up from the pre-merger 87M). Therefore, without a significant improvement in growth and revenues, it will be a challenge to maintain its high price.
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