This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a demographic shift of an ageing population and significant technological automation. So if you think that stocks or equities or ETFs are the best place to put your money in 2022, you might want to think again. The crash of the OTC and small-cap market since February 2021 has been quite an indication of what a correction looks like. According to the Motley Fool what happens after major downturns in the market historically speaking? In each of the previous four instances that the S&P 500's Shiller P/E shot above and sustained 30, the index lost anywhere from 20% to 89% of its value. So what's what we too are due for, reversion to the mean will be realistically brutal after the Fed's hyper-extreme intervention has run its course. Of course what the Fed stimulus has really done is simply allowed the 1% to get a whole lot richer to the point of wealth inequality spiraling out of control in the decades ahead leading us likely to a dystopia in an unfair and unequal version of BigTech capitalism. This has also led to a trend of short squeeze to these tech stocks, as shown in recent years' data. Of course the Fed has to say that's its done all of these things for the people, employment numbers and the labor market. Women in the workplace have been set behind likely 15 years in social progress due to the pandemic and the Fed's response. While the 89% lost during the Great Depression would be virtually impossible today thanks to ongoing intervention from the Federal Reserve and Capitol Hill, a correction of 20% to 50% would be pretty fair and simply return the curve back to a normal trajectory as interest rates going back up eventually in the 2023 to 2025 period. It's very unlikely the market has taken Fed tapering into account (priced-in), since the euphoria of a can't miss market just keeps pushing the markets higher. But all good things must come to an end. Earlier this month, the U.S. Bureau of Labor Statistics released inflation data from July. This report showed that the Consumer Price Index for All Urban Consumers rose 5.2% over the past 12 months. While the Fed and economists promise us this inflation is temporary, others are not so certain. As you print so much money, the money you have is worth less and certain goods cost more. Wage gains in some industries cannot be taken back, they are permanent - in the service sector like restaurants, hospitality and travel that have been among the hardest hit. The pandemic has led to a paradigm shift in the future of work, and that too is not temporary. The Great Resignation means white collar jobs with be more WFM than ever before, with a new software revolution, different transport and energy behaviors and so forth. Climate change alone could slow down global GDP in the 21st century. How can inflation be temporary when so many trends don't appear to be temporary? Sure the price of lumber or used-cars could be temporary, but a global chip shortage is exasperating the automobile sector. The stock market isn't even behaving like it cares about anything other than the Fed, and its $billions of dollars of buying bonds each month. Some central banks will start to taper about December, 2021 (like the European). However Delta could further mutate into a variant that makes the first generation of vaccines less effective. Such a macro event could be enough to trigger the correction we've been speaking about. So stay safe, and keep your money safe. The Last Dance of the 2009 bull market could feel especially more painful because we've been spoiled for so long in the markets. We can barely remember what March, 2020 felt like. Some people sold their life savings simply due to scare tactics by the likes of Bill Ackman. His scare tactics on CNBC won him likely hundreds of millions as the stock market tanked. Hedge funds further gamed the Reddit and Gamestop movement, orchestrating them and leading the new retail investors into meme speculation and a whole bunch of other unsavory things like options trading at such scale we've never seen before. It's not just inflation and higher interest rates, it's how absurdly high valuations have become. Still correlation does not imply causation. Just because inflation has picked up, it doesn't guarantee that stocks will head lower. Nevertheless, weaker buying power associated with higher inflation can't be overlooked as a potential negative for the U.S. economy and equities. The current S&P500 10-year P/E Ratio is 38.7. This is 97% above the modern-era market average of 19.6, putting the current P/E 2.5 standard deviations above the modern-era average. This is just math, folks. History is saying the stock market is 2x its true value. So why and who would be full on the market or an asset class like crypto that is mostly speculative in nature to begin with? Study the following on a historical basis, and due your own due diligence as to the health of the markets: Debt-to-GDP ratio Call to put ratio

The U.S. federal funds rate peaked in 2023 at its highest level since the 2007-08 financial crisis, reaching 5.33 percent by December 2023. A significant shift in monetary policy occurred in the second half of 2024, with the Federal Reserve implementing regular rate cuts. By December 2024, the rate had declined to 4.48 percent. What is a central bank rate? The federal funds rate determines the cost of overnight borrowing between banks, allowing them to maintain necessary cash reserves and ensure financial system liquidity. When this rate rises, banks become more inclined to hold rather than lend money, reducing the money supply. While this decreased lending slows economic activity, it helps control inflation by limiting the circulation of money in the economy. Historic perspective The federal funds rate historically follows cyclical patterns, falling during recessions and gradually rising during economic recoveries. Some central banks, notably the European Central Bank, went beyond traditional monetary policy by implementing both aggressive asset purchases and negative interest rates.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you know when a stock will go up or down? (NOV Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

PDLB is a triple whammy on those three themes.ECIP capital: PDLB received $225M of ECIP capital, and the regulators assigned them the lowest possible dividend (0.5%) on this capital for the first year of payments (announced in June). If we assume PDLB continues to pay 0.5% on this preferred and they have a cost of preferred equity of 10%, then we can calculate the value of this $225M liability as just $11M, with the rest a write-up to equity.This adjustment brings P/TBV from 82% to 46%.Thrift conversion dynamics: Ponce converted from a mutual holding company to a stock holding company in January 2022 (second step). PDLB is an unprofitable and under-levered bank. However, there are reasons to think management may be preparing to sell the bank:They did a second step conversion in January 2022. Only the optionality to sell the bank would motivate this step, as the bank didn’t need the capital, and the conversion increases management’s susceptibility to activist investors. This is highly praised by the best stock analysis websites.Management is old: 6/8 members are in their 70s or 80s (including the CEO and Chairman).Together, the Directors and Officers own >2M shares of stock, worth ~$20M. The CEO owns 580,000 shares, worth ~$6M. His total compensation is ~$1.3M (and he'll need to retire soon anyway). Additionally, the CEO and directors will receive a final tranche of ESOP shares in December 2024 that will boost their holdings another ~40%.Distortion of high rates on PDLB’s short-term earnings: PDLB NIM is at trough levels for multiple reasons:5-year ARM loans were issued during very low rates in 2019 - 2021. 5-year treasury yields were between 0.2% and 1.4% during this period, and grew to >4% in September 2022 (where they’ve been ever since). Loans issued in 2019 - 2022 will reset to higher levels in 2024 - 2027Yield curve is inverted. Ponce lends based on the long end of the curve (five-year rates at 4.1%) and funds on the short-end of the curve (brokered deposits come in at ~5.3%). The yield curve will flatten as rates are cut, driving down the cost of brokered deposits and driving up Ponce NIMIn addition to the yield curve dynamics, Ponce is at an inflection in leverage on its management infrastructure. It built out management capabilities for a much larger bank, and is currently seeing decreasing Q/Q non-interest cost, while assets and interest income are growing nicely.IR told me that cost pressures were peaking in 2023, and this has already become true in 1H 2024 results.Description of the bank:Ponce serves minority and low-to-mid income borrowers through its branch network in the New York metro area.Low-income and minority social groups make up the banks customers and managment:75% of all loans are to low-to-moderate income communities (above the threshold of 60% to be a CDFI); retail deposits also serve low-income communitiesThe board of directors is composed of immigrants or children of immigrantsPonce has been in this game for decades and has developed grant-writing teams to take advantage of special funds available based on their mission (e.g. $4.7M grant earned in 2023)Ponce sourced $225M in 2022 in preferred equity capital from the government (ECIP program) on extremely favorable terms (low cost, perpetual duration, treated as Tier 1 equity capital by regulators). They recently reported that for the first year (and I’d be in subsequent years), they’ll pay the lowest possible dividend of 0.5% (the range is up to 2% for the program). This number is inline with the one quoted by the best stock websites.Ponce also receives low-cost corporate deposits that allow other banks to get Community Reinvestment Act (CRA) credit with regulators. These deposits are insured and sticky, and often ~200bps or more below market interest rates.Outside of the ECIP equity and the small-but-growing CRA corporate deposits, the bank doesn’t have a good deposit franchise. The blended total cost of interest-bearing liabilities in 2023 is 4.0%.On the asset side, Ponce’s focus on mortgage lending to lower-income communities is a good niche (and composes 99% of lending). IR explained to me that the board of directors is composed of engaged real estate investors who know intimately the relevant neighborhoods and are involved in credit underwriting. Ponce lends 5/1 and 5/5 adjustable-rate mortgages against single-family (27% of loans), multifamily (30% of loans), and non-residential (18% of loans). Construction (23% of loans) properties are 36-month fixed-rate loans. LTVs on all these segments are ~55% and debt service coverage ratio >1.25x. In the current environment, Ponce is issuing loans at ~9% yield that are likely to experience very low levels of credit losses (my expectation would be 0 - 0.1% per year in annual credit cost). Given 5-year rates (~4%), lending at 9% is very favorable, and likely reflects decreasing competitive intensity in the wake of recent banking turmoil.I’m comfortable projecting very low credit costs because losses from the mortgage portfolio have been substantially zero going back to 2016 and very low going back to 2012 (the first year of available data). Charge-offs seemed to peak in 2013 at 0.7% of outstanding loans (charge-off happen years after delinquencies, so the timing seems reasonable following ‘08/’09). Given the peak of 0.7% and the more common experience of 0.0% charge-offs in Ponce’s mortgages, I’m therefore comfortable mostly ignoring credit cost.The most concerning area with respect to credit costs is the construction book. Although they scaled the construction business in 2023, it's not a new business for PDLB (they've been doing construction loans on the order of ~100M per year since 2017, and on a smaller scale before that). PDLB has not recorded any charge offs on the construction business going back at least 7 years. PDLB had no new delinquencies on this book in 2023 (I.e. from loans made in 2020). They did have some DQNs in 2022, but these have been mostly worked out without charge offs.Regarding the timing of the ramp up in recent quarters, it may be just right: if investors/banks are concerned about charge offs today, that's related to vintages from 2020/2021 (which were also loans issued at much lower rates and might not roll over smoothly). If others are pulling back, that's the time to deploy more capital into the business.The bank is currently very under-leveraged: Tier-1 equity / RWA is 21% (vs. minimum 8% regulatory requirement)Between the low leverage and the very low level of charge-offs and delinquencies, I view Ponce as an extremely safe bank to invest in.Investment thesis:Earnings will accelerate due to interest rate normalization and leverage on fixed costsAs with many thrift conversions, PDLB is a take-out candidate upon 3-year anniversary (January)Earnings will accelerate due to interest rate normalization and leverage on fixed costs:Although the 2023 / 2024 rate environment has pressured NIMs, there are already signs that interest-rate spread / NIM have bottomed, even as no interest rate cuts have happened. Interest rate spreads have leveled out in the past three quarters at ~1.7%. Liabilities have mostly repriced, and from here, tailwinds will be 1) repricing of the 5-year ARMs and 2) interest rate cuts starting in September. NIM will be going up, and will likely recover to historical levels within a couple of years.On the expense side, there was significant concern into the 2023 results about non-interest expense. Compensation and benefits grew by 13% CAGR from 2019 - 2023. Growth was 10% in 2023, showing deceleration but still to a high level. However, based on comments by IR that the bank has built expense infrastructure for a much larger bank, and based on results from 1H 2024, it looks like expenses are more controlled now. Non interest cost was in the 17.0M - 17.9M range for the last four quarters (prior to recently announced Q2). Q2, on the other hand, showed non-interest expense at 16.1M. Meanwhile, interest earning assets continued to grow at ~12% Y/Y. The combination of flat / decreasing costs and double-digit asset growth is very favorable for expense leverage.Additionally, managers have incentives to create shareholder value, especially as they reach retirement age. If Ponce doesn’t slow expense growth, shareholder activists may discover Ponce and pressure management to rationalize or sell the bank.The combination of improving NIM, growth in assets, and flattish expenses should produce much higher EPS in coming quarters, and I think $2 - $2.50 in EPS by 2026 is likely (if the bank isn’t sold).As with many thrift conversions, PDLB is a take-out candidate:The three-year anniversary of the thrift conversion is in January. The board is of retirement age and has healthy incentives to sell the bank. A buyout is likely a home-run from today’s stock price of $10.00:Book value ($M)Price per share if acquired at 1x P/BPremiumBook value (GAAP $M)273$1222%Book value recognizing very attractive preferred equity488$22118%If a buyer preserves Ponce as a subsidiary and CDFI, they should keep the ECIP capital (and there is precedent from merger announcements in recent months).Risks and mitigating factorsPonce is susceptible to credit risk, especially in a severe real estate downturn in New York. However, from what we can see of the wake of 2008/2009 financial crash, realized losses on the portfolio were quite low. Additionally, current credit metrics are pristine. 90-day delinquencies are just 0.5% of loans. Construction loans were the worst performers at 1.6%, followed by (counter-intuitively) owner-occupied at 1.4%. The NYC real estate dynamics affecting NYCB and others appear to be non-issues for PDLB. However it’s worth keeping a close eye on credit metrics.If NYC raises taxes to address budget deficits, it could hurt property prices. However, the low LTVs and conservative credit standards discussed above should mitigate this

The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you know when a stock will go up or down? (LEGN Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

As of June 17, 2024, the most shorted stock was for, the American holographic technology services provider, MicroCloud Hologram Inc., with 66.64 percent of their total float having been shorted. This is a change from mid-January 2021, when video game retailed GameStop had an incredible 121.07 percent of their available shares in a short position. In effect this means that investors had 'borrowed' more shares (with a future promise to return them) than the total number of shares available for public trading. Owing to this behavior of professional investors, retail investors enacted a campaign to drive up the stock price of Gamestop, leading to losses of billions when investors had to repurchase the stock they had borrowed. At this time, a similar – but less effective – social media campaign was also carried out for the stock price of cinema operator AMC, and the price of silver. What is short selling? Short selling is essentially where an investor bets on a share price falling by: borrowing a number of shares selling these shares while the price is still high; purchasing the same number again once the price falls; then returning the borrowed shares at a profit. Of course, a profit will only be made if the share price does fall; should the share price rise the investor will then need to purchase the shares back at a higher price, and thus incur a loss. Short selling can lead to some very large profits in a short amount of time, with Tesla stock generating over one billion dollars in short sell profits during the first week of March 2020 alone, owing to the financial crash caused by the coronavirus (COVID-19) pandemic. However, owing to the short-term, opportunistic nature of short selling, these returns look less impressive when considered as net profits from short sell positions over the full year. The risks of short selling Short selling carries greater risks than traditional investments, and for this reason financial advisors often recommend against this strategy for ‘retail’ (i.e. non-professional) investors. The reason for this is that losses from short selling are potentially uncapped, whereas losses from traditional investments are limited to the initial cost. For example, if someone purchases 100 dollars of shares, the maximum they can lose is the 100 dollars the spent on those shares. However, say someone borrows 100 dollars of shares instead, betting on the price falling. If these shares are then sold for 100 dollars but the price subsequently rises, the losses could greatly exceed the initial investment should the price rise to, say, 500 dollars. The risks of short selling can be seen by looking again at Tesla, with the company causing the greatest losses over 2020 from short selling at over 40 billion U.S. dollars.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you predict if a stock will go up or down? (PCTY Stock Prediction)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Federal Reserve's balance sheet has undergone significant changes since 2007, reflecting its response to major economic crises. From a modest *** trillion U.S. dollars at the end of 2007, it ballooned to approximately **** trillion U.S. dollars by May 2025. This dramatic expansion, particularly during the 2008 financial crisis and the COVID-19 pandemic - both of which resulted in negative annual GDP growth in the U.S. - showcases the Fed's crucial role in stabilizing the economy through expansionary monetary policies. Impact on inflation and interest rates The Fed's expansionary measures, while aimed at stimulating economic growth, have had notable effects on inflation and interest rates. Following the quantitative easing in 2020, inflation in the United States reached * percent in 2022, the highest since 1991. However, by *************, inflation had declined to *** percent. Concurrently, the Federal Reserve implemented a series of interest rate hikes, with the rate peaking at **** percent in ***********, before the first rate cut since ************** occurred in **************. Financial implications for the Federal Reserve The expansion of the Fed's balance sheet and subsequent interest rate hikes have had significant financial implications. In 2023, the Fed reported a negative net income of ***** billion U.S. dollars, a stark contrast to the ***** billion U.S. dollars profit in 2022. This unprecedented shift was primarily due to rapidly rising interest rates, which caused the Fed's interest expenses to soar to over *** billion U.S. dollars in 2023. Despite this, the Fed's net interest income on securities acquired through open market operations reached a record high of ****** billion U.S. dollars in the same year.

The Fund Management Activities industry is undergoing a period of transformation, characterised by technological disruptions and shifting investor preferences. Firms that have embraced this innovation and demonstrated their ability to adapt have been well positioned to navigate these challenges. That being said, companies have still been plagued by numerous economic headwinds, resulting in particularly volatile revenue in recent years. Revenue is expected to fall at a compound annual rate of 0.8% over the five years through 2024 to €163.6 billion, including a forecast rise of 2.7% in 2024. Economic uncertainty has been rife in recent years, with investors remaining cautious amid muted economic growth, sticky inflation and aggressive interest rate hikes from central banks across Europe. Notably, 2022 was a tough year for capital markets, with the rising base rate environment triggering mass sell-offs in fixed-income markets and clobbering bond values. Stock markets didn’t fare much better, with the MSCI World Index ending the year down by 13.1%. Optimism was hard to come by going into 2023, but capital markets defied expectations, partially due to a solid performance from large cap tech stocks and investors pricing in rate cuts at the tail-end of the year, supporting capital inflows. Although not forecast to record double-digit growth, stock market are positioned to see a modest gain in 2024, with interest rates likely to be cut and inflation coming down. However, there’s the argument that stocks, most notably US stocks, are overvalued, leading to the possibility of a repricing, which would put downwards pressure on prices and weigh on revenue growth. Revenue is slated to swell at compound annual rate of 3.8% over the five years through 2029 to €197.4 billion. Investment activity is set to pick up in the short term as economic growth improves, boosting investor confidence and driving revenue and profit growth. Technological advancements will continue to gather pace in the coming years, with developments like robo-advisers becoming increasingly accurate and supporting investment returns.

Workers’ compensation and other insurance funds businesses have experienced significant changes in recent years, largely driven by economic fluctuations and shifts in investment income. The crash of the US economy in 2020 due to pandemic-related restrictions placed immense pressure on the industry. Business formation plunged and unemployment soared, resulting in a diminished customer base for insurance funds and a steep drop in revenue. Regardless, the Federal Reserve's injection of liquidity into the financial system propelled stock prices upward, boosting investment income for insurance providers. This increase in investment income provided some relief for providers, enabling them to cover expenses and sustain profits despite revenue losses. The relaxation of COVID-19 restrictions spurred economic recovery in 2021, driving unemployment down and corporate profit up. This positive economic climate increased demand for insurance services and enhanced investment income due to robust stock market conditions. However, since 2022, inflation has wreaked havoc, causing businesses and organizations to slash investments in insurance funds amid soaring prices. More recently, rising interest rates have reduced downstream demand due to the emergence of recessionary fears, but revenue and profit have expanded because of growing returns on fixed-income products. Overall, revenue for workers’ compensation and other insurance funds has inched downward at a CAGR of 0.2% over the past five years, reaching $56.6 billion in 2025. This includes a 0.5% rise in revenue in that year. Looking ahead, providers are poised for moderate growth over the next five years. As the US economy stabilizes, with solid GDP growth and potential increases in business formation and employment, the customer base for insurance funds is likely to expand. These favorable economic conditions should bolster consumer confidence and investment in the stock market, leading to greater investment income for the industry. Nonetheless, larger players are expected to dominate, given their ability to invest in cutting-edge technologies like AI for predicting claim risks and optimizing business operations. Smaller providers may face intensified internal competition, prompting some to exit the market, while others could focus on niche offerings or invest in technological advancements to remain viable and competitive. Overall, revenue for workers’ compensation and other insurance funds is expected to expand at a CAGR of 1.3% over the next five years, reaching $60.3 billion in 2030.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you predict if a stock will go up or down? (WIG20 Index Stock Prediction)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Gold rose to 3,320.86 USD/t.oz on July 1, 2025, up 0.53% from the previous day. Over the past month, Gold's price has fallen 1.80%, but it is still 42.51% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

As of April 2024, WisdomTree Core Physical Gold was the leading gold back exchange-traded commodity (ETC) listed on the London stock exchange, providing a return of 13 percent on euro investments annually. Invesco Physical Gold A followed closely in second place, providing a return of 12.86 percent on investments made in euros. What is an exchange-traded commodity? An exchange-traded commodity (ETC) is a commodity such as silver, wheat, oats, and gold traded on the stock exchange. Unlike exchange-traded funds (ETFs) which allows investment in a basket of securities, ETCs allow investment in a single commodity. Gold-backed ETCs aim to track the spot price of gold. This results in the price of the ETC moving up and down in correlation with the underlying gold price. The annual return rate The return on investment (ROI) is a way to measure the performance of an investment. The ROI is calculated by dividing the amount gained or lost from an investment by the original invested amount. This number is then represented as a percentage. Different gains and losses can be generated on foreign investments due to changes in the value of the security in foreign markets. If the local home currency of an investor is rising in value, this leads to lower returns on foreign investments. Similarly, a decreasing home currency will increase the returns on foreign investments. The difference in currency performance, inflation levels in the home market or abroad, and interest rates are all factors that can lead to differing ROI rates.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you know when a stock will go up or down? (SYK Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you predict if a stock will go up or down? (EVR Stock Prediction)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you predict if a stock will go up or down? (LON:TMTA Stock Prediction)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data