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...

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.

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.

Can we predict stock market using machine learning? (FZO 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

Stock-NewsEventsSentiment (SNES) 1.0  is a dataset consisting of market and news time series data for S&P 500 companies over a period of 21 months (October 2020 to July 2022). The dataset contains the following parameters for each company (daily ticks):

• Market data: Stock price and trade volumes
• News data (volume of articles): -- Sentiment (positive/negative) -- New Products -- Layoffs -- Analyst Comments -- Stocks -- Dividends -- Corporate Earnings -- Mergers & Acquisitions -- Store Openings -- Product Recalls -- Adverse Events -- Personnel Changes -- Stock Rumours

Resources:

• Read more about SNES 1.0 here
• Check out Aylien's News API - the main API behind this dataset.
• See the code used for compiling the dataset here.

https://www.kaggleusercontent.com/kf/101833090/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..iQWprmEBO9-W3tJnXlV6Dw.1kJHDt8ySvXNhLL9XhChsuR_tQIGCVkZxipSSHxTdQ0be4Kz5XY-RzXYZO01fGb3WuCpHY3_SF3oDYnruXu2iseopmjjIu4Fyeh_nuHz2Ckr7UBQHkQGmoTNp8NjnXYJ-WrOkZG35CMBzdwJBKHXTQwyUqLfqFVaqSp4deBmcXTW3U_U1lpYs30ULxx7VPSzOdDZKbiXvSPYpJIm5YN8occ1HlrGzXC3QklLrBADnHvsEEte8uNKwFCdNZ3FJX8jyxEuE6jRf0HheGqHCQ_ZgfvNenXpwhoH6Les7gAiYOx4G7gBpE2cnhvloqusJe7gvW4f7T7zv_NCrhTe40SfzO6YxlT49UAok0j4TyNpj9LBn25D4eyC1w4wfqE4w9pUE0IM0HmuBsLrsmdZBgnNzCpRk-whaccNi7gccy32Utv40w1AEYkowNjI-iwlfetSwXBFfltA5ETDWBRCrhCHmWITF3OGsDTuKT0tAizbwfNdjBFiDMHtJPAZ4jTutWX0jioWQVwgdIk8j6rQvHJk3ecG16Kn8kiQgAb_D_ts7oV7aQo1yrmlKxnaSRX6xrS4oC1MHuGoW1ZP4OnnUhkQBBryNKNdMnUk49ifd0PbYbOD2jDy-kiGVanf1nQSDwaXNiDVijHkDOxL1-d3R2hpupVZNSgMnuGUSnA0aF67gig.TKd-E32Ni9ig_a8ZQbuadw/_results_files/_results_4_0.png" alt="">

The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China’s financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market’s return, while the momentum in the bond market positively influences the stock market’s return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China’s financial market.

Total return spillovers between the US stock market and ten markets in the Asia-Pacific region, January 1985 to November 2022.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

Incorporating Combinations of Sentiment Scores Of Financial News And MLP-Regressor For Stock Prediction.#

Junaid Maqbool, Preeti Aggarwal, Ravreet Kaur maqbooljunaid@gmail.com

Abstract.

The stock market is very volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, we have proposed a machine learning model where the financial news is used along with historical stock price data to predict upcoming prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well financial news of four different companies from different sectors to predict next day and next week stock trend and accuracy metrics were checked for a period of 10, 30, and 100 days. Our model was able to achieve the highest accuracy of 0.90 for both trend and future trend when predicted for 10 days. This paper also performs experiments to check which stock is difficult to predict and which stocks are most influenced by financial news and it was found Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others.

Complete research paper can be found at

Incorporating Financial News Sentiments and MLP-Regressor with Feed-Forward for Stock Market Prediction

Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach

Also the pdf of paper is available in the code file as well the data for citation and references

1. Maqbool, Junaid, Preeti Aggarwal, and Ravreet Kaur. "Incorporating Financial News Sentiments and MLP-Regressor with Feed-Forward for Stock Market Prediction." Emerging Technologies for Computing, Communication and Smart Cities: Proceedings of ETCCS 2021. Singapore: Springer Nature Singapore, 2022. 55-67.
2. Maqbool, Junaid, et al. "Stock prediction by integrating sentiment scores of financial news and MLP-regressor: a machine learning approach." Procedia Computer Science 218 (2023): 1067-1078.

Code is publicly available at Github

Methodology: All news links are crawled from google search "Stock Market" for that specific date, and only get the first 2 pages of results, where each page containing 10 results.

Columns:

• date: 2021-10-09 to 2022-10-08 (Cleaned)
• url: the news link (Not cleaned)
• full_text: entire article scrape using Newspaper3k library (Not cleaned)
• summary: apply summary function from Newspaper3k, containing 10 sentences (Not cleaned)
• close: close price from yahoo finance (Cleaned, empty means market not opened)
• volume: ticket volume from yahoo finance (Cleaned, empty means market not opened)

Remarks: 1). Since some of the contents are blocked by paywall, so excluded the news with number of full text character less than 500 (e.g. "$0.99 Subscription for reading!"). However, it is not guaranteed. 2). Some urls may be replicated 3). Some full text content may happen to appear in different links 4). full_text and summary are not guaranteed English

For more information, please visit my Github, thanks!

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.

Can we predict stock market using machine learning? (QRVO 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

China's TikTok is rapidly taking over the market worldwide, and as an internet powerhouse, we should have a better understanding of Chinese tech companies' stocks and be able to predict the price movement of their shares. Not only will this help us understand China, but it will also benefit individual investments.

This article collects information on the stocks of representative technology companies in the Hong Kong financial market in 2022, such as Alibaba, Tencent, Xiaomi and etc.

I have write a demo for your coding, you could do further on this dataset

The macro policy of the stock market is an important market information. The implementation goal of the macro policy of the stock market is mainly to improve the effectiveness of the stock market. However, whether this effectiveness has achieved the goal is worth verifying through empirical data. The exertion of this information utility is closely related to the effectiveness of the stock market. Use the run test method in statistics to collect and sort out the daily data of stock price index in recent 30 years, the linkage between 75 macro policy events and 35 trading days of market efficiencies before and after the macro event are tested since 1992 to 2022. The results show that 50.66% of the macro policies are positively linked to the effectiveness of the stock market, while 49.34% of the macro policies have reduced the effectiveness of the market operation. This shows that the effectiveness of China’s stock market is not high, and the nonlinear characteristics are obvious, so the policy formulation of the stock market needs further improvement.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-12-01 about VIX, volatility, stock market, and USA.

This study examines the market return spillovers from the US market to 10 Asia-Pacific stock markets, accounting for approximately 91 per cent of the region’s GDP from 1991 to 2022. Our findings indicate an increased return spillover from the US stock market to the Asia-Pacific stock market over time, particularly after major global events such as the 1997 Asian and the 2008 global financial crises, the 2015 China stock market crash, and the COVID-19 pandemic. The 2008 global financial crisis had the most substantial impact on these events. In addition, the findings also indicate that US economic policy uncertainty and US geopolitical risk significantly affect spillovers from the US to the Asia-Pacific markets. In contrast, the geopolitical risk of Asia-Pacific countries reduces these spillovers. The study also highlights the significant impact of information and communication technologies (ICT) on these spillovers. Given the increasing integration of global financial markets, the findings of this research are expected to provide valuable policy implications for investors and policymakers.

The UK plaster article market stood at $X in 2022, surging by X% against the previous year. In general, the total consumption indicated a strong increase from 2012 to 2022: its value increased at an average annual rate of X% over the last decade. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. Based on 2022 figures, consumption increased by X% against 2012 indices. Over the period under review, the market reached the maximum level in 2022 and is likely to see steady growth in the immediate term.

Macro-policy information in China’s stock market (1990–2022).

Pakistan's main stock market index, the KSE 100, fell to 167838 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.09% and is up 60.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on December of 2025.

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.

Machine Learning stock prediction: LON:CTA 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 Belarusian paper articles market was estimated at $121M in 2024, rising by 15% against the previous year. Over the period under review, the total consumption indicated noticeable growth from 2012 to 2024: its value increased at an average annual rate of +2.8% over the last twelve years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. Based on 2024 figures, consumption decreased by -7.5% against 2022 indices.

This dataset was used for training and evaluating the RNN-based autoencoder model. (CSV)