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.

Short Interest: NYSE: Mid Month: Stocks: No of Shares data was reported at 15,315.146 Unit mn in Nov 2018. This records a decrease from the previous number of 15,327.142 Unit mn for Oct 2018. Short Interest: NYSE: Mid Month: Stocks: No of Shares data is updated monthly, averaging 13,491.524 Unit mn from Jul 2000 (Median) to Nov 2018, with 220 observations. The data reached an all-time high of 18,608.173 Unit mn in Jul 2008 and a record low of 4,182.378 Unit mn in Aug 2000. Short Interest: NYSE: Mid Month: Stocks: No of Shares data remains active status in CEIC and is reported by New York Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z004: NYSE: Short Interest.

Short interest is a market-sentiment indicator that tells whether investors think a stock's price is likely to fall. It can also be compared over time to examine changes in investor sentiment.

Short interest regulation and reporting requirements vary by country. Countries with Short Interest Data by Position Holder

-Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Netherlands, Poland, Portugal, Spain, Sweden, UK, Japan Data for these countries is reported to local regulators in compliance with ESMA short selling regulations and began for most of these markets on 1 November 2012. The exceptions to this are Spain, which has data going back to 10 June 2010 and Greece, where the history begins on30 May 2013.

Countries with Short Interest Data by Traded Volume/Position

-Canada, China, Chile, Hong Kong, Israel, Malaysia, Mexico, New Zealand, Norway, Peru, Singapore, South Korea, Taiwan, Thailand, Turkey, United States, Brazil, Australia.

Countries Which Permit Short Selling but Have no Activity

-following countries permit short selling, but there is currently no activity. EDI monitors these markets and will provide updates if / when there is activity:

Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, India, Latvia, Lithuania, Luxembourg, Malta, Philippines, Romania, Saudi Arabia, and Slovakia.

Content

Company: Ticker Major index membership: Index Market capitalization: Market Cap Income (ttm): Income Revenue (ttm): Sales Book value per share (mrq): Book/sh Cash per share (mrq): Cash/sh Dividend (annual): Dividend Dividend yield (annual): Dividend % Full time employees: Employees Stock has options trading on a market exchange: Optionable Stock available to sell short: Shortable Analysts' mean recommendation (1=Buy 5=Sell): Recom Price-to-Earnings (ttm): P/E Forward Price-to-Earnings (next fiscal year): Forward P/E Price-to-Earnings-to-Growth: PEG Price-to-Sales (ttm): P/S Price-to-Book (mrq): P/B Price to cash per share (mrq): P/C Price to Free Cash Flow (ttm): P/FCF Quick Ratio (mrq): Quick Ratio Current Ratio (mrq): Current Ratio Total Debt to Equity (mrq): Debt/Eq Long Term Debt to Equity (mrq): LT Debt/Eq Distance from 20-Day Simple Moving Average: SMA20 Diluted EPS (ttm): EPS (ttm) EPS estimate for next year: EPS next Y EPS estimate for next quarter: EPS next Q EPS growth this year: EPS this Y EPS growth next year: EPS next Y Long term annual growth estimate (5 years): EPS next 5Y Annual EPS growth past 5 years: EPS past 5Y Annual sales growth past 5 years: Sales past 5Y Quarterly revenue growth (yoy): Sales Q/Q Quarterly earnings growth (yoy): EPS Q/Q Earnings date

BMO = Before Market Open
AMC = After Market Close: Earnings Distance from 50-Day Simple Moving Average: SMA50 Insider ownership: Insider Own Insider transactions (6-Month change in Insider Ownership): Insider Trans Institutional ownership: Inst Own Institutional transactions (3-Month change in Institutional Ownership): Inst Trans Return on Assets (ttm): ROA Return on Equity (ttm): ROE Return on Investment (ttm): ROI Gross Margin (ttm): Gross Margin Operating Margin (ttm): Oper. Margin Net Profit Margin (ttm): Profit Margin Dividend Payout Ratio (ttm): Payout Distance from 200-Day Simple Moving Average: SMA200 Shares outstanding: Shs Outstand Shares float: Shs Float Short interest share: Short Float Short interest ratio: Short Ratio Analysts' mean target price: Target Price 52-Week trading range: 52W Range Distance from 52-Week High: 52W High Distance from 52-Week Low: 52W Low Relative Strength Index: RSI (14) Relative volume: Rel Volume Average volume (3 month): Avg Volume Volume: Volume Performance (Week): Perf Week Performance (Month): Perf Month Performance (Quarter): Perf Quarter Performance (Half Year): Perf Half Y Performance (Year): Perf Year Performance (Year To Date): Perf YTD Beta: Beta Average True Range (14): ATR Volatility (Week, Month): Volatility Previous close: Prev Close Current stock price: Price Performance (today): Change

VIQ Solutions (VQS) Shares of VQS stock have been in recovery mode since last quarter. That was when the AI-driven tech company saw its stock price plummet after reporting earnings. Fast-forward a few months, and the VIQ Solutions stock price has climbed by more than 100%, with daily volumes increasing this month. There could be a few things in play for VQS stock. As we know, ChatGPT and AI stocks are gaining plenty of speculative interest right now. The massive surge of attention on machine learning has prompted a breakout in plenty of companies with exposure to the space. VIQ provides digital voice and video capture technology and transcription services. Late last month, based on the data provided by the short interest api, the company boosted its AI workflows with a new automatic speech recognition platform to increase accuracy in multi-speaker environments. “Our clients see the value in our ability to implement our integrated solutions and service offerings to transform and analyze digital content and securely generate accurate, actionable information,” said Vahram Sukyas, Chief Technology Officer, VIQ Solutions. This week VIQ expanded its global technology footprint and signed a multi-year contract with Transcription Hub, a transcription services company, to provide internal and commercial workflow solutions to transcription services organizations in India. The platform is designed to decrease turnaround time and yield higher transcription accuracy. Imperial Petroleum Inc. (IMPP) With China reopening from COVID lockdowns (finally), energy stocks are coming back into focus. Gas prices are climbing thanks to a mild winter as well. Imperial Petroleum has experienced its share of energy industry speculation and momentum-fueled moves over the last year. In fact, at one point in 2022, share prices reached highs of over $9. Solid earnings from its last quarter have begun coming back into the picture now, as earnings season is well underway. The third quarter saw Imperial report an Earnings Per Share of 8 cents compared to a loss of 3 cents from a year ago. The company also saw its sales explode. The company did just under $5.8 million in sales for the third quarter of 2021. The 2022 Q3 figures were more than 630% higher at $42.6 million. CEO Harry Vafias also highlighted several key points of the third quarter’s performance. He said, “As a result of having acquired six vessels in the course of ten months, we generated net income of $15.5 million in a single quarter which is 15,400% higher than our profit in Q2 22’ and equivalent to 23% of our current market capitalization; We incurred moderate debt during the quarter, maintaining a healthy capital structure with $42.3 million of debt while preserving a free cash balance available for further fleet expansion of about $92 million. Given the strong market fundamentals and the promising charter rate environment and by taking advantage of our efficient management of our expanded fleet, we believe that we will achieve strong results and generate significant cash flow going forward.” With a more bullish tone in energy, it will be interesting to see how the company’s next round of earnings compares. Spectrum Pharmaceuticals (SPPI) AI and chatGPT stocks aren’t the only things getting attention in the stock market today. “Old standbys” like biotech penny stocks remain a hot topic. They usually become a source of speculative trading trends due to ongoing trials that can make more break certain companies. Spectrum Pharmaceuticals, one of the best value stocks, has performed well this year, having risen over 100% since the beginning of January. The company develops targeted oncology treatment platforms. This week Spectrum announced receipt of a permanent J-code (J1449) for its ROLVEDON injection from the U.S. Centers for Medicare & Medicaid Services. J-codes are reimbursement codes used by commercial insurers, including Medicare, Medicare Advantage, and other government payers, for certain drugs. “A permanent J-code will enable a more efficient and predictable reimbursement in the outpatient setting. The combination of a permanent J-code on April 1, 2023, and ROLVEDON’S inclusion in the National Comprehensive Cancer Network® Supportive Care Guidelines (NCCN Guidelines) announced on December 6, 2022, are key elements in establishing brand awareness and building customer confidence in our novel product,” said CEO Tom Riga. Wearable Devices Ltd. (WLDS) We discussed WLDS stock toward the end of 2022 and other low float penny stocks. Wearable Devices, as one of the best growth stocks for any investors, is developing non-invasive neural input interface technology via wearables, including wristbands. Wearers can control digital devices using things like subtle finger movement to do so. This week the company announced that it received approval for a $900,000 grant budget for developing a manufacturing process of its AI-based neural interface, the Mudra Band. CEO Asher Dahan...

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.

Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) is assigned short-term B3 & long-term Ba3 estimated rating.

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.

Tsakos Energy Navigation Ltd Series E Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Shares par value $1.00 is assigned short-term B2 & long-term Ba2 estimated rating.

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.

JSD Nuveen Short Duration Credit Opportunities Fund Common Shares of Beneficial Interest

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

Consistent growth in assets under management (AUM) has immensely benefited the industry over the past five years. Industry servicers invest capital they receive from a variety of investor types across a broad range of asset classes and investment strategies. Operators collect a fee for the amount of money they manage for their clients and a percentage of gains they are able to generate on invested assets. Managers have shifted investment strategies throughout the period to generate greater returns. Interest rate fluctuations, trade tensions, escalating geopolitical risks and market volatility have contributed to shifting investment strategies. In addition, the industry increasingly monitors social medias and retail order flows to better anticipate market moves, mitigating risk and driving investment returns. Overall, industry revenue climbed at a CAGR of 3.2% to $126.9 billion over the past five years, including an expected incline of 1.6% in 2025 alone. Despite economic volatility throughout the period, the S&P 500 jumped at a CAGR of 11.4%, boosting AUM. However, profit has fallen due to pressure on industry fee structures, as a result, profit comprises 33.1% of revenue in the current year. Although industry professionals question the relevance of benchmarking hedge fund returns against equity performance, given that hedge funds rely on a range of instruments other than stocks, the industry's poor performance relative to the S&P 500 has begun to raise concern from some investors. These trends have affected the industry's structure, with the traditional 2.0% and 20.0% structure of a flat fee on total AUM and a right-to-earned profit deteriorating into a 1.4% and 16.0% arrangement. Industry revenue is expected to grow at a CAGR of 2.7% to $144.7 billion over the five years to 2030. AUM is forecast to continue increasing at a consistent rate, partly due to the diversification benefits that hedge funds provide. Nonetheless, increased regulation stemming from the global financial crisis and an escalating focus on the industry's tax structure has the potential to harm industry profit. Further economic uncertainty stemming from heightened inflation and persistently high interest rates is anticipated to dampen any large-scale growth for the industry as more hedge funds take a hawkish approach in their investment portfolio moving forward. Regardless, the number of new hedge funds is forecast to trend with AUM and revenue over the next five years.

The Australian Securities Exchange (ASX) was established in July 2006 after the Australian Stock Exchange merged with the Sydney Futures Exchange, making it one of the top 20 global exchange groups by market capitalization. ASX facilitates trading in leading stocks, ETFs, derivatives, fixed income, commodities, and energy, commanding over 80% of the market share in the Australian Cash Market, with the S&P/ASX 200 as its main index. We offer comprehensive real-time market information services for all instruments in the ASX Level 1 and Level 2 (full market depth) products, and also provide Level 1 data as a delayed service. You can access this data through various means tailored to your specific needs and workflows, whether for trading via electronic low latency datafeeds, using our desktop services equipped with advanced analytical tools, or through our end-of-day valuation and risk management products.

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.

PIMCO Access Income Fund Common Shares of Beneficial Interest is assigned short-term Baa2 & long-term B1 estimated rating.

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.

EVG Eaton Vance Short Diversified Income Fund Eaton Vance Short Duration Diversified Income Fund Common Shares of Beneficial Interest

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.

Is Small Cap 2000 Set to Soar?

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.

Global Partners LP 9.75% Series A Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests is assigned short-term Ba3 & long-term Ba3 estimated rating.

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 Dow Jones North America Select Junior Gold index is expected to trend higher in the short term, supported by positive momentum and bullish technical indicators. However, investors should be aware of potential risks, including geopolitical tensions, rising interest rates, and economic uncertainties, which could lead to market volatility and downward pressure on gold prices.